Climate Change and Water Resources in South Asia

Climate Change and Water Resources in South Asia

Climate Change and Water Resources in South Asia

Edited by M. Monirul Qader Mirza Adaptation and Impacts Research Group (AIRG) Meteorological Service of Canada, Environment Canada c/o-Institute for Environmental Studies (IES) University of Toronto Canada Q. K. Ahmad Bangladesh Unnayan Parishad (BUP) Niketon, Gulshan-1 Dhaka, Bangladesh

A.A. BALKEMA PUBLISHERS LEIDEN / LONDON / NEW YORK / PHILADELPHIA / SINGAPORE

This edition published in the Taylor & Francis e-Library, 2005. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” Copyright © 2005 Taylor & Francis Group plc, London, UK All rights reserved. No part of this publication or the information contained herein may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, by photocopying, recording or otherwise, without written prior permission from the publishers. Although all care is taken to ensure the integrity and quality of this publication and the information herein, no responsibility is assumed by the publishers nor the authors for any damage to property or persons as a result of operation or use of this publication and/or the information contained herein. Published by: A.A. Balkema Publishers, Leiden, The Netherlands, a member of Taylor & Francis Group plc www.balkema.nl, www.tandf.co.uk, www.crcpress.com Library of Congress Cataloging-in-Publication Data British Library Cataloguing in Publication Data ISBN 0-203-02077-4 Master e-book ISBN

ISBN 0 415 36442 6 (Print Edition)

In memory of my uncles M. Akramuzzaman, Dr Mirza Muzibul Huq and Dr M. Ashrafuzzaman. M. Monirul Qader Mirza

To my sons Rushdy and Urfi and daughter-in-law Farzin. Q. K. Ahmad

Table of Contents

Foreword R. K. Pachauri Foreword Don MacIver Preface About the Editors About the Authors Acronyms

xi xiii xv xix xxi xxiii

1 CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA: AN INTRODUCTION M. Monirul Qader Mirza Q. K. Ahmad 1.1 1.2 1.3 1.4

Introduction Water Availability and Demand in South Asia Climate Change and Water Resources Climate Change and Future Water Challenges

1 2 8 8

2 HYDROLOGIC MODELING APPROACHES FOR CLIMATE IMPACT ASSESSMENT IN SOUTH ASIA M. Monirul Qader Mirza 2.1 2.2 2.3 2.4 2.5 2.6 2.7

Introduction Hydrologic Models Advantages and Limitations of Hydrologic Models in Climate Change Application Application of Hydrologic Models for Climate Change Impact Assessment in Bangladesh Application of Hydrologic Model in India Application of Models in Pakistan Summary and Concluding Remarks

23 23 32 35 45 46 48

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3 ARE FLOODS GETTING WORSE IN THE GANGES, BRAHMAPUTRA AND MEGHNA BASINS? M. Monirul Qader Mirza R. A. Warrick N. J. Ericksen G. J. Kenny 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8

Introduction Hydro-Meteorology of the GBM Basins The Flood Problem The Data Statistical Analyses Methods Results Discussion Conclusions

55 57 59 63 65 65 67 69

4 CLIMATE CHANGE AND WATER RESOURCES ASSESSMENT IN SOUTH ASIA: ADDRESSING UNCERTAINTIES Gary Yohe Kenneth Strzepek 4.1 4.2 4.3 4.4 4.5 4.6 4.7

Introduction Defining Uncertainties Hydro-Climatic Analysis of Flooding in Bangladesh A Hydrologic Model for the Rivers Future Climate Scenarios Assessing Adaptation Under Conditions of Profound Uncertainty Concluding Remarks

77 78 80 83 86 89 99

5 THE IMPLICATIONS OF CLIMATE CHANGE ON RIVER DISCHARGE IN BANGLADESH M. Monirul Qader Mirza 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8

Introduction Objectives Methodology Estimation of Changes in Annual Discharge Effects on Mean Peak Discharge Effects on Depth and Spatial Extent of Flooding Socio-Economic Effects of Changes in Inundation Categories Concluding Remarks

103 107 107 115 119 123 131 132

TABLE OF CONTENTS

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6 CLIMATE CHANGE AND GLACIER LAKE OUTBURST FLOODS AND THE ASSOCIATED VULNERABILITY IN NEPAL AND BHUTAN Motilal Ghimire 6.1 6.2 6.3 6.4 6.5 6.6

Introduction GLOF Hydrology Studies About Glacier Lakes and Their Outburst Events in Nepal and Bhutan GLOF Events’ Impact, Vulnerability and Adaptation Glacier Retreat, GLOF Events and Climate Change Concluding Remarks

137 138 139 144 147 150

7 CLIMATIC CHANGE - IMPLICATIONS FOR INDIA’S WATER RESOURCES M. Lal 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10

Background India’s Geography, Population and Water Needs Climate of India Floods and Droughts Water Resources of India Future Demand and Supply of Water Government Policy and Legislative Tools Coping with Climate Change and Adaptation Research Needs Concluding Remarks

155 156 160 171 177 188 189 190 193 193

8 CLIMATE CHANGE AND WATER RESOURCES MANAGEMENT IN PAKISTAN Asad Sarwar Qureshi 8.1 8.2 8.3 8.4 8.5

Introduction Water Resources in Pakistan Major Challenges Climate Change Impacts on Water Resources: The Way Forward Concluding Remarks

197 200 207 218 228

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9 CLIMATE CHANGE AND WATER RESOURCES MANAGEMENT IN BANGLADESH Hossain Shahid Mozaddad Faruque Md. Liakath Ali 9.1 9.2 9.3 9.4 9.5 9.6 9.7

Introduction Water Resources Problems and Their Management Water Management Practices Major Studies, Policies and Plans Climate Change and Water Resources Sector in Bangladesh Future Framework of Management Concluding Remarks

231 232 237 242 244 246 252

10 ADAPTATION OPTIONS FOR MANAGING WATER-RELATED EXTREME EVENTS UNDER CLIMATE CHANGE REGIME: BANGLADESH PERSPECTIVES Ahsan Uddin Ahmed 10.1 10.2 10.3 10.4 10.5 10.6

Introduction Water-Related Extreme Events and Climate Variability Climate Change and Its Implications for Water Resources Coping with Climate Variability Towards Framework of Future Adaptations Concluding Remarks

255 255 260 266 269 275

11 USING THE ADAPTATION POLICY FRAMEWORK TO ASSESS CLIMATE RISKS AND RESPONSE MEASURES IN SOUTH ASIA: THE CASE OF FLOODS AND DROUGHTS IN BANGLADESH AND INDIA M. Monirul Qader Mirza Ian Burton 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 11.10 INDEX

Introduction Adaptation Policy Framework Vulnerability and Adaptation: A Brief Synthesis Present Vulnerability and Adaptation Measures and Policies in South Asia: Urban Flooding in Dhaka Vulnerability of Gujarat to Drought Hazard Stakeholders’ Participation Present Adaptation Policies Future Climate Change, Risks and Adaptation Adaptation Policy Framework: Opportunities and Challenges Concluding Remarks

279 282 284 287 296 299 303 305 307 310 315

Foreword

South Asia is home to a population of more than a billion and a quarter. The original settlers on the subcontinent made this region their home essentially on the attraction of rich and fertile land and abundant water resources. With rapid growth in the population, particularly during the last century, the scarcity of water resources has reached an alarming level, making this a subject deserving of deep attention and an area where major policy initiatives become essential. Agriculture is still a significant contributor to the GDP of the countries of South Asia, and well over half the population of the region is dependent on agriculture or agriculture-related activities. The dominance of the monsoon as a major source of water supply and the seasonal nature of precipitation in the region, makes the management of water through irrigation a crucial determinant of agricultural activity. With industrial growth and urbanization, the demand for water in the industrial sector and in towns and cities is also increasing rapidly. The problem is compounded by periodic droughts in certain years and excessive floods particularly during the monsoon season. Both these phenomena lead to large-scale destruction of infrastructure, property and lives of livestock and human beings. The problem of climate change is likely to amplify these problems in the future. The Third Assessment Report of the IPCC clearly highlights the likelihood of droughts and floods increasing in the future. The Fourth Assessment Report is likely to shed further light on this problem, particularly given the fact that water has been included as a cross-cutting theme for this report. Possible shifts in the onset and adequacy of monsoons, the retreat of glaciers and changes in magnitude and variability in temperature will introduce significant changes in water resources availability and uses in South Asia. The anthology “Climate Change and Water Resources in South Asia” is a timely contribution to improving knowledge on the impacts of climate variability and changes in water resources in South Asia and related adaptation measures. The editors and the contributors are to be congratulated on an important publication.

R. K. Pachauri Chairman, Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland & Director-General, The Energy & Resources Institute (TERI), New Delhi, India

Foreword

There is a growing concern across the world about climate variability and change, and associated vulnerability, impacts and adaptation for various economic sectors. Over the last decade, the Adaptation and Impacts Research Group (AIRG), Meteorological Service of Canada, Environment Canada has contributed significantly to the science of vulnerability, impacts and adaptation (VIA) research nationally and internationally. In Canada, the AIRG led and contributed to a number of climate change and VIA projects that include: Canada Country Study; Canadian Climate Impacts Scenarios Project; CCME Climate Change Indicators Project; Natural Hazards and Disasters in Canada; Climate Change-Human and Animal Diseases; Climate Change and Water Resources in the Great Lakes and Climate Change and the Canadian Energy Sector. One of the mandates of the AIRG is to contribute to international research projects and initiatives in the field of climate change and VIA research. The AIRG significantly contributed to the Intergovernmental Panel on Climate Change (IPCC) of the United Nations (UN); the Millennium Ecosystem Assessment of the UN; the Canada-China Cooperation in Climate Change (C5) Project; The AIACC AS25 Project, THORPEX-A Global Atmospheric Program and the STARDEX Project. In addition to East Asia and the Caribbean, South Asia is also becoming an area of interest of the AIRG in terms of climate change and VIA studies. This anthology is the third initiative of our international commitment towards South Asia. Previously, Dr. M. Monirul Mirza edited the anthology “Flood Problem and Management in South Asia”and “The Ganges Water Diversion: Envronmental Effects and Implications” published by the Kluwer Academic Publishers, the Netherlands. South Asia is a region of diverse climates. Livelihood and sustenance of development are highly climate driven. Floods, droughts and cyclones regularly batter economic sectors and infrastructure and cause deaths to human and livestock population. Future changes in the South Asian climates and the sea level rise especially the monsoon, will have significant impacts on water supply and demand, floods and droughts, changes in soil moisture, soil degradation, saline water intrusion, pollution of surface and ground waters and faster melting of the Himalayan glaciers. These changes will have profound effects on various economic sectors and the livelihoods of millions of people, especially the poorest section of the South Asian society. In order to reduce vulnerability, there is an urgent need to design and implement adaptation measures. It is also warranted that adaptation be integrated into national development plans of the South Asian nations, as a continuous process. These issues are discussed in the 11 Chapters of this anthology “Climate Change and Water Resources in South Asia”. It is indeed a significant contribution from which scientists, vulnerability, adaptation and impact researchers and

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FOREWORD

policy makers will be benefited. I congratulate the editors, authors, reviewers and publisher of the anthology for their tremendous hard work in making this noble project a success.

Don MacIver Director, Adaptation and Impacts Research Group (AIRG), Meteorological Service of Canada, Environment Canada

Preface

This anthology presents analyses of research works from five countries of South Asia who share a number of transboundary river basins. It contains 11 chapters, which address most of the fundamental issues related to climate variability, climate change and water resources in South Asia. The journey towards this anthology began six years ago when we started working with the Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC) of the United Nations. During the IPCC TAR process, we felt that there was not enough information readily available on the potential effects of future climate change on water resources of South Asia. The initiative received a strong support from Dr. Janjaap Blom, Taylor and Francis Publishers, The Netherlands when the first editor of this anthology met him during the “International Conference on Water Resources Management in Arid Regions” in March of 2002 in Kuwait. In 1992, the UN Framework Convention on Climate Change (UNFCCC) expressed its concern that the enhanced greenhouse effect due to anthropogenic emission would result on average in an additional warming of the Earth’s surface and atmosphere and that might adversely affect natural ecosystems and humankind. A few years later, the Intergovernmental Panel on Climate Change (IPCC) in its Third Assessment Report categorically bolstered this concern. The IPCC-TAR released in 2001 stated “…There is new and stronger evidence that most of the warming observed over the last 50 years is attributed to human activities”. It further expressed that warming in the last century had contributed to the observed sea level rise, through thermal expansion of seawater and widespread loss of sea ice. Evidence of the link between climate change and increasing climate variability has been mounting rapidly. In a climate change regime, the range of uncertainty of climate and weather will increase. Overall, the whole climate and hydrologic system will be impacted. However, there will be regional variations. The implications of climate variability and change for water resources sector, therefore, warrant updated information and a complete understanding in order to design and implement adaptation. Why should climate change be so important for the water sector in South Asia? Monsoon is an integral part of the hydrologic cycle and water availability in South Asia. Global Climate Models (GCM) are in general agreement that future climate change will have a profound impact on monsoon. This will eventually affect availability of water resources as well as development and investment dynamics. The IPCC-TAR indicates the possibility of increases in the frequency and intensity of extreme weather events such as floods, droughts, and heat waves, which are very common in the countries of South Asia. They often severely affect lives and property, create food insecurity, and accelerate the process of poverty in many parts of the region. In April of 2004, scientists in the periodical Nature predicted that a meltdown of the 3 km thick massive Greenland ice sheets due to global warming would swamp many low-lying areas of the globe that include parts of South Asia.

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PREFACE

There are many difficulties in precisely estimating the impacts of climate change on water resources. They include physical and climatic characteristics of a river basin, selection of hydrologic model and scenarios, availability of hydro-meteorological and socio-economic data and computing and financial resources. Chapters included in this anthology followed the standard methods for impact assessment established by the IPCC and by many other regional and sectoral studies. What will be the potential impacts of climate variability and change on water resources of South Asia? Large areas in Nepal, India, and Bangladesh are vulnerable to recurrent floods. In Nepal and Bhutan, Glacier Lake Outburst Floods (GLOFs) are becoming serious threats to human settlements. Southern provinces of Pakistan and Western India are usually affected by acute droughts. Loss of human lives, livestock population, and property are on the rise due to catastrophic natural hazards. Landslides during torrential rains disrupt communication and supply sediments to dams/reservoirs and river channels. Floods and droughts also threaten water quality and eventually human health. While in monsoon there is huge surplus of water in South Asia, the water availability picture in the dry season is just the opposite. In the dry season, the supply of water cannot simply meet the demand, which causes intra-country and inter-country water disputes. In a warmer climate, these problems are expected to exacerbate across South Asia with some degree of uncertainties. In a warmer climate in the future, the excess water in monsoon and drought situation (hydrological, meteorological, and agricultural) in the dry months will cause a number of water allocation and management problems. Flood management will be a major issue in Nepal, India, and Bangladesh. Existing flood mitigation/control structures and non-structural measures will need to be strengthened and tailored to meet the future challenges in a climate change regime. Drought management will become a much more serious challenge for India, Pakistan and to a lesser degree for Bangladesh. Irrigation for agriculture remains by far the largest water consumer in the region. The efficiency of irrigation in the regional countries is generally low, and many perverse incentives constrain efforts to improve the situation. Efficiency of irrigation needs to be improved sufficiently to reduce water demand and structural reforms are required to improve water management. Due to rapid urbanization, domestic water demand is gradually increasing. With rapid economic and urban development water, demand will continue to increase. Climate change will act as an additional factor to the increasing drinking water demand. Water-borne diseases contribute to high infant mortality in South Asia where access to clean drinking water is limited. In the arid regions especially of Western India and Southern Pakistan shortages of water supply become acute during a drought. Regional cooperation on the sharing of water of the transboundary rivers remains a contentious issue in South Asia. Management of water quality of the transboundary rivers is another emerging issue, which will need adequate attention in the future. In recent years, there is encouraging progress in towards organizing joint responses to the common threat of flooding and other hydrological disasters. Scope of cooperation will need to be widened and the cooperative framework strengthened for mutual benefits. Knowledge on vulnerability and adaptation (VA) can inspire people to mobilize resources and initiate/strengthen action to lessen the impacts of climate variability and change. The science of VA has received serious attention in the IPCC process. In recent years, in terms of global warming, present and future VA activities are given equal importance. The concept is that the assessment of present vulnerability and adaptation will help identify the gaps, and addressing them is a step forward towards steeping the future. In the water sector in South Asia, a variety of adaptation/mitigation measures are under implementation. However, an adaptation policy framework per se is missing.

PREFACE

xvii

A framework is necessary in order adaptation policies can be properly formulated with reference to different levels of society - national to local levels. Many reviewers spent a great deal of time in critically reviewing the chapters. Farzana Abdulhusein patiently and carefully prepared the camera-ready copy. Jane Devie, Department of Geography University of Toronto drew many maps and graphs. Professor Marie Sanderson at the Adaptation and Impacts Research Group (AIRG), Environment Canada reviewed some of the chapters and offered constructive comments. We gratefully acknowledge all of these contributions. Funding support for preparing the manuscripts was provided by the AIRG. Our sincere appreciation and thanks to Don MacIver, Director of the AIRG for his support. We are grateful to the contributors of this book who invested enormous amounts of time in preparing the chapters. Without their sincere efforts this book would not have materialized. Finally, the views expressed in this book are those of the authors and do not reflect the views of their respective organizations.

M. Monirul Qader Mirza Adaptation and Impacts Research Group (AIRG) Meteorological Service of Canada, Environment Canada c/o-Institute for Environmental Studies (IES) University of Toronto Canada Q. K. Ahmad Bangladesh Unnayan Parishad (BUP) Niketon, Gulshan-1 Dhaka, Bangladesh

About the Editors

Dr M. Monirul Qader Mirza has extensively researched on hydrological and climate extremes, natural hazards and their management, climate change and water resources and associated vulnerability, impact and adaptation and environmental impacts of water diversions from the transboundary rivers. He received his PhD from the International Global Change Institute (IGCI), University of Waikato, Hamilton, New Zealand on climate change and flooding in Bangladesh in 1998. He contributed as a Coordinating Lead Author (CLA) to the Special Regional Report and the Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC) of the United Nations and to the Millennium Ecosystem Assessment. Presently he is contributing as a CLA to the IPCC’s Fourth Assessment Report, Working Group II. He is currently with the Adaptation and Impacts Research Group (AIRG), Meteorological Service of Canada, Environment Canada. He is also an Adjunct Professor at the Institute for Environmental Studies (IES), University of Toronto, Canada. He has been declared as a Burtoni Fellow of the Meteorological Service of Canada for the year 2004-2005. He has recently been appointed as the Editor of “Adaptation Science”, a quarterly Newsletter of the AIRG. He is a member of the American Society of Civil Engineers and Professional Engineers, Ontario, Canada. Dr Q. K. Ahmad is a socio-economic specialist of international repute and has to his credit a wide range of research work on environment and water resources, climate change, policy planning, food and agriculture, rural development, poverty alleviation, human development, technology and employment generation, women in development and gender issues. He extensively studied various issues related to water resources development and cooperation in the South Asia Region. He received his PhD from the London School of Economics and Political Science, London University in 1976. He is Chairman and Chief Executive, Bangladesh Unnayan Parishad (BUP), Dhaka. He was the President and International Vice-President of the Association of Development Research and Training Institutes of Asia and the Pacific (ADIPA), Kuala Lumpur and Society for International Development (SID), Rome, respectively. During 1998-2001, he acted as a Coordinating Lead Author to the IPCC’s Third Assessment Report. Presently he is contributing as a Lead Author to the IPCC’s Fourth Assessment Report, Working Group II.

About the Authors

Ahsan Uddin Ahmed is the Director, Bangladesh Unnayan Parishad (BUP)-Centre for Water and Environment, Niketon, Gulshan-1, Dhaka, Bangladesh. His research focuses on climate change, vulnerability, impacts and adaptation; regional cooperation in water sharing; environment and resource management for sustainable development. Asad Sarwar Qureshi is presently Head of International Water Management Institute (IWMI) office in Iran where he is focusing on increasing water productivity of dry and marginal lands. He has long been associated with the adaptive research aimed at irrigation management to increase land and water productivity especially in the Indus basin. He has special interests in integrated water management modeling to evaluate the impacts of different water management strategies on crop production and environment. Gary Yohe is the John E. Andrus Professor of Economics at Wesleyan University, Connecticut, USA. He has been working in the climate area for more than 20 years, with specific focus on coping with the sources and implications of the profound uncertainty that clouds our view of how the future will unfold. Gavin J. Kenney is presently working as an independent climate and agriculture consultant in New Zealand. He was previously with the International Global Change Institute (IGCI), University of Waikato, Hamilton, New Zealand and the Environmental Change Unit, University of Oxford, U.K. Hossain Shahid Mozaddad Faruque is currently the Director General, Water Resources Planning Organization (WARPO), Ministry of Water Resources, Government of Bangladesh. Trained as a water resources engineer, he has been associated with the planning of Bangladesh’s water sector for over three decades. Ian Burton is an Emeritus Professor, Department of Geography and Planning and Institute for Environmental Studies (IES), University of Toronto, Canada. He is also a Scientist Emeritus with the Adaptation and Impacts Research Group (AIRG), Meteorological Service of Canada, Environment Canada. He is currently the President of the International Society of Biometeorology. Kenneth Strzepek is a Professor in the Civil, Environmental and Architectural Engineering Department at the University of Colorado, Boulder, USA. His areas of expertise include modeling of river basins with a focus on the implications of climate change and socioeconomic development across the associated watersheds.

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ABOUT THE AUTHORS

M. Lal is currently with the Pacific Centre for Environment and Sustainable Development, University of South Pacific, Suva, Fiji as a Visiting Professor. His research interests include: global and regional climate, modeling the climate and its variability, regional environmental change-integrated approach, vulnerability assessment and regional adaptation and mitigation potentials. He is a Coordinating Lead Author of the Intergovernmental Panel on Climate Change (IPCC) of the United Nations, Fourth Assessment Report, Working Group II. M. Monirul Qader Mirza is currently with the Adaptation and Impacts Research Group (AIRG), Meteorological Service of Canada, Environment Canada. He is also an Adjunct Professor, Institute for Environmental Studies (IES), University of Toronto, Canada. His research mainly focuses on extreme hydro-meteorological events, hydrologic modeling, climate change and associated vulnerability, impact and adaptation. Md. Liakath Ali, Senior National Expert, Program Development Office for Integrated Coastal Zone Management Plan (ICZMP), Water Resources Planning Organization (WARPO), Ministry of Water Resources, Government of Bangladesh. Motilal Ghimire is at the Central Department of Geography, Tribuvan University, Kathmandu, Nepal. Areas of his research interest include: socio-economic conditions of the mountains, mountain hydrology and water resources, extreme hydrological events and application of geographic information system (GIS) in vulnerability assessment. Neil Ericksen is the founding Director of the International Global Change Institute (IGCI), University of Waikato, Hamilton, New Zealand. His research interests are: human response to natural hazards and climate change, governance and environmental management, and resource planning. Q. K. Ahmad is the Chairman of the multidisciplinary research organization Bangladesh Unnayan Parishad (BUP); and President, Bangladesh Economic Association (BEA), Dhaka, Bangladesh. He has to his credit a wide range of research works and publications, including on environment and water resources, regional cooperation, climate change, policy planning, food and agriculture, rural development, poverty alleviation, human development, technology, employment generation, and gender issues. Richard Warrick is the Deputy Director of the International Global Change Institute (IGCI), University of Waikato, Hamilton, New Zealand. His recent research activities have focused on climate related issues, particularly global climate and sea level changes and on the development of integrated models for assessing the effects of climate change and variability at national and regional scales.

Acronyms

ADB ADRC BCM BDCLIM BOD BWDB CCC CCIRG CCCma CDBI COLA CPCB CSE CSIRO CWC DHM EANHMP ENSO EPADC EPWAPDA FANA FAO FAP FCD FCDI FEC FPCO GBM GCM GDP GFDL GLOF GoB GoI GoP GPP GSI HBV

Asian Development Bank Asian Disaster Reduction Center Billion Cubic Meter Bangladesh Climate Model Bio-chemical Oxygen Demand Bangladesh Water Development Board Canadian Climate Centre Climate Change Impact Review Group Canadian Centre for Climate Modeling and Analysis Climate Diagnostic Board of India Center for Ocean Land and Atmosphere Central Pollution Control Board Centre for Science and Environment Commonwealth Scientific Industrial Research Organization Central Water Commission Department of Hydrology and Meteorology East Asia Natural Hazard Management Project El Niño Southern Oscillation East Pakistan Agriculture Development Corporation East Pakistan Water and Power Development Authority Federally Administered Northern Areas Food and Agriculture Organization of the United Nations Flood Action Plan Flood Control and Drainage Flood Control Drainage and Irrigation French Engineering Consortium Flood Plan Coordination Organization Ganges, Brahmaputra and Meghna Global Climate Model Gross Domestic Product Geophysical Fluid Dynamics Laboratory Glacier Lake Outburst Flood Government of Bangladesh Government of India Government of Pakistan Guidelines for Peoples Participation Geological Survey of India Hydrologiska Byrans Vattenbalansavdelning

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ACRONYMS

HEC HYVs IBRD ICIMOD IMD IPCC IRMB ITCZ IWMI JICA LLNL MAGICC MODSIM MOE MPO MSL MWR NERC NIO NWCF NWFP NWMP OECD PET RAJUK SAARC SCENGEN SLR SRES SWAT TDS UBC UKMOH UKTR UNDP UNEP UNESCO UNFCCC UNICEF USEPA WARPO WAPDA WECS WMO WRDTC

Hydrologic Engineering Center High Yielding Varieties International Bank for Reconstruction and Development International Centre for Integrated Mountain Development India Meteorological Department Intergovernmental Panel on Climate Change Institute of Royal Meteorology Belgium Inter-Tropical Convergence Zone International Water Management Institute Japan International Cooperation Agency Lawrence Livermore National Laboratory Model for the Assessment of Greenhouse Gas Induced Climate Change Model Simulation Ministry of Environment Master Plan Organization Mean Sea Level Ministry of Water Resources National Environment Research Council National Institute of Oceanography Nepal Water Conservation Foundation Northwest Frontier Province National Water Management Plan Organization for Economic Cooperation and Development Potential Evapo-Transpiration Rajdhani Unnayan Katripakha South Asian Association for Regional Cooperation Scenario Generator Sea Level Rise Special Report on Emission Scenarios Soil and Water Assessment Tool Total Dissolved Solids University of British Columbia United Kingdom Meteorological Office High Resolution Model United Kingdom Meteorological Transient Model United Nations Development Programme United Nations Environment Programme United Nations Educational Scientific and Cultural Organization United Nations Framework Convention on Climate Change United Nations International Children’s Emergency Fund United States Environment Protection Agency Water Resources Planning Organization Water and Power Development Authority Water and Energy Commission Secretariat World Meteorological Organization Water Resources Development and Training Centre

1 Climate Change and Water Resources in South Asia: An Introduction M. MONIRUL QADER MIRZA Q. K. AHMAD

1.1

INTRODUCTION

The South Asia region contains many large river systems: Ganges, Brahmaputra, Meghna, Indus, Godavari, Mahanadi, and Narmada (Fig. 1.1), which support millions of people. The river systems of South Asia can be classified into four major groups (a) Himalayan rivers; (b) Deccan rivers; (c) Coastal rivers; and (d) Rivers of the inland drainage basin. Table 1.1 lists these major rivers of South Asia, their origins and sources of water. The Himalayan rivers are formed by melting snow and glaciers and have continuous flow throughout the year. Snow and rainfed river basins occupy 2.32 million km2 or 55% of basin areas while the remaining 1.90 million km2 or 45% of basin areas belong to rainfed rivers. Snow and glaciers are partial sources of water for the large rivers: the Ganges, Brahmaputra and Indus, which originate in the Himalayas (Fig. 1.1). The rivers of the Deccan plateau are rainfed and fluctuate in volume, many of them being non-perennial; the coastal rivers, which,especially on the West Coast, are short in length with small catchment areas, most of them being non-perennial; and the rivers of the inland drainage basin in Western Rajasthan, which are ephemeral, drain towards the silt lakes such as Sambhar, or are lost in the desert sands. Water availability in this region is driven by monsoons, which are cyclical wave-like air masses that occur in the sub-tropics, moving from the sea to land during the summer and land to water in winter. The word monsoon comes from the Arabic mausim, meaning ‘season,’ because these storms return every year. Two monsoon systems operate in the region: the Southwest or summer monsoon and the Northeast or winter monsoon (Box 1.1 and Fig. 1.2). The summer monsoon accounts for 70%-90% of the annual rainfall over most of South Asia, except over Sri Lanka and Maldives where the Northeast monsoon is dominant. Apart from the monsoon, the Northern part of South Asia receives considerable precipitation from Western disturbances, and in the Southern parts (especially Sri Lanka), from weather associated with the ITCZ (Inter-Tropical Convergence Zone). Considerable monsoon variability occurs in both space and time. There is also a clear association between El Niño events and weak monsoons. During the period 1871-2001, 11 of 22 drought years were El Niño years (Kumar et al., 2003). Between 1901 and 1990, rainfall was deficient in all seven strong El Niño cases.

2

CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA

Fig. 1.1 South Asia region and major rivers. Source: Ben Crow et al., 1995. Reproduced with permission.

1.2

WATER AVAILABILITY AND DEMAND IN SOUTH ASIA

Per capita/year water availability in South Asia is shown Table 1.2. It demonstrates two issues. There is a sharp contrast among the South Asian nations in terms of water availability and consumption, and all countries have surplus water. The figures, however, mask serious imbalances. They do not reveal the wide variability in time and space (Subba, 2001). Most of these waters are generated in the monsoon (June-September) and flow unused to the sea. Figure 1.3 shows seasonal availability of water for some South Asian rivers. For some selected rivers the ratios of dry season and monsoon flows are: 1:6 (Ganges), 1:4 (Brahmaputra), 1:12 (Narmada) and 1:10 (Godavari). Water scarcity is a serious problem in Pakistan. Several parts of India are water stressed which include the regions in the Indus, Krishna and Ganges sub-basins. Regions with East flowing rivers between Mahanadi and Pennar, and West flowing rivers of Kach and Kathiawar experience water scarcity, while the regions with East flowing rivers between Pennar and Kanyakumari suffer with absolute water scarcity (per capita availability 14 m3/year). Even during the monsoon, a large area in India and Bangladesh suffers from water scarcity and sometimes from drought.

M. M. Q. MIRZA AND Q. K. AHMAD

3

4

CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA

Fig. 1.2 Monsoons and directions of air movement.

It is estimated that up to 80% of the water supply of dry season flow of the Indus, Ganges and Brahmaputra Rivers comes from Himalayan glaciers (Table 1.3) (Subba, 2001). This estimate seems to be high. On an annual basis, for the Ganges and Brahmaputra, the contribution of snow and glaciers is insignificant which is <1% (Mirza, 1997). At the high end, it has been estimated that snow and glacier melt contributes roughly 10% of the total flow generated in the Nepalese rivers (Sharma, 1977; Gyawali, 1989). Since the Western Himalayas receives less monsoon rain but higher winter snowfall than the Central and

M. M. Q. MIRZA AND Q. K. AHMAD

5

Eastern Himalayas, the dependence of the Indus on ice melt from glaciers is greater than that of the Ganges and Brahmaputra. The Chenab, one of the five tributaries of the Indus, is almost completely glacier-fed. Water demand is generally created by three driving forces: increases in population, agriculture, and industrial growth. In future, climate change may act as an added factor by altering water supplies. Each of these factors will be considered below. 1.2.1

POPULATION GROWTH

South Asia is one of the most densely populated regions of the world. The current (2001) population is estimated to be 1,220 million. The ratio of rural and urban population is roughly 3:1 (UN, 1994). Population growth has both direct and indirect effects on water demand (Fig. 1.4). The main direct effect is an increased need of water for domestic purposes (including recreation). Increases in water use could be affected by a number of factors, such as increases in per capita income and rural to urban migration. Generally, economically affluent people use more water. For example, Dhaka’s urban dwellers living in the high income residential areas use 2.5 times more water than these with average incomes. As well, the per capita standard urban water requirement is considered to be 2 times higher than that of rural areas. Overall, there is no doubt that increases in population will create larger water demand. However, there are other factors, which may play a significant role in determining water use patterns. They are: pattern of urbanization, the degree of adoption of water-conservation technology, and institutional factors governing directly or indirectly the degree of demand management (Kulshrestha, 1993). One additional factor, the pricing of water, is an important determinant in future water use and demand. Currently, in South Asia, water is highly subsidized which encourages inefficient water use, thereby creating more demand.

1.2.2

AGRICULTURE

At present, agriculture is the single largest contributor to the GDP of South Asian countries. The highest and the lowest contribution of the agriculture sector is 41% and 20% for Nepal and Sri Lanka, respectively (ADB, 2003). In future, agriculture will likely remain an important sector of the economy in terms of food production as well as employment generation. Water requirements for the agriculture sector are also the highest in South Asia. Future water demand in the agriculture sector will also be driven indirectly by

6

CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA

increases in population through: the demand for food (cereals and corns), and the demand for non-food (industrial) and farm products. The increased demand for food may be met by taking one or more of the following measures suggested by Kulshrestha (1993): expanding the rainfed (dry land) area, improving the productivity of the rainfed (dry land) area, expanding the irrigated area, improving the productivity of irrigated agriculture, and importing food from other countries. The last measure will not create water demand in the food-importing region. In South Asia, there is very limited scope for the expansion of rainfed agricultural land because most of the land is already under cultivation. There may be some scope for increasing the productivity of rainfed land, but currently that is constrained by floods, droughts, temporary inundation from rainfalls, tidal flows and coastal salinity (Hossain and Fisher, 1995). Note that in South Asia yields of rice under rainfed conditions compared with those of irrigated rice yields are low. Therefore, it is assumed that increased demand for food will be met by expanding the area under irrigation. This will have a substantial impact on future water demand.

60000

Ganges

Discharge (m3/sec)

50000

Brahmaputra Godavari

40000

Narmada

30000 20000 10000 0 Jan Feb Mar Apr May Jun

Jul Aug Sep Oct Nov Dec

Month

Fig. 1.3 Mean monthly discharge of the Ganges, Brahmaputra, Godavari and Narmada Rivers. Data source: Bangladesh Water Development Board (BWDB) and Global Runoff Data Center, Germany.

Poultry and livestock products constitute a portion of the food intake in South Asia. India has the largest bovine population in the world and milk is an important drink in India and other countries. It is assumed that the dietary habits of people have been changing recently which people consuming more meat now than before. This is perhaps due not only to the increases in income levels but also decreased per capita availability of fish. The intake of milk and milk products (cheese, butter, clarified butter, yogurt, ice cream, etc.) is increasing and becoming popular in many South Asian countries. Therefore, in future, increases in demand for poultry and livestock products will be translated into an increased number of livestock. This would result in increased water demand for stock watering, growing forages and livestock feeds (Kulshrestha, 1993).

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Table 1.3 Major Himalayan glaciers

Country/State

Glaciers

Himachal Pradesh

Chandra, Bhaga, Chandra-Nahan, Beas Kund Zemu, Kanchenjunga Bicham, Kangto Yamnotri, Gangotri, Satopanath, Milam, Pindari

Sikkim Arunachal Pradesh Garhwal/Kumaon

Nepal Bhutan

Api, Annapurna, Langtang, Khumbu, Yalung Chomolhari

Source: Subba, 2001.

Fig. 1.4 Water and Population Dynamics (Modified from: Sherbinin, 1998).

1.2.3

INDUSTRIAL GROWTH

Industrial development is being fostered as an important element of economic growth. When income levels increase due to industrialization, the demand for products further accelerates the industrialization process. Direct foreign investment is also an important factor in industrialization supporting both domestic requirements and exported products. All these activities and changes eventually increase industrial water requirements and uses. Industrial water demands vary from country to country in South Asia. High water requirement industries are: mining, thermal power, steel and non-ferrous metals, heavy chemicals, fertilizers, petro-chemicals, paper and newspaper, textiles, and cement. India’s industrial water requirement will continue to be the highest in South Asia because of its

8

CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA

population (mainly middle class consumers), size of the economy and versatile investment opportunities for foreign and overseas Indian investors. According to one estimate, industrial water demand in India doubles every ten years [Central Water Commission (CWC), 1989]. In Bangladesh, the rate of increase in industrial water demand has been estimated to be 5% per annum (MPO, 1986). However, these dated estimates need to be revised in the context of a market economy. 1.3

CLIMATE CHANGE AND WATER RESOURCES

Global climate change due to the enhanced greenhouse effect has emerged as one of the most pressing environmental issues for the 21st century. Emissions resulting from human activities are substantially increasing the atmospheric concentrations of greenhouse gases enhancing the natural greenhouse effect and resulting in an additional warming of the Earth’s surface. Over the last 100 years, global mean surface temperature has increased by 0.6o±0.2oC (IPCC, 2001a) (Fig. 1.5). This value is about 0.15oC larger than that estimated by the Second Assessment Report (SAR) for the period up to 1994, owing to the relatively high temperatures of the additional years (1995 to 2000) and improved methods of processing the data. These numbers take into account various adjustments, including urban heat island effects (IPCC, 2001a). The global mean radiative forcing due to greenhouse gases will continue to increase in the current century. The fraction due to CO2 is projected to increase from slightly more than half to about three quarters. The change in direct plus indirect aerosol radiative forcing is projected to be smaller in magnitude than that of CO2. According to the IPCC (2001a), the globally averaged surface temperature is projected to increase by 1.4oC to 5.8oC during the period 1990-2100. Global mean sea level is projected to rise in the range of 9 cm to 88 cm (Fig. 1.5). These results are for the full range of 35 SRES scenarios, based on a number of climate models. Based on global model simulations and for a wide range of scenarios, global average water vapor concentration and precipitation are projected to increase in the 21st century. In South Asia there is a model agreement on increase with an average change between 5% to 20% (IPCC, 2001a). Changes in these two important climatic parameters will have profound implications for hydrology and water resources. The hydrological system is sensitive to changes in climate (Arnell et al., 1996). The interactions between increases in greenhouse gases and the hydrological system are very complex and are shown in Figure 1.6. Increases in temperature will result in changes in evapo-transpiration, soil moisture, and infiltration. Increased atmospheric CO2 may increase global mean precipitation as indicated by all GCMs (IPCC, 2001a). Changes in rainfall could affect water availability in soils, rivers and lakes, with implications for domestic and industrial water supplies, hydropower generation, and agricultural productivity. Increased evapo-transpiration enhances the water vapor content of the atmosphere and the greenhouse effect, and the global mean temperature rises even higher. Land use will also play a key role in increased evapo-transpiration. Possible changes in temperature, precipitation and evapo-transpiration may result in changes in soil moisture, ground water recharge and runoff and could intensify flooding and droughts in various parts of the world (Arnell et al., 1996, IPCC, 2001b). 1.4

CLIMATE CHANGE AND FUTURE WATER CHALLENGES

The Asian monsoon causes the most regional inter-annual variability in the climate system (IPCC, 2001a). Several studies show an increase in the inter-annual variability of

M. M. Q. MIRZA AND Q. K. AHMAD

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daily precipitation in the Asian summer monsoon with increased greenhouse gases (Hu et al., 2000; Lal et al., 2000). Lal et al. (2000) find that there is also an increase in intra-seasonal precipitation variability and that both intra-seasonal and inter-annual increase are associated with increased intra-seasonal convective activity during the summer. On the other hand, Asian summer monsoon precipitation may be decreased as a result of the dampening effect caused by sulfate aerosols (Kattenberg et al., 1996; Roeckner et al., 1999). However, IPCC (2001a) concluded that the magnitude of this change depends on the size and distribution of the forcing. Relatively small climatic changes can cause large water resources problems, particularly in the arid and semi-arid regions of India and Pakistan, flood vulnerable areas of India and Bangladesh. Climate change will have significant implications in South Asia as shown below.

Fig. 1.5 Projected temperature and sea level changes (IPCC, 2001a).

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CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA

1.4.1

DROUGHTS AND FLOODS

Changes in the characteristics and magnitude monsoon and summer precipitation can increase the probability of occurrences of various types of droughts (Box 1.2) in South Asia. Failure of the normal arrival of monsoon and magnitude of rainfall often causes droughts in India (Fig. 1.7). Droughts are also common in Bangladesh, Pakistan and Sri Lanka. In Pakistan 68 mha of land with less than 300 mm rainfall are vulnerable to annual droughts. In 2000, India and Pakistan were hit by severe droughts and economic damage was very high (Box 1.3). Greenhouse Gas Increases

Increases in Radiative Forcing

Increases in Temperature

Snow and Ice Melt

Sea Level Rise

Backwater Effect by Tidal Flow

Changes in Precipitation and Evapo-Transpiration Changes in Drought Changes in Soil Moisture

Changes in Runoff

Changes in River Flow

Changes in Floods

Changes in Ground Water

Fig. 1.6 Relationship between climate change and hydrology. Shaded boxes indicate important parameters for flood and drought generation. Source: modified from Arnell, 1992.

In Nepal floods and landslides occur almost every year. In July 1993, Nepal experienced the worst natural disaster on record. Three days of torrential rainfall in central Nepal triggered disastrous landslides, and caused debris flows and major flooding in main streams and the Terai plains. Nepal is also vulnerable to Glacier Lake Outburst Floods (GLOFs), where 40 mha of land in India are annually vulnerable to floods. In the downstream delta in Bangladesh (located at the confluence of the three large rivers), which drains vast areas of the GBM river basins, the seasonal variation of water availability is very high and largely unpredictable. Large parts of the basins, including Bangladesh are

M. M. Q. MIRZA AND Q. K. AHMAD

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inundated every year by floods in the monsoon (Fig. 1.8). Bangladesh experienced three extreme floods in 1987, 1988 and 1998 (Box 1.4). Box 1.2 Various Types of Droughts

Maidment (1994) and Kulshrestha (1997) defined four types of droughts: (i) Meteorological Drought: when actual rainfall over an area is less than 75% of the long-term climatological mean; (ii) Hydrological Drought: when there is a significant depletion of surface water causing very low stream flow and drying of lakes, reservoirs and rivers. It may also result in recession of spring flows and glaciers due to insufficient regeneration of seasonal snow cover; (iii) Agricultural Drought: when soil moisture is inadequate to support the healthy growth of crops, resulting in very low yield. Water level is lower and ground water is unable to meet the crop needs; and (iv) Economic Drought: concerned with effects on economic and human sectors.

90 80

Drought area (%)

70

Rainfall less normal (%)

60 50 40 30 20 10 82 19

74 19

66 19

51 19

20 19

11 19

04 19

99 18

18

73

0

Fig. 1.7 Departure of rainfall from normal and drought affected area in India. Normal rainfall for the period 1871-1994 = 853 mm (Data source: Indian Institute of Tropical Meteorology (IITM)).

Flooding is largely dependent on extreme rainfall events. Gordon et al. (1992) and Whetton et al. (1993) indicate that global warming may produce changes in the frequency of intense rainfall because of possible changes in the paths and intensities of depressions and storms; and possible increases in convective activity. Climate model experiments suggest that rainfall intensity and number of wet spells are likely to increase with increases in greenhouse gas concentrations (McGuffie et al., 1999). However, current GCMs lack accuracy at smaller resolutions (regional and sub-regional) and therefore involve high uncertainties in projecting local weather extremes (Kattenberg et al., 1996). Despite this limitation, evidence from climate models and hydrological studies suggest that flood frequencies are likely to increase with global warming. The amount of increase is very

12

CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA

uncertain and, for a given change in climate, will vary considerably between catchments (Arnell et al., 1996).

Fig. 1.8 The Ganges basin and Farakka Barrage. Boundaries of the Brahmaputra and Meghna basins are also shown.

M. M. Q. MIRZA AND Q. K. AHMAD

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Box 1.4 Bangladesh Floods in 1987, 1988 and 1998

Disastrous floods inundated as much as 70% of Bangladesh in consecutive years 1987 and 1988 and a decade later in 1998. Heavy local rainfall together with cross-border runoff contributed significantly to flooding process in 1987. However, in 1988 and 1998, runoff mainly from the cross border basin areas of the Ganges, Brahmaputra and Meghna (GBM) Rivers in Bhutan, India and Nepal caused floods. The El Niño Southern Oscillation (ENSO) has a significant influence on the weather and meteorological dynamics in South Asia. During the monsoon months (June-September) of 1988 and 1998, basin areas of the GBM Rivers received rain considerably above normal with the “La Nina” event (colder phase of the ENSO). The economic damage of these three floods was estimated at 1 billion to 4 billion US dollars. Source: Mirza, 2003.

1.4.2

IRRIGATION

Agriculture in South Asia is highly irrigation intensive. Crops in the summer as well as monsoon crops occasionally require irrigation. For example in Bangladesh about 40% of the crops grown in summer need significant irrigation. The primary source of irrigation water is surface water. However, ground water is also becoming a leading source of irrigation. Irrigated areas irrigated by source of irrigation for India is shown Figure 1.9.

Fig. 1.9 Area irrigated in India by sources of water. Figure courtesy of: K. S. Rajan, Institute of Industrial Science, University of Tokyo, Japan.

Agricultural particularly for irrigation water demand, is sensitive to climate change. A change in field-level climate may alter the need for and timing of irrigation. Increased dryness may lead to increased demand, but demand could be reduced if soil moisture content rises at critical times during the cropping season.

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CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA

SRES

A1 Storyline

A2 Storyline

A1 Family

A1F1

A1T

B1 Storyline

A2 Family

A1B

B2 Storyline

B1 Family

A2

B2 Family

B1

B2

Fig. 1.10 SRES storylines and six illustrative “marker” scenarios. Source: Taylor et al., 2003. Note: A1: Rapid Convergent Growth- The A1 scenarios describe a future world of very rapid economic growth and global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. The difference between the A1FI, A1B, and A1T scenarios is mainly in the source of energy used to drive this expanding economy. • A1FI: Fossil-Fuel Intensive: coal, oil, and gas continue to dominate the energy supply for the foreseeable future. • A1B: Balance between fossil fuels and other energy sources. • A1T: emphasis on new Technology using renewable energy rather than fossil fuel. A2: Fragmented World- the A2 scenario describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities. B1: Convergence with Global Environmental Emphasis- the B1 storyline and scenario family describes a convergent world with the same global population that peaks in mid-century and declines thereafter B2: Local Sustainability- the B2 storyline and scenario family describes a world in which the emphasis is on local solutions to economic, social, and environmental sustainability.

M. M. Q. MIRZA AND Q. K. AHMAD

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Döll and Siebert (1999) applied a global irrigation water-use model with a spatial resolution of 0.5o x 0.5o to assess the impact of climate change on net irrigation requirements per unit irrigated area using scenarios from the ECHAM4 climate model. Under this scenario and similarly under the corresponding HadCM3 scenario-net irrigation requirements per unit-irrigated area generally would increase for most areas in India due mainly to increase in temperature and decrease in winter precipitation. Lal et al. (2001) reported a 5%-25% decline in winter rainfall over India and concluded that this may lead to droughts during the dry summer months. A temperature increase of 3.5oC-5.5oC is projected by 2080. The experiments were conducted in the CCSR/NIES coupled A-OGCM using for SRES (Special Report on Emission Scenarios) marker emission scenarios (Box 1.5), which include revised trends for all the principal anthropogenic forcing agents. 1.4.3

GLACIERS AND WATER SUPPLY

Himalayan glaciers that feed the Ganges River appear to be retreating quickly. The estimated retreat of the Dokriani glacier in 1998 was 20 m compared to an annual average of 16.5 m for 1993-1998 (Down to Earth, 1999). Dokriani is just one of the several hundred glaciers that feed the Ganges. The 26 km Gangotri glacier retreated (between 1842 and 1935) at an average rate of 7.3 m/year, while between 1977 and 1990, the rate of recession was went up to 28 m/year (Table 1.4). Table 1.4 Retreating Himalayan Glaciers

Glacier

Magnitude of Retreat (m)

Period (years)

Average Retreat (m/year)

Gangotri Pindari Milam Poting Triloknath Bara Shigri Chota Shigri Shankulpa

364 2,840 1,350 262 400 650 60 518

13 (1977 and 1990) 120 (1845-1966) 107 (1849-1957) 50 (1906-1957) 26 (1969-1995) 18 (1977-1995) 9 (1986-1995) 75 (1881 to 1957)

28 23.6 12.6 5.2 15.4 36.1 6.7 6.9

Source: Bahadur, 1998.

Glaciers in the Himalayas are receding faster than in other parts of the world. If the present rate continues, with less than 1oC increase in temperature the likelihood of many of them disappearing by the year 2035 is very high (Kotlyakov, 1999). The world may be 0.3oC-1oC warmer by the year 2035 (IPCC, 2001a). Global warming and glacier retreat in the Himalayas will have five broad implications. (1) in the short-run, in the process of continued retreat, more water will be supplied to the glacier dependent perennial rivers in the Himalayas. This may generate positive effects on dry season water availability. (2) the chances of glacier lake outburst flood (GLOF) may increase (Hasnain, 1999). (3) rising temperature will contribute to the raising of the snowline, and increasing the risk of flash floods during the wet season. (4) in the long-run, dry season flow in upstream Himalayan rivers could be greatly reduced, posing serious eco-environmental problems (Hasnain, 1999). Note that with increase in population, water demand will likely be higher in the

16

CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA

long-run. Therefore, the gap between demand and supply will be wider. 5) in the shortrun, with increase in dry season discharge, sediment supply in the rivers may increase. This may pose a threat to the existing dams and reservoirs in the region. More melting means higher silt loads, reducing the life of dams and reservoirs. 1.4.4

FOOD SECURITY

Agriculture is highly sensitive to climate variability in South Asia. Food production is well adjusted to the mean climatic conditions and can cope with moderate variations. Climatic variability creates hazards to which the agricultural ecosystems are not well adapted (WMO and FAO, 1996). High temperatures accelerate reduction in soil moisture, damage crops and reduce yields. Low temperatures, frost and prolonged episode of fog curtail yields and destroy crops. Extreme rainfall events and flooding cause inundation and water-logging of productive land. Floods and drought cause significant damage to crop production leading to food insecurity in South Asia. For example, a flood in Bangladesh devastated the Kharif II crop in 1974, led to a famine, malnutrition and disease, claiming about 250,000 lives. A severe drought in 1979 created a famine like situation. About 3.5 million tons of crops were destroyed by floods in 1998 (Mirza, 2002) and droughts in 2000 in Gujarat and Pakistan generated food shortages. In the wake of future climate change, crops will be more vulnerable to damage from climate variability and change, extreme weather events, lack of soil moisture and inadequate water supplies for irrigation. The likelihood of food insecurity will also increase especially among the marginal farmers and people employed in the agriculture sector. 1.4.5

WATER AND SOIL QUALITY

Water quality is a function of chemical, physical, and biological characteristics (IPCC, 2001b). However, quality is determined using standards set for agriculture, industrial and human consumption. Major water pollutants include organic material, which causes oxygen deficiency in water bodies, nutrients which cause excessive growth of algae in lakes and coastal waters and toxic heavy metals and organic compounds. In South Asia, the major sources of water pollution are irrigated agriculture, industrial discharges, natural nutrients, sediments and intrusion of saline water to inland areas. Future climate change could contribute to accelerated pollution of water and soil. Agricultural inputs are most likely to be affected by climate change because a changing climate might alter agricultural practices. A changing climate may also alter chemical processes in the soil, including chemical weathering (White and Blum, 1995). Warmer, drier conditions, for example, promote mineralization of organic nitrogen (Murdoche et al., 2000) and thus increase the potential supply to the river or ground water. Water temperature by and large controls biological and chemical processes in river water. Higher temperatures alone would lead to increases in concentrations of some chemical species but decreases in others (IPCC, 2001b). Dissolved oxygen concentrations are lower in warmer water, and higher temperatures also would encourage the growth of algal bloom (through eutrophication) which consume large amounts of oxygen during the decomposition process. Stream water quality, however, also will be affected by stream flow volumes, affecting both concentration and total load.

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Water pollution by intrusion of saline water has become a major problem in the coasts of Bangladesh and India. In the coastal districts of Bangladesh especially in the Southwest region, water and soil salinity are on the rise. Due to reduced dry season flow in the Gorai River (a distributary of the Ganges) as a result of upstream withdrawal in India, water salinity in the Southwest region has increased significantly. In this region, soil salinity occurs in the dry period due to penetration of tidal water from the sea and also due to the capillary rise of saline water from the underground water table (Mirza and Hossain, 2004). In the coastal regions of Tamil Nadu, India, salinity of ground water due to the intrusion of seawater into the subsurface aquifer has been identified as a major problem (Subramanian, 2000). Due to the excess withdrawal of ground water, the water table has declined allowing seawater to penetrate. Similarly, ground water pollution by saline seawater is a major problem in Gujarat, Western India. In the coasts of West Bengal and Orissa, freshwater is often polluted by high tides and storm-surges. In the future due to sea level rise, coastal water salinity problem will be aggravated. IPCC (2001b) estimated that with a rise of 45 cm, about 11% of Bangladesh may be inundated by saline water. On the other hand, a 100 cm rise in sea level could inundate about 6,000 km2 in India (Teri, 1996). A rise in sea level and coastal inundation will certainly increase pollution of coastal waters. 1.4.6

DAMS AND RESERVOIRS

Because climatic change will affect the sub-processes of the sediment chain, more soil may be eroded in the future than at present. Increased precipitation, creating more erosion, mass wasting and bed and bank cutting of rivers will add to the sedimentation rates. Reservoirs will be particularly vulnerable, as the storage space available will be filled up at a much faster rate with the exacerbated sedimentation. In South Asia, India has the largest nd number of dams (more than 1,500) and reservoirs, Pakistan ranks 2 with five and Nepal and Bangladesh have built only one each: the Kaptai and Kulekhani Dams, respectively. In South Asia, reservoirs are losing storage space much faster than was assumed in their designs. Indian reservoirs, on an average, are losing storage space at the rate of 1.49 acre-feet/mile2/year. The life of the Kaptai Reservoir in Bangladesh was estimated to be 300 years, but today it is calculated at only 186 years (Mirza, 1986). The Kulekhani reservoir, which had a life expectancy of 100 years, lost 1 million m3 of its gross storage capacity in just 12 years after its impoundment, which began in 1981. The reservoir had a designed storage of 12 million m3. In 1993, a 15-hour cloudburst over the Kulekhani River triggered a massive flow of debris, with landslides and tremendous bank cutting, pouring almost 4.8 million m3 of sediment into the reservoir (Nippon Koei, 1994). The specific sediment yield of this torrent was 38,000 m3/km2, but the siltation rate assumed in the design of the reservoir was just 700 m3/km2/year (JICA, 1974). The result was the almost total saturation of the dam’s 12 million m3 storage capacity. Built with a US$ 120 million loan from the World Bank, the dam currently employs catchment-wide sedimentation control activities and relocation of the tunnel intake of the plant. The high rate of sedimentation means that the economic life of the reservoirs planned to be built in the region will be reduced. Twenty-nine of these reservoirs are proposed to be built in the Nepal Himalayas. Given that the Himalayas is still a hydrological “black box”, the economics of reservoir projects based on environmental designs are incomplete and their life span is much less than that assumed in the design (Gyawali and Dixit, 1997). Two lessons can be drawn from the information presented above on sedimentation:

18

CLIMATE CHANGE AND WATER RESOURCES IN SOUTH ASIA

estimates of regional sedimentation need to incorporate not only the contributions of sheet, rill and gully erosion but also of bishyari, Jokulhaup and landslides as regular sources in the sediment budget and in a warmer climate, due to increase in sedimentation, the threshold rate of sedimentation is likely to be higher than today. Uncertainty in defining the new level of threshold will remain. A more thorough analysis of reservoir economy must guide the decisions regarding the source of future water supply. Using reservoirs for providing water supply should be questioned. An effort should be made to analyze the efficacy of reservoirs vis-à-vis the alternatives for meeting water requirements through local water resource management especially ground water storage. Because the consequences of sedimentation are, they must be analyzed collectively and cooperatively by the countries of the region. Response to the problem of sedimentation must go beyond the conjecture that increased sedimentation is caused by deforestation and that its extent may be reduced by watershed management. Watershed management does play a role, but it influences water-sediment flow only at the micro level. It should, therefore, be awarded its true importance in enhancing local economy and not considered as the only remedy for mitigating sedimentation at the regional level. Dam safety relates to the calculation of the critical runoff threshold that a spillway could handle and the frequency of occurrence of the threshold in a future warmer climate in which the dimensions of the existing spillway may be inadequate to accommodate the increased magnitude of floods (WMO, 1987). Although, the relationship between dam safety and hydrological catastrophes in the river basins in South Asia is not well catalogued, the performance of spillways of Kulekhani Hydropower Project and Bagmati Irrigation Barrage in Nepal during the floods of 1993 may provide some basis for estimating critical runoff thresholds. In 1993 flood, the Bagmati Barrage, designed for a flood of 8,000 m3/sec, was over-topped. The Kulekhani spillway was designed to handle a peak flood of 2,400 m3/sec and experienced 1,340 m3/sec (Nippon Koei, 1994). 1.4.7

WATER SHARING AND TREATIES/AGREEMENTS

Water has been a contentious issue among the nations in South Asia who share transnational river basins. India and Bangladesh have 54 transnational rivers. Many important tributaries of the Ganges River originate in the Nepal Himalayas and supply water to the downstream countries of India and Bangladesh. Origins of main tributaries of the Brahmaputra River originate in Bhutan. The Indus River system is shared by India and Pakistan. Disputes regarding sharing of water of transnational rivers are highly driven by resources’ constraints. As indicated in Section 1.2, water demand usually mismatches the supply due to high seasonality (dry season vs. monsoon) of river flows. As the supply of water is finite, the problem is further compounded by increased population pressure, agriculture, rocketing urban water supply and industrial needs and environmental water requirements. As Bangladesh occupies the tail end of the transnational river basins such as the Ganges, it receives residual water supplies. A significant amount of water is withdrawn in the upstream part of the river basin in Uttar Pradesh and Bihar. Further downstream at Farakka (West Bengal) (Fig. 1.8) on average 52% of the water is diverted through a Barrage for making Kolkata Port navigable. After the partition of India in 1947, dispute arose on the sharing of water of the Indus River system. India requires water supplies from the tributaries of the Ganges River, which originate in Nepal, and the latter requires energy supply to sustain its economy. However, water of the transnational rivers of these two countries is not as contentious between India and Bangladesh and Pakistan.

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The implications of reduced water in transnational rivers for sharing will have two facets. First, intra-country water conflict will increase e.g. the case of Sindh and Punjab Provinces of Pakistan and water of the Indus River, and water disputes between Karnataka and Tamil Nadu States in India for Cauvery water. Second, bilateral conflicts dealing with rights and the sharing of transnational rivers’ water will occur. India and Pakistan have resolved the Indus water problem in 1960. However, in case of reduced resources, pressure will increase to re-negotiate terms and conditions. There is no permanent water sharing treaty/agreement on any rivers between India and Bangladesh. The two countries signed a 30-year treaty on the Ganges waters in 1996 and its further extension is negotiable. There is not enough water in the Ganges River at Farakka in the dry season to satisfy the water requirements of both countries. However, there is a potential to augment the flows by building dams/reservoirs in Nepal and India. Construction of a string of dams/reservoirs may cause significant environmental and human resettlement problems. The water sharing problems of 53 other rivers are unresolved and there is no possibility of any near-term solution. In a warmer climate in the future, the reduced water supply in these rivers will complicate negotiations regarding sharing arrangements. Upstream countries usually take unilateral actions such as diversions and withdrawals, which cause additional problems. Postel (2003) identified four triggers of conflict on water of transnational rivers in a future warmer climate. They are: • • • •

Unilateral action to change hydrology of a transnational water resources; Existing international institutions unable to respond to the change; Conflict potential is greatest if both are present; and Greater the size of the unilateral action, greater the potential for conflict.

REFERENCES Arnell, N. W.: Hydrology and Climate Change. In P. Calow and G. E. Petts (editors). Rivers Handbook: Hydrological and Ecological Principles, Boston, Blackwell Scientific Publications, 1992, pp.173-186. Arnell , N. W., Bates, B., Lang, H., Magnuson, J. J., and Mulholland, P.: Hydrology and Freshwater Ecology. In: Climate Change 1995: Impacts, Adaptation and Mitigation of Climate Change: Scientific-Technical Analyses. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change (R. T. Watson, M. C. Zinyowera and R. H. Moss Eds.), Cambridge University Press, Melbourne, Australia, 1996. Asian Development Bank (ADB): Key Indicators 2003. Education for Global Participation, ADB, Manila, 2003. Bahadur, J.: Himalayan Glaciers, Vigyan Prasar, New Delhi, 1998. Central Water Commission (CWC): Water Resources of India, CWC, New Delhi, 1987. Central Water Commission (CWC): Water and Related Statistics, CWC, New Delhi, 1989. Crow, B., Lindquist, A. and Wilson, D.: Sharing the Ganges: The Politics and Technology of River Development, University Press Limited, Dhaka, 1995. Döll, P. and Siebert, S.: Global Modeling of Irrigation Water Requirements, University of Kassel, Kassel, Germany, 1999. Down to Earth: Glaciers: Beating Retreat. Down to Earth 7(23) (1999). Food and Agriculture Organization (FAO): FAO Aquastat (http://www.fao.org/ag/agl/agl/ag1/aquastatweb/main/html/aquastat.htm), 2002. Gordon, H. B., Whettton, P. H., Pittock, A. B., Fowler, A. M., and Haylock, M. R.: Simulated Changes in Daily Rainfall Intensity Due to Enhanced Greenhouse Effect. Climate Dynamics 8 (1992), pp.83-102.

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Gyawali, D. and Dixit, A.: Natural Disaster and Social Resilience: 1993 Cloud Burst Over Central Nepal, Community Response to Vulnerability, Nepal Water Conservation Foundation, Kathmandu, 1997. Gyawali, D.: Water in Nepal, East West Center, Hawaii, USA, 1989. Hasnain, S. I.: Report on Himalayan Glaciology, International Commission on Snow and Ice (ICSI), U.K., 1999. Hossain, M. and Fisher, K. S.: Rice Research for Food Security and Sustainable Development in Asia: Achievements and Future Challenges. Geojournal 35(3) (1995), pp.286-298. Hu, Z-Z., Latif, M., Roeckner, E. and Bengtsson, L.: Intensified Asian Summer Monsoon and Its Variability in a Coupled Model Forced by Increasing Greenhouse Gas Concentrations. Geophysical Research Letters 27 (2000), pp.2681-2684. Intergovernmental Panel on Climate Change (IPCC): Climate Change 2001: The Scientific Basis, Cambridge University Press, U.K., 2001a. Intergovernmental Panel on Climate Change (IPCC): Climate Change 2001: Impacts, Adaptation, and Vulnerability, Cambridge University Press, U.K., 2001b. Japan International Cooperation Agency (JICA): Feasibility Report on Kulekhani Hydroelectric Project, JICA, Tokyo, Japan, 1974. Kattenberg, A., Giorgi, F., Grassl, H., Meehl, G. A., Mitchell, J. F. B., Stouffer, R. J., Tokioka, T., Weaver, A. J., Wigley, T. M. L.: Climate Models-Projections of Future Climate. In: Climate Change 1995: The Science of Climate Change. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, U.K., 1996. Kotlyakov, V. M.: Variations of Snow and Ice in the Past and Present on a Global and Regional Scale, International Commission of Snow and Snow (ICSI), ICSI, U.K., 1999. Kulshrestha, S. M.: Drought Management in India and Potential Contribution of Climate Prediction, Joint COLA/CARE Technical Report No. 1., COLA and CARE, Calverton, MD, USA, 1997. Kulshrestha, S. N.: World Water Resources and Regional Vulnerability: Impact of Future Changes, IIASA, Luxembourg, Austria, 1993. Kumar, K. R., Kumar, K. K., Ashrit, R. G., Patwardhan, S. K., and Pant, G. B.: Climate Change in India: Observations and Model Projections. In: Climate Change and India: Issues, Concerns and Opportunities (P. R. Shukla, Subodh K. Sharma and P. V. Ramana Eds.), Tata McGraw-Hill Publishing Company Limited, New Delhi, India, 2003. Lal, M., Meehl, G. A. and Arblaster, J. M.: Simulation of Indian Summer Monsoon Rainfall and Its Intra-Seaonal Variability. Regional Environmental Change 1(3-4) (2000), pp.163-179. Lal, M., Nozawa, T., Emori, S., Harasawa, H., Takahashi, K., Kimoto, M., Abe-Ouchi, A., Nakajima, T., Takemura, T. and Numaguti, A.: Future Climate Change: Implications for Indian Summer Monsoon and Its Variability. Current Science 81(9) (2001), pp.1196-1207. Maidment, R.: Handbook of Hydrology, McGraw-Hill Inc., New York, 1994. McCurry, S.: Monsoon, Thames and Hudson, Singapore, 1988. McGuffie, K., Henderson-Sellers, A., Holbrook, N., Kothavala, Z., Balachova, O. and Hoekstra, J.: Assessing Simulations of Daily Temperature and Precipitation Variability with Global Climate Models for Present and Enhanced Greenhouse Climates. International Journal of Climatology 19 (1999), pp.1-26. Mirza, M. M. Q.: Watershed Management and Sediment Control, Unpublished Paper, Department of Water Resources Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1986. Mirza, M. M. Q.: Modeling the Effects of Climate Change on Flooding in Bangladesh. Unpublished PhD Thesis, International Global Change Institute (IGCI), University of Waikato, Hamilton, New Zealand, 1997. Mirza, M. M. Q.: Drought in South Asia: Some Lessons. Daily Star Features (2 June, 2000), Dhaka, 2000. Mirza, M. M. Q.: Global Warming and Changes in the Probability of Occurrence of Floods in Bangladesh and Implications. Global Environmental Change 12 (2002), pp.127-138.

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Mirza, M. M. Q.: Three Recent Extreme Floods in Bangladesh: A Hydro-Meteorological Analysis. In: Flood Problem and Management in South Asia (M. M. Q. Mirza, A. Dixit and A. Nishat Eds.), Kluwer Academic Publishers, Dordrecht, The Netherlands, 2003, pp.35-64. Mirza, M. M. Q. and Hossain, M. A.: Adverse Effects on Agriculture in the Ganges Basin in Bangladesh. In: The Ganges Water Diversion: Environmental Effects and Implications (M. M. Q. Mirza Ed.), Kluwer Academic Publishers, Dordrecht, The Netherlands (in press), 2004. Master Plan Organization (MPO): National Water Plan: Summary Report, MPO, Dhaka, 1986. Murdoche, P. S., Baron, J. S., and Miller, T. L.: Potential Effects of Climate Change on Surface Water Quality in North America. Journal of the American Water Resources Association 36 (2000), pp.347-366. Nakicenovic, N.: Special Report on Emissions Scenarios, Cambridge University Press, U.K., 2000. Nippon Koei: Master Plan Study on Sediment Control for Kulekhani Watershed-Main Report, Nippon Koei, Tokyo, Japan, 1994. Postel, S.: Presentation on Climate Change and Effects on Water Quality and Quantity: The Escalating Need for Conflict Management, World Watch Institute, Washington, D.C., USA, 2003. Roeckner, E., Bengtsson, L., Feichter, J., Lelieveld, J. and Rodhe, H.: Transient Climate Change with a Coupled Atmosphere-Ocean GCM Including the Tropospheric Sulfur Cycle. Journal of Climate 12 (1999), pp.3004-3032. Sharma, C. K.: River Systems of Nepal, Sangeeta Sharma, Kathmandu, 1977. Sherbinin, A. D.: Water and Population Dynamics: Local Approaches to a Global Challenge. In: Water and Population Dynamics: Case Studies and Policy Implications (A. D. Sherbinin and V. Dompka Eds.), American Association for the Advancement of Science (AAAS), Washington, D.C., USA, 1998. Subba, B.: Himalayan Waters: Promise and Potential, Problems and Politics, Panos South Asia, Kathmandu, 2001. Subramanian, V.: Water Quantity and Quality in South Asia, Kingston International Publishers, Surrey, U.K., 2000. Tata Energy Research Institute (TERI), The Economic Impact of One Meter Sea Level Rise on Indian Coastline-Methods and Case Studies. Report submitted to the Ford Foundation, New Delhi, 1996. Taylor, B., Barton, M. and Neilsen, D.: Climate Analysis and Scenarios, Expanding the Dialogue on Climate Change & Water Management in the Okanagan Basin. In: S. J. Cohen and T. Neale Eds., Interim Report, Environment Canada, Agriculture and Agri-Food Canada and University of British Columbia, 2003. United Nations (UN): Annual Populations (1994 Revision)-Median Estimate, United Nations, New York, 1994. Whetton, P. H., Fowler, A. M., Haylock, M. R., and Pittock, A. B.: Implications of Climate Change Due to Enhanced Greenhouse Effect on Floods and Droughts in Australia. Climate Change 25 (1993), pp.289-317. White, A. F. and Blum, A. E.: Effects of Climate on Chemical Weathering in Watersheds. Geochimica et Cosmochimica Acta 59 (1995), pp.1729-1747. World Meteorological Organization (WMO): Water Resources and Climate Change: Sensitivity of Water Resource Systems to Climate Change and Variability, WMO, Geneva, 1987. World Meteorological Organization (WMO) and Food and Agriculture Organization of the United Nations (FAO): Climate and Food Security, World Food Summit, Rome, 13-17 November, 1996.

2 Hydrologic Modeling Approaches for Climate Impact Assessment in South Asia M. MONIRUL QADER MIRZA

2.1

INTRODUCTION

The hydrologic and water resources problems in South Asia are discussed in Chapter 1. It is anticipated that the problems will be exacerbated if basin-wide temperature and precipitation would change due to climate change. Quantification of possible changes in river discharge (mean or peak) is achieved with the application of hydrologic models. Four types of hydrologic model - empirical, water-balance, conceptual lumped-parameter and process-based distributed models - are used for hydrologic modeling. A model is usually selected depending on the purpose of the application which includes: runoff-simulation; sediment transport and morphological changes; estimating ground water and changes in ground water volume; forecasting flood volume, depth and duration; assessing changes in land-use; and assessing impacts of changes in climate. Availability of data and resources are also governing factors in a model selection process. This chapter discusses the comparative advantages and limitations of various hydrologic models and their suitability for estimating changes in mean annual and mean peak discharge under selected climate change scenarios for the river basins in South Asia. It examines reduction of input variables for empirical modeling through the sensitivity analysis of runoff to changes in temperature and precipitation. This chapter also discusses application of hydrologic models in Bangladesh as a case study to assess climate change impacts. 2.2

HYDROLOGIC MODELS

In planning for water resources and extreme events like floods and droughts, it is essential to know the precipitation-runoff processes in the vegetation, land surface and soil components of the hydrologic cycle. These processes differ in arid, semi-arid and humid climates. Even within a single climate zone, physical processes can vary widely because of the diversity of vegetation, soils and microclimates. Hydrologic models describe these processes by partitioning the water among the various pathways of the hydrologic cycle (Dooge, 1992). Mathematically, hydrologic models incorporate a set of assumptions, equations and procedures intended to describe the performance of a prototype (real-world) system (Linsley et al., 1988). Because of the

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increase in computing capacity, complex mathematical descriptions of the physical processes of the hydrologic cycle can now be incorporated into hydrologic models. However, because of variations in physical parameters and the limitations of our knowledge and understanding about the complexity of the hydrological processes, no ‘hydrologic model’ is able to reproduce fully the prototype processes. Accuracy of the model is highly dependent on factors such as: adequacy of empirical, statistical and mathematical descriptions of the physical processes; the quantity and quality of input data; the extent of basin coverage; and the magnitude of variability in physical parameters. There are two main aims for using simulation modeling in hydrology. The first is to explore the implications of making certain assumptions about the nature of the real-world system. The second is to predict the behavior of the real-world system under a set of naturally occurring circumstances (Beven, 1989). In order to meet these aims, different types of hydrologic models are required. There are four types of hydrologic models - empirical, water-balance, conceptual lumped-parameter and process-based distributed models. The choice of model type depends partly on the purpose of the application including: simulating runoff, sediment transport and morphological changes; estimating ground water and changes in ground water volume; forecasting flood volume, depth and duration; assessing changes in land-use; and assessing impacts of changes in climate. The choice of model also depends on the availability of data and resources. The various types of hydrologic models and their advantages and limitations are discussed below. 2.2.1

EMPIRICAL MODELS

In hydrological modeling, empirical models are generally developed and used for prediction and estimation purposes. These models do not explicitly consider the physical laws governing the processes involving precipitation, temperature, vegetation and soils (Singh, 1988). However, they do implicitly incorporate the fundamental physical fact that, generally, variations in runoff tend to respond proportionally to the variations in climate. Empirical models are developed based on a ‘black box’ modeling approach where empirical equations are used to relate runoff and rainfall, and only the input (rainfall) and output (runoff) have physical meanings. Through statistical techniques, empirical models reflect only the relations between input and output for the climate and basin conditions during the time period for which they were developed. These models provide a much more simplified view of reality, particularly when regression techniques are employed (Kirkby et al., 1987). The accuracy of models largely depends on the magnitude of error inherent in the input and output data. As the empirical models are developed with input and output data within a certain range and time period, caution should be exercised regarding the extension of the relationship for climate conditions different from those used for the development of the function (Leavesley, 1994). Models developed for a particular river basin cannot be applied to a different basin. Although empirical models are often criticized for these limitations, they are widely used compared to other models. Despite their limitations, empirical models have some distinct advantages over other types of hydrologic models. For example, they are relatively easy to develop, require less data, can be calibrated simply, require fewer resources, and do not need a huge computing capacity. When other models cannot be developed or used because of the paucity of data, empirical models can be developed for various purposes. In many situations, empirical models can yield accurate results and can, therefore, serve a useful purpose in

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decision-making (Singh, 1988). In hydrology, empirical models are generally useful in estimating the mean annual flood, monthly and annual mean discharge and bankful discharge (Garde and Kothyari, 1990; Kothyari and Garde, 1991; Mosley, 1979 and 1981; Schumm, 1969; Thomas, 1970; Rodda, 1969; Leopold and Millier, 1956; Natural Environment Research Council (NERC), 1975; Beable and Mckerchar, 1982). There are two important issues which need to be taken into account before developing an empirical model for estimating discharge and floods. First, empirical models require very good spatial distribution of precipitation. Ideally, this can be achieved by acquiring long-term records of precipitation for a large number of stations uniformly distributed over a river basin, covering high and low elevations. Similarly, long-term records of temperature are also necessary if temperature effects are to be considered. Second, a fairly good record of discharge (or runoff) from downstream stations is needed. However, if there is any diversion of flows through the distributary (ies) or by any other means in the upstream areas, this has to be taken into account depending on the magnitude of the diversion. 2.2.2

WATER-BALANCE MODELS

Water-balance models were first developed by Thornthwaite (1948) in the 1940s and were subsequently revised by Thornthwaite and Mather (1955) and by others. Palmer (1965) used a water-balance model similar to that of the Thornthwaite model while developing an index of meteorological drought. Thomas (1981) presented an alternative water-balance model with several new features. These water-balance models have very simple structures and are characterized by a limited number of parameters. This kind of model is essentially a ‘book-keeping procedure,’ which uses the following fundamental equation to estimate the balance between the precipitation (as rain and snowmelt), loss of water by evapo-transpiration, stream flow and recharge into the ground water:

where P is the precipitation, R is the runoff, G is ground water runoff, ∆S is the changes in storage (snow and soil water) and E is evapo-transpiration. The typical structure of a water-balance model is shown in Figure 2.1. The models can be simple to complex depending on the details of each of the components of the equation (2.1). Most water-balance models calculate direct runoff from precipitation and lagged runoff from the basin storage in the computation of the total runoff (R). The sensitivity and accuracy of water-balance models often depend on the method of calculating potential evapo-transpiration (PET). Various PET-models are available among which Penman (1948), Thornthwaite (1948), Blaney and Criddle (1950), Monteith (1964), Priestley and Taylor (1972), and Hargreaves (1974) are important (see cited references for descriptions of these models). The selection of the PET model is largely dependent on the availability of sufficient climate data, which varies from place to place. Most models compute E as a function of potential ET and water available in soil storage (S). Various methods are in use for calculating E from the PET and soil moisture deficit relationship, including linear, layered and exponential methods. One advantage of water-balance models is that they can potentially be used to determine changes in seasonal snow storage and melt. Within a water-balance model, the storage and melting processes of snow are described by two types of model: energy-balance and temperature-index models.

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Precipitation

Snow Storage

Evapo-Transpiration

Soil Storage

Surface Runoff

Soil Storage

Base Flow/ Delayed Flow

Total Runoff Fig. 2.1 Typical structure of a water-balance model.

The energy-balance models simulate the flow of mass and energy in the snow cover. The energy-balance approach for calculating snowmelt applies the law of conservation of energy to a control volume. The control volume has its lower boundary as the snow-ground interface and its upper boundary as the snow-air interface. The use of a volume allows the energy fluxes into the snow to be expressed as internal energy changes (Gray and Prowse, 1993). The energy balance model is physically or meteorologically more explicit than the temperature-index model. It contains parameters that can be extrapolated to a certain degree of confidence from weather maps or from regional climate models (Kuhn, 1993). Various studies have used energy-balance models to estimate runoff from snowmelt (Fitzharris and Grimmond, 1982; Granger and Gray, 1990; Gray and O’Neill, 1974; and Gray and Landine, 1987). Details of an energy-balance model can be found in Gray and Prowse (1993). The second type of model is the temperature-index snowmelt model (Equation (2.2)). Despite its simplicity, the model is widely used in forecasting discharge in snow-covered basins. Using monthly data, for example, Kwadijk (1993) applied a temperature-index snowmelt model in order to assess the impact of climate change on the Rhine River basin and found close fit between the simulated values and observed data. While modeling the effects of climate change on water resources in the Sacramento River basin in the USA, Gleick (1987) found poor performance of a temperature-index snowmelt model using monthly data. The temperature-index models for rain-free and rain conditions are as follows: (i) Rain-Free Condition

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where M = snowmelt in mm Mf = snowmelt factor Ti = index temperature Tb = base temperature (set as 0oC) (ii) During Rain For a rain event, the melt factor is modified as follows: Mf = (0.74 + 0.007P) (Ti – Tb) where

P = precipitation (in mm)

Snowmelt is calculated by:

Overall, water-balance models incorporate soil-moisture characteristics of regions, allow monthly, seasonal, and annual estimates of hydrologic parameters, and use readily available data on meteorological phenomena, soil, and vegetation characteristics. They can often provide efficient estimates of surface runoff when compared to measured runoff, reliable evapo-transpiration estimates under many climate regimes, and estimates of ground water discharge and recharge rates. Typical data requirements are precipitation, temperature, sunshine hour, wind speed, information on characteristics of vegetation (which may include type of vegetation for estimating rooting depths), and soil (such as field capacities and wilting points). While generally the water-balance models require huge amounts of data, they can nevertheless be applied in reasonably large areas with sparse data (Hare and Hay, 1971; Brash and Murray, 1980). For example, Hare and Hay (1971) applied the Lettau’s (1969) empirical model to approximate precipitation in order to analyze the anomalies in the large-scale annual water-balance over Northern North America. Brash and Murray (1980) estimated adjusted equilibrium precipitation from an energy-balance equation. The estimated precipitation was then used to estimate water yield in the Taieri catchment in New Zealand and found to be very closely matched with the measured data. Note, however, that these energy balance techniques require reliable net radiation data, which are not readily available for the major river basins in South Asia. By integrating hydrologic advances with existing water-balance techniques, new insights into hydrologic processes and environmental impacts can be gained for climate impact assessments. Furthermore, water-balance models are well suited to the current generation of microcomputer software and hardware. A number of water-balance models have been developed to assess the impact of climate change on river runoff and soil moisture stress from wet to dry regions (Mather and Feddema, 1986; McCabe and Wolock, 1992; Thompson, 1992; Flaschka et al., 1987; McCabe and Ayers, 1989; Conway, 1993 and Kwadijk, 1993). These studies show various magnitudes of runoff and soil moisture sensitivities on monthly time-scales to possible changes in climate. Overall, such studies demonstrate that the water-balance approach holds good potential for application in the river basins of South Asia (subject to availability of the required data) in order to assess effects of climate change on hydrology and water resources.

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2.2.3

HYDROLOGIC MODELING APPROACHES

CONCEPTUAL LUMPED-PARAMETER MODELS

Conceptual lumped-parameter models are developed based on approximations or simplifications of physical laws. These models embody a series of functions which are considered to describe the relevant catchment processes. The algorithms are usually simplified by the use of empirical relations in order to speed the solution and to adapt the model to cope with the point-to-point variations in the hydrologic processes within the catchment (Crawford and Linsley, 1968; Boughton, 1968; Linsley et al., 1988; Leavesley, 1994). They contain parameters, some of which may have direct physical significance and can, therefore, be estimated by using concurrent observations on input and output. Some widely-used models of this category are: the Sacramento Soil Moisture Accounting model (Burnash et al., 1973), the Institute of Royal Meteorology Belgium (IRMB) model (Bultot and Dupriez, 1976), the HBV model (Bergstorm, 1976), the Hydrologic Simulation Program-FORTRAN (HSPF) model (USEPA, 1984), the Erosion Productivity Impact Calculator (EPIC) model (Williams et al., 1984) and the MODHYDROLOG (Chiew and McMahon, 1993). A schematic diagram of a conceptual lumped-parameter model (MODHYDROLOG) is shown in Figure 2.2. In the conceptual lumped-parameter models, the vertical and lateral movement of water with respect to time is incorporated. Variations in respect of space are ignored. The vertical processes of water movement include interception storage and evaporation, infiltration, soil-moisture storage, evapo-transpiration, percolation to ground water storage, snow-pack accumulation and melt, and capillary rise. The horizontal processes include surface runoff, interflow, ground water flow, and stream flow. Components of the vertical and lateral processes are integrated. The model development starts with the vertical processes. Interception storage is assumed and calibrated usually by trial and error. Empirical algorithms are used for calculating the evaporation from the surface storage. For calculating infiltration calculation, two methods are in practice. First, the maximum infiltration rate is assumed from the field observations and then the infiltration rate is expressed as a function of soil storage (Boughton, 1968). Second, some prominent infiltration models, such as Green-Ampt (1911), Philip (1957 and 1969) and Holtan (1961), can be used directly. For example, the Hydrologic Engineering Center’s HEC-1 model uses the Green-Ampt and Holtan’s infiltration models. One of the important limitations of using these models is the need to estimate a number of parameters, some of which have to be estimated either from laboratory experiments or from field observations. The other vertical and horizontal components that need to be developed are evapo-transpiration, percolation and base flow. Evapo-transpiration is usually calculated as a function of soil moisture storage, soil moisture storage capacity and potential evapo-transpiration (Chiew and McMahon, 1993). A constant is used to calculate the percolation to ground water storage. Another constant is used to estimate the base flow from the ground water storage. The base flow constant is usually determined by calibrating the estimated flows with the observed values. Lumped-parameter models have some distinct advantages. They do not necessarily require direct use of mathematical equations of physical processes and they take into account more physical processes than water-balance models. They also have been shown to be capable of making acceptable estimates of stream flow, evapo-transpiration, soil moisture deficits, and storage changes, including changes in ground water storage, for smaller river basins.

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Fig. 2.2 Schematic representation of the MODHYDROLOG daily rainfall-runoff model. Source: Courtesy of Chiew and McMahon, 1994.

Although lumped-parameter models are widely used, they have a number of limitations. These include: (1) the equations of a lumped-parameter model can only be approximate representations of the real world and must introduce some error arising from the model structure; (2) spatial heterogeneities in system responses may not be well reproduced by catchment-averaged parameters (Sharma and Luxmore, 1979; Freeze, 1980); (3) the accuracy with which a model can be calibrated or validated is very dependent on the observations of both inputs and outputs (Ibbit, 1972; Hornberger et al., 1985). Since input variables, particularly evapo-transpiration estimates, may be subject to considerable uncertainty; (4) there is a great danger of over-parameterization if attempts are made to simulate all hydrological processes thought to be relevant and to fit those parameters by optimization against an observed discharge record (Hornberger et al., 1985), so three to five parameters should be sufficient to reproduce most of the information in a hydrological record; and (5) the calibrated parameters of such models may be expected to show a degree of interdependence, so that equally good results may be obtained with different sets

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of parameter values, even though a model has only a small number of parameters (Ibbitt and O’Donnell, 1971; Pickup, 1977; and Sorooshian and Gupta, 1983). Another potential disadvantage is that the use of lumped-parameter rainfall-runoff models depends essentially on the availability of sufficiently long meteorological and hydrological records for their calibration. Such records are not always available. Their calibration also involves a significant element of curve fitting, thus making any physical interpretation of the fitted parameter values extremely difficult. There are other limitations, too. Because of their inherent structure, these models also make very little use of contour, soil, and vegetation maps, or of the increasing body of information related to soil physics and plant physiology. These models are not suitable for predicting the effects of land-use changes on the hydrological regime of a catchment, particularly when only a part of the catchment is affected. In the case of the lumped models, parameter values are highly dependent on both the model structure and the period of calibration (Beven and O’Connell, 1982). Therefore, as with other hydrologic models, it is not advisable to extrapolate events that are outside the conditions over which the model parameters are estimated. 2.2.4

PHYSICALLY-BASED DISTRIBUTED MODELS

Neither the empirical nor the lumped models are capable of addressing the physical processes of the basin which control the basin response, as they do not account for the spatial distribution of basin parameters. This limitation prompted the development of physically-based models aimed at improving the understanding of catchment processes. A schematic diagram of the Système Hydrologique Européen (SHE) distributed model is shown in Figure 2.3.

Fig. 2.3 Schematic representation of the SHE model. Source: Adapted from Abbot et al., 1986.

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Physically-based distributed models require descriptive equations for the hydrological processes involved (Freeze and Harlan, 1969). The equations on which distributed models are developed generally involve one or more space coordinates. They thus have the capability of forecasting the spatial pattern of hydrologic conditions within a catchment as well as the simple outflows and bulk storage volumes. In general, the descriptive equations are non-linear differential equations that cannot be solved analytically for cases of practical interest. Therefore, for simplification, some empirical discretization is made. Indeed, the complexities of hydrological systems are such that all the model components ultimately rely on an empirical relationship. As discussed by Freeze and Harlan (1969), the development of a computational model to simulate physical processes is carried out by: (1) defining a physical system isolating a region of consideration with simplified boundaries and neglecting all physical processes non-essential to the phenomenon being studied; (2) representing the idealized and simplified physical system by a mathematical model, including governing differential equations and boundary/initial conditions; (3) converting the mathematical model into a numerical model using one of the numerical methodologies (finite difference, finite element, boundary element, and characteristics methods) which is most appropriate to the problem; and (4) writing a computer code based on the selected computational algorithm to obtain numerical results in still graphic or animated form. In other words, before the computational model is developed, numerous idealizations, simplifications, approximations and discretizations have to be made. Regarding calibration of the physically-based model, the theoretical idea is that the model has the potential to estimate parameter values by field measurements without having to carry out parameter optimization as required by the simpler models of the lumped, conceptual type (Abbott et al., 1986). But in reality, the situation is different. Such an ideal situation requires comprehensive field data covering all parameters and a model discretization to an appropriate scale (Refsgaard et al., 1992). For example, the SHE model was applied to the Wye catchment in England and in six small catchments in the Narmada basin in India (Bathurst, 1986 and Refsgaard et al., 1992). In these catchments, during the application, optimizations were carried out because of inadequate representation of the hydrological processes, insufficient data, and the possible difference in scale between the measurement and the model grid scale (Bathurst, 1986 and Refsgaard et al., 1992). The distributed nature of physically-based models offers some advantages over other types of models. For example, they are capable of forecasting the effects of land-use changes, the effects of spatially variable inputs and outputs, the movement of pollutants and sediments, and the hydrological response of ungauged catchments. Regarding land-use changes in a catchment, deforestation rarely takes place abruptly over a complete basin; it is more common for piecemeal changes to take place over a considerable period of time. In a distributed model the effects of such changes can be examined in their correct spatial context. It is clear from the above discussion that physically-based models require much more information than their empirical, water-balance or lumped-conceptual counterparts. Thus, calibration and validation emerge as major tasks. Extensive field measurements require huge amounts of resources and time, and computing capacities are high. Finally, despite the greater effort required to parameterize, validate and run physically-based models, the simulated results often provide only slightly better (or sometimes worse) correspondence with measured values than lumped-conceptual models (Beven, 1987; Logue, 1990; and Wilcox et al., 1990). Perhaps this results from the equations used to describe the physical variability and the high degree of temporal and spatial variability of critical input

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parameters. Ironically, the description of physical variability is presumed to be a strength for physically-based models (Beven, 1985; Bathurst and O’Connell, 1992). Regarding extrapolation of physically-based distributed models, Beven and O’Connell (1982) mentioned that, because of the physical basis of the model parameters, the measured parameters’ values might be extrapolated to other locations or time periods. However, response of the physical parameters at other locations or other time periods may not be same. Therefore, physically-based distributed models also have limitations regarding extrapolation. Comparisons of various hydrologic models are tabulated in Table 2.1. In this section, the advantages and limitations of various hydrologic models have been discussed. In the next section, the applicability of some hydrologic models for assessing the impact of climate change on water resources is discussed. 2.3

ADVANTAGES AND LIMITATIONS OF HYDROLOGIC MODELS IN CLIMATE CHANGE APPLICATION

A number of studies have been carried out to assess the impacts of climate changes using empirical, water-balance and lumped-parameter models (Revelle and Waggoner, 1983; Mather and Feddema, 1986; McCabe et al., 1990; McCabe and Wolock, 1992; Thompson, 1992; Flaschka et al., 1987; MaCabe and Ayers, 1989; Conway, 1993; and Kwadijk, 1993). All these studies used monthly precipitation and temperature time-series data for the assessment. Models were calibrated to the observed data and then validated against the other observed dataset in order to assess the capacity of the model to generate current hydrological output (for example, runoff). Finally, the models were used to predict the possible effect of future climate change on water resources. Most of the models used GCM-based and hypothetical climate scenarios for sensitivity analysis. In the applications noted above, the model parameters were estimated from the current climate as a basis for predicting future conditions. This is one of the major limitations of modeling the effects of climate change. The behavior of physical parameters of a catchment is not necessarily stationary overtime. For example, most pedological processes operate over a very long time-scale, but changes in organic matter content and soil structure may become apparent over a time-scale of less than 10 years (Climate Change Impact Review Group (CCIRG), 1991). Higher temperatures and increased rainfall would lead to a loss of soil organic matter and hence a decrease in ability of the soil to hold moisture; higher temperatures would also encourage clayey soil to shrink and crack, thus assisting the passage of water into and through the soil profile (CCIRG, 1991). Another issue is the response of vegetation to climate changes. For example, Idso and Brazel (1984) estimated that plant evapo-transpiration may be decreased by one-third for a doubling of carbon-dioxide due to partial stomatal closure in plants, increasing their water use efficiency and conserving soil moisture for increased runoff to rivers and streams. Thus, as CO2 concentrations change over time, so might the relationships between climate and hydrology. Indeed, Dooge (1992) suggested that research should not be used to develop more complex models until the issue of the “antitranspirant effect” of higher atmospheric CO2 enrichment is effectively resolved. Which type of model should be chosen for assessing changes in runoff from scenarios of climate change? Empirical models can be applied successfully if the processes are ignored and the objective is limited to predicting runoff or discharge on monthly or annual time-scales. Empirical models require less data than the other models. The model performance during the calibration and validation period is highly dependent on good spatial and temporal coverage of the input data.

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Water-balance models are equally suitable for monthly and annual time-scales, but require more data. Calibration and validation are relatively more time and resource consuming as compared to empirical models. Conceptual lumped-parameter models need more data than either empirical or water-balance models. These models can be applied at shorter time-scales (say on a daily or hourly basis), a distinct advantage over empirical and water-balance models. However, calibration and validation procedures for these models are much more complicated. Physically-based distributed models require a vast amount of data, which are often impracticable to collect. These models need laboratory experimentation to estimate parameter values. Calibration and validation procedures are much more complex than for other models and computing (and other resource) demands are higher. Finally, physically-based models may not necessarily improve the accuracy of outputs compared to other models. This last point is particularly important to consider when choosing between complex and simpler models. For example, while applying the SHE model in the Narmada basin in India, Refsgaard et al. (1992) concluded that the simulated results of the rainfall-runoff were of the same degree of accuracy as would have been expected with similar hydrological models of the lumped-parameter type. They concluded that the results obtained in the Narmada basin do not justify the application of an advanced model, such as the SHE, where the objective is limited to rainfall-runoff modeling. 2.4

APPLICATION OF HYDROLOGIC MODELS FOR CLIMATE CHANGE IMPACT ASSESSMENT IN BANGLADESH

Based on the comparative advantages, limitations, and suitability of various hydrologic models with respect to research purposes, data availability, scale and resources, Mirza (1997) applied a suite of empirical model and MIKE 11-GIS hydrodynamic model for: (1) determining the sensitivity of mean annual and mean peak river discharges in the Ganges, Brahmaputra and Meghna (GBM) basins (Fig. 2.4) in Bangladesh to future climate changes; and (2) estimating the consequent changes in flood magnitude, depth and extent.

Fig. 2.4 The Ganges, Brahmaputra and Megna basins.

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Broadly, the objectives of the application were centered on relationships between precipitation, temperature and discharge, and the time-scale of concern was annual. Based on the above discussion the use of lumped-parameter or process-based distributed models for these purposes was not found practicable. Note that complex hydrologic models generally operate over small time intervals, such as an hour or day. Consequently, the field measurement, determination, estimation and optimization procedures can be onerous. For this reason, complex models are usually more suitable for smaller, more manageable catchments. For example, in the application of the SHE model to the small Wye catchment, it was still necessary to specify about 2,400 parameter values (Beven, 1989). By comparison, the combined GBM basins are approximately 1.75 million sq. km in area. For these river basins, the number of parameter values that would need to be specified using the same modeling approach is unmanageably large. The water-balance approach had a good potential for application in the GBM basins in order to determine the effects of climate change on annual discharge and flooding in Bangladesh. But the use of the water-balance approach was hindered by the lack of adequate hydro-meteorological (radiation, wind speed and humidity) and land-use data. Although the water-balance approach has been employed successfully in smaller basins with sparse data, as discussed in Section 2.2, the shear size and geographical diversity of the GBM basins (1.75 million sq. km) dictates against its use and it would probably create more uncertainties than it would resolve. Therefore, given the time and resources available, Mirza (1997) decided to apply a simpler empirical approach in combination with the MIKE 11-GIS hydrodynamic model. It is worth noting that simple empirical models have already been developed and applied successfully for similar purposes in the Himalayan region (Khosla, 1994; Garde and Kothyari, 1990 and Kothyari and Garde, 1991). Following sub-sections describe the processes involved in empirical models and simulating their results in the MIKE 11-GIS hydrodynamic model. 2.4.1

THE RELATIVE SENSITIVITY OF RUNOFF TO PRECIPITATION AND TEMPERATURE

What is the relative importance of precipitation and temperature in affecting runoff in the Ganges, Brahmaputra and Meghna basins? Precipitation and temperature (as it affects evaporation and transpiration) are the principal climate driving forces in generating runoff. Therefore, runoff is mainly sensitive to these two meteorological inputs. The sensitivity of runoff to temperature and precipitation changes can be approximately determined by using the water-balance equation (see equation (2.1)). In many cases it is assumed that the catchment is watertight and that no inflow or outflow of ground water occurs. On an annual basis it is often assumed that no change in storage takes place from year to year. Therefore, equation (2.1) reduces to:

In equation (2.3), E cannot be determined directly. However, if the annual open-water evaporation in any place is known or determined using the PET model of Penman (1948), E can be estimated by the following empirical model (Pike, 1964):

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The equation for the future runoff under the climate change can be written as:

The percentage change from the present day runoff can be determined using the following expression:

In order to estimate the sensitivity of runoff to temperature and precipitation, one station has been selected from each of the river basins - the Ganges, Brahmaputra and Meghna. These stations are New Delhi, Gauhati and Sylhet. For the sensitivity analysis an approximately 5% change in precipitation is associated with each degree change in global mean temperature, in accordance with global estimates from GCM simulations (IPCC, 1990), in order to select the range of precipitation change. The sensitivity of runoff has been calculated applying equations (2.6), (2.7) and (2.8). The results of the sensitivity analysis are presented in Figures 2.5a, 2.5b, and 2.5c. In general, the gentle slopes of equal percentage change lines show that runoff change is more sensitive to precipitation change than to temperature change. The results also show that, in percentage terms, runoff is more sensitive to precipitation and temperature changes in relatively dry stations than wet stations. As an example, in the case of the New Delhi station (a drier station) no change in temperature and a 4% increase in precipitation changes runoff by +11%, while for the Gauhati and Sylhet (the wetter stations) the changes in runoff are +6% and +8%, respectively. In the extreme case, a 5oC increase in temperature and a 20% increase in precipitation could increase runoff by 29% at the New Delhi station, whereas for Gauhati and Sylhet stations the expected changes are 22% and 21%, respectively. 2.4.2

THE EMPIRICAL MODEL DEVELOPMENT PROCESS

The sensitivity analysis in preceding sub-section shows that runoff in the Ganges, Brahmaputra and Meghna River basins appears to be much more sensitive to changes in precipitation than to changes in temperature. Based on this analysis, it was decided to use only precipitation as the independent variable in developing empirical models for the three river basins. 2.4.2.1 STEP I: DATA IDENTIFICATION AND ACQUISITION The sensitivity analysis described above has facilitated the reduction of variables by excluding temperature. The main data requirements are identified as annual precipitation, annual mean discharge and annual peak discharge. Annual precipitation can be derived from daily or monthly records, or by directly using annual totals depending on availability. Similarly, annual mean discharge can be calculated from the daily observations or monthly mean values. Peak discharge will be the highest daily observed value in a year.

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(a)

(b)

(c) Fig. 2.5 Sensitivity of runoff to temperature and precipitation changes in the: (a) Ganges basin (New Delhi), (b) Brahmaputra basin (Gauhati) and (c) Meghna basin (Sylhet).

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2.4.2.2 STEP II: DATA QUALITY ASSESSMENT Data quality requires an assessment of the length of records, homogeneity and missing observations. In India, Bangladesh and Nepal, station history is seldom available, which makes an assessment of homogeneity difficult. Standard procedures (Salinger, 1980) can be applied to fill in missing observations. If more than one dataset is available, the best dataset can be selected based on various criteria including the length of record, spatial coverage and volume of missing observations. 2.4.2.3 STEP III: EMPIRICAL MODEL BUILDING This step involves building empirical models for the purpose of assessing possible changes in flood discharge in Bangladesh due to changes in precipitation. Three empirical relationships are required: a) the relationship between annual precipitation and annual mean discharge; b) the relationship between annual mean discharge and annual peak discharge; and c) the stage-discharge rating equations. Future changes in flood discharge and stages can be determined using the empirical relationships for (a), (b), and (c). The empirical relationships in (a) and (b) can be linear or non-linear while in (c) the stage-discharge relationship is non-linear. These can be checked by plotting the y variable against the x variable(s). Linearity or non-linearity can be bi-variate or multi-variate depending on the number of independent variables. For the regression model building, non-linearity can be transformed to linearity by applying standard transformation methods (McCuen and Snyder, 1987; Box and Cox, 1964). Standard procedures were followed in developing the empirical models and their adequacy for prediction purpose were also examined (Mirza, 1997; Mirza et al., 2003). Results of climate change impacts are presented and discussed in Chapter 6 on Bangladesh Country Study. Stage-discharge relationships were not developed because these are already built into the MIKE 11 model, which is used to estimate the effects of changes in peak discharge and local precipitation on flood extent and depth as a consequence of climate change. 2.4.3

SIMULATION WITH THE MIKE 11-GIS MODEL

The French Engineering Consortium (FEC) (1989) first proposed to use geographic information systems (GIS) for flooded area and inundation depth mapping for Bangladesh in the late 1980s. Subsequently, GIS was widely used in the Flood Action Plan (FAP) studies for various purposes, including flood mapping. Reasonably accurate flood mapping requires inputs (such as discharge and water levels) from flood models. In Bangladesh, by and large, flood modeling has been carried out using the MIKE 11 flood model, maintained by the Institute for Water Modeling (IWM in Dhaka). The MIKE 11 software package models the flows and water levels in rivers and estuaries. It is used as a tool to simulate flooding behavior of rivers and floodplains. The models numerically represent the river and floodplain topography and are calibrated to recorded flood levels and discharges (FAP 25, 1994). The MIKE 11 model is based on an efficient numerical solution of the complete non-linear equations for 1-D flows. A network configuration represents the rivers and floodplains as a system of connected branches. The inputs are daily discharge and water levels at the boundary of Bangladesh and rainfall over the area covered by the model. At discrete points along the branches, flood levels (at h-points) and discharges (at Q-points) are calculated at hourly time-steps as a function of time (Fig. 2.6). However, flood models do not themselves generate the flood maps.

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Branch h-point Q-point

Figure 2.6 A MIKE 11 network is an interconnected system of branches representing rivers and floodplains. Along the branches h-points and Q-points are located. Flood levels are calculated at h-points and discharge at Q-points. Source: FAP 25, 1994.

In order to generate flood maps, GIS techniques (available with ARC/INFO GIS) are applied in combination with the MIKE 11 model. In a MIKE 11-GIS model, the inputs from the MIKE 11 model include: information on flood model network, cross-section databases, and results from flood simulations (which include water levels and discharge over time throughout the river system). A wide variety of data can be held in the GIS, such as: ground elevations in the form of Digital Elevation Model (DEM); rivers; roads; beels/lakes; settlements; and satellite images. Critical information on river and floodplain topography, rainfall, discharges and water levels are fed into the MIKE 11 model from the GIS (Fig. 2.7). Thus, for the purpose of floodplain mapping, information from both systems (MIKE11 and GIS) is related in the combined MIKE 11-GIS model (Fig. 2.8). More particularly, the MIKE 11 cross-sectional databases are exported to the MIKE 11-GIS model to display cross-sectional profiles, and for merging river cross-sections with the DEM of floodplain profiles. Imported flood model simulation results are used for flood mapping, graphing, and statistical output (FAP 25, 1994). The elevations in a DEM used by the MIKE 11-GIS model are derived from three basic types of topography: floodplains; high ground; and features which depress or raise the floodplain (for example, rivers, khals, beels, roads, embankments and settlements). The floodplain is characterized by very flat topography, while the high ground is typically steep. 2.4.4

SIMULATION OF CHANGES IN FLOOD DEPTH AREAL EXTENT

Empirical models were used to calculate changed mean and 20-year peak discharge values. It is obviously not realistic, however, to carry out flood simulation with constant high discharges, as this will lead to an overestimate of inundation areas and depths. Rather, the peak discharge must be associated with a realistic temporal distribution of discharges throughout the season, as input to the model. In order to overcome this problem, boundary inflows for the three rivers were selected for a “typical” year. (The term “typical” is attributed to temporal distribution of discharge rather than the magnitude). Available records show that the inflows of the 1991 monsoon represent a temporal distribution which may be considered fairly “typical” with regard to the usual peaking time of the three rivers

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(Chapter 1). In 1991, discharges of the Ganges and Brahmaputra peaked in September and July, respectively.

River Network (Line Coverage) Rain Gauges (Point Coverage)

Catchments (Polygon Coverage)

DEM (Grid)

Fig. 2.7 Organization of the GIS data. Source: FAP 25, 1994.

For model simulations, the 1991 discharge values were multiplied by the scaling factors given in Table 2.2. The scaling factors were calculated by dividing the mean discharge values (current and determined from the GCM scenario applications) of each river by the 1991 peak discharges of the Ganges, Brahmaputra and Meghna Rivers at Hardinge Bridge, Bahadurabad and Bhairab Bazaar, respectively. The recorded peak discharge in 1991 was: Ganges, 56,000 cumecs; Brahmaputra, 84,100 cumecs; and Meghna, 14,500 cumecs (BWDB, 1995). For simulating floods, the MIKE 11 model needs local rainfall as an input in addition to the discharge values of the major rivers entering Bangladesh. In the rainfall-runoff model contained within the MIKE 11, rainfall data for a total of 86 rainfall stations maintained by the Bangladesh Water Development Board (BWDB) were used. These stations are distributed over the 100,000 sq. km. area covered by the General Model for Bangladesh. The rainfall dataset covers the 25-year period 1967-1992. The rainfall-runoff model consists of 48 catchments, of which only 7 actually represent the catchment areas of the Ganges, Brahmaputra and Meghna Rivers in Bangladesh. Mean areal rainfall for each catchment was calculated by the IWM from the 86 rainfall stations using the Thiessen polygon method. The mean areal rainfall was then used as direct input to the model. The rainfall-runoff model was run using a 24 hourly time-step. The procedure (Annex 2.1) for calculating daily rainfall values for the “control run” and “climate change scenarios” (mean and 20-year rainfall), was developed by a BDCLIM team of researchers at the International Global Change Institute (IGCI), University of Waikato, New Zealand. For the calculation, the rainfall records of the 86 stations for the period 1967-1992 were considered. For the control run, mean rainfall for each of the 365 days was calculated. For calculating 20-year rainfall values, the ratio of the 20-year and mean rainfall was determined and the mean rainfall for each of the 365 days was then multiplied by the computed ratio. For the climate change simulations, each of the current values was multiplied by the scaling factors.

h

Flood Simulation

t

5.35 5.75 5.90 6.20 5.80

0.00 6.00 12.00 18.00 24.00

11 G I S

M I K E

Fig. 2.8 Steps being followed in the MIKE 11-GIS model to produce flood maps. Source: FAP 25, 1994.

River System

11

M I K E

h

Time

Flood Maps

42 HYDROLOGIC MODELING APPROACHES

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APPLICATION OF HYDROLOGIC MODEL IN INDIA

Gosain and Rao (2003) applied the SWAT (Soil and Water Assessment Tool) distributed hydrologic model on major river basins in India. For simulation of discharge, daily weather data from HadRM2 was used. A total of 40 years of simulation over 12 river basins were conducted (Fig. 2.9). The simulation period was splitted into two equal 20 years period belonging to “control” and “future”. Each river basin was also subdivided into reasonable sized sub-basins in order to account for spatial variability of possible change in climate.

Fig. 2.9 The 12 river basins in India where the Soil and Water Assessment Tool (SWAT) model was applied. Source: Shukla et al., 2003. Reproduced with permission.

2.5.1

DATA USED FOR STUDY

For making assessment of water resource availability at particular locations of the river basin, the data inputs for the SWAT model (Box 2.1) are: terrain, land-use, soil and weather. Data (spatial scale 1:250,000) for all the river basins of India (except for the Brahmaputra and Indus Rivers) was used in the model. The snowbound areas of the Ganges River basin could not include in the model due to the lack of required data. The following data elements were used. DEM (Digital Elevation Model): A DEM represents a digital file consisting of terrain elevations for ground positions at regularly spaced horizontal intervals. Contours taken from a 1:250,000 scale ADC world topographic map was used to generate DEM for the study.

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Box 2.1 The SWAT Model

SWAT (Soil and Water Assessment Tool) is a conceptual continuous model. The model is useful to the water resource managers in assessing the impact of management on water supplies and non-point source pollution in large river basins. The model operates on a daily time step and allows a basin to be subdivided into grid cells or natural sub-watersheds. Major components of the hydrologic balance and their interactions are simulated including surface runoff, lateral flow in the soil profile, ground water flow, evapo-transpiration, channel routing, and pond and reservoir storage. The primary considerations in model development were to stress land management, water quality loadings, flexibility in basin discretization and continuous time simulation.

Stream Network Layer: Large-scale contour/DEM data was not available. Therefore, the actual stream network was used. This option helped in conforming to the shapes of the sub-basins, which were close to the prototype situation. Appropriate threshold values were used for generating the stream networks for various river basins. Watershed (sub-basin) Delineation: Automatic delineation of watersheds was done by using the DEM as input and the final outflow point on each river basin as the pour point. Weather Data: The weather data generated in transient experiments of the climate carried out by the Hadley Center for Climate Prediction, U.K. The data at a resolution of 0.44o x 44o latitude by longitude grid points was obtained from the Indian Institute of Tropical Meteorology (IITM). The daily weather data on maximum and minimum temperature, rainfall, solar radiation, wind speed and relative humidity at all the grid locations were processed to use in the hydrologic model. The RCM grid was superimposed on the sub basins in order to derive the weighted means of the inputs for each of the sub basins. The centroid of each sub basin was then taken as the location for the weather station to be used in the SWAT model. This procedure was used for both the “present/control” (1981-2000) and “future/GHG” (2041-2060) climate data. Land Cover/Land-Use Layer: Classified land cover using remote sensing by the University of Maryland Global Land Cover Facility with resolution of 1 km grid cell size was used (Hansen et al., 1999). Soil Layer: Soil map was adopted from FAO Digital Soil Map of the World and Derived Properties (Version 3.5, November, 1995) with a resolution of 1:5,000,000 was used (FAO, 1995). 2.6

APPLICATION OF MODELS IN PAKISTAN

Masood and Ullah (1991) applied the UBC-Mangla watershed model to forecast inflows to the Mangla reservoir, Indus basin (Box 2.2), Pakistan. The UBC model was developed by the University of British Columbia, Canada and was extensively used there in assessing impacts of climate change on water resources (Morrison et al., 2002; Micovic and Quick, 1999; Loukas and Quick, 1999). The general flow chart of the UBC model is shown in Figure 2.10. In assess climate impacts on average inflows, three climate scenarios were

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used (for details see the Chapter 8 on Pakistan). The model has seven major components which form a logical subdivision of the hydro-meteorological modeling and evaluation process. Details on the model can be seen at http://www.civil.ubc.ca/home/ubcmodel/ main.htm. •

•

•

• •

• •

The meteorological sub-model distributes the input data to all elevation zones of the watershed. This distribution process controls the total volume of moisture which is input to the model, and specifies the variation of temperature with elevation, which controls whether precipitation falls as rain or snow and also controls the melting of the snow packs and glaciers. The soil moisture sub-model controls the evaporation losses and the subdivision of the rainfall and snowmelt into the four components of runoff: fast, medium, slow and very slow components. The model computes the soil moisture deficit and which controls the non-linear subdivision of rain and melt into the runoff components. The watershed routing sub-model determines the time distribution of runoff. Each of the four components of runoff determined by the soil moisture sub-model is subjected to storage routing using either cascades or single linear reservoirs. Because these reservoirs are linear, conservation of mass is guaranteed and an accurate water budget is maintained. The output and evaluation sub-model is designed to give flexible access to many aspects of the calculated watershed behavior. The semi-automatic calibration sub-model requires some user guidance to ensure that parameters are restricted to reasonable ranges. The calibration process is a constrained iterative search optimization which evaluates a maximum of four parameters at a time. The updating sub-model is based on a combination of feedback information from flow measurement and snow cover data from snow course or satellite. The routing sub-model, based on the UBC Flow Model, combines watershed flows and routes these flows through a river, lake and reservoir system.

A generalized interactive model called MODSIM1 was also used to model climate impacts on water resource systems in the Indus basin. MODSIM is a capacitated network flow model in which components of the system are represented by an interconnection of nodes (diversion points, reservoirs, points of inflow/outflow, demand locations, stream gauges, etc.) and links that have a specified direction of flow and maximum capacities (canals, pipelines, and natural reaches). In order to consider the demands, inflows, and desired reservoir operating rules, MODSIM internally creates a number of “accounting” nodes and linkages that are intended to ensure mass balance throughout the network. The network can be visualized as a resource allocation system through which the available water resource can be moved from point to point to meet various demands. The Indus basin was divided into 57 nodes and 70 links, including 30 demand nodes which represented the canal system in the region. All major contributors to the Indus

1

MODSIM is a river basin network simulation model developed by Dr. John Labadie at Colorado State University, USA based on an earlier model, SIMYLD II, developed for the Texas Water Development Board. MODSIM was developed to enable the simulation of large-scale, complex water resource systems, including considerations for water rights priorities, reservoir operations, and important institutional and legal factors that affect river basin planning functions.

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basin, from Tarbela and Mangla downward, were included in the network. The input files for the model included the maximum, minimum, and initial capacities of each reservoir. Nine storage reservoirs were represented in the model, but six of them had zero capacity. There were only three reservoirs in the system, and their capacities were declining as a result of sedimentation. Four climate change scenarios were used to assess the impacts. Inflows were calculated for each decade between 2000 and 2050. These inflows were then used as inputs to the system, both with the change in reservoir capacities and with no new reservoir added to the system. Box 2.2 The Indus basin

The Indus River basin stretches from the Himalayan Mountains in the North, to the dry alluvial plains of the Sindh Province of Pakistan in the South. The basin is shared by India and Pakistan. The alluvial plains of the Indus basin cover an area 2 of 207, 200 km , which is approximately 25% of the land area of Pakistan (WCD, 2000). Five main rivers that join the Indus from the Eastern side are Jhelum, Chenab, Ravi, Beas and Sutlej. Besides these, two minor rivers - Soan and Harrow also drain into the Indus. On the Western side, a number of small rivers join Indus, the biggest of which is river Kabul with its main tributaries i.e. Swat, Panjkora and Kunar. Several small streams such as Kurram, Gomal, Kohat, Tai, Tank, etc. also join the Indus on the right side. The main source of inflow to the Indus River is snowmelt and glacier flow. The Indus River and its tributaries on an average bring about 170 BCM of water annually. River flow is highly variable and the range is 120 BCM-230 BCM. The network has three major reservoirs: the Tarbela, Mangla, and Chasma. It also includes 19 barrages or head works, 12 link canals, 43 canal commands, and over 107,000 watercourses. The Indus Plain is characterized by arid to semi-arid climate with significant variability from upstream to downstream. Mean minimum temperatures in the upper plain are 2°C in the winter while mean maximum temperatures in the summer is 49°C. Mean annual rainfall is low, ranging from 90 mm in the lower plain to 510 mm upstream in Lahore.

2.7

SUMMARY AND CONCLUDING REMARKS

Modelers use four types of hydrologic models - empirical, water-balance, conceptual lumped-parameter and process-based distributed models - for precipitation-runoff modeling. All these models have advantages and limitations with regard to data requirements, the description of physical processes, calibration, resources, computing capacity and flexibility. Empirical models require a relatively easier development process than the other models, require less data, need fewer resources and do not require huge computing capacity. Water-balance models incorporate some physical processes. However, their data and other resources requirements are greater than the empirical models. Conceptual lumped-parameter models take into account more physical processes than the water-balance models. They oversimplify the values of physical parameters during the calibration which may not match the real world situation. Physically-based distributed models address the physical processes in a comprehensive fashion. Their requirements of data, resources and computing capacity are very much higher than the other models.

M. M. Q. MIRZA Temperature

Snowfall

Rainfall

Snowmelt & Glacier Melt

Infiltration Control

49

1. Meteorological Data

Input Distribution by Elevation Zone Evapo-Transpiration Losses

2. Watershed Moisture

Balance Computations

3. Runoff Component

Allocation

Soil M oisture Control

Very Slow Runoff

Flash Behavior from High Intensity Rainfall

Slow Runoff

Medium Runoff

Fast Runoff

Interflow

Surface Runoff

4. Time Distribution

of Runoff Deep Zone Ground Water

5. Modification by

Watershed Storages

Upper Zone Ground Water

Lake or Reservoir Routing Control

6. Evaluation with

Recorded Stream Flow Data

Generated Stream Flow (Input to Channel System)

Fig. 2.10 A general flow chart of the UBC Watershed Model. Adapted from Quick, 1995.

Results from the empirical hydrologic models were used to simulate peak floods in Bangladesh with the MIKE 11-GIS hydrodynamic model. Although water-balance, lumped-parameter and physically-based distributed models have some advantages over the empirical models, their applications were constrained by high requirement for data, resources and computing capacity. The combined area of the basins is about 1.75 million sq. km which are distributed over China, India, Nepal, Bhutan and Bangladesh. In reality it is rather impossible to have access to precipitation, temperature, soil, land-use and other databases managed by various organizations in the co-basin countries. Analysis shows that runoff in the Ganges, Brahmaputra and Meghna basins is more sensitive to changes in precipitation than temperature. Analysis further reveals that under climate change, runoff of a drier river basin may be more sensitive than a wetter basin to changes in precipitation. Based on the analysis, temperature has been excluded from the empirical model building process.

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Annex 2.1 Procedure for calculating daily rainfall values for the “control run” and “climate change scenarios”.

Step 1: Long-term mean rainfall for each station for each of the 365 days was calculated using the daily rainfall record for the period 1967-1992. Step 2: The annual rainfall totals for each of the 25 years (1967-1992) for each station was calculated using the daily rainfall records. Step 3: From the annual rainfall totals in Step 2, the mean annual rainfall and standard deviation for each station were determined. Step 4: Using the 1967-1992 mean rainfall and standard deviation, the 20-year rainfall for each station was calculated by: 20-year rainfall = (1.645 * sd (1967-1992) + mean (1967-1992)). Step 5: The ratio between the 20-year rainfall (from Step 4) and the mean (1967-1992) rainfall was determined. Step 6: The 365 daily means (from Step 1) were scaled by multiplying by the ratio (Step 5). For the simulation of the climate change scenarios, the two datasets (Step 2 and Step 6) were modified by multiplying by the scaling factors given in Table 2.2. The mean areal rainfall for each catchment was then calculated using the Thiessen polygon method.

REFERENCES Abbott, M. B., Bathurst, J. C., Cunge, J. A., O’Connell, P. E. and Rasmussen, J. L.: An Introduction to the Système Hydrologique Européen “SHE” 1: History and Philosophy of a Physically-Based, Distributed Modeling System. Journal of Hydrology 87 (1986), pp.45-59. Bangladesh Water Development Board (BWDB): Daily Discharge Data for the Ganges, Brahmaputra and the Meghna Rivers, Dhaka, BWDB, 1995. Bathurst, J. C. and O’Connell, P. E.: Future of Distributed Modeling, The Système Hydrologique Européen. Hydrological Processes 6(3) (1992), pp.265-277. Bathurst, J. C.: Physically-Based Distributed Modeling of an Upland Catchment Using the Système Hydrologique Européen. Journal of Hydrology 87 (1986), pp.79-102. Beable, M. E. and Mckerchar, A. I.: Regional Flood Estimation in New Zealand, Water & Soil Technical Publication 20, Wellington, New Zealand, Ministry of Works & Development, 1982. Bergstorm, S.: Development and Application of a Conceptual Runoff Model for Scandinavian Catchments, Department of Water Resources Engineering. Lund Institute of Technology, Bulletin Series A-52, Norrkoping, Sweden, Swedish Meteorological and Hydrological Institute, 1976. Beven, K. J.: Distributed Models. In M. G. Anderson and T. P. Burt (editors), Hydrological Forecasting, New York, John Wiley & Sons, 1985, pp.405-435. Beven, K. J.: Towards a New Paradigm in Hydrology. IASH Publication 164 (1987), pp.393-403. Beven, K. J.: Changing Ideas in Hydrology: The Case of the Physically-Based Models. Journal of Hydrology 105 (1989), pp.105-172. Beven, K. J. and O’Connell, P. E.: On the Role of Physically-Based Models in Hydrology, Institute of Hydrology Report Number 81, Wellingford, 1982.

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Blaney, H. F. and Criddle, W. D.: Determining Water Requirements in Irrigated Areas from Climatological and Irrigation Data, USDA Soil Conservation Service Technical Paper Number 96, 1950. Boughton, W. C.: A Mathematical Catchment Model for Estimating Runoff. Journal of Hydrology (NZ) 7 (1968), pp.75-100. Box, G. E. P and Cox, D. R.: An Analysis of Transformation. Journal of Royal Statistical Society B26 (1964), pp.211-252. Brash, D. N. and Murray, D. L.: Indirect Methods For Estimation of Catchment Precipitation, New Zealand Geographer 36(2) (1980), pp.57-67. Bultot, F. and Dupriez, G. L.: Conceptual Hydrological Model for an Average-Sized Catchment Area, I. Concepts and Relationships. Journal of Hydrology 29 (1976), pp.251-272. Burnash, R. J. C., Ferral, R. L. and McGuire, R. A.: A Generalized Stream Flow Simulation System, Conceptual Modeling for Digital Computers. Sacramento, CA, US Department of Commerce, National Weather Service and State of California, Department of Water Resources, 1973. Chiew, F. H. S. and McMahon, T. A.: Comparison of Six Rainfall-Runoff Modeling Approaches. Journal of Hydrology 147 (1993), pp.1-36. Chiew, F. H. S. and McMahon, T. A.: Application of the Daily Rainfall-Runoff Model MODHYDROLOG to 28 Australian Catchments. Journal of Hydrology 153 (1994), pp.383-416. Climate Change Impact Review Group (CCIRG): The Potential Effects of Climate Change in the United Kingdom, London, CCIRG for the Department of Environment, HMS, 1991. Conway, D.: The Development of a Grid-Based Hydrologic Model of the Blue-Nile and the Sensitivity of the Nile River Discharge to Climate Change, Unpublished Ph.D Thesis, Norwich, University of East Anglia, U.K., 1993. Crawford, N. H. and Linsley, R. K.: Digital Simulation in Hydrology: Stanford Watershed Model IV., Stanford University, Department of Civil Engineering, Technical Report 39, 1968. Dooge, J. C. I.: Hydrologic Models and Climate Change. Journal of Geophysical Research 97(D3) (1992), pp.2677-2686. Fitzharris, B. B. and Grimmond, C. S. B.: Assessing Snow Storage and Melt in a New Zealand Mountain Environment, IAHS Publication 138 (1982), pp.161-168. Flaschka, I. C., Stockton, C.W. and Boggess, W. R.: Climate Variation to Surface Water Resources in the Great Basin Region. Water Resources Bulletin 23 (1987), pp.47-57. Flood Action Plan 25 (FAP 25): Flood Management Model: Final Report, Volume I: Main Report, Dhaka, FPCO, 1994. Food and Agriculture Organization of the United Nations (FAO): Digital Soil Map of the World and Derived Soil Properties (Version 3.5). Food and Agriculture Organization, United Nations, Rome, Italy, 1995. Freeze, R. A.: A Stochastic-Conceptual Analysis of Rainfall-Runoff Processes on a Hill-Slope. Water Resources Research 16 (1980), pp.391-408. Freeze, R. A. and Harlan, R. L.: Blueprint for Physically-Based, Digitally Simulated Hydrologic Response Model. Journal of Hydrology 9 (1969), pp.237-258. French Engineering Consortium (FEC): Pre-Feasibility Study for Flood Control in Bangladesh, Vol. 2: Present Conditions, Paris, FEC, 1989. Garde, R. J. and Kothyari, U. C.: Flood Estimation in Indian Catchments. Journal of Hydrology 113 (1990), pp.135-146. Gleick, P.: The Development and Testing of a Water-Balance Model for Climate Impact Assessment: Modeling the Sacramento Basin. Water Resources Research 23(6) (1987), pp.1049-1061. Gosain, A. K. and Rao, S.: Impacts of Climate Change on Water Sector. In: Climate Change and India: Vulnerability Assessment and Adaptation (P. R. Shukla et al. eds), Universities Press, New Delhi, 2003, pp.159-192. Granger, R. J. and Gray, D. M.: A Net Radiation Model for Calculating Daily Snowmelt in Open Environments, Nordic Hydrology 21 (1990), pp.217-234. Gray, D. M. and Landine, R. J.: An Energy-Budget Snowmelt Model for the Canadian Prairies. Canadian Journal of Earth Science 25(9) (1987), pp.1292-1303.

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Gray, D. M., and O’Neill, A. D. J.: Applications of Energy Budget for Predicting Snowmelt Runoff. Advanced Concepts Technical Study Snow Ice Resources, National Academy of Sciences, Washington, D.C., 1974, pp.108-118. Gray, D. M. and Prowse, T. D.: Snow and Floating Ice. In: Handbook of Hydrology (D. R. Maidment ed.), McGraw-Hill Inc, New York, 1993. Green, W. H. and Ampt, G. A.: Studies on Soil Physics: Flow in Air and Water Through Soils, Journal of Agricultural Science 4 (1911), pp.1-24. Hansen, M. C., DeFries, R. S., Townshend, J. R. G. and Sohlberg, R.: 1 km Global Land Cover Dataset Derived from AVHRR. Global Land Cover Facility, University of Maryland Institute for Advanced Computer Studies, College Park, Maryland, U.S.A, 1999. Hare, F. K. and Hay, J. E.: Anomalies in the Large-scale Annual Water Balance Over Northern North America. Canadian Geographer 15(2) (1971), pp.79-94. Hargreaves, G. H.: Estimation of Potential and Crop Evapo-Transpiration. Transaction of American Society of Agricultural Engineering 17 (1974), pp.701-704. Holtan, H. N.: A Concept for Infiltration Estimates in Watershed Engineering, USDA Bulletin, 1961, pp.41-51. Hornberger, G. M., Beven, K. J., Cosby, B. J. and Sappington, D. E.: Shenandoah Watershed Study: Calibration of a Topography-Based, Variable Contributing Area Hydrological Model to a Small Forested Catchment. Water Resources Research 21 (1985), pp.1841-1850. Ibbit, R. P.: Effects of Random Data Errors on the Parameter Values for a Conceptual Model. Water Resources Research 8 (1972), pp.70-78. Ibbitt, R. P. and O’Donnell, T.: Designing Conceptual Catchment Models for Automatic Fitting Methods. IASH Publication 101 (1971), pp.461-475. Idso, S. B. and Brazel, A. J.: Rising Atmospheric Carbon-Dioxide Concentrations May Increase Stream Flow. Nature 312 (1984), pp.51-53. Intergovernmental Panel on Climate Change (IPCC): Climate Change: The IPCC Scientific Assessment, Cambridge, U.K., Cambridge University Press, 1990. Kwadijk, J.: The Impact of Climate Change on the Discharge of the River Rhine, Netherlands Geographical Studies, 1993. Khosla, A. N.: Analysis and Utilization of Data for the Appraisal of Water Resources. Journal Irrigation Power, India, October, 1994, pp.410-422. Kirkby, M. J., Naden, P. S., Burt, T. P. and Butcher, D. P.: Computer Simulation in Physical Geography, New York, John Wiley & Sons, 1987. Kothyari, U. C. and Garde, R. J.: Annual Runoff Estimation for Catchments in India. Journal of Water Resources Planning and Management 117(1) (1991), pp.1-10. Kuhn, M.: Methods of Assessing the Effects of Climatic Changes on Snow and Glacier Hydrology. IAHS Publication 218 (1993), pp.135-144. Kwadijk, J.: The Impact of Climate Change on the Discharge of the River Rhine, Utrecht, Netherlands Geografische Studies 171, 1993. Leavesley, G. H.: Modeling the Effects of Climate Change on Water Resources-A Review. Climate Change 28 (1994), pp.159-177. Leopold, L. B. and Millier, J. P.: Ephemeral Streams-Hydraulic Factors and Their Relation to the Drainage Network, United States Geological Survey Professional Paper 282A, 1956. Lettau, H.: Evapo-Transpiration Climatonomy. A New Approach to Numerical Prediction of Monthly Evapo-Transpiration, Runoff and Soil Moisture Storage. Monthly Weather Review 97 (1969), pp.691-699. Linsley, R. K., Kohler Jr., M. A. and Paulhus, J. L. H.: Hydrology for the Engineers, London, McGraw Hill Book Company, 1988, p.492. Logue, K.: R-5 Revisited, Re-Evaluation of a Quasi-Physically Based Rainfall-Runoff Model with Supplementary Information. Water Resources Research 26 (1990), pp.973-987. Loukas, A and Quick, M. C.: The Effect of Climate Change on Floods in British Columbia. Nordic Hydrology 30 (3) (1999). Mather, J. R. and Feddema, J.: Hydrologic Consequences of Increase in Trace Gases and CO2 in the Atmosphere. In U.S. Environmental Protection Agency and the UN Environment Programme, Washington, D.C., Effects of Changes in Stratospheric Ozone and Global Climate Vol.3, 1986.

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McCabe Jr., G. J. and Ayers, M. A.: Hydrologic Effects of Climate Change in the Delware River Basin. Water Resources Bulletin 25 (1989), pp.1231-1242. McCabe Jr., G. J. and Wolock, D. M.: Effects of Climate Change and Climate Variability on the Thornthwaite Moisture Index in the Delware Basin. Climate Change 20 (1992), pp.143-153. McCabe Jr., G. J., Wolock, D. M., Hay, H. E. and Ayers, M. A.: Effects of Climate Change on the Thornthwaite Moisture Index. Water Resources Bulletin 26 (1990), pp.633-643. McCuen, R. H. and Snyder, W. M.: Hydrologic Modeling: Statistical Methods and Applications, New Jersey, Prentice-Hall, 1987. Micovic, Z. and Quick, M. C.: A Rainfall and Snowmelt Runoff Modeling Approach to Flow Estimation at Ungauged Sites in British Columbia. Journal of Hydrology 226 (1999), pp.101-120. Mirza, M. M. Q.: Modeling the Effects of Climate Change on Flooding in Bangladesh, Unpublished Ph.D Thesis. International Global Change Institute (IGCI), University of Waikato, New Zealand, 1997. Mirza, M. M. Q., Warrick, R. A. and Ericksen, N. J.: The Implications of Climate Change on Flood Discharges of the Ganges, Brahmaputra and Meghna Rivers in Bangladesh. Climatic Change 57 (2003), pp.287-318. Morrison, J., Quick, M. and Foreman, M.: Climate Change in the Fraser River Watershed: Flow and Temperature Projections. Journal of Hydrology 263 (2002), pp.230-244. Monteith, J. L.: Evaporation and Environment. The State and Movement of Water in Living Organisms, New York, Academic Press, 1964, pp.205-234. Mosley, M. P.: Semi-Determinate Hydraulic Geometry of River Channels, South Island, New Zealand. Earth Surface Processes and Landforms 6 (1981), pp.127-137. Mosley, M. P.: Prediction of Hydrologic Variables From Channel Morphology, South Island Rivers. Journal of Hydrology (NZ) 18 (1979), pp.109-120. Natural Environment Research Council (NERC): Flood Studies Report, London, NERC, 1975. Palmer, W. C.: Meteorological Drought, Research Paper. U.S. Weather Bureau 45 (1965). Penman, H. L.: Natural Evapo-Transpiration from Open Water, Bare Soil and Grass. Proceedings of Royal Society of London Series A. 193 (1948), pp.120-145. Philip, J. R.: The Theory of Infiltration: The Infiltration Equation and Its Solution. Soil Science 83 (1957), pp.345-357. Philip, J. R.: Theory of Infiltration. Advances in Hydroscience 5 (1969), pp.215-296. Pickup, G.: Testing the Efficiencies of Algorithms and Strategies for Automatic Calibration of Rainfall-Runoff Models. Hydrological Sciences Bulletin 22 (1977), pp.257-274. Pike, J. G.: Estimation of Annual Runoff from Meteorological Data in a Tropical Climate. Journal of Hydrology 2 (1964), pp.116-123. Priestly, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Monthly Weather Review 100 (1972), pp.81-92. Refsgaard, Seth, S. M., Bathurst, J. C., Erlich, M., Storm, B., Jorgensen, G. H. and Chandra, S.: Application of the SHE to Catchments in India, Part 1: General Results. Journal of Hydrology 140 (1992), pp.1-23. Quick, M. C.: The UBC Watershed Model. In: Computer Models of Watershed Hydrology (V. P. Singh ed.) Water Resources Publications, Highlands Ranch, Colorado, U.S.A., 1995, pp.233-280. Revelle, R. R. and Waggoner, P. E.: Effects of a Carbon Dioxide Induced Climate Change on Water Supplies in the Western United States. In Changing Climate: Report of the Carbon Dioxide Assessment Committee, Washington, D. C., National Academy Press, 1983. Rodda, J. C.: The Significance of Characteristics of Basin Rainfall and Morphology in a Study of Floods in the United Kingdom. UNESCO Symposium on Floods and Their Computation 2 (1969), pp.834-845. Salinger, J. M.: New Zealand Climate. The Instrumental Method, Unpublished Ph.D Thesis, New Zealand, Victoria University of Wellington, 1980. Schumm, S. A.: River Metamorphosis. Journal of Hydraulic Engineering 95 (1969), pp.255-273. Sharma, M. L. and Luxmore, R. J.: Soil Spatial Variability and Its Consequences on Simulation of Water-Balance. Water Resources Research 15 (1979), pp.1567-1573.

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Singh, V. P.: Hydrologic Systems: Rainfall-Runoff Modeling, Vol. I, New Jersey, Prentice Hall, 1988. Sorooshian, S. and Gupta, V. K.: Automatic Calibration of Conceptual Rainfall-Runoff Models: The Question of Parameter Observability and Uniqueness. Water Resources Research 19 (1983), pp.260-268. Thomas, D. M. and Benson, M. A.: Generalization of Stream Flow Characteristics from Drainage Basin Characteristics, U.S. Geological Survey Water Supply Paper 1970, 1970. Thomas, H. A.: Improved Methods for National Water Assessment, Report to U.S. Water Resources Council, Washington, D.C., 1981. Thompson, S. A.: Simulation of Climate Change Impacts on Water-Balances in the Central United States. Physical Geography 13 (1992), pp.31-52. Thornthwaite, C. W.: An Approach Toward a Rational Classification of Climate. Geographical Review 38 (1948), pp.55-94. Thornthwaite, C. W. and Mather, J. R.: The Water-Balance, Publications in Climatology, Drexel Institute of Technology. Laboratory of Climatology VII, 1 (1955). U.S. Environmental Protection Agency (USEPA): User’s Manual for Hydrological Simulation Program-FORTRAN (HSPF) EPA-600/3-84-066, Athens, GA Environmental Research Laboratory, 1984. Wilcox, B. P., Rawls, W. P., Brakensiek, D. L. and Wight, J. R.: Predicting Runoff from Rangeland Catchments: A Comparison of Two Models. Water Resources Research 26(10) (1990), pp.2401-2410. Williams, J. R., Jones, C. A. and Dyke, P. T.: A Modeling Approach to Determine the Relationship Between Erosion and Soil Productivity. Transactions of the American Society of Engineering 27 (1984), pp.129-144. World Commission on Dams (WCD): Case Study - Pakistan: The Tarbela Dam and Indus River Basin (http://www.dams.org/kbase/studies/pk/pk_exec.htm), 2000.

3 Are Floods Getting Worse in the Ganges, Brahmaputra and Meghna Basins? M. MONIRUL QADER MIRZA R. A. WARRICK N. J. ERICKSEN G. J. KENNY 3.1

INTRODUCTION

The Ganges, Brahmaputra and Meghna/Barak (GBM) river systems occupy about 175 million hectares (mha) of South Asia (Fig. 3.1) and supports more than 500 million people (Verghese and Iyer, 1993). They are unique in the world with respect to water and sediment supplies, channel processes, and instability. While the Brahmaputra ranks fourth among the largest rivers of the world with regard to mean annual discharge, the Ganges ranks thirteenth (Mirza, 1997). The estimated annual sediment yield of the Brahmaputra is 1,028 tons/km2, the highest among the world’s largest rivers. On the other hand, the sediment yield of the Ganges is only 502 tons/km2 although its basin area is two times that of the Brahmaputra (Barua, 1994). The swinging and avulsion of the courses of the Ganges and Brahmaputra Rivers in recent history have significant influence on the morphology of their alluvial floodplains (Rahman, 1993; Brammer, 1996). They are characterized by high flows during the monsoon and low flows during the dry season. For example, the ratio of monsoon flow to dry season flow of the Ganges River at Hardinge Bridge in Bangladesh is 6:1 (Mirza and Dixit, 1997). The high flows often cause floods in many parts of these vast river basins. Sitting at the confluence of the three major rivers, Bangladesh (area 148,000 sq. km) is considered to be the most flood-affected country in the world followed by India. Every year, slightly over one-fifth of its land area becomes flooded and in extreme cases, more than two-thirds of the country is affected. In upstream India (area 3,280,000 sq. km), floods annually inundate an area larger than half of Bangladesh. Available information shows that in recent years, flood damage in Nepal, India and Bangladesh is increasing. Substantial increases in flood damage in Nepal during the 1980s were reported by the Asia Development Bank (ADB, 1991). For India, the Center for Science and Environment (CSE, 1992) reported that the annual flood damage had increased 40 times from the 1950s to the 1980s (Fig. 3.2). According to Mirza (1991a), compared to the 1960s and 1970s, flood damage in Bangladesh was the greatest in the 1980s (Fig. 3.3). These increases have largely been attributed to worsening flood events (increased river discharge and spatial extent) in the GBM basins in India and Bangladesh Reprinted with permission from Environmental Hazards 3 (2001), pp.37-48.

56

ARE FLOODS GETTING WORSE?

(CSE, 1992; BBJTO, 1989; RBA, 1980; and Ives, 1991). These claims, do not, however, appear to be based on systematic analyses of relevant data. Therefore, this paper examines whether floods in the GBM basins are getting worse by applying statistical tests to: 1) the peak discharge data of the three rivers recorded at various stations in India, Nepal and Bangladesh; and 2) the flooded area data. The latter were used to determine changes in spatial severity of flooding in India and Bangladesh. Peak discharge recording stations and period of records are shown in Table 3.1. It is possible that the reported increases in flood problems are due to increased human activities in flood-prone areas. But that element of flood hazard is not the subject of this paper.

Fig. 3.1 The Ganges, Brahmaputra and Meghna basins. Location of some discharge measurement stations have also been shown.

Damage (in million rupees)

50000 40000 30000 20000 10000 0 1950

1960

1970

Year

1980

1990

2000

Fig. 3.2 Flood damage in India during 1953-1999 (Source: CWC, 1989; ADRC, 2000a).

M. M. Q. MIRZA ET AL.

57

Fig. 3.3 Flood damage in Bangladesh during 1954-1998 (Source: Mirza, 1991a; ADRC, 2000a).

3.2

HYDRO-METEOROLOGY OF THE GBM BASINS

Of the three river basins, the Ganges is the largest. Its 109.5 mha basin area is distributed over China, Nepal, India and Bangladesh. The Ganges River rises South of the main Himalayan divide near Gangotri at a height of 4,500 m in the Uttar Pradesh (UP), India. In Nepal, India and Bangladesh, mean annual precipitation in the basin is 1,860 mm, 908 mm and 1,568 mm, respectively. Mean annual runoff of the Ganges River at Farakka, India and Hardinge Bridge, Bangladesh, is estimated to be 415 x 103 million cubic meters (mcm) and 352 x 103 mcm, respectively (Mirza, 1997). The highest annual peak discharge (80,230 m3sec-1) was recorded at Hardinge Bridge in 1998 (See Fig. 3.1 for the location of some of the stations referred to in this paper.) The Brahmaputra basin area is 58 mha. It is regarded as one of the world’s largest braided river systems in terms of discharge, sediment transport, and channel processes (JMBA, 1989). The river originates at an elevation of 5,150 m in a large glacier mass in the Kailash range of the Himalayas, very close to Manassarovar Lake. Mean annual precipitation in the basin area in India and Bangladesh is 2,500 mm and 2,400 mm, respectively. Mean annual runoff of the Brahmaputra at Pandu, India and Bahadurabad, Bangladesh is estimated to be 511 x 103 mcm and 643 x 103 mcm, respectively. The highest peak discharge was 98,600 m3sec-1 recorded at Bahadurabad in 1988. The Meghna/Barak basin is the smallest of the three basins, with an area of 8 mha. The headstream of the river in India is known as Barak and originates on the Southern slope of the mountain range to the North of Manipur, India. In Bangladesh, the river is known as Meghna and flows Southwest to meet the Padma (combined flow of the Ganges and Brahmaputra Rivers) at Chandpur. Mean annual precipitation of the basin in India and Bangladesh is 2,640 mm and 3,574 mm, respectively. Mean annual runoff of the Barak in India is 41 x 103 mcm (measured at Badarpurghat) (Kothyari and Garde, 1991). At Bhairab Bazaar in Bangladesh, the mean annual runoff is estimated to be 151 x 103 mcm. The highest peak discharge at Bhairab Bazaar was 19,900 m3sec-1 recorded in 1993.

* non-random.

The Meghna The Surma-Meghna Flooded Area (mha) India Bangladesh

The Kosi The Brahmaputra

The Ganges

River

1885-1971 1949-1980 1934-2000

Hardwar Farakka Hardinge Bridge Barahkshetra Pandu Bahadurabad Bhairab Bazaar Kanairghat 1953-1997 1954-1999

1948-1978 1955-1974 1956-1999 1964-1998 1969-1993

Period of Record

Station

26.20 25.15 24.03 -

29.58 25.00 23.06

Latitude (deg. N)

Table 3.1 Statistical properties of the peak discharge and flooded area data

91.50 89.66 59.98 -

78.10 87.91 89.03

Longitude (deg. E)

7.28 3.03

10,190.00 50,524.00 67,389.00 14,072.00 2,224.00

6,639.00 56,516.00 51,184.00

Mean (m3sec-1)

0.47 0.68

0.44 0.20 0.18 0.19 0.16

0.47 0.17 0.18

Coefficient of Variation (CV)

+0.17 +0.16

- 0.18 + 0.20 +0.03 - 0.23 + 0.35

- 0.42* + 0.05 + 0.22

Lag-1 Autocorrelation Coefficient

58 ARE FLOODS GETTING WORSE?

M. M. Q. MIRZA ET AL.

3.3

59

THE FLOOD PROBLEM

Flooding of catastrophic proportions often occurs in the GBM river basins. Extreme precipitation in the monsoon, together with the physical settings of the river basins has caused many severe floods in the last few decades. Causes and characteristics of floods vary between the highlands in Nepal, the middle ground in India, and the flat deltaic terrain in Bangladesh. In Nepal, the flood problem is mainly restricted to the Terai region along the border it shares with India. Rivers in the Terai region are very unpredictable and cause heavy flood damage as a result of intensive downpours on the Southern slopes of the Siwalik Himalaya (SAARC, 1992). The high Himalayan Mountains of Nepal are affected by Glacier Lake Outburst Floods (GLOF). These floods do not cause much damage to human settlements because the upper mountainous areas are sparsely populated. In contrast, floods in the Terai occur regularly and cause considerable damage to densely populated floodplains. In India, floods in the Ganges region are caused by the following factors either singly or in combination: excessive precipitation, inadequate river channel capacity, obstruction in streams, inadequate waterways at confluences, human encroachments and lack of adequate drainage, failure of flood control embankments and deforestation (Rangachari, 1993; Chowdhury, 1989; Dhar and Nandargi, 1998). Similar factors cause floods in the Brahmaputra region, but they are compounded by local physiographic features. The region is interspersed with a large number of streams, flooding from which inundates the intervening narrow valleys. The riverbeds in some cases are higher than the surrounding valley land. Therefore, any breach or spilling causes deep flooding in the valleys. The Brahmaputra region in India is highly prone to earthquakes and this often causes landslides. These seriously disturb the drainage system. The Barak region lies between the Khasi and Jaintia Hills in the North and Mizo Hills in the South. The river often overflows its banks inundating low areas on either side. There is a series of bowl-shaped depressions, locally known as “Haors”, which fill with floodwater. The gradient of the river is extremely flat and the outfall at the border with Bangladesh is congested. In recent years, the role of deforestation in the upstream areas in causing flooding in the downstream areas of the GBM basins has triggered interesting debates (BWDB, 1987; Carson, 1985; Thompson and Warburton, 1985; Hamilton, 1987; Hofer, 1998; Ives and Messerli, 1989; Messerli and Hofer, 1995; Rogers et al., 1989). BWDB (1987) indicated that deforestation in the upstream contributed significantly to the increased rates of sediment supply and accretion. However, the existing publications do not report any significant recent increase in the sediment load of the larger rivers and their tributaries, or in the magnitude of annual flooding and levels of river discharge (Ives and Messerli, 1989). Thompson and Warburton (1985) questioned the linkage between massive floods in the plains and land-use activities upstream in the Himalayas. However, they noted that there was some technical uncertainty encountered when analyzing the human components of erosion, flooding and shifting hydrological patterns. Hofer (1998) concluded that land-use changes in the Himalayas were not responsible for the floods far downstream in India and Bangladesh. In the aftermath of the devastating floods in Bangladesh in 1988, Rogers et al. (1989) remarked that there were no grounds for considering deforestation in the Himalayas as a significant cause of the flooding in the delta of the river system. Carson (1985:36) mentioned, “…Flooding and sedimentation problems in India and Bangladesh are a result of the geomorphic character of the rivers and man’s attempts to contain the rivers. Deforestation likely plays a minor, if any, role in the major monsoon flood events on the lower Ganges.” The role of deforestation in the sedimentation and flooding processes

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1999

1993

1989

1985

1981

1977

1973

1969

1965

1961

120 100 80 60 40 20 0 1954

Flooded Area (000 sq.km)

in South Asia is a highly contentious issue and it needs adequate scientific research and attention. Of the three countries affected by flooding in the GBM river basins, Bangladesh is the most vulnerable because of its geographic location, high monsoon cross-border flow, and the physiographic characteristics of its deltaic floodplains. Half of the country is under 12.5 m above the mean sea level (CBJET, 1991). Because of its flatness, floodwaters cannot drain quickly. The three rivers together may generate as much as 200,000 m3sec-1 of peak discharge. The problem becomes more complicated when the peak flow of each of the three rivers synchronises. In such a case, the flooded area may exceed 60% of the country (as occurred in 1988 and 1998), about three times the normally flooded area (Fig. 3.4). Although the river levels fall rapidly from September through November, water levels on adjoining floodplains fall more slowly because of low gradients, congested drainage, and substantial depression areas. The latter may stay submerged until December to January and some throughout the whole dry season (November to May). Floods cause considerable damage in the GBM basins and four main economic sectors - agriculture, housing, industry and transportation infrastructure are particularly vulnerable. Flood related damage puts considerable strain on the economies of the countries that share the GBM basins. This is particularly true in terms of diversion of resources for recovery activities and the loss in growth of Gross Domestic Products (GDP). For example, during the 1998/1999 fiscal year in Bangladesh, GDP growth declined to 4.6% from 5.2% of the previous year due to the devastating floods of 1998. Industry sector growth, however, decreased by 3.4% during the same period as a result of flood-induced disruptions in the manufacturing subsector (ADB, 2000).

Year

Fig. 3.4 Flooded area in Bangladesh during 1954-1999 (Source: BWDB, 2000b).

In Nepal, government statistics show an increasing trend in damage to public and private property from floods and landslides in recent years. The estimated damage to property increased from US$ 1.0 million in 1983 to US$ 100.0 million in 1989 (ADB, 1991). In India, out of the 34 mha of “flood-prone” area, some 23 mha are in the GBM basins. Fifteen Indian states and the union territory of Delhi lie in the basin. However, four states alone account for over two-thirds of the flood-prone area: Uttar Pradesh, Bihar, West Bengal and Assam (Rangachari, 1993). Data compiled by India’s Central Water Commission (CWC) show that during 1953-1987, the average area affected by floods was

M. M. Q. MIRZA ET AL.

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1997

1993

1989

1985

1981

1977

1973

1969

1965

1961

1957

200 175 150 125 100 75 50 25 0 1953

Flooded Area (000 sq.km)

7.66 mha (22.5% of the flood-prone area), of which 3.51 mha were cropped (CWC, 1989). Recent data published by the Ministry of Water Resources, Government of India shows that during 1953-1997 annual average flood affected area has declined to 7.42 mha (MWR, 2000) from that of the 1953-1987 average (Fig. 3.5). This is due to a decline in flooded area in the period 1987-1997. Available evidence indicates increasing flood damage in recent years in India. State governments estimated that flood damage in 1987 and 1988 was US$ 1.5 billion and US$ 2.5 billion, respectively (Rangachari, 1993). In 1953 it was 524 million rupees and remained around that level until the middle of the 1960’s when damage tracked upward (Fig. 3.2) (CWC, 1989).

Year

Fig. 3.5 Flooded area in India during 1953-1997 (Source: MWR, 2000).

In Bangladesh, the area prone to floods in the GBM basin is 6.14 mha. This is 42% of the country’s geographical area. On an annual average, 20.5% of Bangladesh (3.03 mha) becomes inundated. The loss caused by floods in Bangladesh in a normal year is about US$ 175 million; but in extreme cases, the damage may exceed two billion dollars. The 1998 flood damage was the worst in history, totaling in the range of US$ 2 billion to 2.8 billion (ADRC, 2000a; MOFA, 1998). The industry and infrastructure sectors were worst hit, followed by agriculture (MDMR, 1998). The flood damage in Bangladesh for the period 1954-1998 is shown in Figure 3.3. Flood damage estimation methods in Nepal, India and Bangladesh only take into account the direct damage. Death, trauma, accidents, post-flood health and nutrition problems are not considered direct damage as their monetary valuations are unaccounted for. Almost every year, a significant number of people die due to floods. During 1953-1987, the annual average loss of human lives in India due to floods was 1,439 (CSE, 1992). In West Bengal, India 1,262 people had died and another 117 were reported missing during the devastating monsoon floods of 2000 (UNICEF, 2000). In Nepal, during the period of 1981-1999, a total of 5,453 people lost their lives with the highest, 2,307 people, in 1993 (ADRC, 2000a). In the catastrophic floods of 1998 in Bangladesh, the number of reported deaths was 1,050 (ADRC, 2000a). The number of deaths caused by floods in India, Bangladesh and Nepal is summarized in Table 3.2. Flood damage is an indicator of flood hazard, which in turn, is a function of potential flood events in relation to human use of flood-prone land. This includes activities aimed at alleviating the flood problem, such as embanking river channels and elevating floor levels of buildings. Thus, flood hazard effects (registered as property damage, social disruption, and human injury) rise or fall with changes in the parameters of the flood event

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ARE FLOODS GETTING WORSE?

(e.g., discharge and areal extent) or human use of flood-prone land (e.g., type and density), or both. In the wake of increasing flood damages in the GBM basins, special emphasis has been given to flood problems in both India and Bangladesh. The Government of India created the Rashtriya Barh Ayog (National Commission on Floods) in 1976. Devastating floods in India in 1987 led to the setting up of two committees to look into the problem (Rangachari, 1993; Mirza, 1991a). Bangladesh formulated a Water Master Plan in 1964 that recommended 59 flood control projects as a result of the consecutive floods of 1954 and 1955 (Mirza, 1991b). However, from the mid-seventies to the late-eighties, flood control received little attention in Bangladesh. In response to the devastating flood of 1988, Bangladesh carried out 28 studies under the “Flood Action Plan (FAP)” during the period 1989-1995. All of these efforts were based on the claim by government (as well as non-government agencies) that floods in the GBM basin areas were getting worse (CSE, 1992; BBJTO, 1989; RBA, 1980; Ives, 1991). As floods are generally accompanied by over bank spilling, assessment of peak discharges in the major rivers is the best way to determine whether changes in flood events have occurred or not. In order to detect changes, Cumulative Deviation, Worsley Likelihood Ratio, Kruskal-Wallis and Mann-Whitney U tests (Annex 3.1) were applied to the peak discharge series of the three rivers. Similar tests were also applied to the flooded areas in India and Bangladesh to see if there were associated changes in the spatial (areal) extent of flooding. Table 3.2 Number of deaths due to floods in India, Bangladesh and Nepal during the period 1953-2000

Year

India

1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975

37 279 865 462 352 389 619 510 1,374 348 432 690 79 180 355 3,497 1,408 1,076 994 544 1,349 387 686

Bangladesh

Nepal

112 129

60

117 30

39 221

276

87 120 50 427 1,987

350

M. M. Q. MIRZA ET AL.

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Table 3.2 Continued

Year

India

Bangladesh

Nepal

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

1,373 11,316 3,396 3,637 1,913 1,376 1,573 238 1,661 1,804 1,200 1,835 2,050 1,097 203 1,024 572 1,862 2,845 1,479 1,506 2,526 2,131 500 2,159

103 13 17

130

655

245 1,200 300 150 3,680 2,379 180 231 450 15 366 43 900 55 179 1,050 48 100

750 92 186 200 46 22 358 27 25 51 2,307 140 768 311 170 144

Source: India: 1953-1987 (CWC, 1989) and 1988-1999 (ADRC, 2000a). Death toll for 2000 was taken from (ADRC, 2000b; UNICEF, 2000). Bangladesh: ADRC, 2000a except for 1954, 1955, 1962, 1968, 1970 and 1974 (Islam, 2000). Nepal: until 1999 (ADRC, 2000a.). Figure for 2000 was taken from ADPC, 2000.

3.4

THE DATA

Annual peak discharge data for the Ganges, Brahmaputra and Meghna Rivers were collected from UNESCO (1976), IAHS-AISH (1984), Bangladesh Water Development Board (BWDB) (1995, 2000a), French Engineering Consortium (FEC) (1989a), Raghunath (1985) and Nepal Water Conservation Foundation (NWCF, 1996). Flooded area data were collected from the Central Water Commission (CWC, 1985), Ministry of Water Resources (MWR, 2000) and BWDB (1993, 2000b). The periods of records of the collected peak discharge data for various rivers and stations varied, as shown in Table 3.1, but fall within the period 1885 to 2000. For India, flood damage data were collected from the Central Water Commission (CWC, 1989) and Asian Disaster Reduction Center (ADRC, 2000a). Flood damage data for Bangladesh were taken from Mirza (1991a) and Asian Disaster Reduction Center (ADRC, 2000a). Two observations were missing in the peak discharge data series for the Ganges River. One observation was missing for each of the Farakka (1969) and the Hardinge Bridge (1971) sites. These were filled by determining the correlation coefficient and then applying

64

ARE FLOODS GETTING WORSE?

the method of Salinger (1980).1 Similarly, one observation was also found missing for each of the Pandu (1964) and the Bahadurabad (1971) sites for the Brahmaputra River. These were also filled by the method applied for the missing data of the Ganges River. For the Meghna River at Bhairab Bazaar, four observations (1977-1980) were missing. These missing observations were filled by applying the precipitation-peak discharge regression model (Mirza, 1997).2 Errors involved with the discharge measurement, processing and storage of data are not generally reported and documented. Standard equipment, methods and specifications are being used in discharge and water level measurements in Nepal, India and Bangladesh. However, in Bangladesh, due to changing bed forms, velocity measurements from non-anchored boats and inaccurate measurement of depths for current meter may cause ≤10% and ≤15%-20% uncertainty in discharge and water level measurements, respectively (Sir William Halcrow and Partners, 1991; FAP 24, 1993). The magnitudes of errors in the measurement of discharge and water levels in India and Nepal are not known. But, due to similar characteristics of river channels, they are assumed to be the same as those of Bangladesh. Statistical properties of the peak discharge data are shown in Table 3.1. Annual peak discharge of the Ganges River at Hardwar is found to be highly variable, followed by the Kosi River at Barahkshetra. The almost equal coefficients of variation of the Brahmaputra, Meghna and Surma-Meghna Rivers indicate that they drain the catchment areas with similar characteristics. Lag-1 autocorrelation coefficient was determined using the equation shown below. This coefficient is used to determine the presence of “persistence” in the data. A negative value of r1 is indicative of marked high frequency (i.e. short-period) oscillations. On the other hand, positive values indicate Markov linear type persistence (Mirza et al., 1998). The presence of this type of persistence in a peak discharge series means that a large (or small) peak discharge for one year is more likely to be followed by a large (or small) for the next year:

where Xi is annual peak discharge at year i, n is the sample size, and discharge.

is mean peak

The randomness of the series can be tested to identify presence of trend or cycle using the one-tail 95% confidence limit of the Gaussian distribution (Mitchell et al., 1966). The test value (r1 ) t is computed from:

1 The missing observation for one year was calculated using the ratio of the mean peak discharge of the two stations with a missing record to the adjacent data-possessing station multiplied by the peak discharge of that year. 2 The regression model for estimating annual peak discharge is Qp = -10531 + 3.41*P1 + 5.69* P2 (R2 = 87%). Where P1 is average precipitation in the North Assam meteorological sub-division and P2 is the average precipitation in the South Assam meteorological sub-division and Bangladesh part of the basin.

M. M. Q. MIRZA ET AL.

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If the sample value of r1 is larger than the value of (r1 ) t , then the series is considered non-random. 3.5

STATISTICAL ANALYSES METHODS

The Cumulative Deviation (Buishand, 1982), Worsley Likelihood Ratio (Worsley, 1979), Kruskal-Wallis (Coshall, 1989) and Mann-Whitney U (Kite, 1988; Essenwanger, 1985) tests (Annex 3.1) were applied to detect changes in the mean of the peak discharge and flooded areas. Details of these tests are described below. For individual station and the flooded areas, the entire time series was considered for the Cumulative Deviation and the Worsley Likelihood Ratio tests. The available data were divided into 10-year periods for the Kruskal-Wallis test. For the Mann-Whitney U test, data were divided into two equal segments. The test statistics of the Mann-Whitney and Kruskal-Wallis tests were calculated based on the ranks of the observations, not their actual values. The cumulative deviation and Worsley Likelihood Ratio tests assume that the observations are random and normally distributed. However, the tests can be applied with some reasonable departures from the normality. The Lag-1 autocorrelation values were within the desired range (except the Ganges River at Hardwar). They indicated that the observations of peak discharge at most of the stations were approximately random and normally distributed. For all four tests, critical values for a two-sided probability were used. The null hypothesis was rejected only if a change was detected at a 95% confidence level. Results of the statistical tests are presented in Table 3.3. 3.6

RESULTS

All four statistical tests indicate a downward trend in the peak discharge of the Ganges River at Hardwar in India. Note that this is a discharge measurement station located very far upstream. The Kruskal-Wallis test detected increases at Farakka, 18 km upstream of the Bangladesh border. Three statistical tests (Cumulative Deviation, Worsley Likelihood Ratio and Kruskal-Wallis) indicated increases in the Ganges River flood peak discharge at Hardinge Bridge, in Bangladesh (Table 3.3). As the discharge of the Ganges River at Farakka has been regulated by a barrage3 since 1975, these changes might have not occurred entirely as a result of natural phenomena. The Kosi River (an important tributary of the Ganges) in Nepal showed no change in peak discharge. Similarly, all four statistical tests demonstrated decreases in the peak discharge of the Brahmaputra River at Pandu in India. However, there was no change indicated at Bahadurabad further downstream. In the Surma-Meghna at Kanairghat, the statistical tests indicated increases in the peak discharge. Downstream at Bhairab Bazaar, there was no change in peak discharge of the Meghna River.

3 The barrage was built by India across the Ganges River at Farakka (18 km from the Bangladesh border) in order to divert 1,133 m3sec-1 of water to restore the navigability of the port of Kolkata. It was commissioned in April of 1975.

Hardwar Farakka Hardinge Bridge Barahkshetra Pandu Bahadurabad Kanairghat Bhairab Bazaar The Ganges The Brahmaputra The Meghna -

a. Peak Discharge The Ganges

Change (-) No Change Change (+) No Change Change (-) No Change Change (+) No Change Change (+) No Change No Change Change (-)

Cumulative U Deviation

Note: The shaded rows denote locations within the border of Bangladesh.

Bangladesh

b. Flooded Area India

The Bhahmaputra The Surma-Meghna The Meghna

The Kosi

Station/Basin

Peak Discharge/Flooded Areas Change (-) Change (+) Change (+) No Change Change (-) No Change Change (+) No Change Change (+) No Change No Change No Change

Kruskal-Wallis

Statistical Tests

Change (-) No Change Change (+) No Change Change (-) No Change Change (+) No Change No Change No Change Change (+) No Change

Worsley Likelihood Ratio Change (-) No Change No Change No Change Change (-) No Change Change (+) No Change Change (+) No Change No Change Change (-)

Mann-Whitney

Table 3.3 Results of the statistical tests applied to the peak discharge data of the Ganges, Brahmaputra and Meghna Rivers and the flooded areas in India and Bangladesh

66 ARE FLOODS GETTING WORSE?

M. M. Q. MIRZA ET AL.

67

With regard to flooded areas, three statistical tests - Cumulative Deviation, Kruskal-Wallis and Mann-Whitney U showed detectable increases in flooded areas in the Ganges basin. No change was detected in the Brahmaputra basin. Only the Worsley Likelihood Ratio test identified an increase in the Meghna basin in India. In Bangladesh, Cumulative Deviation and the Mann-Whitney U tests indicated decreases in the flooded areas in Bangladesh. Overall, the results do not indicate any conclusive change in the peak discharge or flooded area time series within Bangladesh (Table 3.3). However, at the upstream stations in India two rivers showed an increase in peak discharge. This was not registered at downstream stations in Bangladesh (except at Hardinge Bridge where three of the four tests showed increases). As mentioned above, however, these increases are likely to be due to the regulation of discharge by the Farakka Barrage. Are changes in climate responsible for increases in flood peaks and flooded areas in the upstream areas of the Ganges, Brahmaputra and Meghna Rivers? Firm evidence of a long-term regional trend in area-averaged precipitation is yet to be found. Mooley and Parthasarathy (1983) examined above- and below-average annual precipitation extremes between 1871 and 1980 for 360 precipitation stations all over India, except for the Northern mountainous districts. They included the Gangetic Plain, Bengal and Assam in their analysis. No statistically significant trends or oscillations were found. Their conclusion was that annual precipitation totals were distributed randomly overtime. During 1988-1997, India experienced nine abnormal monsoons; six were higher than normal and three were 1% to 10% less than a normal (Dhar and Nandargi, 1998). There is no indication of any trend towards increased monsoon precipitation in the upstream river basins that could have exacerbated flooding in either upstream areas or in Bangladesh in downstream. 3.7

DISCUSSION

The results should be considered with caution, since the period of analysis from the data available at the eight river gauging stations ranges from 20 years to 87 years. Nevertheless, claims that worsening flood problems in the GBM basins are due to increasing flood events are not borne out by this analysis. The results do not indicate any conclusive change in the peak discharge or flooded area overtime. Factors other than increasing flood event characteristics (peak discharge and flooded area) must be responsible for the increase in flood damage (Figs. 4.2 and 4.3) recorded for countries within the GBM basins. Two main factors have been identified as possible contributors to the record of increasing flood damage: (i) improvement in flood damage assessment techniques; and (ii) increases in human settlement in flood-prone areas. 3.7.1

IMPROVEMENT IN FLOOD DAMAGE ASSESSMENT TECHNIQUES

Flood damage assessment techniques have undergone a series of improvements in the last five decades, particularly in India and Bangladesh. In the past, the Ministry of Water Resources in India carried out flood damage assessment based on: area flooded, frequency of floods, probable depth and duration of inundation, crop area damaged and value of damaged crops, amount of damage to houses, number of cattle heads lost and damage to public utilities. A bottom-up reporting process was followed. For example, block supervisors used to report to sub-divisional officers. Between 1957-1964, several high-level committees were constituted to improve the techniques of data collection,

68

ARE FLOODS GETTING WORSE?

processing and reporting (RBA, 1980). These committees emphasized the need to send out teams at the end of every flood season to contact other agencies collecting relevant data, visit flood affected areas to ascertain the damage from local populations, and make their own assessment. Rashtriya Barh Ayog (RBA, 1980) recommended that complete enumeration methods be followed by the State Governments, and made other recommendations to reduce over- and under-estimations. It also recommended use of remote sensing techniques for flood damage assessment whenever possible. Bangladesh has also improved its flood monitoring and assessment procedures since the early 1960s, when flood control was given an institutional shape. The First Water Master Plan of 1964 (EPWAPDA, 1964) prepared the foundation for flood control processes in Bangladesh. The National Water Plan (MPO, 1986) and the Flood Action Plan (FPCO, 1993) have significantly improved the flood damage data collection, processing, interpretation and reporting techniques. For example, since 1980, the Government of Bangladesh has been using Advanced Very High Resolution Radiometer (AVHRR) data for assessing flooded areas and damage. The Bangladesh Water Development Board has a comprehensive network of data collection through its field offices (Rasid and Pramanik, 1993). 3.7.2

INCREASED HUMAN SETTLEMENT IN FLOOD-PRONE AREAS

It is estimated that by 1991, about 530 million people lived in the GBM basins in Nepal (4%), India (75%) and Bangladesh (20%). Most of them dwelt on floodplains susceptible to annual floods. In India, the number of people affected by the average annual floods rose from 16 million to 53 million in the 30-year period leading up to the late 1980s (CSE, 1992). In Bangladesh it has increased from 13 million to 38 million for the same period. This translates into about 10 million additional households at risk from annual flooding. When people living in areas reached by larger than normal floods are considered, the population and dwellings now at risk due to population growth, escalates dramatically. Although the exact totals are not known, the rapid increase in the number of dwellings at risk over the last 30 years will have contributed significantly to the increase in the amount of flood damage reported in that period. People living in, or associated with these at threat floodplains draw their livelihood from them. Such activities include agriculture, industries, public infrastructure, and commercial outlets. For example, during the 1950s and 1960s, India’s cultivated area increased from about 119 mha in 1950-1951 to 141 mha in 1970-1971. Since then, it has remained relatively stationary, although in some areas it intensified through the “green revolution” (CSE, 1992). In India, agriculture accounts for 48% of the average annual flood damage. The average crop area affected annually by floods increased sharply from less than 2 mha (30% of total) in the 1950s to over 4.5 mha (>50%) in the 1980s (CSE, 1992). Similarly, the agricultural area in Bangladesh has expanded in the last 30 years. The net-cropped area increased from 8.43 mha in 1972/1973 to 8.61 mha in 1989/1990. The French Engineering Consortium (FEC, 1989b) has estimated that agriculture and dwellings each accounts for roughly 30% of the total damage. The remaining 40% is from damage to infrastructure, such as roads, railways, and water works. While it is highly likely that damage estimates have improved and accounted for a significant portion of the escalating flood damage figures reported for countries within the GBM basins, it is most likely that increasing settlement and associated economic activities in flood-prone areas is the more important contributor. It is these human use elements of

M. M. Q. MIRZA ET AL.

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the flood hazard to which an explanation must be sought for rising flood damage and not the flood events themselves. 3.8

CONCLUSIONS

The findings in this article have a number of policy level implications for the government agencies of the countries that share the GBM basins and the donors/aid agencies involved. First, governments should formulate and implement policies to discourage further settlement in the flood-vulnerable areas. This requires concrete action plans in terms of delineation of the floodplains based on risk factors, enactment of proper legislation and allocation of suitable lands to settle additional people. Second, plans should be formulated to protect economic and industrial centers in order to reduce flood damage. Adoption and implementation of such plans requires political decisions as well as significant investment. Third, flood damage mitigation programmes should provide more emphasis to local level actions. Historically, a top-down flood management approach has been in practice in the countries that share the GBM basins. People at the lowest level are seldom consulted in developing any flood management plan. Therefore, in many cases, stipulated benefits are not delivered to the vulnerable population and areas (Adnan, 1991). Fourth, flood research should also focus on understanding the relationship between climate variations and societal factors in the observed flood damage. Presently, flood research in the GBM basins is more focused on structural solutions rather than exploring any possible relationship between hydro-meteorological and societal factors. Cooperation between upstream and downstream countries in flood management could also reduce vulnerability to a great extent (Verghese and Iyer, 1993). Although some cooperative arrangements exist between Bangladesh and India, other countries of the GBM basins - Nepal and Bhutan should be brought under a comprehensive regional flood management plan. This requires executive initiative and decision from the highest political levels. Fifth, flood damage adjustment research should receive more focus at the government level. Although independent researchers (Paul, 1997; Rasid and Mallik, 1995) have conducted some studies on this topic, flood research specifically on damage adjustment at the government level is almost absent. Direct government funded research institutions could undertake the necessary research studies. This will require the allocation of additional financial and human resources by governments. Sixth, donors and aid agencies should put emphasis on projects that build capacity at local levels to reduce vulnerability and damage. In the countries of the GBM basins, donors and aid agencies usually exert a substantial influence in shaping socio-economic development policies of the respective governments. Therefore, they could bring about a significant shift in the flood management policies by focusing more on projects for capacity building at local levels, in pursuance of a bottom-up planning process.

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ARE FLOODS GETTING WORSE?

Annex 3.1

(i) Cumulative Deviation Test (Buishand, 1982): The purpose of this test is to detect the existence of a jump after m observations:

(3.1)

The basic assumption for this test is that the observations are independent and normally distributed. The test can still be applied, however, when there are slight departures from normality. Given the observations X1 ...... X n , we let

(3.2)

The test statistic

is percentage points of the statistic Q are given in

Buishand (1982). (ii) Worsley Likelihood Ratio Test (Worsley, 1979):

computing The test statistic is values for the test statistic W.

(3.3) Worsley (1979) gives critical

(iii) Kruskal-Wallis Test (Coshall, 1989): The test is applied for determining equality of sub-sample means. For this test the time series data is divided into m sub-samples with lengths nj ( j = 1, 2, ..., m) and Rij is the rank of the ith observation of the jth sub-sample in the ordered complete sample. The test statistic is:

M. M. Q. MIRZA ET AL.

71

(3.4)

where, Rj is the total ranks in the jth sub-sample, i.e. Rj = ∑ Rij, and N = sample size.

is the total

When ties are involved in the ranking procedure, H is divided by

where

T = t3 - t and t is the number of tied observations in a tied group of scores. Under the null hypothesis, for larger nj, the statistic follows the Chi-Square Distribution with (m-1) degrees of freedom. (iv) Mann-Whitney U Test (Kite, 1988; Essenwanger, 1985): This test is suggested for determining progressive change in the mean value with time. If two sub-samples are not well mixed and the entire sample is ranked, the elements of one sub-sample display relatively low rank numbers while those of the other display relatively high rank numbers. The test statistic U, reflects the low value arising when the sub-samples are not well mixed. Therefore, if the observed U value is less than a certain critical value Ucr, the hypothesis that there is no location difference between the sub-samples is rejected. The test statistic is calculated as the smaller of U1 and U2

(3.5) and (3.6) where n1 ⫽ size of the first sub-sample, n2 ⫽ size of second sub-sample, R1 = sum of ranks attributed to members of the first sub-sample in the ranks total sub-sample. When both sub-samples are larger than 20, U is approximately normally distributed with mean n1n2/2 and standard deviation The test statistic Z is calculated as:

(3.7)

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ARE FLOODS GETTING WORSE?

The Mann-Whitney test is based on a continuous distribution and if tied observations are encountered, a correction must be made. If t is the number of observations tied for a given rank and T = 1/12 (t3 - t) then the Z statistic is

(3.8)

where

is the sum of T’s over all groups of tied observations.

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REFERENCES Adnan, S.: Floods, People and Environment: Institutional Aspects of Flood Protection Programmes in Bangladesh, Research and Advisory Services, Dhaka, Bangladesh, 1991. Asian Development Bank (ADB): Disaster Mitigation in Asia and the Pacific, ADB, Manila, 1991. Asian Development Bank (ADB): Annual Report 1999, ADB, Manila, 2000. Asian Disaster Preparedness Center (ADPC): Bulletin on Nepal Floods, ADPC, Bangkok, 2000. Asian Disaster Reduction Center (ADRC): Disaster Database (http://www.adrc.or.jp/nations), 2000a. Asian Disaster Reduction Center (ADRC): Latest Disaster Information (http://www.adrc.or.jp/nations), 2000b. Bangladesh-Bhutan Joint Team of Officials (BBJTO): Report on Flood Control and Flood Mitigation. Government of Bangladesh and His Majesty’s Government of Bhutan, Dhaka and Thimpu, 1989. Bangladesh Water Development Board (BWDB): Flood in Bangladesh: Investigation, Review and Recommendation for Flood Control, BWDB, Dhaka, 1987. Bangladesh Water Development Board (BWDB): Annual Flood Report 1992, BWDB, Dhaka, 1993. Bangladesh Water Development Board (BWDB): Discharge Data for Various Rivers in Bangladesh, BWDB, Dhaka, 1995. Bangladesh Water Development Board (BWDB): Discharge Data for Various Rivers in Bangladesh, BWDB, Dhaka, 2000a. Bangladesh Water Development Board (BWDB): Flooded Area Data, BWDB, Dhaka, 2000b. Barua, D. K.: On the Environmental Controls of Bangladesh River Systems. Asia Pacific Journal on Environment and Development 1(1) (1994), pp.81-98. Brammer, H.: The Geography of the Soils of Bangladesh, University Press Ltd., Dhaka, 1996. Buishand, T. A.: Some Methods for Testing the Homogeneity of Rainfall Records. Journal of Hydrology 58 (1982), pp.11-27. Carson, B.: Erosion and Sedimentation Processes in the Nepalese Himalaya. International Center for Integrated Mountain Development (ICIMOD), Occasional Paper No.1, ICIMOD, Kathmandu, 1985, p.36. Central Water Commission (CWC): State-Wise Data on Flood Affected Areas and Damages 1953 to 1984, CWC, New Delhi, 1985. Central Water Commission (CWC): Water and Related Statistics, CWC, New Delhi, 1989. Center for Science and Environment (CSE): Floods, Floodplains and Environmental Myths, CSE, New Delhi, 1992. China -Bangladesh Joint Experts Team (CBJET): Study Report on Flood Control and River Training Project on the Brahmaputra River in Bangladesh, Vol. 1 and Vol. 2. Government of Bangladesh and Government of China, Dhaka and Beijing, 1991. Chowdhury, A. M.: Flood 1988. In: Flood in Bangladesh (M. Ahmad Edited), Community Development Library, Dhaka, 1989, pp.235-240. Coshall, J.: The Application of Non-Parametric Statistical Tests in Geography, University of East Anglia, Norwich, Norfolk, U.K., 1989. Dhar, O. N. and Nandargi, S.: Floods in Indian Rivers and Their Meteorological Aspects. In: Kale, V. S. (Editor), Flood Studies in India, Geological Society of India, Bangalore, 1998, pp.1-25. East Pakistan Water and Power Development Authority (EPWAPDA): Master Plan. International Engineering Company Inc., San Francisco, 1964. Essenwanger, O. M.: World Survey of Climatology. General Climatology 1B, Elsevier, Amsterdam, 1985. Flood Action Plan (FAP) 24: Hydrological Study: Phase I. Flood Plan Coordination Organization (FPCO), Dhaka, 1993. Flood Plan Coordination Organization (FPCO): Disaster Preparedness Program. FPCO, Dhaka, 1993. French Engineering Consortium (FEC): Pre-Feasibility Study for Flood Control in Bangladesh, Annexes, FEC, Paris, 1989a. French Engineering Consortium (FEC): Pre-Feasibility Study for Flood Control in Bangladesh, Vol. 5, Financial and Economic Aspect, FEC, Paris, 1989b.

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Hamilton, L.: What are the Impacts of Himalayan Deforestation on the Ganga-Brahmaputra Lowlands and Delta? Assumptions and Facts. Mountain Research and Development 7(3) (1987), pp.256-263. Hofer, T.: Do Land-Use Changes in the Himalayas Affect Downstream Flooding? Traditional Understanding and New Evidences. In: Kale, V. S. (Editor), Flood Studies in India, Geological Society of India, Bangalore, 1998, pp.119-141. IAHS-AISH: World Catalogue of Maximum Observed Floods. IASH-AISH Publ. No. 143, UNESCO, Paris, 1984. Islam, N.: Flood ’98 and the Future of Urban Settlements in Bangladesh. In: Ahmad, Q. K., Chowdhury, A. K. A., Imam, S. H., Sarker, M. (Eds.), Perspectives on Flood 1998, University Press Ltd., Dhaka, 2000, pp.52-65. Ives, J. D. and Messerli, B.: The Himalayan Dilemma: Reconciling Development and Conservation, Routledge, New York, 1989. Ives, J. D.: Floods in Bangladesh: Who is to Blame? New Scientist, April 13th, 1991, pp.30-37. Jamuna Multipurpose Bridge Authority (JMBA): Jamuna Bridge Project, Phase II Study: Feasibility Report, Vol. II, JMBA, Dhaka, 1989. Kite, G. W.: Frequency and Risk Analysis in Hydrology. Water Resources Publications, Fort Collins, USA, 1988. Kothyari, U. C. and Garde, R. J.: Annual Runoff Estimation for Catchments in India. Journal of Water Resources Planning and Management 117(1) (1991), pp.1-10. Master Plan Organization (MPO): National Water Plan. Ministry of Irrigation, Water Development and Flood Control, Dhaka, 1986. Messerli, B. and Hofer, T.: Assessing the Impact of Anthropogenic Land-Use Changes in the Himalayas. In: Chapman, G. P., Thompson, M. (Eds.), Water and the Quest for Sustainable Development in the Ganga Valley, Global Development and the Environment Series, Mansell, London, 1995, pp.64-89. Ministry of Disaster Management and Relief (MDMR), Bangladesh: Estimated Sector-Wise Damages Due to Flood 1998, MDMR, Dhaka, 1998. Ministry of Foreign Affairs (MOFA), Bangladesh: Damages Caused by Flood 1998 (http://www.bangladeshonline.com/gob/flood98/foreign_1.htm), 1998. Ministry of Water Resources (MWR): Flood Affected Areas (http://warmin.nic.in/development/ flood_affected.htm), 2000. Mirza, M. M. Q.: Environmental Impacts of Floods, Droughts and Deforestation in the Himalayan Region. In: Veziroglu, T. N. (Editor) Energy and Environmental Progress-I., Vol. 6. Nova Science Publishers, New York, 1991a, pp.551-570. Mirza, M. M. Q.: Flood Action Plan of Bangladesh - The Embankment Issue. Water Nepal 2(2/3) (1991b), pp.25-28. Mirza, M. M. Q.: Modeling the Effects of Climate Change on Flooding in Bangladesh, Unpublished D.Phil Thesis. International Global Change Institute (IGCI), University of Waikato, Hamilton, New Zealand, 1997. Mirza, M. M. Q. and Dixit, A.: Climate Change and Water Resources in the GBM Basins. Water Nepal 5(1) (1997), pp.71-100. Mirza, M. M. Q., Warrick, R. A., Ericksen, N. J. and Kenny, G. J.: Trends and Persistence in Precipitation in the Ganges, Brahmaputra and Meghna basins in South Asia. Hydrological Sciences Journal 43(6) (1998), pp.845-858. Mitchell Jr., J. M., Dzeedzeevskii, B., Flohn, H., Hofmeyr, W. L., Lamb, H. H., Rao, K. N. and Wallen, C. C.: Climatic Change. Technical Note No. 79, WMO, Geneva, 1966. Mooley, D. A. and Parthasarathy, B.: Droughts and Floods Over India in Summer Monsoon Seasons 1871-1980. In: A. Street-Parrot, M. Beran, R. Ratchiffe (Eds.), Variations in the Global Water Budget. D. Reidel, Dordrecht, the Netherlands, 1983, pp.239-252. Nepal Water Conservation Foundation (NWCF): Peak Discharge Data for the Kosi River at Barahkshetra, NWCF, Kathmandu, 1996. Paul, B. K.: Flood Research in Bangladesh in Retrospect and Prospect: A Review. Geoforum 28(2) (1997), pp.121-131. Raghunath, H. M.: Hydrology. Wiley Eastern, New Delhi, 1985.

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Rahman, M. A.: The Tale of a Delta, the Rivers, the Donor Dictature and FAP: The Congenital Profligacy. In: Proceedings of Open Discussion on Flood Action Plan, 12 November. Institution of Engineers, Dhaka, Bangladesh, 1993. Rangachari, R.: Flood Management. In: B.G. Verghese, R.R. Iyer (eds.), Harnessing Eastern Himalayan Rivers: Regional Cooperation in South Asia, Konark Publishers, New Delhi, 1993, pp.86-98. Rasid, H. and Mallik, A.: Flood Adaptations in Bangladesh - Is the Compartmentalization Scheme Compatible with Indigenous Adjustments of Rice Cropping to Flood Regimes. Applied Geography 15(1) (1995), pp.3-17. Rasid, H. and Pramanik, A. H.: Areal Extent of the 1988 Flood in Bangladesh: How Much Did the Satellite Imagery Show? Natural Hazards 8 (1993), pp.189-200. Rashtriya Barh Ayog (RBA): Report, Vol. 1. Ministry of Energy and Irrigation, New Delhi, 1980. Rogers, P., Lydon, P. and Seckler, P.: Eastern Waters Study: Strategies to Manage Flood and Drought in the Ganges-Brahmaputra Basin, ISPAN, Virgina, USA, 1989. Salinger, J. M.: New Zealand Climate: The Instrumental Method, Unpublished Ph.D Thesis, Victoria University of Wellington, New Zealand, 1980. Sir William Halcrow and Partners: River Training Studies of the Brahmaputra River. First Interim Report, Annex I, Part 4: Analysis of Discharge Measurements, Sir William Halcrow and Partners Ltd., Dhaka, 1991. South Asian Association for Regional Cooperation (SAARC): Regional Study on the Causes and Consequences of Natural Disasters and the Protection and Preservation of the Environment, SAARC Secretariat, Kathmandu, 1992. Thompson, M. and Warburton, M.: Uncertainty on a Himalayan Scale. Mountain Research and Development 5(2) (1985), pp.115-135. United Nations Educational Scientific and Cultural Organization (UNESCO): World Catalogue of Very Large Floods, UNESCO, Paris, 1976. United Nations International Children’s Emergency Fund (UNICEF): West Bengal Flood Emergency Situation Report, 24 Oct 2000, UNICEF, India, 2000. Verghese, B. G. and Iyer, R. R.: Harnessing Eastern Himalayan Rivers: Regional Cooperation in South Asia, Konark Publishers, New Delhi, 1993. Worsley, K. J.: On the Likelihood Ratio Test for a Shift in Location of Normal Populations. Journal of American Statistical Association 74 (1979), pp.365-367.

4 Climate Change and Water Resource Assessment in South Asia: Addressing Uncertainties GARY YOHE KENNETH STRZEPEK

4.1

INTRODUCTION

Any human or natural system’s environment varies from day to day, month to month, year to year, decade to decade, and so on. It follows that systematic changes in the mean conditions that define those environments can actually be experienced most noticeably through changes in the nature and/or frequency of variable conditions that materialize across short time scales and that adaptation necessarily involves reaction to this sort of variability. This is the fundamental point in Hewitt and Burton (1971), Kane et al. (1992), Yohe et al. (1999), Downing (1996) and Yohe and Schlesinger (1998). Some researchers, like Smithers and Smit (1997), Smit et al. (2000), and Downing et al. (1997), use the concept of “hazard” to capture these sorts of stimuli, and claim that adaptation is warranted whenever either changes in mean conditions or changes in variability have significant consequences. For most systems, though, changes in mean conditions over short periods of time fall within a “coping range” - a range of circumstances within which, by virtue of the underlying resilience of the system, significant consequences are not observed for short-term variability (see Downing et al. (1997) or Pittock and Jones (2000)). There are limits to resilience for even the most robust of systems, of course. It is therefore as important to characterize the boundaries of a system’s coping range as it is to characterize how the short-term variability that it confronts might change over the longer term. This chapter is designed to reflect the sensitivity to short-term climate variability (expressed in terms of the changes in frequency of flooding events in Bangladesh along the Ganges, Brahmaputra and Meghna Rivers) to long-term secular change (expressed in terms of long-term trends in maximum monthly flows) along a wide range of not-implausible climate futures. It therefore explores a case for which the boundaries of a coping range are easily defined by flooding thresholds. When we ultimately turn a discussion of how to evaluate adaptation options that might expand the coping range (exposure to flooding) or reduce the cost of flooding (sensitivity to flooding in terms of multiple metrics), we will do so in a way that can accommodate enormous uncertainty. We begin by characterizing the sources of uncertainty in our perception of how the future climate might evolve and our associated expectations about the frequency of flooding. Section 4.3 reviews historical records of annual mean flows, annual peak monthly

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flows and flooding events. A statistically calibrated reduced-form relationship between monthly peak flow and the likelihood of flooding in any one year will summarize these data. Section 4.4 follows with a description of a simple hydrologic model that relates precipitation and temperature to river flow on a monthly basis; calibration and scaling issues are also reviewed. Major sources of uncertainty in generating scenarios of future climate change are described in Section 4.5. Following a methodology developed in Yohe et al. (1999), a systematic sampling across 14 general circulation models across three alternative carbon-emissions scenarios associated with two alternative sulfate scenarios, three alternative climate sensitivities, and two alternative sulfate forcing factors will produce a wide range of future flow scenarios (684 in number). Subsequent analysis will work with 8 representative scenarios for peak monthly flows selected from the full sample. The representatives will not be chosen to reflect a probabilistic portrait of what the future might hold. They will, rather, be selected to span a full-range of “not-implausibility” futures so that the associated inter-temporal trajectories of the annual likelihood of flooding events absent any additional adaptation presented in Section 4.5 offer pictures of profound uncertainty - possible futures that cannot, at this point, be dismissed as impossible. The scenarios will, in particular, reflect the possibility that maximum flows may or may not climb continuously over time; indeed, they reflect the distinct possibility that the monthly maxima may actually begin to fall after 2050. Further adaptation can be expected to guard against any increase in the frequency of flooding, so Section 4.6 describes how these representative trajectories might be employed to characterize the relative efficacy of various adaptation options overtime before a concluding section offers some thoughts about context. 4.2

DEFINING UNCERTAINTIES

Figure 4.1 offers a schematic portrait of how the drivers of climate change might influence the likelihood of flooding events in Bangladesh. Various emissions trajectories of greenhouse gases and sulfate aerosols are shown there to produce a range of climate futures, determined in large measure by uncertainty about climate sensitivity and the radiative forcing of the sulfates. These climate futures produce ranges of change in monthly precipitation and temperature which, in turn, produce a set of futures expressed in terms of maximum monthly flows in any given year. Since the severity of possible flooding events in any year can be related statistically to these maximum flows, trajectories of the likelihood of small, modest, and extreme flooding are ultimately produced. The expanding size of the loci in Figure 4.1 illustrates pictorially how the uncertainty that clouds our understanding of each step in the causal chain cascades down the causal flow. If, for example, we knew the path of future emissions exactly, we could not precisely define associated climate change. If we knew how climate change would evolve over the next decades, we still could not accurately describe how associated patterns of precipitation and temperature would be altered and how those changes might be translated into river flows. And even if we knew exactly how flows might change, we could not accurately predict how the likelihood of flooding events might change. A second cascade of uncertainty, derived from the methods with which researchers try to describe each of the links depicted in Figure 4.1, must also be recognized. First of all, there may not be one accepted model of any given link in the causal structure. Instead, multiple modeling structures - abstractions of the real world - may exist, and they sometimes produce wildly different answers to the very same questions. This simple phenomenon is valuable in examining the relative value of one particular model or another,

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but it introduces model uncertainty for analysts who are looking across model results for a coherent view of the future. In addition, the ability of any particular model to offer credible scenarios is limited by the statistical boundaries that surround estimates of the critical parameters (call this calibration uncertainty). These limitations are well understood, of course, but they can be exacerbated when any one parameterization (with associated error bounds) is used to produce predictions of critical state variables (call this prediction uncertainty). Things get even worse when researchers take account of uncertainty about the track that the critical drivers of the model might take in the future (call this projection uncertainty). This compounding effect, really the point of Figure 4.1, can be especially troublesome when these drivers move beyond past experience and therefore out of the sample range upon which the model was calibrated. Finally, underlying social and economic structures might change overtime; and if they do, this evolution undermines the credibility of using historically-founded modeling structures as representations of future conditions to produce what might be called contextual uncertainty. Emissions

Concentrations

Precipitation

Temperature

Maximum Flow

Flooding Likelihood

Fig. 4.1 The cascade of uncertainty from emissions to a source of vulnerability.

Our depiction of climate uncertainty in terms of the annual likelihood of flooding will, at least implicitly, confront each of these sources of uncertainty by the time we describe a framework within which to evaluate adaptation options. Calibration, prediction and projection uncertainties will, for example, cloud our understanding of the link between flow in the rivers and the likelihood of flooding events. Model and projection uncertainties will cascade through the scenarios with which we create representative “not-implausible”

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portraits of future climate change in terms of flow, but calibration and prediction uncertainties will also have an effect behind the scenes. Finally, the evaluation approach described in Section 4.6 must accommodate contextual uncertainty. 4.3

HYDRO-CLIMATIC ANALSIS OF FLOODING IN BANGLADESH

Bangladesh is very vulnerable to flooding, principally due to intense monsoon precipitation that falls on the watershed of the Ganges, Brahmaputra and Meghna (GBM) Rivers. Figure 4.2 shows how these rivers converge into a single delta within Bangladesh. Mirza (2003) reports that the GBM watershed covers 1.75 million square kilometers of Bangladesh, China, Nepal, India and Bhutan. According to Ahmed and Mirza (2000), 20.5% of the area of Bangladesh is flooded each year, on average; and in extreme cases, floods about 70% of Bangladesh can be under water.

Fig. 4.2 The Ganges, Brahmaputra and Meghna Rivers.

The goal of this paper is to analyze the impact of not-implausible climate change scenarios on the flood frequency in Bangladesh. Mirza (2003) took a statistical approach to relate monsoon precipitation to peak flood flows. This paper will use a conceptual hydrologic rainfall-runoff model that incorporates evapo-transpiration, snowmelt, soil moisture and surface and sub-surface flows. Separate models of the Ganges and Brahmaputra Rivers are developed and described in the next section. The hydrologic model needs to be driven by a climate data, of course, but COSMIC reports only spatially averaged climate change variables at a nation scale. To cope with this problem, Nepal was selected as the representative country for three reasons. First of all, Nepal is located almost directly in the geographic center of the GBM watershed. Secondly, its monsoon precipitation characteristics, in quantity and timing, are representative of the average characteristics over much of the GBM basins. Finally, using the COSMIC data from China or India, two very large countries over which COSMIC averages climate variables are not representative of the conditions in the GBM watershed.

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81

UNCERTAINTIES IN THE HISTORICAL CLIMATE RECORD

The COSMIC scenario generator provides a base year of 1990, but does not provide any information on the statistics of climate record for the country. It is nonetheless necessary to have data on the moments and probability distributions of the hydro-climatic variables to perform a flood frequency analysis. To supplement the COSMIC scenario data for Nepal, we employed historical climate data gathered by the Tyndall Center for Climate Change Research and recorded in their TYN CY 1.1 dataset. Mitchell et al. (2004) reported that the TYN CY 1.1 data provide a summary of the climate of the 20th century for 289 countries and territories including monthly time series data for seven climate variables for the 20th century (1901-2000). Interestingly, the dataset creators provide the following warning: “This dataset is intended for use in trans-boundary research, where it is necessary to average climatic behavior over a wide area into statistics that are representative of the whole area.” This warming endorses the use of TYN CY 1.1 and COSMIC data for Nepal as appropriate for this modeling approach. 4.3.1.1

CLIMATE VARIABILITY

Table 4.1 presents the statistics for the annual precipitation and mean annual temperature for Nepal from the TYN CY 1.1 monthly time series data for the 20th century (1901-2000). The data shows that mean annual temperature varies very little with a COV of 0.04 and a lag-one correlation of 0.47. Precipitation exhibits variability at the total annual level. More importantly for predicting the likelihood of flooding events, though, maximum monthly precipitation per year is even more variable and strongly (positively) skewed with a high coefficient of variation. Table 4.1 Climate Statistics 1901-2000

Mean Mode Median Standard Deviation Skewness Lag-One Auto Correlation Coefficient of Variation Maximum Minimum

4.3.1.2

Annual Precipitation (mm)

Maximum Monthly Precipitation (mm)

Mean Annual Temperature ºC

2,097.1 2,600.2 2,084.8 264.9 0.102 0.096 0.13 2670.4 1396.0

556.1 489.1 533.9 98.2 0.433 -0.100 0.18 813.4 360.6

8.17 8.22 8.20 0.37 0.07 0.47 0.04 9.29 7.20

FLOOD FREQUENCY

Figure 4.3 shows that the flooded area in Bangladesh varies greatly from year to year. Flood risk is characterized by the probability that a certain level of flood will occur each

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year. The risk factor is generally expressed as a return period of T = 1/(probability of occurrence). The return period is determined from the cumulative density function of flood frequency. For flood frequency analyses, FAP (1992) recommends using the Gumbel Type I Distribution (EV1) for the major rivers in Bangladesh; it is defined by:

⎡ ⎛ x − u ⎞⎤ F ( x ) = exp ⎢ − exp⎜ ⎟⎥ − ∞ < x < ∞ ⎝ α ⎠⎦ ⎣

α=

6 S π

u = X - 0.5772a

120 100 80 60 40

1999

1993

1989

1985

1981

1977

1973

1969

1965

1961

20 0 1954

Flooded Area (000 sq.km)

where S is the standard deviation and 7 is the mean. The mean and standard deviation of the flood peak as well as the parameters of the EV1 distribution were determined using 100-year time series of climate data with the rainfall-runoff model. Using these statistics and the EV1 distribution, flood flows for the 2-year, 10-year, 50-year and 100-year return periods were calculated. They are presented in Table 4.2.

Year

Fig. 4.3 Bangladesh Flood Area from 1954 through 1999.

4.3.2

FLOODED AREA AND SEVERITY

High river flows themselves are not a problem unless they overtop their banks and flood area in the adjoining floodplain. The determination of flood flows used the science of hydrology, while determining the extent of and depth of flooding was based on the science of hydraulics. Mirza et al. (2003) reported on the application of the MIKE 11-GIS hydrodynamic model for Bangladesh to determine flooded area as a function of peak flood flows in the Brahmaputra-Ganges-Meghna Rivers system. Figure 4.4 shows the data from their work and the non-linear relationship that was developed between peak flow and flooded area with results in an R2 of 0.59. Flooded Area (million of hectares) = 4.3095* ln[Flow (cms)] – 45.906

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With a relationship between peak flow and flooded area, we have created a link between climate variables and the extent of flooding. Subsequent analysis of climate change will examine the impact of potential climate change on flooding in Bangladesh with full recognition of the possibility that this impact may not be symmetric with respect to all levels of flood risk. Table 4.3 shows four levels of flooding (low, modest, moderate and severe) that were mapped to correspond to the 2-year, 10-year, 50-year and 100-year return periods, respectively. Table 4.2 Flood flow frequency statistics 1901-2000

P - Annual Probability of Flood Exceeding Q T - Return Period for Q (years) Q - Peak Flood Flow (cms)

0.5

0.1

0.02

0.01

2 115,000

10 140,000

50 162,500

100 172,000

F lo od ed Ar ea M illo n hectare

6 5 4 3

y = 4.3095Ln(x) - 45.906 2

R = 0.5912

2 1 0 100000

110000

120000

130000

140000

150000

Pea k F loo d (C MS)

Fig. 4.4 The relationship between flood flows and flooded areas in Bangladesh. Table 4.3 Flood flow frequency statistics 1901-2000

P - Annual Probability of Flood Exceeding Q T - Return Period for Q (years) Q - Peak Flood Flow (cms) A- Flood Area (ha 10^6) Level of Flooding

4.4

0.5

0.1

0.02

0.01

2 115,000 4.311256 Low

10 140,000 5.158979 Modest

50 162,500 5.801248 Moderate

100 172,000 6.046099 Severe

A HYDROLOGIC MODEL FOR THE RIVERS

Mirza et al. (2003) examined the potential climate change impacts for river discharges in Bangladesh using an empirical model to analyze changes in the magnitude of floods of the Ganges, Brahmaputra and Meghna Rivers. The present analysis uses a conceptual rainfall-runoff model, WATBAL, to analyze changes in the magnitude of floods for the same watershed. Yates (1997) describes the model. It has been applied in over forty

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country studies of climate change impact on runoff including the Nile River basin, a river basin of the same spatial scale as the GBM basin. More specifically, the WATBAL model predicts changes in soil moisture according to an accounting scheme based on the one-dimensional bucket conceptualization depicted schematically in Figure 4.5. Yates and Strzepek (1994) compared this relatively simple formulation to more detailed distributed hydrologic models and found them in close agreement with absolute and relative runoff. The advantage of this lumped water-balance model lies in its use of continuous functions of relative storage to represent surface outflow, sub-surface outflow, and evapo-transpiration in the form of a differential equation (see Kaczmarek (1993) or Yates (1996)). The monthly water-balance contains two parameters related to surface runoff and sub-surface runoff. A third model parameter, maximum catchment water-holding capacity (Smax), was obtained from a global dataset based on the work of Dunne and Willmott (1996).

Fig. 4.5 A schematic conceptualization of the water-balance model.

The precise structure of WATBAL is easily described. To begin with, the monthly soil moisture balance is written as:

where

Peff Rs Rss Ev Smax z

= effective precipitation (length/time), = surface runoff (length/time), = sub-surface runoff (length/time), = evaporation (length/time), = maximum storage capacity (length), and = relative storage (1 ≥ z ≥ 0).

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A non-linear relationship describes evapo-transpiration based on Kaczmarek (1990):

Following Yates (1996), surface runoff is described in terms of the storage state and the effective precipitation according to:

where ε is a calibration parameter that allows for surface runoff to vary both linearly and non-linearly with storage. Finally, sub-surface runoff is a quadratic function of the relative storage state:

where a is the coefficient for sub-surface discharge. In certain regions, snowmelt represents a major portion of freshwater runoff and greatly influences the regional water availability. Ozga-Zielinska et al. (1994) provide a two parameter, temperature based snowmelt model which was used to compute effective precipitation and to keep track of snow cover extent. Two temperature thresholds define accumulation onset through the melt rate (denoted mf i ). If the average monthly temperature is below some threshold Ts, then the all the precipitation in that month accumulates. If the temperature is between the two thresholds, then a fraction of the precipitation enters the soil moisture budget and the remaining fraction accumulates. Temperatures above some higher threshold Tl give a mfi value of 0, so all the precipitation enters the soil moisture zone. If there is any previous monthly accumulation, then this is also added to the effective precipitation.

where,

and snow accumulation is written as,

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In writing equations (4.5) through (4.7), mfi Ai Pmi Peffi Tl Ts i

= melt factor, = snow accumulation, = observed precipitation, = effective precipitation, = upper temperature threshold at which precipitation is all liquid (°C), = lower temperature threshold at which precipitation is all solid (°C), = month

The model was calibrated from the TYN CY 1.1 data for the Ganges and Brahmaputra separately over using data from monthly flow from the 1970 and 1980 and produced R2 statistics of 0.89 and 0.87 for the Brahmaputra and Ganges, respectively. Since the climate change scenarios in COSMIC begin with a base year of 1990, the COSMIC base had to be correlated with the TYN CY 1.1 average data. Panels A and B of Figure 4.6 show the relationship between historical average and COSMIC base year data for temperature and precipitation, respectively.

Fig. 4.6 Panel A - Correlation of COSMIC 1990 to historical monthly temperature.

4.5

FUTURE CLIMATE SCENARIOS

Schlesinger and Williams (1998 and 1999) designed the COSMIC program so that researchers could produce literally thousands of “not-implausible” climate scenarios that are internally consistent. Each scenario is defined by a specific global circulation model (of the 14 included in COSMIC) driven by one of seven emissions scenarios for greenhouse gases that span virtually the entire range of published scenarios. Each scenario is also defined by one of three associated sulfate emission trajectories and by choosing a sulfate forcing parameter between 0 watts per meter and -1.2 watts per meter squared and a climate sensitivities between 1o and 4.5o (for a doubling of effective carbon-dioxide concentration from pre-industrial levels). It would be imprudent if not impossible to conduct integrated analyses along each one, so there is a fundamental need to limit the

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number of scenarios under study while still spanning the range of “not-implausibility”. In this application, 8 scenarios were therefore chosen and dubbed “representative” of an underlying set of 684 possibilities, but care must be taken in interpreting their content. They were not chosen to be representative in any statistically significant sense. They were, instead, chosen to represent the diversity displayed by the multitude of internally consistent “not-implausible” climate futures that published climate models can produce.

Fig. 4.6 Panel B - Correlation of COSMIC 1990 to historical monthly precipitation.

Panel A of Figure 4.7 depicts the full set of 684 scenarios in terms of maximum monthly flows in 2050 and 2100 - monthly flows that were computed by inserting COSMIC monthly precipitation and temperature pathways into the hydrologic model described in Section 4.4. Panel A also plots a 45o line along which these two annual maxima would be identical. Notice that many, but by no means all, of the ordered pairs lie below this demarcation. These pathways indicate the possibility that monthly flows might actually decline with secular climate change in the later half of the century even if they began the century with an increasing trend. It seems that reduced precipitation in the lowlands more than accommodate increased runoff of melting snowfall in the spring in the later decades. Panel B of Figure 4.7 reflects the same range of “not-implausible” futures with 8 representative scenarios whose underlying parameterizations which are displayed in Table 4.4. They clearly do not reflect the relative frequency of model run output across the full sample; instead, they reasonably span the range of possible outcomes. Figure 4.8 provides an alternative depiction of the diversity that these representative scenarios capture in terms of transient trajectories of maximum monthly flows in 10-year increments from 2000 through 2100. The three panels of Figure 4.9 offers insight into the likelihood of modest, moderate, and severe flooding events in any year along each of the 8 scenarios. The values portrayed there were derived for each year along each flow pathway from the statistical correlation described in Section 4.3. Notice that they fall, for every year along each pathway, as you move from modest to severe events. This is because some of the modest events are, statistically speaking at least, included in episodes of moderate and severe flooding; quite simply, the area that would be vulnerable to modest flooding would surely be exposed

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during moderate and severe floods. While none of these likelihoods reflected any additional adaptation to the threat of flooding, it is now certainly appropriate to begin thinking about interventions over the medium- or long-term (like building dikes or instituting programs of systematic and repeated dredging) that would be designed to reduce one or more of these likelihoods. Contemplating precisely how and when

Fig. 4.7 Panel A - The distribution of flow pathways from COSMIC displayed in terms of maximum monthly flows anticipated in 2050 and 2100.

450000 400000 350000

Flow in 2 1 0 0

300000 250000 200000 150000 100000 50000 0 100000

120000

140000

160000

180000

200000

220000

FLow in 2 0 5 0

Fig. 4.7 Panel B - Representative scenarios for the distribution portrayed in Panel A displayed in terms of maximum monthly flows anticipated in 2050 and 2100.

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alternative adaptations might be implemented and evaluating their relative efficacy are the topics of the next section. 4.6

ASSESSING ADAPTATION UNDER CONDITIONS OF PROFOUND UNCERTAINTY

Several unifying methodologies have been developed recently to aid researchers who are trying to evaluate adaptation options in the face of extreme uncertainty. The United Nations Development Programme (2003), for example, just completed several years of work involving multiple experts from across the globe in its creation Adaptation Policy Framework (APF). The basic structure of the APF is illustrated schematically in Table 4.5; it is particularly well suited for the risk-hazard approach characterized in the introduction. It is therefore particularly well suited for handling the profound uncertainty depicted in Figure 4.9. The need to reduce vulnerability to flooding in Bangladesh set the stage for this work, so we have a reasonably focused context within which Step I of the APF might be accomplished. Descriptions of current and (possible) future climate conditions, as required in Steps II and III, are similarly provided in Sections 4.3 and 4.5, respectively. We now turn to assessing adaptation options (Step IV). The risk-hazard approach to assessing how adaptation might increase a system’s long-term sustainability in the face of climate change and climate variability builds on the notion that its exposure to the impacts of climate, its baseline sensitivity to those impacts, and its adaptive capacity determine its vulnerability. The trajectories displayed in Section 4.5 offer a wide range of “not-implausible” baselines along which we can judge the relative efficacy of various adaptations in terms of reducing the annual likelihood of flooding events. For some adaptations designed to reduce exposure (like building dikes and/or levies), tracking the sensitivity of the correlation estimated in Section 4.3 to higher thresholds for flooding events could measure the change in flooding frequency. The diversity of futures, as well as the reported differentiation in the severity of flooding events, would add richness to the analysis and depth to the range of alternatives to be considered. Building dikes along the rivers could, for example, reduce exposure to modest flooding, but do nothing to diminish the likelihood of moderate or severe events. It follows that the likelihood of flooding in areas vulnerable during modest episodes would not fall to zero, but the likelihood of moderate events (that themselves would include some chance of severe inundation). Building a different set of dikes inland from the rivers could meanwhile reduce exposure to moderate or extreme flooding, but do nothing to diminish the likelihood of modest events. Finally, the observation that the likelihood of modest or moderate flooding might actually begin to decline at some point in the future adds a time dimension to the problem. Investments in flood protection for these risks might therefore have to be maintained over decades rather than centuries. In addition, the value of protecting against only modest events (in terms of reducing their likelihood) would climb (as the likelihood of moderate inundation fell). In any case, the message is that the inter-temporal character and expense of the investments required to achieve any specific protection goal could be quite different depending on how the future unfolds. In other adaptations that target exposure (like building dams or periodically dredging the rivers), the hydrologic model presented in Section 4.4 would have to be adjusted. Throughout any analysis, though, the proposed changes in variability or coping capacity would have to be run through each of the climate scenarios of Section 4.5 to produce new flooding frequency trajectories for specific representative climate scenarios. Differences between these trajectories and the corresponding baselines could then be used to

UKMO POLLS GISS UIUC BMRC CCC CCC CCC

Scenario

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Medium

Low

Medium

Medium

High

Low

High

Low

Carbon Emission Scenario

High

High

Low

High

High

High

Low

Low

Sulfate Emissions 2.5o 4.5o 2.5o 4.5o 2.5o 2.5o 2.5o 4.5o

-1.0W/m2 -1.0 W/m2 -1.0W/m2 -1.0W/m2 -1.0W/m2 -1.0W/m2 -1.0W/m2

Climate Sensitivity

-1.0W/m2

Sulfate Forcing

Notes: GCM’s are identified by their acronyms; details can be found in Schlesinger and Williams (1999). Emissions scenarios are qualitatively identified relative to the distribution described in Yohe et al. (1999).

Global Circulation Model

Table 4.4 Characterization of the representative scenarios

90 WATER RESOURCE ASSESSMENT: ADDRESSING UNCERTAINTIES

G. YOHE AND K. STRZEPEK

91

400000

M a x im um M onthly Flow

350000 Sc enario 1

300000

Sc enario 2

250000

Sc enario 3 Sc enario 4

200000 Sc enario 5 Sc enario 6

150000

Sc enario 7

100000

Sc enario 8

50000 0 2000

2020

2040

2060

2080

2100

Ye ar

Fig. 4.8 The representative scenarios reflected in terms of transient trajectories of maximum monthly flows.

Fig. 4.9 Panel A - The likelihood of a modest flooding event in any year.

characterize the degree to which any adaptation would reduce flooding frequency overtime. Moreover, casting the same adaptations across the wide range of possible baselines can be used to test its robustness against profound uncertainty over the long-run. Analysis of a different set of adaptations designed to reduce sensitivity of surrounding systems to flooding events would not have to adjust these portraits of future climate change

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(unless they were to be combined with exposure-limiting options). In these cases, however, a final link between flooding and some metrics of social, economic, or ecological impact would be required to produce adaptation baselines and to reflect the effect of adaptation. The metrics of impact would, of course, now be the appropriate indicators of relative efficacy.

Fig. 4.9 Panel B - The likelihood of a moderate flooding event in any year.

P r o b a b ilit y o f a S e v e r e F lo o d

1.000

0.800 S c e n a r io 1 S c e n a r io 2 S c e n a r io 3

0.600

S c e n a r io 4 S c e n a r io 5

0.400

S c e n a r io 6 S c e n a r io 7 S c e n a r io 8

0.200

0.000 2000

2020

2040

2060

2080

2100

Ye a r

Fig. 4.9 Panel C - The likelihood of a severe flooding event in any year.

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Table 4.5 The Adaptation Policy Framework of the United Nations Development Programme

Step I: Scope the Project Define key systems Review and evaluate existing assessments Step II: Assess Current Vulnerability Assess climate risks, impacts and damages (note climate variability and change) Identify socio-economic and natural resource drivers Assess experience with adaptation Assess adaptive capacity in the context of policy and development needs Step III: Characterize Future Conditions Characterize future climate trends, risks and opportunities Characterize future socio-economic trends Characterize future environmental trends Characterize a range of development options Step IV: Prioritize Policies and Measures Characterize a broad adaptation approach Evaluate the feasibility and efficacy of alternative adaptations Prioritize measures and adaptations within and across sectors Step V: Continue the Adaptation Process Incorporate adapting to climate risks into development plans Review, monitor and evaluate policies, measures and adaptations 4.6.1

MOVING TOWARD A MORE COMPLETE ASSESSMENT OF VULNERABILITY AFTER ADAPTATION

A complete assessment of the ability adaptation to reduce vulnerability cannot stop with estimates of relative eff icacy, because these evaluations must include some consideration of relative feasibility. Recent work by Yohe and Tol (2002) builds on the notion the determinants of adaptive capacity that are path dependent and geographically idiosyncratic to suggest how to accomplish this task. The determinants of adaptive capacity that they cite include: 1. 2. 3. 4. 5. 6.

The range of available technological options for adaptation, The availability of resources and their distribution across the population, The structure of critical institutions, the derivative allocation of decision-making authority, and the decision criteria that would be employed, The stock of human capital including education and personal security, The stock of social capital including the definition of property rights, The system’s access to risk spreading processes,

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7.

8.

The ability of decision-makers to manage information, the processes by which these decision-makers determine which information is credible, and the credibility of the decision-makers, themselves, and The public’s perceived attribution of the source of stress and the significance of exposure to its local manifestations.

The approach that Yohe and Tol (2002) suggest also relies fundamentally on the notion that a system’s adaptive capacity is fundamentally determined by the weakest link - the underlying determinant that provides the least support in its ability to cope with variability and change in local environmental conditions. To apply these notions to a specific adaptation context, Yohe and Tol construct an index of the potential contribution of any adaptation option (to be denoted by j) to an indicator of overall coping capacity (denoted by PCCj ) from a step-by-step evaluation of feasibility factors - index numbers that are judged to reflect its strength or weakness vis à vis the last seven determinants of adaptive capacity. These factors were subjective values assigned from a range bounded on the low side by 0 and on the high side by 5 according to systematic consideration of the degree to which each determinant would help or impede its adoption. Let these factors be denoted by ffj (k) for determinants k = 2,… ,8. An overall feasibility factor for adaptation (j) could then be reflected by the minimum feasibility factor assigned to any of these determinants; i.e.,

Each factor inserted in equation (4.8) indicates whether the local manifestations of each determinant of adaptive capacity would work to make it more or less likely that adaptation (j) might be adopted. A low feasibility factor near 0 for determinant #k would, for example, indicate a shortcoming in the necessary preconditions for implementing adaptation (j), and this shortcoming would serve to reduce its feasibility. A high feasibility factor near 5 would indicate the opposite situation; assessors would, in this case, be reasonably secure in their judgement that the preconditions included in determinant #k could and would be satisfied. Notice that the structure of equation (4.1) makes it clear that high feasibility factors for a limited number of determinants would not be sufficient to conclude that adaptation (j) could actually contribute to sustaining or improving an overall coping capacity. The overall feasibility of adaption (j) could still be limited by deficiencies in meeting the requirements of other determinants - the weakest link. The ability of adaptation (j) to influence a system’s exposure or sensitivity to an external stress will meanwhile be reflected in an efficacy factor EFj - a subjective index number assigned from a range running from 0 to 1. Efficacy factors, like changes in the expected frequency of flooding events supported by analyses just described and computed along the exposure pathways reported in Section 4.5, reflect the likelihood that adaptation (j) will perform as expected to influence exposure and/or sensitivity compounded by the likelihood that actual experience would exceed critical thresholds if it were adopted. Yohe and Tol define potential contribution of any adaptation to a system’s social and economic coping capacity as the simple product of its overall feasibility factor and its efficacy factor; i.e.,

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Note that the potential contribution is a unit-less number bounded from below by 0 and above by 5. 4.6.2

AN EXAMPLE OF THE INDICATOR APPROACH

Tol et al. (2001) reported on an extensive assessment of adaptation against the increased risk of climate-induced flooding in the Rhine Delta; and their work can support an instructive application of the vulnerability model to examine this issue. Six feasible options for the Netherlands were identified by major consultancies: (1) store excess water in Germany; (2) accept more frequent floods; (3) build higher dikes; (4) deepen and widen the riverbed; (5) dig a fourth river mouth; and (6) dig a bypass and create a Northerly diversion. The Netherlands is the 11th largest economy in the world (measured in terms of purchasing power parity), and the distribution of resources across the population is irrelevant because flood protection is administered by the national government. The structure of critical institutions, the derivative allocation of decision-making authority, and the decision criteria could be more problematic. However, water management and land-use planning are administered by separate agencies; as a result, pressure to expand into the floodplain can limit the options for water management because of conflicts among many stakeholders. Indeed, public works are increasingly decided through direct participation of the population; long postponements result, and radical solutions are disadvantaged. The stock of human capital, including education and personal security, is very high in the Netherlands, though; and Dutch water engineers are among the best in the world. The stock of social capital is also high. The Netherlands is a consensus-oriented society in which the collective need is an effective counterweight to individual interests. Property rights are clearly defined, and the judiciary is independent. The system’s access to formal risk spreading processes is limited because flood insurance cannot be purchased. Decision-makers are quite capable of managing information and determining which is credible; as a result, their decisions are generally taken to be credible. Dutch bureaucrats are typically well educated and supported by able consultancies; but an “old-boy” network of professors, civil servants and consultants controls water management practices. The public, as well as the water managers, are well aware of climate change and its implications for flood risk. Table 4.6 offers expert judgment into how these macro-scale observations might be translated into the micro-scale determinants of each of the options listed above. The strength of each determinant was scored on a subjective scale from 0 on the low side to 5 on the high side. The low score for storing water is a reflection of the international cooperation that would be required to implement and to manage such a scheme. Accepting floods, creating a fourth mouth for the river, and constructing a bypass also scored low marks, but their deficiencies were far less ubiquitous; instead, specific determinants like distributional ramifications and/or risk spreading were sources of weakness. Higher dikes and manipulating the riverbed were awarded higher scores, but neither is perfect. Indeed, manipulating the riverbed would appear to be most feasible, but it is hampered by a relatively low efficacy factor; i.e., such a plan could not eliminate the risk of flooding. On the other hand, higher dikes face participation difficulties on the feasibility side, but could offer extremely effective flood protection. The results of organizing an examination of adaptive capacity around its underlying determinants are thus surprisingly pessimistic. Each alternative, for one reason or another, has a weakness that can be discovered by a process that looks at each determinant in turn.

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Table 4.6 Quantifying the details of adaptation options for the lower Rhine Delta

Options Store Water

Accept Floods

Higher Dikes

Riverbed

4th Mouth

Bypass

1. Resources Total costs Distributiona

3 1

5 3

4 4

4 5

1 1

2 1

2. Institutions Structureb Participationc Criteriad

1 2 2

4 2 1

5 3 5

4 5 4

2 1 3

3 2 2

3. Human Capital

1

2

5

4

4

3

4. Social Capital

1

3

4

5

2

2

5. Risk Spreading

2

1

5

4

4

3

6. Information Management Credibility

1 1

3 2

5 4

4 5

2 3

2 3

3

3

5

5

3

3

1

1

3

4

1

1

Efficacy Factor (EF)

0.8

1.0

1.0

0.6

0.8

0.6

Coping Index (PCC)

0.8

1.0

3

2.4

0.8

0.6

Determinant

7. Awareness Feasibility Factor (FF)

e

Source: Table 4.4 in Yohe and Tol (2002). Notes:

a b

c

d e

4.6.3

The distribution of the costs and benefits of implementing an option. The degree to which the current mandates of bureaucracies are inadequate for the problem, essentially, how much integration of land-use and water management is needed for successful implementation. The degree to which the decision-making process is likely to be hindered by “not in my backyard” phenomena. The degree to which the option fits in with current decision-making criteria. Ranking (minimum of the weighted scores).

COMPUTING EFFICACY FACTORS FROM ALONG THE REPRESENTATIVE SCENARIOS

The introduction to this section described generically a few of the adaptation targets that might be pursued along the Ganges, Brahmaputra and/or Meghna Rivers, but it stopped short of explaining exactly how to use the likelihood pathways of Figure 4.9 to compute what have now be termed efficacy factors. Suppose, for the sake of illustration, that protection against only modest flooding were pursued. If it were designed to be completely successful, then the efficacy factor would equal to one minus the likelihood of moderate flooding (portrayed in Panel B of Fig. 4.9) even if these investments were coupled with inland protection against moderate or severe inundation. Panel A of Figure 4.10 shows these factors - the likelihood of avoiding flooding. By way of contrast, Panels B depicts the efficacy of protecting against modest and moderate flooding with projects that might be

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constructed alongside the river; they are equal to one minus the likelihood of extreme flooding. They are higher, indicating a more effective program, but this program could be significantly more expensive to implement. The boundary of the area vulnerable to moderate flooding could, for example, be much larger than the boundary of the area vulnerable to moderate flooding. Comparisons of these two trajectories for each scenario can, however, be used to assess the net value of incurring this greater expense. Panel C of Figure 4.10 shows the difference between the efficacies of the two strategies and indicates the degree to which such an expanded investment project would reduce the likelihood of modest flooding. This is, of course, the first step in computing the expected benefit of moving protection against modest flooding closer to the riverbank. Notice that the pattern displayed in Panel C clearly shows the importance of time profiles. In particular, the value of moving protection against moderate flooding close to the river erodes significantly overtime after peaking sometime around the middle of the century for most scenarios.

Fig. 4.10 Panel A - Efficacy factor of protecting against modest flooding with or without protection against moderate or severe flooding inland from the river.

4.6.4

TRUTH IN ADVERTISING - THE UNDERLYING ASSUMPTIONS OF THE INDICATOR APPROACH

The construction of this indicators of the sort just described clearly depends on subjective judgments of the relative strengths of underlying determinants. This can be a virtue, though, for applications in which quality data are scarce. The method also depends critically on the notion that adaptive capacity is ultimately determined by the “weakest link” - a hypothesis that requires some justification. Yohe and Tol reported some suggestive empirical results from international comparisons. They found, for example, that poorer people are more likely to fall victim to natural violence than are richer people. They also found that more densely populated areas are more vulnerable. Moreover, they found a positive relationship between income inequality and vulnerability; i.e., people in more

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Fig. 4.10 Panel B - Efficacy factor of protecting against modest and moderate flooding along the riverbed with or without protection against severe flooding inland from the river.

Fig. 4.10 Panel C - Reduction in the likelihood of modest flooding achieved by moving protection against moderate flooding to the riverbed with or without protection against severe flooding inland from the river.

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egalitarian societies seem to be less likely to fall victim of natural violence than are people in a society with a highly skewed income distribution. A growing body of literature has reached similar conclusions regarding income inequality and mortality (see for example, Lynch et al., 2000; Kaplan et al., 1996; Ross et al., 2000). Even more recently, McGuire (2002) looked for statistically significant explains for variability in infant mortality across developing countries. First principles of the microeconomics of product markets provide even stronger evidence in support of the hypothesis. Market responses to price signals of surplus or scarcity need not be orchestrated from above. They happen automatically as rational actors individually pursue their own best interest; as a result, markets can be viewed as a paragon of autonomous adaptation to external stress. To an economic theorist, however, the list of the determinants of adaptive capacity holds special meaning. It is a list of sources for market inefficiency and/or (perhaps) failure. Indeed, weakness in any determinant could doom a market to under-performance or even collapse. To see how, simply move through the list of determinants provided above. Note, to begin with, that the price elasticities of demand (and supply, for that matter) for any market good increase with time because the list of available response options expands. If energy prices rose, for example, short-term responses might be confined to driving less or lowering the indoor temperature. Over the long-term, though, individuals might add insulation to their house, replace old windows, buy a more fuel-efficient car, and so on; the result would be a larger quantity response to the higher price. We can also rest assured that access to resources with which to underwrite the implementation of alternative responses can increase these critical elasticities. Social capital is required to construct and to sustain the definition of property rights and the institutional foundations upon which market transactions rely; and human capital is necessary if market participants are to respond “rationally”. Both of these types of capital require appropriately designed institutions as well as decision-makers whose primary goal is to safeguard the integrity of the marketplace. Agents’ abilities to process information and to separate signal from noise is equally important; theory tells us that inefficiencies and market failures can result from the application of asymmetric information; these are the realms of moral hazard and principal-agent problems. Finally, the inability to spread risk (the result of market distortions or the vagaries of adverse selection) can also bring a market to a halt. 4.7

CONCLUDING REMARKS

We have not, in this paper, analyzed the potential efficacy of any specific adaptation with which decision-makers might be able to reduce the likelihood of flooding in Bangladesh. We have, though, described one method by which analyses of possible adaptations could be conducted to accommodate the cascade of uncertainty that explodes from a variety of sources to cloud our vision of how the future will unfold. Model, calibration and projection uncertainty can be captured in the range of “not-implausible” climate futures generated by COSMIC. Calibration and prediction uncertainties can be reflected in translating the hydrologic model to the likelihood of flooding and in driving it through time by COSMIC outputs; and contextual uncertainty can certainly be recognized by careful application of the Adaptation Policy Framework. Moreover, focusing attention on representative transient scenarios explicitly brings a critical time dimension to bear on the analyses. “Who know what and when?” are some of the critical questions, but their answers will not provide any insight into relative vulnerability until they are coupled with some idea of what decision-makers might do with that information and how effective those

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actions might be overtime in reducing climate-driven risks. Bringing some consistent methodology to the subjective consideration of these final questions, informed by the range of futures drawn from the COSMIC transients, is the point of constructing time series of coping capacity indices.

ACKNOWLEDGMENTS The National Science Foundation of the United States supported both Yohe and Strzepek in this work under contract SBR 95-21914 with the Center for Integrated Study of the Human Dimensions of Global Change at Carnegie Mellon University.

REFERENCES Ahmed, A. U. and Mirza, M. M. Q.: “Review of Causes and Dimensions of Floods with Particular Reference to Flood ’98: National Perspectives”. In Ahmad, Q. K., Chowdhury, A. K. Azad, Imam, S. H., and Sarker, M. (eds.), Perspectives on Flood 1998, University Press Limited, Dhaka, 2000, pp.67-84. Downing, T. E. (ed): Climate Change and World Food Security, Springer, Berlin, 1996, 662 pages. Downing, T. E., Ringius, L, Hulme, M. and Waughray, D.: “Adapting to Climate Change in Africa”. Mitigation and Adaptation Strategies for Global Change 2 (1997), pp.19-44. Dunne and Willmott: “Global Distribution of Plant-Extractable Water Capacity of Soil”. International Journal of Climatology 16 (1996), pp.841-859. FAP (Flood Action Plan) 25: Flood Hydrology Study, Flood Plan Coordination Organization (FPCO), Dhaka, 1992. Hewitt, J. and Burton, I.: The Hazardousness of a Place: A Regional Ecology of Damaging Events, University of Toronto, Toronto, 1971, 312 pages. Kane, S. J., Reilly, J. and Tobey, J.: “An Empirical Study of the Economic Effects of Climate Change on World Agriculture”. Climatic Change 21 (1992), pp.17-35. Kaplan G. A., Pamuk E. R., Lynch J. W., Cohen R. D. and Balfour J. L.: “Inequality in Income and Mortality in the United States: Analysis of Mortality and Potential Pathways”. British Medical Journal 312 (1996), pp.999-1003. Kaczmarek, Z.: “On the Sensitivity of Runoff to Climate Change and Variability”, IIASA Working Paper WP-90-058, Laxenburg, Austria, 1990. Kaczmarek, Z.: “Water-Balance Model for Climate Impact Analysis”. ACTA Geophysica Polonica 41 (1993), pp.1-16. Lynch, J. W., Smith, G. D., Kaplan, G. A and House, J. S.: “Income Inequality and Mortality: Importance to Health of Individual Income, Psychosocial Environment, or Material Conditions”. British Medical Journal 320 (2000), pp.1200–1204. McGuire, J.: “Democracy, Social Provisioning, and Under-5 Mortality: A Cross-National Analysis”, Wesleyan University Working Paper, Department of Government, Middletown, CT, USA, 2002. Mirza, M. M. Q.: “The Three Recent Extreme Floods in Bangladesh: A Hydro-Meteorological Analysis”. Natural Hazards 28 (2003), pp.35-64. Mirza, M. M. Q., Warrick, R.A. and Ericksen, N.J.: “The Implications of Climate Change on Floods of the Ganges, Brahmaputra and Meghna Rivers in Bangladesh”. Climatic Change 57 (2003), pp.287-318. Mitchell, T. D., Carter, T. R., Hones, P. D., Hulme, M. and New, M.: “A Comprehensive Set of High-Resolution Grids of Monthly Climate for Europe and the Globe: The Observed Record (1901-2000) and 16 Scenarios (2001-2100). Journal of Climate, Forthcoming, 2004.

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Ozga-Zielinska, M., Brzezinski, J. and Feluch, W.: “Meso-Scale Hydrologic Modeling for Climate Impact Assessments: A Conceptual and a Regression Approach”, IIASA CP 94-10, Laxenburg Austria, 1994. Pittock, B. and Jones, R. N.: “Adaptation to What and Why?” Environmental Monitoring and Assessment 61 (2000), pp.9-35. Ross, N. A., Wolfson, M. C., Dunn, J. R., Berthelot, J. M., Kaplan, G. A. and Lynch, J.A.: “Relation Between Income Inequality and Mortality in Canada and in the United States: Cross Sectional Assessment Using Census Data and Vital Statistics”. British Medical Journal 320 (2000), pp.898-902. Schlesinger, M. and Williams, L.: “COSMIC - Country Specific Model for Intertemporal Climate”, Computer Software, Electric Power Research Institute, Palo Alto, CA, USA, 1998. Schlesinger, M. and Williams, L.: “Country Specific Model for Inter-Temporal Climate”. Climatic Change 41 (1999), pp.55-67. Smit, B., Burton, I., Klein, R. J. T. and Wandel, J.: “An Anatomy of Adaptation to Climate Change and Variability”. Climatic Change 45 (2000), pp.223-251. Smithers, J. and Smit, B.: “Human Adaptation to Climatic Variability and Change”. Global Environmental Change 7 (1997), pp.129-146. Tol, R. S. J., van der Grijp, N. M., Olsthoorn, A. A., and van der Werff, P. E.: “Adapting to Climate Change: A Case Study on Riverine Flood Risks in the Netherlands”. In R. S. J. Tol and A. A. Olsthoorn (eds.), Floods, Flood Management and Climate Change in the Netherlands, Institute for Environmental Studies, Vrije Universiteit, Amsterdam, Netherlands, 2001. United Nations Development Programme (UNDP): The Adaptation Policy Framework, New York, 2003. Yates, D.: “WATBAL: An Integrated Water-Balance Model for Climate Impact Assessment of River Basin Runoff ”. International Journal of Water Resources Development 12 (1996), pp.121-139. Yates, D.: “Approaches to Continental Scale Runoff for Integrated Assessment Models”. Journal of Hydrology 201 (1997), pp.289-310. Yates, D. and Strzepek, K.: “Comparison of Models for Climate Change Assessment of River Basin Runoff ”, IIASA Working Paper 94-46, Laxenburg, Austria, 1994. Yohe, G., Jacobsen, M. and Gapotchenko, T.: “Spanning ‘Not-Implausible’ Futures to Assess Relative Vulnerability to Climate Change and Climate Variability”. Global Environmental Change 9 (1999), pp.233-249. Yohe, G. and Schlesinger, M.: “Sea Level Change: The Expected Economic Cost of Protection or Abandonment in the United States”. Climatic Change 38 (1998), pp.437-472. Yohe, G. and Tol, R.: “Indicators for Social and Economic Coping Capacity - Moving Toward a Working Definition of Adaptive Capacity”. Global Environmental Change 12 (2002), pp.25-40.

5 The Implications of Climate Change on River Discharge in Bangladesh M. MONIRUL QADER MIRZA

5.1

INTRODUCTION

5.1.1

WATER RESOURCES PROBLEM OF BANGLADESH

Bangladesh lies in the delta of three large rivers - the Ganges, Brahmaputra and Meghna (GBM), which is often termed as a “land of rivers and water.” With a complex network of 230 rivers, including 57 cross boundary rivers, about 92.5% of the 175 million hectares (mha) of combined basin area of the GBM Rivers (Fig. 5.1) is beyond the boundary of Bangladesh and is located in China, Nepal, India and Bhutan. Therefore, Bangladesh acts as a drainage outlet for the cross-border runoff. More than 90% of the annual runoff is generated outside of Bangladesh. However, there is a high seasonal difference in the availability of water. For example, for the Ganges River, the ratio of dry and monsoon runoff is 1:6 (Fig. 5.2). This illustrates that Bangladesh has an abundance of water in the monsoon while the country still faces surface water scarcity in the dry season. Irrigated agriculture is highly dependent on dry season surface water availability. On average, annually floods engulf roughly 20.5% of the area of the country, or about 3.03 mha (Mirza, 2003). In extreme cases, floods may inundate about 70% of Bangladesh, as it occurred during the floods of 1988 and 1998 (Ahmed and Mirza, 2000). Hydrological droughts are very common in the rivers of Bangladesh. The magnitude of precipitation over the GBM basins is very high and more than three-quarters occurs during the summer monsoon (June-September) (Table 5.1). The resulting huge volume of cross-border monsoon runoff, together with the locally generated runoff and some physical factors, either singly or in combination, causes floods in Bangladesh. The physical factors, either singly or in combination, include snow and glacier melt, El Niño Southern Oscillation (ENSO) induced conditions, loss of drainage capacity due to the siltation of principal distributaries, backwater effect, unplanned infrastructure development, deforestation and the synchronization of flood peaks of the major rivers. Recently Mirza (2003), compared three recent extreme floods (1987, 1988 and 1998) in Bangladesh and found that the intense monsoon precipitation was the principal cause of flooding. However, there are differences in opinions concerning the role of deforestation in upstream areas in the flooding process in Bangladesh. Deforestation of steep slopes in the Himalayas is assumed to lead to accelerated soil erosion and landslides A part of this chapter was published in the Climatic Change 57 (2003), pp.287-318 and reprinted with permission.

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during monsoon precipitation. This in turn is believed to contribute to devastating floods in Bangladesh (Khalequzzaman, 1994; Hamilton, 1987). Hofer (1998) concluded that land-use changes in the Himalayas were not responsible for floods in India and Bangladesh. With regard to sedimentation, the existing publications do not report any significant recent increase in the sediment load of the major rivers and their tributaries (Ives and Messerli, 1989).

Fig. 5.1 The Ganges, Brahmaputra and Meghna basins.

80000 70000 60000 50000 40000 30000 20000 10000 3/1/68

2/1/68

1/1/68

12/1/67

11/1/67

10/1/67

9/1/67

8/1/67

7/1/67

6/1/67

5/1/67

4/1/67

0

Fig. 5.2 Hydrograph of the Ganges (lighter solid line) and Brahmaputra (thicker solid line) Rivers for the typical water year 1967-1968. The values are in m3/sec. Data source: Bangladesh Water Development Board (BWDB, 1995).

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Table 5.1 Mean annual precipitation in the Ganges, Brahmaputra and Meghna basins

Basin

Country

Mean Annual Precipitation (mm)

Ganges

Nepal India Bangladesh

1,860 450-2,000 1,570

Brahmaputra

Tibet (China) Bhutan India Bangladesh

400-500 500-5,000 2,500 2,400

Meghna/Barak

India Bangladesh

2,640 3,575

Source: Mirza, 1997.

Bangladesh generally experiences four main types of floods: flash, riverine, rain and storm-surge (Fig. 5.3). Eastern and Northern areas of Bangladesh adjacent to its border with India are vulnerable to flash floods. Rivers in these regions are characterized by sharp rises and high flow velocities resulting from exceptionally heavy rainfall occurring over the hilly and mountainous regions in the neighboring India. Riverine floods occur when flood water of the major rivers and their tributaries and distributaries spill. With the onset of the monsoon in June, all of the major rivers start swelling to the brim and bring flood water from the upstream basin areas. Rain floods are caused by intense local rainfall of long duration in the monsoon months. Heavy pre-monsoon rainfall (April-May) causes local runoff to accumulate in depressions. Later (June-September), local rainwater is increasingly ponded on the land by the rising water levels in the adjoining rivers. Coastal areas of Bangladesh, which consist of large estuaries, extensive tidal flats, and low-lying offshore islands, are vulnerable to storm-surge floods, which occur during cyclonic storms. Cyclonic storms usually occur during April-May and October-November. Flood is a necessity as well as a danger in Bangladesh. For example, normal floods help the growth of rice crops because of the fertilization produced by nitrogen supplying blue-green algae, which grow in the ponded clear flood water (World Bank, 1989). The extra moisture provided by large floods to higher lands also benefits rabi crops such as vegetables, lintels, onion, mustard, etc. (Brammer, 1990). Rabi refers to a cropping season from November-May. But, high flood levels can cause substantial damage to key economic sectors: agriculture, infrastructure and housing. Based on the reported crop damage due to floods, average annual loss is estimated to be 0.47 million tons (Paul and Rasid, 1993). However, in a year of an extreme flood such as 1998, food grain loss may exceed 3.5 million tons (Ahmed, 2001). The total monetary loss caused by the extreme floods of 1998 and 1988 was US$ 3.4 billion and US$ 2.0 billion, respectively or 10% of the GDP of Bangladesh in the respective years (Bhattacharya, 1998; World Bank, 1989). For a country like Bangladesh with a transitional economy and a low per capita income ($360 in 2001) (World Bank, 2003), this amount of loss is very high. Although flood affects people of all socio-economic status, the rural and urban poor have been the hardest hit.

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Fig. 5.3 Bangladesh and various flood types.

5.1.2

RATIONALE OF THE RESEARCH

Future climate change may affect water resources availability and extreme hydrological events such as floods in Bangladesh in many ways. The IPCC (2001) indicated a likelihood of increased intensity of extreme precipitation over the South Asian region. All climate models simulate an enhanced hydrological cycle and increases in annual mean rainfall over South Asia (under non-aerosol forcing). In all periods of simulation (GHG and GHG + aerosol forcing), summer precipitation shows an increase. The magnitude of increase in summer precipitation with GHG + aerosol forcing is smaller than that seen in the GHG forcing. The difference in change with aerosol forcing is due to its dampening

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effect on Indian summer monsoon precipitation (Lal et al., 2001; Cubasch et al., 1996; Roeckner et al., 1999). Annual runoff may increase as a result of increased precipitation. However, uncertainty remains in dry season availability of river flow as it is related to a number of factors. They include: amount of monsoon precipitation and ground water recharge, amount of snowfall, temperature gradient, snowmelt, evaporation, upstream water demand, etc. More frequent extreme precipitation could increase the possibility of flash floods. Increased precipitation in the GBM basins may increase the magnitude, depth and spatial extent of riverine and rain floods. Based on a series of theoretical and model-based studies, including the use of a high resolution hurricane prediction model, it is likely that peak wind intensities will increase by 5% to 10% and the mean and peak precipitation intensities by 20% to 30%, in some regions (IPCC, 2001). Therefore, stronger storm-surges can aggravate coastal flooding. Of all of these flood types, the riverine floods are the most pervasive and have long-term impacts on land-use, the economy and most development strategies for Bangladesh. Thus, it is with the changes in riverine flooding that the effects of climate change may be most strongly felt. In the past, a number of studies on climate change and its possible implications on Bangladesh have been undertaken (Ahmad and Warrick, 1996; ADB, 1994; and Resource Analysis, 1993). The consensus was that over the past 100 years, the broad region encompassing Bangladesh had warmed by 0.5oC (Ahmad and Warrick, 1996). However, overall increases in precipitation were not found (Mirza et al., 1998). These studies also indicated that with increases in precipitation in Bangladesh and surrounding areas due to climate change, flooding in Bangladesh might worsen. However, no specific research has assessed changes in flooding in terms of magnitude, depth and spatial extent in Bangladesh taking into account possible changes in precipitation in the cross-border basin areas of the GBM Rivers. 5.2

OBJECTIVES

As indicated above, the annual runoff in the GBM basins may be changed due to possible changes in future climate and it may also exacerbate the flood problem in Bangladesh. Most experiments using GCMs show increases in monsoon precipitation as a consequence of enhanced greenhouse effect. However, it is not known exactly what the magnitude of climate change will be in the future or how it will affect precipitation, and thereby flooding in Bangladesh. Therefore, a study was carried out under the BDCLIM (Bangladesh Climate) project to examine possible changes in flooding in Bangladesh under climate change. The BDCLIM is a large integrated model system developed for assessing the effects of future climate change scenarios on Bangladesh (Warrick et al., 1996). Taking into account the range of uncertainty in the climate scenarios, the overall goals of this research include: 1) determining the sensitivity of mean annual and mean peak discharge at the boundary of Bangladesh to future climate change and 2) estimating the consequent changes in depth and spatial extent of flooding in Bangladesh. 5.3

METHODOLOGY

In order to meet the first objective, four major steps were followed. First, an empirical relationship between precipitation and discharge was determined. Second, climate change scenarios were constructed for the three river basins using the results of CSIRO9 (McGregor et al., 1993), UKTR (Murphy and Mitchell, 1995), GFDL (Whetherland and

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Manabe, 1986), and LLNL (Whener and Convey, 1995) GCMs in the SCENGEN software of the Climatic Research Unit (CRU), University of East Anglia, U.K. (CRU, 1995). Third, the climate change scenarios were applied to empirical models in order to determine the magnitude of changes in discharge at the boundaries of Bangladesh. Fourth, the MIKE 11-GIS hydrodynamic model was forced with current and future peak discharges to simulate river flood stages and depth and spatial extent of flooding within Bangladesh. The MIKE 11 is a professional engineering software tool that simulates flows, water quality and sediment transport in river basins, estuaries, irrigation systems, channels and other water bodies. The Danish Hydraulic Institute (DHI) developed the software. The GIS interface was developed and applied during the Flood Action Plan (FAP) Study (1990-1995) in Bangladesh. The model has been calibrated and validated in a Bangladesh context by the Surface Water Modeling Center (SWMC), Dhaka and is currently being used for water resource development, planning and management. 5.3.1

DEVELOPMENT OF EMPIRICAL DISCHARGE MODELS

As a first step for determining the sensitivity of mean peak discharge at the boundary of Bangladesh, different approaches of modeling were envisaged. The empirical modeling approach was compared to the water-balance, lumped-parameter and physically-based distributed models and found to be preferable on the basis of the constraints imposed by the large areal extent of the river basins and the lack of available data and resources. Sensitivity analyses for three selected stations in the Ganges, Brahmaputra and Meghna River basins was carried out using the model R = P - E. Here R = runoff, P = precipitation and E = actual evapo-transpiration, which was calculated using the relationship

P E=

(1 + (

P 2 ) ) PE

(Pike, 1964), where PE = potential evapo-transpiration. The analysis showed that runoff was far more sensitive to precipitation than to temperature (Mirza, 1997; Mirza and Dixit, 1997) (Fig. 5.4). Therefore, temperature was excluded as an explanatory variable for empirical model building but it may be considered as an explanatory variable as part of a future research undertaking. The results of the sensitivity analysis also shows that, in percentage terms, runoff is more sensitive to precipitation and temperature changes in relatively dry stations than wet stations. As an example, in the case of the New Delhi station (a drier station in the Ganges basin) no change in temperature and a 4% increase in precipitation changes runoff by +11%, while for the Gauhati and Syhet (the wetter stations in the Brahmaputra and Meghna basins, respectively), the changes in runoff are +6% and +8%, respectively. In the extreme case, a 5oC increase in temperature and a 20% increase in precipitation could increase runoff by 29% at the New Delhi station, whereas for Gauhati and Syhet stations the expected changes are 22% and 21%, respectively. Accordingly, time-series data for precipitation were collected from various primary and recognized secondary sources for the three river basins. Sources of precipitation data were: 1) Carbon Dioxide Information Analysis Center (CDIAC)/Oak Ridge National Laboratory (ORNL), Tennessee, USA; 2) Climatic Research Unit (CRU), University of East Anglia, U.K.; 3) Nepal Water Conservation Foundation (NWCF), Kathmandu; 4) The Bangladesh Water Development Board (BWDB), Dhaka; 5) United Nations; and

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(a)

(b)

(c) Fig. 5.4 Sensitivity of runoff to temperature and precipitation changes in the: (a) Ganges basin (New Delhi), (b) Brahmaputra basin (Gauhati) and (c) Meghna basin (Syhet).

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6) Center for Ocean-Land-Atmosphere Studies (COLA), Maryland, USA. Discharge data was received from the Bangladesh Water Development Board. Details of these datasets are given in Mirza, 1997. Selection of the dataset for the development of empirical models was made with regard to length of record, spatial coverage and missing observations. The selected datasets were the COLA dataset and the NWCF dataset for the Ganges basin; the COLA dataset and selected four stations from the UN dataset within Bangladesh for the Brahmaputra basin; and the COLA dataset and the UN and BWDB datasets for the Meghna basin. Missing observations were between 1%-12% for the NWCF, UN and BWDB datasets. These observations were filled in by applying the method stated by Salinger, 1980. After filling in the missing observations, the means and standard deviations were computed for the complete time series and compared with those of the incomplete time series. The difference in the means and standard deviations were found to be statistically insignificant at a 5% level of significance. The precipitation and discharge data were examined with respect to their adequacy of empirical modeling. Statistical tests show that the precipitation observations in all meteorological sub-divisions are normally distributed. Over the periods of record, one meteorological sub-division (The East Madhaya Pradesh) (V10 in Fig. 5.5a) in the Ganges basin shows a statistically significant decreasing trend. In the Brahmaputra basin, a decreasing trend is found only in the precipitation time series of South Assam (V2 in Fig. 5.5b). However, the basin-wide average precipitation series does not show any discernible trend. On the other hand, each of these two sub-divisions covers a small area over the respective river basin. Therefore, they would not have a major effect on the predictive capability of the empirical models. Precipitation observations of all meteorological sub-divisions are found to be random, with a few exceptions. Analysis shows the presence of Markov linear type “persistence” only in the observations of the North Assam and South Assam meteorological sub-divisions in the Brahmaputra basin (Mirza, 1997). Annual mean and peak discharge series have been found to be normally distributed for the GBM Rivers. Statistical tests indicate that the difference in mean annual discharge of the Ganges River at Hardinge Bridge for the pre- and post-Farakka period is not statistically significant. Therefore, on an annual basis, the regulation effect of the Farakka Barrage (Fig. 5.1) can be overlooked (Mirza, 1997). The barrage was constructed at Farakka (18 km from the border of Bangladesh) and commissioned in April of 1975 to divert 1,134 m3/sec water to make the Hooghly-Bhagirathi River channel (on which the port of Kolkata is situated) navigable (Mirza, 2002). A sequence of empirical models that describe the relationship between precipitation and annual mean and peak discharge was developed. One of the advantages of such a relationship is, for example, that in absence of precipitation data, peak discharge can be estimated from known values of annual discharge. Initially, in order to examine the independence of the explanatory variables, annual mean discharges of the Ganges River at Hardinge Bridge and Brahmaputra River at Bahadurabad in Bangladesh (Fig. 5.1) were regressed on the meteorological sub-division wide annual precipitation data. Initial examination indicated the presence of multi-collinearity in the precipitation data. This is the condition where at least one explanatory variable is highly correlated with another explanatory variable or with some combination of other explanatory variables (Maidment, 1993). Multi-collinearity may cause a number of consequences. (1) In extreme cases, the least square point estimates can be far from the true values of the regression parameters, and some estimates may even have the incorrect sign; (2) increases in standard error of regression coefficient estimators occur as the correlations among the independent

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Fig. 5.5a Independent and contiguous precipitation regions of the Ganges basin.

variables increase; (3) serious rounding errors in the calculation of the least square point estimates are produced; and (4) significance tests and confidence intervals for regression coefficients, due to increases in the standard errors of coefficient estimates, are affected. The principal components analyses (Dunteman, 1989; Manly, 1986) of the precipitation data were carried out to minimize the problems of collinearity and to generate relatively independent, contiguous precipitation regions (Table 5.2 and Fig. 5.5c). Selection of components and a procedure for regionalization are discussed in Cattel, 1966; Kaiser, 1960; Morgan, 1971; Ogallo, 1989 and Regemortel, 1995. Multiple regression models were then developed for estimating mean annual discharge for the Ganges and Brahmaputra Rivers. For the Meghna River, a multiple regression model was developed between annual precipitation and the peak discharge. This was due to the absence of adequate annual discharge data. In order to determine mean annual peak discharge in relation to mean annual discharge, regression models between annual mean and peak discharges were developed for the Ganges and Brahmaputra Rivers. Standard procedures (Berry and Feldman, 1985; Bowerman and O’Connell, 1990; Cook and Wesberg,

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1982) were followed to examine the model parameters. The precipitation annual mean discharge regression models for the Ganges, Brahmaputra and Meghna basins are given in Table 5.3.

Fig. 5.5b Independent and contiguous precipitation regions of the Brahmaputra basin. Table 5.2 New variables (regions) derived by the principal components analysis

New Variables River Basin

Ganges Brahmaputra Meghna

*

Variables (Sub-Divisions)

Region 1

Region 2

V1-V11* V1-V4** V1-V3***

V3, V5-V11 V1, V2 and V4 V2 and V3

V1 and V4 V3 V1

Region 3 V2 -

V1 - Sub-Himalayan West Bengal; V2 - Gangetic West Bengal; V3 - Bihar Plateau; V4 - Bihar Plain; V5 - East Up; V6 West Up; V7 - Haryana; V8 - East Rajasthan; V9 - West Madhaya Pradesh; V10 - East Madhaya Pradesh; and V11- Nepal ** V1 - North Assam; V2 - South Assam; V3 - Sub-Himalayan West Bengal; and V4 - Teesta Basin in Bangladesh *** V1 - North Assam; V2 - South Assam; and V3 - Meghna basin (Bangladesh part)

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Fig. 5.5c Independent and contiguous precipitation regions of the Meghna basin.

5.3.2

CONSTRUCTION OF CLIMATE CHANGE SCENARIOS

For the first objective outlined above, the second step was to construct climate change scenarios for the three river basins. Seven alternatives for scenario generation suggested by Carter et al., 1994 and WMO, 1987 were reviewed. These alternatives include direct use of GCM runoff changes, GCM-generated regional temperature and precipitation changes, addition of GCM-predicted changes to baseline conditions, scaling of the standardized patterns of change from GCMs, temporal analogues, spatial analogues and hypothetical scenarios. For this research, the empirical models were developed based on the spatial distribution of precipitation in the three river basins. Therefore, preference was given to the method of scenario construction, which predicts spatial changes in precipitation. For this purpose, the results of the GCMs are useful in that they indicate possible spatial changes in climate. For the scenario construction, a method of scaling “standardized” patterns of precipitation derived from GCMs was adopted. Hulme (1994) recommended standardizing GCM results for climate change scenario construction in order to overcome the problem of variation of equilibrium global mean temperature and overcome the problem of variation of equilibrium global mean temperature and precipitation changes from GCM to GCM. This arises mainly because of the way the GCMs treat clouds and oceans. Moreover, some of the atmospheric GCMs (For example, LLNL and MPILSG -

Annual MeanPeak Discharge

where Qp is annual peak discharge

Qp = 14,844 + 3.26 Qa (R2 =49.3%) (4)

The model excludes the part of the basin in China.

Qp = 6,816 + 3.00 Qa (R2 =51.5%) (5)

Multiple regression of Region 1 and Region 2 produced negative parameter (not significant) for the latter, which was unrealistic from physical point of view. This might have caused by error inherent in the data. Therefore two regions were treated as one homogeneous region.

The model excludes the part of the basin in Bhutan and China.

where Qa is the estimated annual mean discharge at Bahadurabad and P is the area weighted annual precipitation in the basin.

where Qa is the estimated annual mean discharge and P1, P2 and P3 are the area weighted annual precipitation for the Region 1, Region 2 and Region 3, respectively.

-

where Qp is annual peak discharge and P1 is the area weighted annual precipitation in Region 1 (North Assam) and P2 is the average of the precipitation in Region 2 (South Assam and the Bangladesh part of the basin upstream of Bhairab Bazar).

Qp = -10531 + 3.43 *P1 + 5.69 * P2 (R2=87.1%) (3)

Qa = 7201 + 5.23 * P (R2 = 62.33%) (2)

Qa = -6856 +8.02* P1 + 3.55 * P2 + 5.41* P3 (R2 =81.5%) (1)

Annual PrecipitationMean Discharge

Meghna

Brahmaputra

Ganges

Model

Table 5.3 Precipitation-annual mean discharge and annual mean-peak discharge regression models

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MPI large scale geotropic ocean) were coupled with Ocean General Circulation Models (OGCMs), while others include prescribed ocean heat. This produces substantial inter-model differences in their simulation of current climate and response to a doubling of CO2. The results of 11 GCMs were compared, and four were selected - CSIRO9 (3.2 x 5.6 o L9), UKTR (2.5 x 3.75 o L19), GFDL (2.25 x 3.75 o L14) and LLNL (4 x 5o L15). This maximizes the range of predicted changes in precipitation amounts and spatial variability within the GBM basins window. The other selection criterion was goodness-of-fit of a GCM with respect to regional bias (control-observed). The CSIRO9, UKTR and GFDL models showed a slight negative bias for summer precipitation but showed a close fit, compared to the other GCMs. Note that the selected four GCM experiments were based on only GHG forcing. These spatial patterns of precipitation change were then “standardized” to account for the different climate sensitivity values of the GCMs. This gave a pattern of change per degree of global warming. The standardized patterns were then scaled for global mean temperature changes of 2oC, 4oC and 6oC giving a total of 12 scenarios (4 GCMs x 3 DTs) for each river basin and the Bangladesh window. 5.3.3

APPLICATION OF THE CLIMATE CHANGE SCENARIOS TO THE EMPIRICAL MODELS

The third step was to apply the constructed climate change scenarios to the empirical models to determine the magnitude of changes in discharges at the boundary of Bangladesh. This was carried out in two stages: (1) changes in the mean annual discharge were estimated by applying the scenarios of changes in the mean precipitation generated from the results of four GCMs. These GCMs represent the range of uncertainty in climate model projections of future climate change; and (2) the calculated mean annual discharge was used to estimate changes in the mean annual peak discharge. 5.3.4

ESTIMATION OF CHANGES IN DEPTH AND EXTENT OF FLOODING IN BANGLADESH

The fourth step was to force the MIKE 11-GIS model with current and future peak discharges to simulate depth and spatial extent of flooding in Bangladesh. This was carried out in three stages: (1) current and future mean precipitation was scaled for the Bangladesh window as the MIKE 11-GIS model needed input of local precipitation for the simulation purpose (Table 5.4); (2) current and future mean peak discharges were scaled to 1991 discharges for the Ganges, Brahmaputra and Meghna Rivers (Table 5.5). For scaling purposes, the year ‘1991’ was selected because the monsoon of that year represented a temporal distribution which was considered fairly ‘typical’ with regard to the usual peaking time of the three rivers; and (3) the MIKE 11-GIS model was forced with current and future peak discharges to simulate present and future depth and extent of flooding. 5.4

ESTIMATION OF CHANGES IN ANNUAL DISCHARGE

The precipitation change scenarios for the Ganges and Brahmaputra basins are applied to the empirical models in order to assess the possible changes in the mean annual discharge for 2oC, 4oC and 6oC increases in global temperature. For the Ganges basin, the empirical model (Table 5.6) was developed between annual precipitation in the basin area in India and Nepal and annual mean discharge at Hardinge Bridge in Bangladesh. This station is

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located very close to the border of Bangladesh with India. Therefore, the measured discharge takes account of the total cross-border inflow. Annual mean discharge is predicted from the area weighted annual precipitation from three regions comprising the total basin area (excluding parts of the basin area in China and Bangladesh).

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An empirical model was developed between annual precipitation in the Brahmaputra basin area (in India and Bangladesh) and the annual mean discharge of the Brahmaputra at Bahadurabad, Bangladesh. The discharge measurement station (Bahadurabad) takes into account the discharge generated in the Bangladesh part of the basin. The discharge is predicted from the area weighted annual precipitation in the four meteorological sub-divisions of the Brahmaputra basin. The overall results with regard to changes in the mean annual discharge for the Ganges and Brahmaputra Rivers are presented in Table 5.6. It is evident from Table 5.6 that the mean discharge of the Brahmaputra River is less sensitive than the Ganges River to the changes in precipitation. The results of the empirical model support the contention that runoff or discharge of a wetter basin will be less sensitive to climate changes than a relatively drier basin. Details regarding the changes in mean annual discharges for the Ganges and Brahmaputra basins for the four selected GCMs are discussed below. The Ganges Basin Three precipitation change scenarios for global temperature increases of 2oC, 4oC and 6oC were considered for determining changes in the mean annual discharge. Changes in the magnitude of mean discharges from present to the future conditions for the Ganges basin for the four GCMs (CSIRO9, UKTR, GFDL and LLNL) are shown graphically in Figure 5.6. For all four models, the figure shows an increase in discharge as global temperature and thereby basin-wide precipitation, increases. The figure also shows a marked

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0

2

4

6

increase in discharge (for each of the scenarios) across the four models, with LLNL showing least change and UKTR showing most change. Three of the models show significant increases in mean discharge. The application of scenarios from the UKTR model shows that a 39% change in precipitation (at a 6oC rise in global mean temperature) produces a 63.4% change in the mean discharge. In absolute terms, this implies an increase in mean discharge from the current 11,606 m3/sec to 18,970 m3/sec. In contrast, the LLNL model shows the least changes for all temperature scenarios. The LLNL model shows that for a 6oC rise in global mean temperature, there is a 1.5% increase in precipitation, which gives an increase in mean discharge of 2.4% (from 11,606 m3/sec to 11,888 m3/sec). As seen from Figure 5.6, the other models (CSIRO9 and GFDL) fall almost linearly between these two extremes.1

Fig. 5.6 Predicted mean annual discharge for the Ganges River at Hardinge Bridge for the four selected GCMs under global mean temperature increases of 2oC, 4oC and 6oC.

The Brahmaputra Basin The changes in mean annual discharge in the Brahmaputra River due to changes in the mean annual precipitation predicted by the four GCMs are displayed graphically in Figure 5.7. Two general patterns are evident from the figure. First, a very marked difference in the mean discharge predicted by the UKTR and GFDL models as compared with the CSIRO9 and LLNL models. The UKTR and GFDL models show large, step-like increases in discharge as temperature increases (2oC, 4oC and 6oC). Second, the LLNL model shows a very small increase in discharge across the temperature changes, while the CSIRO9 model shows a decrease. The largest increase in the basin-wide precipitation are predicted by the UKTR model. With a 30.6% precipitation change for a 6oC global warming, the mean discharge may increase from 19,350 m3/sec, to 23,069 m3/sec. The smallest increase in the

1

The CSIRO9 model scenarios indicate somewhat lower changes in the mean discharge for a 6oC warming, from the current 11,606 m3/sec to 16,310 m3/sec. Predicted changes for mean annual discharge for the GFDL is somewhat slightly smaller than for the CSIRO9 model. The mean annual discharge may increase from 11,606 m 3/sec to 14,151 m 3/sec at 6 oC rise in global mean temperature.

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0

2

4

6

basin-wide precipitation are predicted by the LLNL model. With a 4.2% precipitation change for a 6oC global mean temperature change, the mean annual discharge may increase from 19,350 m3/sec to 19,863 m3/sec (Fig. 5.7).

Fig. 5.7 Predicted mean annual discharge for the Brahmaputra River at Bahadurabad for the four selected GCMs under global mean temperature increases of 2oC, 4oC and 6oC.

Interestingly, the CSIRO9 model indicates decreases in the mean discharge as temperature increases (2oC, 4oC and 6oC). This is because the CSIRO9 model predicts a slight decrease in precipitation in the Brahmaputra basin for increases in global mean temperature. This reduces the mean discharge of the Brahmaputra by a negligible quantity. For example, with a 1.5% decrease in the mean precipitation (at a 6oC rise in global mean temperature), the predicted mean discharge (19,335 m3/sec) is almost identical to the current mean (19,350 m3/sec). 5.5

EFFECTS ON MEAN PEAK DISCHARGE

Using equation 4 and 5 in Table 5.3, mean peak discharges for the Ganges and Brahmaputra Rivers were estimated from the mean annual discharges under various climate change scenarios for the four GCMs (presented in the previous sub-section). For the Meghna, changes in peak discharges were calculated using equation 3 (Table 5.3) with regard to changes in the mean precipitation. Then, the relative changes (in percent) were calculated with respect to the current mean discharges for the Ganges, Brahmaputra and Meghna Rivers (tabulated in Table 5.7). 5.5.1

THE GANGES RIVER

For the Ganges River, three (CSIRO9, UKTR and GFDL) of the four GCMs indicate substantial changes in the mean peak discharge under 2oC, 4oC and 6oC changes in global mean temperature (Fig. 5.8). Among the GCMs, the UKTR model shows the highest possible change in peak discharge for all temperature scenarios. For a 6oC rise in global mean temperature, the increase in peak discharge is 45.6%. In terms of absolute magnitude,

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0

2

4

6

this change may increase the mean peak discharge to 76,686 m3/sec from the current mean of 52,680 m3/sec. The model predicted value is slightly above the highest recorded peak discharge (76,000 m3/sec) of the Ganges River that occurred in 1987. The LLNL model shows the lowest possible change in peak discharge across all temperature scenarios (2oC, 4oC and 6oC). Even at 6oC, the change is only 1.5%, giving a peak discharge of 53,628 m3/sec compared with the current 52,680 m3/sec. The other two models (CSIRO9 and GFDL) fall between the UKTR and LLNL extremes. The CSIRO9 model gives the second highest changes. With the same magnitude in global mean temperature increase (6oC), the mean peak discharge increases to 68,017 m3/sec (29% increase) from the current mean of 52,680 m3/sec. The third largest increases were predicted in the GFDL model. At a 6oC temperature increase, the mean peak discharge is expected to increase to 60,898 m3/sec from the current mean.

Fig. 5.8 Predicted mean peak discharge for the Ganges River at Hardinge Bridge for the four selected GCMs under 2oC, 4oC and 6oC temperature rise.

5.5.2

THE BRAHMAPUTRA RIVER

Using equation 5 (Table 5.3), mean peak discharges were estimated from the mean annual discharges under various climate change scenarios for the four selected GCMs. For the mean annual discharge pattern in Figure 5.9, the mean peak discharge pattern in Table 5.7 and Figure 5.9 shows an enormous difference in outcomes across the four models. Again, the UKTR and GFDL models indicate large changes in the mean peak discharge while the CSIRO9 and LLNL models predict negligible changes. Among the GCMs, the UKTR model shows the highest possible change (17%) in the mean peak discharge for a 6oC global mean temperature rise. With this change, peak discharge of the Brahmaputra may increase by 13% to 76,022 m3/sec from the current 64,866 m3/sec (Fig. 5.5). This would be equivalent to the mean peak discharge of the Ganges River at Hardinge Bridge at a 6oC global mean temperature rise for the same GCM (Fig. 5.9). The GFDL model gives the next highest changes. With a 2oC-6oC temperature rise, the predicted change in the mean peak discharge could be between 4%-12% (in terms of absolute magnitude, between 67,487 m3/sec and 72,728 m3/sec) (Fig. 5.9).

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0

2

4

6

122

Fig. 5.9 Predicted mean peak discharge for the Brahmaputra River at Bahadurabad for the four selected GCMs under global mean temperature increases of 2oC, 4oC and 6oC.

The CSIRO9 model shows the smallest changes (negative) in peak discharge across the temperature scenarios (2oC, 4oC and 6oC). It was mentioned previously that among the four GCMs, only the CSIRO9 model predicts a decrease in precipitation in the Brahmaputra basin. Perhaps it was due to inadequate representation of the mountainous topography of the Brahmaputra basin. As a result, mean discharge and mean peak discharge may decrease (Table 5.7 and Fig. 5.9). However, even for a 6oC rise in global temperature, the reduction is expected to be slight, only 0.06%. In terms of absolute magnitude, the change is only 47 m3/sec. Such a change would be expected to have negligible or no effect on the mean flooded area and depth in Bangladesh. 5.5.3

THE MEGHNA BASIN

Most of the GCMs indicate high precipitation changes in the Meghna basin under the climate change scenarios (Table 5.4). Using these scenarios, changes in the mean peak discharges were estimated using equation 3 (Table 5.3). The overall results show large increases in the mean peak discharges for the UKTR and GFDL and small increases for the CSIRO9 and LLNL models. The UKTR and GFDL models imply almost equal increases in the mean peak discharge (19.9% increase) at 2oC temperature change (Table 5.7). This may lead to an increase in peak discharge from the current mean of 14,060 m3/sec to 16,861 m3/sec and 16,843 m3/sec, respectively for the UKTR and GFDL models. For a 6oC global temperature increase, the peak discharge may increase to 22,470 m3/sec and 22,400 m3/sec for the UKTR and GFDL models, respectively (Fig. 5.10). The CSIRO9 and LLNL models show much smaller increases in the peak discharge than the other two models. With a 2oC temperature rise, the estimated mean peak discharge for these models is expected to increase to 15,171 m3/sec and 15,958 m3/sec, respectively, from the current mean of 14,060 m3/sec. At the highest 6oC temperature increase, the peak discharge for the CSIRO9 and LLNL models, show increases to 17,382 m3/sec and 19,744 m3/sec, respectively.

123

0

2

4

6

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Fig. 5.10 Predicted mean peak discharge for the Meghna River at Bhairab Bazaar for the four selected GCMs under global mean temperature increases of 2oC, 4oC and 6oC.

5.6

EFFECTS ON DEPTH AND SPATIAL EXTENT OF FLOODING

For mean peak discharges, the Surface Water Modeling Center (SWMC) in Dhaka, Bangladesh, using the MIKE 11-GIS model, carried out 13 simulations (one for the “control” run and 12 runs for the “climate change scenarios”). The model covered most of the Ganges, Brahmaputra and Meghna River basins in Bangladesh, approximately 9.11 million ha. In the three river basins, flood vulnerable area was estimated at 6.72 million ha (BWDB, 1987). This is about 80% of the total area vulnerable to flooding in Bangladesh. 5.6.1

CHANGES IN MEAN FLOODED AREA

The MIKE 11-GIS model results show that the current mean flooded area is 3.77 million ha (Fig. 5.11) based on the mean discharge of 52,680 m3/sec, 64,866 m3/sec and 14,060 m3/sec for the Ganges, Brahmaputra and Meghna Rivers, respectively, together with local rainfall in the river basins. The mean flooded area produced by the MIKE 11-GIS model seems to be very reasonable in relation to observational records (see Section 5.1.1). With regard to the mean flooded area, the model results indicate three main outcomes: • • •

the largest change in flooded area occurs between 0oC and 2oC; there is a clear difference in flooded area outcomes from the UKTR and GFDL models when compared with the CSIRO9 and LLNL models; and the Brahmaputra and Meghna Rivers will play a major role in future flooding.

Surprisingly, the model results indicate that most changes in the mean flooded areas occur between 0oC and 2oC in relation to the increases in the peak discharges of the Ganges, Brahmaputra and Meghna Rivers (Table 5.8 and Fig. 5.12) rather than at higher temperature increases. In the range of 0oC-2oC, 2oC-4oC and 4oC-6oC increases in

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IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH

temperature, increases in flooded area for per degree warming is ( ∆A ) 0.44 mha to o

∆T C

0.55 mha, 0.015 mha to 0.09 mha and 0.015 mha to 0.075 mha, respectively. In general, increases in peak discharge between 0oC-2oC will engulf most of the flood vulnerable areas. Therefore, at higher temperature increases, proportionate increases in discharge will not be able to increase the spatial extent of flooding as it will possibly be limited by the elevation of the lands. The second point to be made from the analyses of the flooded area is that there is a clear distinction to be seen in the outputs from the UKTR and GFDL models when compared to the CSIRO9 and LLNL models. The former two models show greater discharge, and thereby higher flooded area, than the latter (Table 5.8).

Fig. 5.11 Spatial pattern of flood extent and depth for current mean peak discharge.

Results of the inter-model comparison show that, although there is little difference in results between the UKTR and GFDL models, the UKTR model gives the largest increases in the mean peak discharge for 2oC, 4oC and 6oC temperature changes. Consequently, the MIKE 11-GIS model yields the highest changes in the mean flooded area for the UKTR model. For a 2oC temperature increase, the expected change in the mean flooded area is +29%. This is perhaps caused by higher increases in the peak discharge of the Ganges River. This helps increase the flooded area in the Brahmaputra basin by slowing down its drainage at Baruria Transit. The change is expected to be +39% for a 6oC temperature rise (Fig. 5.13). For the GFDL model, the changes are 28% and 37% in the flooded area, respectively.

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Table 5.8 Area (in mha) inundated under 2oC, 4oC and 6oC temperature increases for the four GCMs

Mean Flooded Area o

Model

0C

2oC

4oC

6oC

CSIRO9

3.77

4.65

4.68

4.71

UKTR

3.77

4.87

5.08

5.24

GFDL

3.77

4.84

5.02

5.17

LLNL

3.77

4.68

4.73

4.78

Fig. 5.12 Changes in the combined mean discharges of the Ganges, Brahmaputra and Meghna Rivers (under control and climate change scenarios) and the mean flooded areas. Values within boxes indicate changes for a 2oC rise in temperature.

It can be seen that the CSIRO9 model results indicate the lowest possible changes in the flooded area, although these changes are not greatly different from the LLNL model. For a 2oC increase in temperature, the mean flooded area increases by 23% under the CSIRO9 model. For the higher temperature increases, the changes are negligible. For example, for a 6oC temperature increase, the mean flooded area increases by 25%, which in absolute terms, is only 0.06 mha more than the area expected to be inundated under a 2oC temperature rise. For the LLNL model, with a 2oC and 6oC temperature rise, the flooded area may change by 24% and 27%, respectively. In absolute terms, the difference between flooded areas under these two scenarios is only 0.1 mha (Table 5.8). For the LLNL model, under the three global mean temperature scenarios, changes in the flooded area are largely produced by the peak discharges of the Meghna River. Changes in peak discharges for the Ganges and Brahmaputra are negligible.

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IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH

Fig. 5.13 Spatial pattern of flood extent and depth for the UKTR model (6oC rise in global mean temperature).

The third point to emerge from the analysis of flooded areas is that the Brahmaputra and Meghna peak discharges play a major role in flooding. This can be seen from an inspection of Figures 5.12 and 5.13, which compare flooded areas from mean peak values under current temperature with that of the 6oC scenarios for the UKTR model. The peak discharge of the Ganges slows down the drainage of the Brahmaputra River through the Baruria Transit. This helps to increase the spatial extent, depth and duration of flood in the Brahmaputra basin, and thus the Brahmaputra water cannot be drained out quickly. Further downstream in Chandpur, the combined flow of the Ganges and Brahmaputra obstructs drainage from the Meghna basin. This phenomenon creates problems in the Meghna basin similar to those of the Brahmaputra. A significant backup of water through the Meghna basin lasts until gradients are established which allow the drainage of flood water from the Meghna basin. Note that under the 2oC, 4oC and 6oC temperature rise scenarios, both the CSIRO9 and LLNL models imply slight decreases in the Brahmaputra peak discharge (Table 5.7). Although, for the peak discharges of the Ganges, the CSIRO9 model implies changes many times greater than those for the LLNL model, slight increases in the flooded area for the LLNL model were perhaps caused by the increases in the peak discharge of the Meghna River. 5.6.2

CHANGES IN THE INUNDATION CATEGORIES

Under the climate change scenarios, four selected GCMs indicate substantial changes in the land inundation categories F0, F1, F2 and F3 (Tables 5.9 and 5.10 and Fig. 5.14).

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In response to increased peak discharge, mean flooded area may increase significantly. However, the rate and net changes in the mean flooded area is expected to be higher than that of the flooded area of higher return periods. The results of simulations for the mean and 20-year flooded area are discussed below. The analysis of inundation categories for the 12 model simulations indicate that: • • • • • • • • •

drastic changes in most of the inundation categories may occur between 0oC and 2oC global mean temperature rise; rates of change are expected to be smaller with higher temperature increases; at 2oC and 4oC temperature changes, the UKTR and GFDL models show similar changes in the medium flood category; at 2oC, 4oC and 6oC temperature changes, the CSIRO9 and LLNL models show a similar pattern of change for all flood categories; at 2oC, 4oC and 6oC temperature rises, the UKTR model shows the highest changes in the deep flood category; under a 6oC temperature rise, most of the mean flooded areas may be deeply flooded in Bangladesh; land area under prolonged inundation (<9 months) may increase; changes in the inundation categories may result in reduced cropping intensity in Bangladesh; and as a result of changes in the inundation categories, the agricultural sector of Bangladesh may suffer substantial loss of land productivity.

Table 5.9 Various land classes in Bangladesh

Crop Suitability

Land Type of Inundation Class

Range of Inundation Depth

Highland (F0)

Less than 30 cm (Flood Free)

Land suited to HYV T. aman in wet season, wheat and HYV boro in rabi season

Medium Highland (F1)

30 cm to 90 cm (Shallow Flooded)

Land suited to local varieties of aus and T. aman in wet season; wheat and HYV boro in rabi season

Medium Lowland (F2)

90 cm to 180 cm (Moderately Flooded)

Land suited to B. aman in wet season and wheat and HYV boro in rabi season

Lowland (F3)

greater than 180 cm (Deeply Flooded)

Land suited to B. aman in wet season and HYV boro in rabi season

Source: MPO, 1987.

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IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH

Table 5.10 Changes in the extent of mean flooded areas under climate change scenarios for the four GCMs, relative to current condition (Control Run 1)

2oC Temperature rise GCM

Fo (0-30 cm)

F1 (31-90 cm)

F2 (91-180 cm)

F3>180 cm

CSIRO9 UKTR GFDL LLNL

4.45 (-17%) 4.23 (-21%) 4.27 (-20%) 4.42 (-17%)

0.96 (0%) 0.89 (-7.5%) 0.91 (-5.4%) 0.96 (0%)

1.63 (+30%) 1.68 (+36%) 1.68 (+36%) 1.64 (+32%)

2.07 (+32%) 2.31 (+47%) 2.25 (+43%) 2.08 (+32%)

4oC Temperature rise GCM

Fo (0-30 cm)

F1 (31-90 cm)

F2 (91-180 cm)

F3>180 cm

CSIRO9 UKTR GFDL LLNL

4.42 (-17%) 4.03 (-25%) 4.09 (-23%) 4.37 (-17%)

0.95 (-2%) 0.81 (-20%) 0.85 (-12%) 0.94 (-2%)

1.63 (+31%) 1.67 (+35%) 1.68 (+36%) 1.66 (+34%)

2.11 (+34%) 2.61 (+66%) 2.48 (+43%) 2.13 (+36%)

6oC Temperature rise GCM

Fo (0-30 cm)

F1 (31-90 cm)

F2 (91-180 cm)

F3>180 cm

CSIRO9 UKTR GFDL LLNL

4.40 (-18%) 3.86 (-27%) 3.94 (-26%) 4.32 (-19%)

0.93 (-4%) 0.73 (-25%) 0.78 (-19%) 0.93 (-3%)

1.64 (+32%) 1.61 (+31%) 1.65 (+33%) 1.67 (+35%)

2.16 (+38%) 2.91 (+85%) 2.74 (+75%) 2.18 (+39%)

The first point is that for all four GCMs, changes in the inundation categories are largest in the range of 0oC to 2oC (Figs. 5.14 and 5.15 and Table 5.10). It is evident from the figures that the non-flood category (F0) may decrease substantially, while the other flood categories, especially F2 and F3, would increase markedly. The F0 land category is expected to change in the range of -21% to -17%. This is due to substantial increases in the peak discharge of the three main rivers as well as local rainfall (see Tables 5.4 and 5.7). Individually, the Meghna basin has largely contributed to these changes. Note that most of the area of this basin is inundated annually. In absolute terms, the increase is in the range of 0.46 mha to 0.68 mha. The highest changes are expected for the F3 category, which could be in the range of +32% to +47% (between 0.5 mha to 0.74 mha). This is followed by the F2 category, where the changes may be in the range of +30% to +36% (between 0.39 mha to 0.44 mha). The lowest change (-7.5% to 0%) may occur in the F1 category. The second point is that although the peak discharges of the three rivers could increase substantially at a higher temperature, the rate of change in flood inundation categories slows (Figs. 5.14, 5.16 and 5.17), as predicted by for most GCMs. For a 4oC temperature rise, the Fo land category changes in the range of -25% to -17% compared to

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129

-21% to -17% at a 2oC temperature rise. Changes in the F1 category are expected to be in the range of -20% to -2%. The F2 and the F3 categories increase by +31% to +36% and +34% to +66%, respectively. Changes in the F1 category are expected to be in the range of -20% to -2%. At a 6oC temperature rise, changes in the F0 category may be in the range of -27% to -18%, which is very negligible. The F1 category changes to a -25% to -4% range from a -20% to -2%. The F2 category, in a broad sense, will remain unchanged, according to two models (CSIRO9 and UKTR) and may change a little for the other two models (GFDL and LLNL). The rate of change in the F3 category will remain almost same as that seen in a 4oC temperature rise.

Fig. 5.14 Changes in Fo, F1, F2 and F3 inundation categories in Bangladesh (2oC, 4oC and 6oC rise in global mean temperature).

The third point is that under 2oC and 4oC temperature changes, the UKTR and GFDL models show a similar pattern of change for the medium or F2 inundation category (Figs. 5.14, 5.15 and 5.16). For a 2oC temperature rise, a 36% change may occur for the two models. Similarly, at a 4oC increases in temperature, changes are predicted to be by 35% and 36% for the UKTR and GFDL models, respectively. The fourth point is that the CSIRO9 and LLNL models show a similar pattern of change at 2oC, 4oC and 6oC increases in temperature (Figs. 5.14, 5.15, 5.16, 5.17 and Table 5.10). At a 2oC temperature change, the CSIRO9 and LLNL models imply equal changes (-17%) for the Fo and no change for the F1 inundation category, respectively. The F2 category may change by 30% and 32%, respectively. For both models, the F3 category may change by 32%. For a 4oC temperature rise, the F0 inundation category may change by -17% and -18%, respectively, for the CSIRO9 and LLNL models. This is perhaps due to increased peak discharges and local rainfall being unable to inundate new areas, due to land elevation. For F1, F2 and F3, the changes are -2% and -2%, +31% and +34%, and +34% and +36%, respectively. Similarly, for a 6oC temperature rise, changes in the F0 inundation category may increase to -18% and -19%, respectively for the CSIRO9 and LLNL models.

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IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH

A slightly greater change is expected for the F1 category under the CSIRO9 model than that for the LLNL model. An equal rate of increase (+1% each) may occur for these two models, in the F2 category. On the other hand, 4% and 3% increases in the F3 category may be expected according to the CSIRO9 and LLNL models, respectively. In absolute terms, these changes are negligible.

Fig. 5.15 Changes in flooded area for the Fo, F1, F2 and F3 inundation categories (2oC rise in global mean temperature).

Fig. 5.16 Changes in flooded area for the Fo, F1, F2 and F3 inundation categories (4oC rise in global mean temperature).

The fifth point is that for the three temperature change scenarios, the UKTR model indicates drastic changes in the flooded areas under the F3 inundation category (Figs. 5.15, 5.16 and 5.17). At 2oC, 4oC and 6oC temperature changes, for the F3 inundation category, the changes may be as great as 47%, 66% and 85%, respectively. The sixth point is that under the climate change scenarios, deeply flooded areas will be the largest of all flooded areas (Figs. 5.15, 5.16, 5.17 and Table 5.10). Of the total mean

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131

flooded area (3.77 mha), the area under the F3 category was found to be the largest, at 1.57 mha. The inundated area under the F1 and F2 categories was found to be 0.96 mha and 1.24 mha, respectively. This implies that in Bangladesh, deeply flooded areas (F3) constitute approximately 42% of the total mean flooded area. The shallow (F1) and medium flooded (F2) areas constitute the remaining 58%. Under the climate change scenarios, the highest changes are expected for the UKTR model. With a 6oC global mean temperature rise, the deeply (>180 cm) flooded area may be 55%. For the GFDL model, it may be 53%. For the CSIRO9 and LLNL models, the area under the deep flood category is expected to be slightly smaller than 50%. This indicates that under the climate change scenarios, a major portion of the mean flooded area may remain under deep water.

Fig. 5.17 Changes in flooded area for the Fo, F1, F2 and F3 inundation categories (6oC rise in global mean temperature).

The seventh point is that under climate change scenarios, greater areas may remain inundated for prolonged periods, compared to the current situation. Currently, the F3 land category remains underwater for about 9 months (World Bank, 1989). In percentage terms, it is predicted to be the highest (55% of the flooded area) for the UKTR model, for a 6oC temperature rise. In absolute terms, the area to remain under deep water is very substantial. The percentage of the flooded areas under various periods of inundation for the F1, F2 and F3 land categories for the current and future scenarios is shown in Table 5.11. The period of inundation given in Table 5.11 has been determined based on the current volume of floods and the drainage capacity of the channels and rivers. However, drainage of flood water may be impaired if the peak discharges of the major rivers and rainfall in Bangladesh increase under climate change scenarios. Therefore, inundation periods may be longer than currently observed. 5.7

SOCIO-ECONOMIC EFFECTS OF CHANGES IN INUNDATION CATEGORIES

Changes in the land inundation categories may substantially affect the agriculture sector in Bangladesh. The effect of changes may be more pronounced for the monsoon rice crops and rabi varieties. Table 5.9 shows land categories suitable for various crops in different cropping seasons.

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IMPLICATIONS ON RIVER DISCHARGE IN BANGLADESH

Table 5.11 Percentage of the mean flooded area under various inundation periods for a 6oC rise in temperature

GCM

Seasonal (F1)

Seasonal (F2 )

<9 months (F3)

Current

25.0

33.0

42.0

CSIRO9

20.0

35.0

45.0

UKTR

14.0

31.0

55.0

GFDL

15.0

32.0

53.0

LLNL

19.5

35.0

45.5

Changes in land categories may affect cropping intensity in Bangladesh. Farmers do not plant when the risk of flooding is too high. A flood can damage the aus (a type of rice crop which is grown in the kharif-I season during April-July) crop at the end of the growing period and the aman (a type of rice crop which is grown in the kharif season during April-November). There are two types of aman transplanted and broadcasted and they are grown in the highlands and lowlands, respectively at the beginning of the growing period. Floods may limit the growth of HYVs (high yielding varieties) between June and October, wherever, risk of flooding is too high (above 60 cm more with a 20% probability of exceedence) (FEC, 1989). Under the climate change scenarios, increases in the mean flood volume and depth will increase the volume and depth of floods within higher return periods. Therefore, risk of inundation with higher flood depths may increase. Loss of F0 and F1 land categories would reduce area under wheat and winter vegetables. If the gross cultivated area in the monsoon season were reduced, the cropping intensity may reduce unless compensated by the boro (a rice crop usually planted in January-February and harvested in April-May) crop. Changes in the F0 and F1 land categories may affect the HYVs of rice cultivation. Under the climate change scenarios, reduction in the F0 and F1 categories is expected to be within the range of -21% to -17% and -25% to -3%, respectively for the four GCMs (Table 5.10). This may have a significant effect on the HYV aman rice production in Bangladesh. Productivity of lands may be affected by changes in the inundation categories. Based on land type, per hectare productivity varies from with irrigation (Taka 24,100-35,000) and without irrigation (Taka 8,600-20,200) (Table 5.12). The F0 land category (irrigated) may be seriously affected if it is transformed into the F1 category. The non-irrigated F0 category may gain very little productivity. On the other hand, with a move from F2 into F3, loss in productivity for non-irrigated F2 may be twice that of the irrigated. 5.8

CONCLUDING REMARKS

Future peak discharges under climate change indicate the possibility of more serious flooding in Bangladesh. Flood-prone areas in Central and Northeastern Bangladesh would be more vulnerable in terms of depth and spatial extent of flooding, due to increases in peak discharge of the Brahmaputra and Meghna Rivers. Increases in the peak discharge of the Ganges may exacerbate flooding in the central region of Bangladesh if it were to occur simultaneously with the peak discharge of the Brahmaputra.

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Table 5.12 Gross value of output per hectare by land type (in Taka)*

Land Type

Irrigated

Non-Irrigated

Fo

35,500.00

19,100.00

F1

29,100.00

20,200.00

F2

28,100.00

15,500.00

F3

24,100.00

8,600.00

Source: World Bank, 1989. Estimates are of 1991 prices with two crops in a year.

Increases in mean peak discharge in the three rivers indicate significant changes in the spatial as well as depth of inundation in Bangladesh. Faster changes may occur at low temperature increases (at 2oC) than those at higher temperature changes. Increased mean peak discharge of the Brahmaputra and Meghna Rivers may largely contribute to this by engulfing the various land classes vulnerable to inundation. The mean flooded area may increase in the range of 20%-40%, at a 6oC rise in temperature. In the future, 55% of the flooded area may be deeply flooded. Greater changes may occur in the non-flood (F0), moderately (F2) and deeply (F3) flooded land categories. These changes may introduce significant changes in the rice agriculture in Bangladesh. Cropping intensity and production of the high yielding varieties (HYV) of rice may be reduced substantially. In terms of population, more people will be vulnerable in future, as an increased number of people will be living in the floodplains of Bangladesh. More houses and infrastructure will be exposed to flooding and the likelihood of increased damage is high. This underscores the need for strengthening flood management policies and adaptation measures in Bangladesh to reduce increased flood hazard due to climate change.

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Morgan, R. P. C.: Rainfall in West Malaysia-A Preliminary Regionalisation Using Principal Components Analysis. Area 3(4) (1971), pp.222-227. Murphy, J. M. and Mitchell, J. F. B.: Transient Response to the Hadley Center Coupled Ocean-Atmosphere Model to Increasing Carbon Dioxide-Part II: Spatial and Temporal Structure of Response. J. Climate 8 (1995), pp.57-80. Ogallo, L. J.: The Spatial and Temporal Patterns of the East African Seasonal Rainfall Derived from Principal Component Analysis. International Journal of Climatology 9 (1989), pp.145-167. Paul, B. K. and Rasid, H.: Flood Damage to Rice Crops in Bangladesh. The Geographic Review 83(2) (1993), pp.151-159. Pike, J. G.: Estimation of Annual Runoff from Meteorological Data in a Tropical Climate. J. Hydrology 2 (1964), pp.116-123. Regemortel, G. V.: Regionalization of Botsowana Rainfall During the 1980s Using Principal Component Analysis. International Journal of Climatology 5 (1995), pp.313-323. Resource Analysis: The Vulnerability Analysis of Bangladesh to Climate Change and Sea Level: Rise: Summary Report. Resource Analysis and Bangladesh Center for Advanced Studies (BCAS), Amsterdam, the Netherlands, 1993. Roeckner, E., Bengtsson, L., Feitcher, J., Leliveld, J. and Rodhe, H.: Transient Climate Change with a Coupled Atmosphere-Ocean GCM Including Tropospheric Sulphur Cycle. J. Climate 12 (1999), pp.3004-3032. Salinger, J. M.: New Zealand Climate: The Instrumental Method, Unpublished PhD Thesis, Victoria University of Wellington, New Zealand, 1980. Warrick, R. A., Kenny, G. J., Sims, G. C., Ericksen, N. J., Ahmad, Q. K. and Mirza, M. M. Q.: Integrated Model Systems for National Assessments of the Effects of Climate Change: Applications in New Zealand and Bangladesh. In L. Erda, W. C. Bolhofer, S. Huq, S. Lenhart, S. K. Mukerjee, J. B. Smith and J. Wisniewski (eds.): Climate Change Vulnerability and Adaptation in Asia and the Pacific, Kluwer Academic Publishers, Dordrecht, the Netherlands, 1996, pp.215-227. Wetherald, R. T. and Manabe, S.: An Investigation of Cloud Cover Change in Response to Thermal Forcing. Climate Change 8 (1986), pp.5-23. Whener, M. F. and Convey, C.: Description and Validation of the LLNL/UCLA Parallel Atmospheric GCM, Technical Report UCRL-D-123223, Lawrence Livermore National Laboratory, 1995. World Bank: Bangladesh: Action Plan for Flood Control, World Bank, Washington, D.C., 1989. World Bank: World Development Report, World Bank, Washington, D.C., 2003. World Meteorological Organization (WMO): Water Resources and Climate Change: Sensitivity of Water-Resource to Climate Change and Variability, WMO, Geneva, 1987.

6 Climate Change and Glacier Lake Outburst Floods and the Associated Vulnerability in Nepal and Bhutan MOTILAL GHIMIRE

6.1

INTRODUCTION

Natural disasters have become a part of the worldwide spectacle of a globalize media. The Glacier Lake Outburst Floods (GLOF) induced disaster has also become a part of it, for its impact and risk on the people and economy of the mountain cannot be in no way underestimated. The GLOF events in the Himalayas have been occurring since long as evidenced by many landform features downstream. For example the GLOF event of the Barun Khola in Nepal was not known, but the accumulation of debris along the river valley is an indication GLOF event. Similarly, the debris accumulation in Pokhara Valley gives clues to the GLOF event some 450 years ago in the Seti due to collapse of moraine of a glacier lake in Machhapuchhare range in the Himalayas. In Bhutan, several evidences also show that GLOF event have been the common phenomenon, although the past events are not recorded in the modern chronicle or many events are unknown to people. A Swiss, Geologist Augusto Gansser, during his expedition to Bhutan Himalayas in the 1960s and 1970s had an opinion about the 1957 Punakha flood. He felt that it was due to an outburst from Taraina Tso in Western Lunana (Gansser, 1970 quoted in Mool et al., 2001). A GLOF event could be traced back to the 1935 event in Sun Kosi Basin which destroyed cultivated land and livestock in Nepal. Several catastrophic GLOF events that originated in China or Nepal in 1964, 1977, and 1980 (Yamada, 1993) were also experienced, but these events were not considered seriously. A GLOF event of 1981 in the Boqu River (Sun Koshi) in China was one that slightly raised the brows of development planners and policy makers, for it had destroyed a large section of the China-Nepal road as well as the Friendship Bridge, and impacted 30 km downstream in Nepal. But it was in 1984 an outburst of glacier lake (Dig Tsho, below Langmoche Glacier in Khumbu) that caused a severe disaster to lives and property downstream has strongly revealed the stakeholders its disaster potential. The lake was drained suddenly and sent a 10 m to 15 m high surge of water and debris down the Bhote Koshi and Dudh Koshi Rivers, for more than 90 km. An estimated 1 million m3 of water was released, creating an initial peak discharge of 2,000 m3/sec; two to four times the magnitude of maximum floods due to heavy monsoon rains. This spectacular natural event destroyed the nearly completed Namche Small Hydel worth NRs 40 million. It eliminated all the bridges, for 42 km downstream; four or five people lost their lives (Fushimi et al., 1985; Galey, 1985; Ives, 1986).

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GLACIER LAKE OUTBURST FLOODS AND VULNERABILITY

GLOF events in Bhutan are also common due to similar Himalayan environment as in Nepal. Awareness about its potential threat goes back to 1970s as evidenced by a brief study done by a joint team of experts from Indo-Bhutan on possible outburst of a glacier lake in Lunana region. Recently, Bhutan experienced a GLOF event on October 7, 1994, the event that partially bursted from Lugge Tsho located in Eastern Lunana (Watanabe and Rothacher, 1996). It had incurred loss of lives and huge property along Punakha Wangdue Valley. Since then concern about GLOF and its threat to development effort seemed to scale up among the stakeholders which is indicated by consequent several studies and mitigation effort that were carried out (Mool et al., 2001). 6.2

GLOF HYDROLOGY

As implicit from the above discussion, GLOF is a catastrophic discharge of water from the glacier lakes due to failure or breach of ice or moraine dam formed at the end of these lakes that may cause a huge disaster. Mool (1993) defined as “the flood due to sudden bursting of glacier lakes which are ice dammed or moraine dammed”. It is a phenomenon of glacier lakes, a ponding of glacier melt water in depression area of glaciers surrounded by the lateral and end moraines or may formed at the side of lateral moraine of the extended glaciers due to interception of the tributaries by its lateral moraine (Yamada, 1993). The first type of glacier lakes, are called moraine dammed glacier lake, while the second type of glacier lakes are referred as ice dammed glacier lakes. Almost all types of glacier lakes in Nepal are moraine dammed, it is because of the fact that the Himalayan glaciers produce very rich debris that make relatively large lateral and end moraine compared to others glaciers in the world. Ice dammed lakes are very rare, and are considered less dangerous than a moraine dammed lake. According to Yamada (1993), apart from the above reasons, a pond may be formed on, in or under glacier under some critical condition; such glacier lakes might be negligible to consider being flood hazard. But such small ponds called “supra glacier” formed within a glacier may eventually grow and connect each other to form a large lake which might be potentially dangerous. A history of past GLOF events of moraine dammed lakes indicates that they are initially derived from supra glacier lakes (Mool et al., 2001). Glacier lakes may not remain as they are all the time, a glacier lake may disappear, once the dam is destroyed or sedimentation fills the lake completely. They are formed and maintained in certain stages of glacier activities corresponding to climatic variation (Yamada, 1993). One theory states that during the so-called “Little Ice Age” which lasted until 1850, glaciers were extensive and due to gradual change in climate since mid 19th century, majority of mountain glaciers has been thinning and retreating which resulted in the formation of glacier lakes behind the end moraines of these retreated glaciers (Röthlisberger and Geyh, 1985). The recent global warming phenomenon have ushered scientist to consider the expansion of glacier lakes in recent times and their outburst as an affect of warming trend. Glacier lakes formation and their outburst are conducive in the High Mountain of Tropics and Sub-Tropics in the Himalayas and Andes since snow in these areas is very sensitive to small change in temperature. Any small rise in temperature would cause a retreat of glacier and enhances the formation of glacier lakes at the earlier toe of the glacier behind the end moraine dam. It is reported that glaciers in the Himalayas are in the order of 10 km-25 km in length, generally are longer than in the Alps, and have a relatively flat longitudinal profile in their lower part. Often they have a large end moraine, which enhances the formation of lakes behind these dams during climatic changes. The gradual

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rise in the glacier lake may lead to glacier lake outburst flood by over powering the dam due to increasing water pressure. The rises in water level in the glacier lakes are generally attributed to (Mool et al., 2001): • • • • • • •

Warming of temperature, Intensive precipitation events, Decrease in seepage across moraine due to sedimentation, Blocking of an outlet by an advancing tributary glacier, Melting of ice-core moraine wall or subterranean thermal activities, Inter-basin sub-surface flow of water from one lake to another lake due to height difference and availability of flow path, and Others local specificities.

It is quite clear that a climate change and variability is one of the causes of rise in water level in the lakes. There are many hypotheses about the bursting mechanism which are presented in the chart in Figure 6.1 (WECS, 1994). 6.3

STUDIES ABOUT GLACIER LAKES AND THEIR OUTBURST EVENTS IN NEPAL AND BHUTAN

Works on glacier inventory in Nepal began in the late 60s, the initiation was made by the Japanese team (Japanese Glacier Research Group (1968-1973) and Glaciological Expedition Nepal: GEN (1973-1974) (Higuchi et al., 1978). But these studies virtually did not describe about glacier lakes and their outburst events. Some historical data on glacier lake outburst data was offered by the Chinese Investigation Team (1973-1974) in its interior report (Yang, 1982 quoted in Xu and Quingua, 1994). In Nepal, the catastrophic outburst of Dig Tsho Lake in Eastern Nepal on 4 August 1985, after similar events in 1977 and 1981 (Xu, 1985; Galey, 1985; Ives, 1986) made outburst events serious disaster and environmental issue to national and international community as well. This concern heralded a series of studies on glacier lakes in Nepal. Fushimi et al. (1985); Galey (1985); Xu (1985); Vuichard and Zimmerman (1986, 1987); and Ives (1986) highlighted on the past or recent GLOF events in Nepal and Tibet and their threat to people and infrastructures at downstream. Government agencies like Water and Energy Commission Secretariat, Nepal Electricity Authority and Chinese counterpart, Lanzhou Institute of Glaciology and Geocrylogy (1988) made a preliminary assessment of glaciers and glacier lakes in the Pumqu (Arun) and Pioque (Bhote Kosi) River basins in both China and Nepal. It was a first step for Nepal to join the research of GLOF. In 1990 and 1991, with support from Japan International Cooperation Agency (JICA), WECs have carried out several inventories of glacier lakes in the Arun, Honku Drangka, Hinku Drangka, Dudh Koshi, Lantang Khola, Chilime, and Marsyangdi Basins through flight observation. The flight observation report recommended detailed examination of the dangerous glacier lakes by site visit. As a result, several lakes such as Lower Barun, Imja, Thulagi, Dig Tsho, and Tam Pokhari glacier lakes were studied. The general features of these potentially dangerous lakes are presented in Table 6.1. Much of the studies in the later years were carried on these lakes by (semi)-government institutes including professional consultancies, individual, and students. For instance, the description about the Imja Lake in Khumbu region is found in the studies of Hammond (1988), Yamada (1993), Watanabe et al. (1994), Watanabe et al. (1995), Kettelmann and Watanabe (1998). From the study of topographic maps, aerial photos, and

Tunnel under ice

Over topping caused by upper glacier calving, ice fall or rock fall

Piping Over topping caused by upper glacier calving, ice fall or rock fall

Moraine Dam

Fig. 6.1 Process of Glacier Lake Outburst Floods (GLOFS).

Ice melts glacier retreats

Glacier Ice Dam

Causes of Outburst For

GLOFs

Ice core melts resulting in piping

Over topping caused by upper glacier calving, ice fall or rock fall

Ice-Core Moraine Dam

140 GLACIER LAKE OUTBURST FLOODS AND VULNERABILITY

27° 48’ N 87° 07’ E 4,570 50 118 1.250 0.625 0.78 28 50 35 -

Latitude Longitude Altitude (m above sea level (masl)) Depth (m) Average Maximum Length (km) Width (km) Area (km2) Stored Water (106*m3) Drainage Area (km2) Approximate Age (years) GLOF Release (106*m3)

Source: Mool et al., 2001.

Lower Barun

Features

27° 59’ N 86° 56’ E 5,000 47.0 99 1.3 0.5 0.60 28.0 45 -

Imja

Table 6.1 Some features of studied glacier lakes in the Nepal Himalayas

27° 50’ N 86° 28’ E 4,580 55.1 131 3.2 0.5 1.39 76.6 77.6 45 -

Tsho Rolpa

28° 30’ N 84° 30’ E 4,146 41.8 81 2.0 0.45 0.76 31.8 55.4 45+ -

Thulagi

27° 52’ N 86° 35’ E 4,365 20 1.21 0.44 0.5 10 50 8

Dig Tsho

27° 44’ N 86° 15’ E 4,432 45 left after GLOF 1.15 0.5 0.47 21.25 45+ 17

Tam Pokhari

MOTILAL GHIMIRE 141

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GLACIER LAKE OUTBURST FLOODS AND VULNERABILITY

the imageries, the development of Imja Glaciers have been reconstructed (Yamada, 1993). The lake has increased in size from 0.03 km2-0.60 km2 during 1955-1992 (Fig. 6.2). A recent study warns this lake to be potentially dangerous as it is in contact with the tongue of glacier (Mool et al., 2001) which is likely to increase water volume and pressure, and trigger lake outburst.

Imja Glacier Lake, 1955-63 (Yamada, 1993)

Tsho Rolpa Lake, Rolwaling Nepal, 1957-2000 (WECs, 1993; Mool et al 2001) a. 1957-59

Expansion of Raphstreng Tsho Glacier Lake from 1956-1994 (Ageta et al 1999)

f. 1979

a. 1955-63 1.02 km2

0.23 km2 b. 1960-68

g. 1983-84

0.61 km2 c. 1972

h. 1988-90

1956-58 October 1988 (Toposheet): 1968 (MOS-I)

December 1993 (SPOT XS)

b. 1975

c. 1984

c. 1992

1.16 km2

0.62 km2

1.27 km2

d. 1974

i. 1993

0.78 km2 e. 1975-77

j. 1999

1.37 km2

1.55 km2 k. 2000

0.80 km2

0

1

1967 1989-90 December 1994 (Gansser, 1970) (DGM, 1996I) (Spot 3)

0

2 km 0

1

2

3

1 km

1.40 km2 4 km

Fig. 6.2 Glacier lake development process.

About Tsho Rolpa Glacier Lake’s geomorphology, lake development process, hydro-meteorology, and hazard assessment could be found in the studies of Damen (1992), Modder and van Olden (1995, 1996a, 1996b, and 1996c), WECS (1993a), Reynolds Geosciences Ltd (1994), Mool (1995a), Budhathoki et al. (1996), Chikita et al. (1997), Yamada (1993, 1998), DHM (1997c, 1998b, and 2000). Monitoring the development process of Tsho Rolpa Lake from the study of maps, aerial photos and imageries it has been reported that during 1957-1992 period the lake has increased from 0.23 km2 to 1.37 km2 (WECS, 1993a) and by 2000 the lake has grown to 1.55 km2 (Mool et al., 2001). Studies in Lower Barun Glacier Lakes were done by WECS (1993a, 1997), and NEA (1995). WECS (1993a) recommended that the lake is increasing and is associated with larger mother glacier. So any project downstream of Lower Barun Glacier Lake requires detail investigation of the lake and downstream valley. Similarly, WECS (1995c), DHM(1997c), Hanisch et al. (1998) had studied Thulagi Glacier. WECS (1995c) study reveals the gradual increase of lake during the last 45 years; comparing the maps of 1958 and field work in 1992, it revealed that the lake has increased from 0.22 km2-0.76 km2 and the glacier has retreated by 1.37 km within the last three decades. However, the study by DHM (1997b) sees no danger from the lake in foreseeable future because it is dammed by extended ice bodies which can neither be rapidly breached by lake water pressure or by erosional forces of river. It can only be removed by large scale melting of ice core which requires a period of hundreds to thousand years.

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The outburst of Dig Tsho Lake in 1985 and the accompanying damage set in train a lot of field investigations to understand the glacier lake morphology and outburst mechanism (Galey, 1985; Ives, 1986; Vuichard and Zimmerman, 1986, 1987; WECS, 1987b). After outburst the lake is considered to be safe as it has been drained completely (WECS, 1987b). However, Mool et al. (2001) argues the reappearance of lake at the tongue to the glacier poses concern and therefore, surrounding moraine and the activity of the lake should be studied in detail. Dwivedi et al. (1999) reported about the bursting mechanism, discharge of water volume and the loss/damage caused by Tam Pokhari Glacier Lake. Mool et al. (2001), compared the lakes and interpreted from the topographic maps of 1963, satellite imagery of 1992-1993, and the topographic maps of 1996 (based on 1992 aerial photo) that the lake area had increased from 0.138 km2 to 0.472 km2. In Bhutan, the first glacier expedition was briefly made in 1960s by Gansser (1970). He identified a number of dangerous lakes, which could flood in the lower valleys. He attributed 1957 flood in Punakha Wangdi Valley to the outburst from Tarina Tsho, Western Lunana. In 1970s and 1980s, joint study team of Geological Survey of Bhutan (GSB) and the Geological Survey of India (GSI) carried out several investigations to assess hazard and socio-economic risk of glacier lakes in Lunana area. These studies concluded that there was no danger of outburst of Lunana Lake in the near future but recommended periodic checks every 2 or 3 years due to presence of ice cores in the moraine dams. After the partial outburst of Lugge Tsho located in Eastern Lunana which has affected life and damaged property along the Punakha - Wangdue Valley (Watanabe and Rothacher, 1996). Some government agencies of Bhutan carried out research on cause and effect of outburst and to recommend short- and long-term mitigation measures (Dorji, 1996a, 1996b; National Environment Commission, 1996). Meanwhile, in 1996 after the many years gap of first glacier inventory, Phuntso Norbu, Division of geology and mines prepared an inventory of glaciers and glacier lakes which was edited and updated by Geological Survey of Bhutan (1999). On the basis of these studies, expansion of glacier lakes were reported by Ageta et al. (1999) (quoted in Mool et al., 2001; Karma et al., 2003). For instance, the area of Rapstreng Tsho Glacier Lake was 0.15 km2 in 1960s, in 1986 the lake was 1.65 km long and 0.96 km wide and 80 m deep and in 1995 the lake had the maximum length of 1.94 km, width 1.13 km and the depth of 107 km (Fig. 6.2). Most recently in 2001, international institutes like ICIMOD and UNEP came up with inventory of glaciers and glacier lakes covering the entire part of Nepal and Bhutan from the study of topographic maps, aerial photos, satellite imagery and literature available (Mool et al., 2001). The study made total inventory of 3,252 glaciers with total area of 5,332.89 km2. These glaciers contain 2,323 nos. of glacier lakes with total area of 75.70 km2 in Nepal. Out of them, 20 have been identified as potentially dangerous in Nepal (Fig. 6.3). Likewise, the study found 677 glaciers with total area of 1,316.71 km2 in whole of Bhutan. The ice reserve has been estimated to be 127.25 km2. The glacier lakes have been identified in the numbers of 2,674; out of them 24 lakes have been identified as potentially dangerous (Fig. 6.4). To sum up the past researches on glacier lakes were mainly focused on the following aspects: • •

Inventory of glacier lakes based on topographic maps, aerial photographs, satellite images, flight observation and field data, Assessment of cause and impact of the recent GLOF events and the possible outburst of glacier lakes,

144

GLACIER LAKE OUTBURST FLOODS AND VULNERABILITY

• • • •

Bathymetric mapping, Hydro-meteorological conditions using field instrumentation, Glaciological condition and geomorphologic analysis of moraine dams, and Risk of GLOF events to proposed hydropower projects.

A = Nagma Pokhari (Tamor); B = (unnamed) (Tamor); C = Lower Barun (Arun); D = Lumding (Dudh Koshi); E = Imja (Dudh Koshi); F = Tam Pokhari (Dudh Koshi); G = Dudh Pokhari (Dudh Koshi); H = (unnamed) (Dudh Koshi); I = (unnamed) (Dudh Koshi); J = Hungu (Dudh Koshi); K = East Hungu 1 (Dudh Koshi); L = East Hungu 2 (Dudh Koshi); M = (unnamed) (Dudh Koshi); N = West Chamjang (Dudh Koshi);O = Dig Tsho (Dudh Koshi); P = Tsho Rolpa (Tama Koshi); Q = (unnamed) (Budhi Gandaki); R = Thulagi (M arsyangdi); S = (unnamed) (Kali Gandaki); T = (unnamed) (Kali Gandaki)

Fig. 6.3 Potentially dangerous lakes of Nepal.

6.4

GLOF EVENTS’ IMPACT, VULNERABILITY AND ADAPTATION

GLOF events in the Himalayas not only signify the damage or disaster from flood, but in recent times these events correlate with glacier retreat which again proximate the global warming trend. The glacier retreat implies a serious concern for water availability for household, agriculture, power and industry for 400 millions living in downstream over a great Indo-Gangetic and Brahmaputra Plain. The water demand for agriculture, industry and urban sector in Nepal, India and Bangladesh is progressively growing and a decline in snow cover would mean a condition of water deficit which a serious threat to food security, energy availability and industry. In the High and Trans-Himalaya region the decline in snow cover would cause serious impact to mountain ecosystem and the livelihood base of the local people which based on snowmelt water fed agriculture and pasture for livestock grazing.

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Measured in the term of past damage and loss over the last half-century, the damage compared to other natural hazard is less and also less frequent (Table 6.2). However, the disaster potential of GLOF has increased in recent times due to growth of settlements along the river valleys, construction of motor roads, bridges, and canals at downstream. About 1.56 million people live within the territory of Nepal (within 3 km from glacier fed rivers) downstream of the blocked or moraine dammed lakes (Fig. 6.5). Several hydropower projects which are either in operation, or under construction or are proposed are associated to rivers that have moraine dammed lakes at their head (Table 6.3). It is argued that these moraine dammed lakes may develop into potentially dangerous lake. About 5 existing, 1 under construction and 3 proposed hydropower projects are associated to the rivers that have potentially dangerous lakes at their sources within Nepal (Yamada, 1993; Mool et al., 2001). N

China

B H U TA N

Potentially dangerous glacier lakes Baun boundary River International boundary

India

Fig. 6.4 Potentially dangerous glacier lakes of Bhutan.

As discussed in the earlier section, investigation on glacier lakes and the attempt to identify potentially dangerous lake began since the last two decade. Out of those identified as potentially dangerous only on a few such lakes mitigation measures have been carried out. In Nepal, Tsho Rolpa is the only glacier lake on which detailed study and mitigation measures are carried out. A first lay man hazard assessment of this lake was done in 1992 (Modder and van Olden, 1995) and in 1993 a hydro-meteorological station was installed. Later in 1994, a British study team made a scientific study on the assessment of the hazard at Tsho Rolpa and recommended that the lake level should be reduced by at least 15 m over 3 to 5 years (Reynolds, 1994). It estimated that occurrence of a GLOF from Tsho Rolpa Lake, could damage up to 100 km downstream from the lake, threatening about 10,000 human lives, thousands of livestock, agricultural land, bridges, including some components of the Khimti Hydroelectric Project and other infrastructures. As a result, siphons and early warning systems were tested (Mool et al., 2001). The first flood warning system in the country was installed in May of 1998 to warn the people living downstream from Tsho Rolpa Glacier Lake, in the potential GLOF hit area

Sun Koshi Arun Arun Dudh Koshi Tamor

Sun Koshi Arun Dudh Koshi Tama Koshi Dudh Koshi

1964 1964 1968 1977 1980

1981 1982 1985 1991 1998

Source: Yamada, 1993; Mool et al., 2001.

River Basin

Year

Zhangzangbo Jinco Dig Tsho Chubung Tam Pokhari

Tara-Cho Gelhaipco Ayaco Nare Nagma Pokhari

Lake

Table 6.2 Past GLOF events and their impact

Tibet (China) Tibet (China) Nepal Nepal Nepal

Tibet (China) Tibet (China) Tibet (China) Nepal Nepal

Source 6.67 ha of wheat field, livestock, etc. Damaging road, 12 trucks, etc. Road, bridges, etc. Mini hydropower plant Villages destroyed 71 km from source Hydropower station Livestock, farmland Hydropower station, 14 bridges, etc. Houses, farmland, etc. Human lives and more than NRs 156 million

Losses

146 GLACIER LAKE OUTBURST FLOODS AND VULNERABILITY

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147

Fig. 6.5 Potentially vulnerable Village Development Committees (within 2.5 km distance from the river with headwater associated to dammed glacier lakes) (Source: Survey Department, HMG Nepal; Mool et al., 2001 (about glacier lakes)).

along the Rolwaling and Tama Kosi Valleys as well as at the Khimti Hydroelectric Project (BC Hydro, 1998). In 1998 the Department of Hydrology and Meteorology, HGMN undertook the task of lowering the lake water by 3 m by cutting an open channel in the end moraine. This project was funded by the Netherlands Government (DHM, 1999). Likewise in Bhutan as a preliminary stage of planned adaptation to GLOF hazard, studies were carried out since 1970s. Mitigation measures to prevent the bursting of the lake were implemented in 1996 on the Lake Raphstreng Tsho only. In order to lower the risk of flood outburst, the water level of the lake was reduced by 4 m by excavating channel outlet. In 1999, with an aim to understand more about GLOF hazard, a multidisciplinary approach of assessing geo-risks of the Raphstreng/Thorthormi Tsho area was carried on Austro-Bhutanese Cooperation (Häuslar et al., 2000, quoted in Mool et al., 2001). The study concluded that the present day risk for an outburst from Raphstreng is low, but the risk of an outburst of Thorthormi Glacier Lake in the future is considered high and it could occur in 15 years-20 years considering the present trend of climate change. 6.5

GLACIER RETREAT, GLOF EVENTS AND CLIMATE CHANGE

Studies about glaciers since early 1960s show that the glaciers in the Himalayan have been retreating since departure of the Little Ice Age in the mid-nineteenth century (Fushimi et al.,1980; Yamada, 1992; Ageta et al., 1999; Zhen and Feng, 2000; Karma et al., 2003). Recent observations have shown many glaciers in the Himalayas retreating rapidly, and Himalayan glaciers are considered to be vulnerable to the recent

Under Construction

Installed Capacity 144 15 14 75 24, 14.1 36 60 10.5 20 14 70 750 402, 335 and 308 600 10800 300 6480 100 300 40 10 35

Total Lakes

1 1 2 12 -

1 65 65 4 22 25 -

Potentially Dangerous 2 2 1 1 1 1

Morraine or Blocked 21 32 1 9 1 1 1 1 9 1 1

Source: International Hydrological Association (IHA), 2000; Mool et al., 2001 (About glacier lakes).

Kali Gandaki Gandak Modi Khola Marshyangdi Trisuli Devighat Upper Bhote Kosi Khimti Khola Sun Kosi Chilime Upper Modi Middle Marsyangdi West Seti Arun III, Upper and Lower Budhi Gandaki Karnali (Chisapani) Upper Karnali Pancheshwar Tamur/Mewa Dudh Kosi Likhu 4

Hydropower Projects

Table 6.3 Hydropower projects and moraine or blocked and potentially dangerous glacier lakes in Nepal

Existing

Proposed

148 GLACIER LAKE OUTBURST FLOODS AND VULNERABILITY

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149

global warming. A study carried out by Yamada (1992) suggests that the retreating rate in the glaciated parts of East Nepal has increased in 1980s as compared to earlier decades. This accelerated retreat closely conforms to obvious rise in temperature in Nepal since the late 1970s (Shrestha et al., 1999 and 2002) (Figure 6.6). Similarly, glacier retreat in Mt. Qomolangma (Mt. Everest in Nepal) has been confirmed by the Sino-American expedition to in 1997; it found that since 1966 to 1998 the Rongbuk Glacier has retreated by 170 m~270 m which implies the global warming trend (Wen et al., 1998). Karma et al. (2003) reveals that the percentage of glaciers retreating in India and Bhutan Himalayas has been between 87% to 100%, while that in East Nepal is 57.3% (Table 6.4). Recent data shows that the average glacier retreat rate in Bhutan is higher, about 30 years higher than in East Nepal (Table 6.5). According to Karma et al. (2003) in Bhutan, the total areal shrinkage from 1963 to 1993 for 66 debris free glaciers is 8% of the initial total and the shrinkage rate of small glaciers were higher than those of the larger glaciers.

Fig. 6.6 Annual temperature trends for Trans-Himalaya and Himalaya (Modified from Shrestha et al., 1999). Table 6.4 Tongue activity of the glaciers in the Himalayas

Region

Kashmir Himachal Gharwal East Nepal Sikkim Bhutan Arunachal

Number of Glaciers

Retreat (%)

Stationary (%)

Advance (%)

17 52 177 485 255 103 62

100 96.2 97.7 57.3 99.6 87.3 96.8

0 1.9 2.3 34.9 0.4 12.7 3.2

0 1.9 0 7.8 0 0 0

Source: Karma et al., 2003.

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GLACIER LAKE OUTBURST FLOODS AND VULNERABILITY

Across the Himalayas, global warming is real, and so is the impact. According to the DHM, the temperature is annually rising at the rate of 0.12oC in the Nepal Himalayas, while the warming rate for the mid-hills and the Tarai of the country stands at 0.03oC and 0.06oC, respectively. Table 6.5 Average rate of glacier retreat in Nepal and Bhutan

Region

Period (Years)

Nepal

33 (1959-1992) 30 (1963-1993)

Bhutan

Variation (retreat) Rate (m/yr) Vertical Horizontal

No. of Glacier

1.72

6.61

58

2.23

7.36

86

Source: Karma et al., 2003.

One study suggest that since the Little Ice Age the total glaciated area in Southeastern Tibetan Plateau has been reduced to equivalent of 50 of glacier at present (Zhen and Feng, 2000). According to IPCC average air temperature in 2100 AD will be higher than that during the 20th century, with a 2.1 K rise in monsoonal temperate glacier area in the Southeastern Tibet. It is predicted that monsoon temperate glacier area will be reduced 75% as compared with the glaciers at present. This implies that most temperate monsoonal glacier (Zhen and Feng, 2000) and the Himalayan glaciers in the temperate and sub-tropical location is likely to face the same fate. It has been reported that across the Himalayas in Nepal, global warming is real and it is evidence by the annual warming rate of 0.12oC in the Nepal Himalayas which is higher than that for the lower hills and plains. The rise in temperature implies that glacier will retreat which will result in the formation of glacier lakes and there is a possibility of their catastrophic outburst, causing significant environmental hazards in many Himalayan valleys (Mool et al., 2001). History of the development processes glacier lakes such as Imja and Tsho Rolpa in Nepal, Raphstreng Tsho Lake, Thorthormi Tsho Lake and Lugge Tsho Lake (Yamada, 1993; WECS, 1993a). Ageta et al. (1999) reveal that these lakes have grown considerably bigger in size in the 80s which coincides with the pronounced warming trend since the mid-70s. However, there still exist some anomalies of glacier expansion in some regions, so, linking glacier retreat and glacier formation with global warming is still in premature stage, and therefore, it has to verified by more investigations. It has to be reckoned that some of this warming is part of a natural climatic cycle and the GLOF events in 1964, 1970-1972, 1981-1982 and 1988 (Fig. 6.7) in Tibetan Himalayas coincide roughly to 9-year or 10-year periodicity of climatic variation in temperature and precipitation (Xu and Quingua, 1994). 6.6

CONCLUDING REMARKS

GLOFs have been the common geomorphic extreme events in Nepal and Bhutan since long. But in recent time these have become a serious threat to socio-economy and infrastructures in downstream as manifested by the recent events’ impact. The growing population and the expanding infrastructures such as road, bridges and many existing and proposed ambitious hydropower projects in the river valleys capped by such glacier lakes, have definitely increased the menace of the GLOF hazards in Nepal and Bhutan. This threat implies a serious challenge to the development endeavors.

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Fig. 6.7 Relation of outburst events with climate change by Xigatze climate stations, Tibet. Cartographic reproduction by Xu and Quingua, 1994.

In recent time GLOF events concur with the glacier retreat which again proximate the global warming trend, although this is yet a hypotheses which has to be verified with many scientific studies which will take place in the future. Nevertheless, with the research experience, so far, it has been able forewarn the people as an indicative of glacier retreat which implies a serious concern for water availability for food security, energy availability, and industries in the downstream. A few commendable studies on Himalayan glaciers (since 1970s) and GLOFs (since late 1980s) has been carried out, but these studies are confined to certain region and are based on short observations, limited temporal data on hydro-meteorology and the indirect evidences. Therefore, it is still early to come up with firm conclusions about the state-of-affairs. However, these studies have made the government and the community at large aware of the risk of GLOFs, implicated glacier retreat, and climate change; and has urged to look for better adaptation measures.

REFERENCES Ageta, Y., and Iwata, S.: The Assessment of Glacier Lake Outburst Flood (GLOF) in Bhutan, Report of Japan-Bhutan Joint Research 1998. Japan/Bhutan: Institute of HydrosphericAtmospheric Sciences of Nagoya University, Department of Geography of Tokyo Metropolitan University, and Geological Survey of Bhutan, 1999. BC Hydro: Final Project Completion Report. Tsho Rolpa, GLOF Warning System Project, Kathmandu, Nepal, 1998. Budhathoki, K. P., Dongol, B. K., Devkota, L. P., Dhital, N. P., Joshi, S. R., Maskey, P. R., Damen, M. van Westen, C. J., Supervisors: Aerospace Survey and GIS for GLOF Hazard Zonation, Rolwaling and Tamakosi Valleys, Dolakha District, Nepal. Field Work Report Submitted as a Partial Requirement of the Special Postgraduate Diploma Course on ‘Mountain Hazard Zonation in the Himalayas with Emphasis on GLOF’ (September 5, 1995 to July 4, 1996) to the ITC, The Netherlands, 1996. Chikita, K., Yamada, T., Sakai A. and Ghimire, R. P.: ‘Hydrodynamic Effects on the Basin Expansion of Tsho Rolpa Glacier Lake in the Nepal Himalaya’. Bulletin of Glacier Research (Data Center for Glacier Research, Japanese Society of Snow and Ice) 15 (1997), pp.59-69.

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Damen, M.: Study on the Potential Outburst Flooding of Tsho Rolpa Glacier Lake, Rolwaling Valley, East Nepal, The Netherlands: Netherlands-Nepal Friendship Association, International Institute for Aerospace Survey and Earth Sciences, ITC, 1992. Department of Hydrology and Meteorology (DHM): Thulagi Glacier Lake Study, Final Report. DHM, HMG/N in Cooperation with Federal Institute for Geo-Sciences and Natural Resources (BGR), Hannover, Germany, 1997b. Department of Hydrology and Meteorology (DHM): Tsho Rolpa GLOF Risk Reduction Project, Formulation Mission Final Report, 1997c. Department of Hydrology and Meteorology (DHM): Tsho Rolpa GLOF Risk Reduction Project, Implementation Report, 1998b. Department of Hydrology and Meteorology (DHM): Climatological Records of Nepal, 1995–1996, Nepal: DHM, HMGN, 1999a. Department of Hydrology and Meteorology (DHM): Tsho Rolpa GLOF Risk Reduction Project, Design Build and Project Management Contracts, Quarterly Progress Report No. 5, 2000. Dorji, Y.: Glaciers and Glacier Lakes Feeding Phochhu and the Risk Associated with These Lakes, Bhutan: Geological Survey of Bhutan, 1996a. Dorji, Y.: Lunana Mitigatory Project. A Preliminary Assessment of the Risk of Flood, Bhutan: Division of Geology and Mines, 1996b. Dwivedi, S. K., Acharya, M. D. and Joshi, S. P.: ‘Preliminary Report on the Tam Pokhari GLOF of September 3rd, 1998’. WECS Bulletin 10(1) (1999), pp.11-13. Fushimi, H., Ikegami, K., Higuchi, K. and Shankar, K.: ‘Nepal Case Study: Catastrophic Floods’. IAHS Publication 149 (1985), pp.125-130. Fushimi, H., and Ohata, T.: ‘Fluctuations of Glaciers from 1970 to 1978 in the Khumbu Himal’, Special Issue in Seppyo. Journal of the Japanese Society of Snow and Ice 41(4) (1980), pp.67-70. Galey, V. J.: Glacier Lake Outburst Flood on the Bhote/Dudh Kosi, August 4, 1985, WECS Internal Report, Kathmandu, WECS, 1985. Gansser, A.: ‘Lunana: The Peaks. Glaciers and Lakes of Northern Bhutan’, In The Mountain World 1968/1969 (1970), pp.117-131. Geological Survey of Bhutan: Glaciers and Glacier Lakes in Bhutan, Vols 1 and 2. Thimpu, Bhutan: Geological Survey of Bhutan, Ministry of Trade and Industry, Royal Government of Bhutan, 1999. Hammond, J. E.: Glacier Lake in the Khumbu Region, Nepal: An Assessment of the Hazards, MA Thesis, Boulder, USA: Department of Geology, University of Colorado, 1988. Hanisch, J., Delisle, G., Pokhrel, A. P., Dixit, A. M., Reynolds, J. M. and Grabs, W. E.: ‘The Thulagi Glacier Lake, Manasulu Himal, Nepal-Hazard Assessment of a Potential Outburst’. In D. Moore and O. Hungr (eds), Proceedings of Eighth International Congress International Association for Engineering Geology and the Environment, 21-25 September, Vancouver, Canada, 1998, pp.2209-2215. Häusler, H., Leber, D., Schreilechner, M., Morawetz, R., Lentz, H., Skuk, St., Meyer, M., Janda, Ch. and Burgschwaiger, E.: Final Report of Raphstreng Tsho Outburt Flood Mitigatory Project (Lunana, Northwestern Bhutan): Phase II, Vienna, Austria: Institute of Geology, University of Vienna, 2000. International Hydrological Association (IHA): www.hydropower.org/CountryReports/Nepal/ 9_2.Nepal_4.htm, 2000. Higuchi, K., Fushimi, H., Ohatga, T., Iwata, S., Yokoyama, K., Higuchi, H., Nagoshi, A. and Iozawa, T.: ‘Preliminary Report on Glacier Inventory in the Dudh Kosi Region’, Special Issue in Seppyo. Journal of the Japanese Society of Snow and Ice 40 (1978), pp.71-77. Ives, J. D.: Glacier Lake Outburst Floods and Risk Engineering in the Himalaya, Occasional Paper No. 5, Kathmandu: ICIMOD, 1986. Karma, T., Ageta, Y., Naito, N., Iwata, S. and Yabuki, H.: Glacier Distribution in the Himalayas and Glacier Shrinkage from 1963 to 1993 in the Himalayas, Bulletin of Glaciological Research. Japanese Society of Snow and Ice 20 (2003), pp.29-40.

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Kettelmann, R. and Watanabe, T.: ‘Approaches to Reducing the Hazard of an Outburst Flood of Imja Glacier Lake, Khumbu Himal’. In S. R. Chalise and N. R. Khanal (eds), Proceeding of the International Conference on Ecohydrology of High Mountain Areas, Kathmandu, ICIMOD, Nepal, 24-28 March, 1998, pp.359–366. Modder S. and van Olden, Q.: Geo-Technical Hazard Analysis of a Natural Moraine Dam in Nepal, Interim Report, The Netherlands: Free University of Amsterdam, 1995. Modder, S. and van Olden, Q.: Engineering-Geomorphological Analysis of Moraine Dam in the Nepal Himalayas. A Detail Survey (scale 1:1500) at Tsho Rolpa Glacier Lake, Rolwaling Valley, Dholakha District, East Nepal, Part 1: Text. MSc Thesis, The Netherlands: Faculty of Earth Sciences, Vrije Universiteit Amsterdam, 1996a. Modder, S. and van Olden, Q.: Engineering-Geomorphological Analysis of Moraine Dam in the Nepal Himalayas. A Detail Survey (scale 1:1500) at Tsho Rolpa Glacier Lake, Rolwaling Valley, Dholakha District, East Nepal, Part 2: Appendices. MSc Thesis, The Netherlands: Faculty of Earth Sciences, Vrije Universiteit Amsterdam, 1996b. Modder, S. and van Olden, Q.: Preliminary Presentation of Geotechnical Data and Maps (Separate) of the Tsho Rolpa End Moraine Complex, The Netherlands: Vrije Universiteit Amsterdam, 1996c. Mool, P. K.: ‘Glacier Lake Outburst Floods in Nepal’. Water and Energy Commission Secretariat (WECS) Bulletin 4(1) (1993), pp.8-11. Mool, P. K.: ‘Glacier Lake Outburst Floods in Nepal’. Special Issue in Journal of Nepal Geological Society 11 (1995a), pp.273-280. Mool, P. K., Bajracharya, S. R. and Joshi, S. P.: Inventory of Glaciers, Glacier Lakes and Glacier Lake Outburst Floods, Monitoring and Early Warning Systems in the Hindu Kush Himalaya Region, ICIMOD/UNEP, Kathmandu, Nepal, 2001. Mool, P. K., Wangdap, W., Bajracharya, S. R., Kunzang, K., Gurung, D. R. and Joshi, S. P.: Inventory of Glaciers, Glacier Lakes and Glacier Lake Outburst Floods, Monitoring and Early Warning Systems in the Hindu Kush Himalaya Region, ICIMOD/UNEP, Kathmandu, Nepal, 2001. Nepal Electricity Authority (NEA): Report on the Field Trip to the Lower Barun Glacier Lake on April 17th, 1995. Arun III Hydroelectric Project, Detailed Engineering Services, Joint Venture Arun III Consulting Services, Lahmeyer International, Energy Engineering International, and Electric Power Development Company Ltd., 1995. National Environmental Commission: A Brief Report on the Expedition to Roduphu and Sichhe Glacier Lakes in the Headwaters of Mo-Chhu, Gasa Dzongkhag, Thimpu, Bhutan: Division of Roads, Geology and Mines Division, Survey of Bhutan, National Environment Commission, 1996. Reynolds, J. M.: Geo-Sciences Ltd.-Hazard Assessment at Tsho Rolpa, Rolwaling Himal, Northern Nepal, Technical Report No: J9402.002 submitted to WECS, Kathmandu, Nepal, 1994. Röthlisberger, F. and Geyh, M. A.: ‘Glacier Variations in Himalayas and Karakorum’. Zeitshrift für Gletscherkunde und Glazialgeologie 21 (1985), pp.237-249. Shrestha, K. L., Shrestha, M. L., Shakya, N. R. and Ghimire, M. L.: Country Paper on Regional Background and National Case Studies, Water Resource in South Asia: An Assessment of Climate Change - Associated Vulnerabilities and Coping Mechanisms, Asia Pacific Network, Fred J. Hansen Institute for World Peace, ASIANICs, START, 2002. Shrestha, A. B., Wake, C. P., Mayewski, P. A. and Dibb, J. E.: ‘Maximum Temperature Trends in the Himalaya and Its Vicinity: An Analysis Based on Temperature Records from Nepal for the Period 1971-1994.’ Journal of Climate 12 (1999), pp.2775-2787. Vuichard, D. and Zimmerman, M.: ‘The Langmoche Flash Flood, Khumbu Himal, Nepal’. Mountain Research and Development 6(1) (1986), pp.90-94. Vuichard, D. and Zimmerman, M.: ‘The 1985 Catastrophic Drainage of a Moraine Dammed Lake, Khumbu Himal, Nepal: Cause and Consequence’. Mountain Research and Development 7(2) (1987), pp.91-110. Watanabe, T., Ives, J. D. and Hammond, J. E.: ‘Rapid Growth of a Glacier Lake in Khumbu Himal, Himalaya: Prospects for a Catastrophic Flood’. Mountain Research and Development 14(4) (1994), pp.329-340.

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Watanabe, T., Kameyama, S. and Sato, T.: ‘Imja Glacier Dead-Ice Melt Rates and Changes in a Supra-Glacier Lake, 1989-1994, Khumbu Himal, Nepal: Danger of Lake Drainage’. Mountain Research and Development 15(4) (1995), pp.293-300. Water and Energy Commission Secretariat (WECS): Study of Glacier Lake Outburst Floods in the Nepal Himalayas, Phase I, Interim Report, May 1997, WECS Report No. 4/1/200587/1/1, Seq. No. 251, Kathmandu, Nepal: WECS, 1987b. Water and Energy Commission Secretariat (WECS): Interim Report on the Field Investigation on the Tsho Rolpa Glacier Lake, Rolwaling Valley, WECS Report No. 3/4/021193/1/1, Seq. No. 436, Kathmandu, Nepal: WECS, 1993a. Water and Energy Commission Secretariat (WECS): Report for the Field Investigation on the Tsho Rolpa Glacier, Rolwaling Valley, February 1993-June 1994, WECS N551.489 KAD, Kathmandu, Nepal: WECS, 1994. Water and Energy Commission Secretariat (WECS): Preliminary Report on the Thulagi Glacier Lake, Dhana Khola, Marsyangdi Basin, WECS Report No. 473, Seq. No. 2/3/170795/1/1, Kathmandu, Nepal, 1995c. Water and Energy Commission Secretariat (WECS): Study and Topographic Mapping of Lower Barun Glacier Lake, Vol 1., Kathmandu, Nepal, 1997. Xu, Daoming: Characteristics of Debris Flows Caused by Outbursts of Glacier Lakes in Boqu River in Xizang, China, 1981, Lanzhou Institute of Glaciology and Cryopedology, Academia Sinica, 1985. Xu, Daoming and Quingua, F.: Dangerous Glacier Lakes and Their Outburst Features in the Tibetan Himalayas. Data Center for Glacier Research, Japanese Society of Snow and Ice. Bulletin of Glacier Research 12 (1994), pp.1-8. Yamada, T.: Report for the First Research Expedition to Imja Glacier Lake - 25 March to 12 April 1992, WECS Report No. 3/4/120892/1/1, Seq. No. 412, Kathmandu, Nepal: WECS/JIC, 1992. Yamada, T.: Glacier Lakes and their Outburst Floods in the Nepal Himalaya, Kathmandu, Nepal: WECS, Kathmandu, Nepal, 1993. Yamada, T.: Glacier Lake and Its Outburst Flood in Nepal Himalaya, Data Center for Glacier Research, Japanese Society of Snow and Ice, Tokyo, Monograph No. 1, 1998. Yang, Z.: Problems of Recent Activities of Debris Flow and Prevention in Xixang, China. Proceeding of First National Congress on Debris Flow Harness (1981), Sichuan Press, Beijing, 1982, pp.92-105. Watanabe, T. and Rothacher, D.: The 1994 Lugge Tsho Glacial Lake Outburst Flood, Bhutan Himalaya. Mountain Research and Development 16(1) (1996), pp.77-81. Wen, R. J., He, Q. D. and Fan, J. Z.: Climatic Warming Causes the Glacier Retreat in Mt. Qomunglongma, C.A.S., Science Press Beijing. Journal of Glacaiology and Geocryology 20(2) (1998). Zhen, S. and Feng, S.: Response of Monsoonal Temperate Glacier in China to Global Warming Since Little Ice Age, C.A.S., Science Press Beijing. Journal of Glacaiology and Grycryology 22(3) (2000).

7 Climatic Change - Implications for India’s Water Resources M. LAL

7.1

BACKGROUND

Water is vital to all forms of life on earth, from the simplest of living organisms to the most complex of human systems. Lack of freshwater to drink, for use in industry and agriculture and for multitude of other purposes where water is essential, is a limiting factor - perhaps the most important factor - hindering development in many parts of the globe. In South Asia, increasing water shortage and declining water quality from pollution during the past few decades has drawn attention to the inherent fragility and scarcity of water and led to concern about water availability to meet the requirements of the 21st century. Because of increasing population and changing patterns of water use in South Asia, additional demand is likely to be accompanied by a sharp decline in per capita water availability. While consumption of 1,000 m3 of water per year and per capita is considered a standard for “well-being” in the developed world, projection of annual water availability per capita by the year 2025 for South Asia is a mere 730 m3. This trend is declining in all parts of the world, including those that are considered to have ample water resources. With the growing recognition of such issues as the possibility of global climate change, an increasing emphasis on the assessment of future availability of water on various spatial and temporal scales is needed. A warmer climate will enhance the hydrological cycle, which implies higher rates of evaporation, and a greater proportion of liquid precipitation compared to solid precipitation; these physical mechanisms, associated with potential changes in precipitation amount and seasonality, will affect soil moisture, ground water reserves and the frequency of flood or drought episodes. The supply of water is limited and governed by the renewal processes associated with the global hydrological cycle. Future projections of changes in monsoon rainfall patterns are tenuous in currently available global climate models. Moreover, it has been recognized now that the superimposed modes of climatic variability (e.g., El Niño and Southern Oscillation), which can disturb mean rainfall patterns on timescales ranging from seasons to decades, are important mechanisms to take into account but are not well predicted by the global climate models. Water resources will come under increasing pressure in South Asia due to the changing climate. Changes in climatic conditions will affect demand, supply and water quality. In regions that are currently sensitive to water stress (arid and semi-arid regions of

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India), any shortfall in water supply will enhance competition for water use for a wide range of economic, social and environmental applications. In the future, larger population will lead to heightened demand for irrigation and perhaps industrialization at the expense of drinking water. Disputes over water resources may well be a significant social consequence in an environment degraded by pollution and stressed by climate change. 7.2

INDIA’S GEOGRAPHY, POPULATION AND WATER NEEDS

India is a land with diverse geographical and climatic endowments. This large expanse of land (with 328 mha gross area) is bounded by the Himalayan range in the North and the sea on three sides encompasses varied geographical and climatic zones ranging from the hot desert of Thar in the Northwestern corner to the cold desert of Ladakh in the extreme North, the arid region of the Rann of Kutch in the West to the world’s wettest place, Cherrapunji, in the Northeast (Fig. 7.1). With the icy continent of Antarctica as its major neighbor to the South with vast stretch of the Indian Ocean in between, India has the world’s tallest wall (the Himalayas) on its Northern boundary. Adjoining the Himalayas further North is the Tibetan plateau that is large, massive and about 5 km high - a gigantic slab of rock protruding up to the middle of the troposphere and acting as a large sized heat source at the mid-tropospheric level. Physiographically, India comprises of seven regions, viz., (1) Northern Mountains (the Himalayas), (2) Indo-Gangetic Plains, (3) Central Highlands, (4) Peninsular Plateau, (5) East Coast, (6) West Coast, and (7) the Islands (Andaman & Nicobar group in the Bay of Bengal and Lakshadweep group in the Arabian Sea). India also has the world’s largest estuary and mangroves, the Sundarbans in the East and biologically rich mountain ranges of the Western Ghats along its West Coast. Apart from this, India is a home to a billion people that is projected to increase to 1.7 billion by 2050 according to the high scenario assuming a fertility rate of 2.1%. The surface water and ground water resources in India play vital roles in agriculture, fisheries, livestock production, forestry, and industrial activity. Water and agriculture sectors in India are likely to be most sensitive to monsoon rainfall. There have been considerable spatial and temporal variations in rainwater availability in recent years as a result of observed swing in the onset, continuity and withdrawal patterns of monsoon. The pace of the green revolution seems to have started slowing down due to immense pressure on India’s land and water resources and indiscriminate addition of restorer inputs such as inorganic fertilizers, pesticides etc. and their inefficient use. Agriculture’s share in Gross Domestic Product (GDP) of India has also declined recently, thus marking a structural shift in the composition of the GDP. Though traditionally, agriculture accounted for two-fifths of the GDP; it accounted for only 31% in 1990-1991 and 25% in 2001 (ADB, 2003). India’s GDP has shown robust growth (never less than 5% since 1990-1991) which suggests that non-agricultural sectors (particularly the service sector) have grown at the expense of agriculture. However, as in all developing countries, about 72% (2001 Census) of India’s population still lives in rural areas. The main source of income for this majority is either directly or indirectly dependent on agriculture. Hence agricultural progress and stability, which has strong links to availability of water resources, holds the key to rural and agrarian prosperity in India. 7.2.1

KEY DEVELOPMENT SECTORS AND WATER SOURCES

In spite of a spurt in industrial growth and activity in the last 30 years, the livelihood of millions of people in rural India is still drawn from the agriculture sector. Besides this,

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there are also major linkages between agriculture and industry. Agriculture supplies the raw materials for employment-intensive industries. It stimulates and sustains industrial output through rural household demands for consumer goods and services. It influences industry through government savings and public investment. Besides irrigation supplies, large water reservoirs are also required to generate hydropower. But unlike irrigation the consumptive use of water in this sector is mainly limited to the evaporative losses. Many of the large reservoirs like Bhakra, Hirakud, Nagarjunasagar, Koyna, Pong, Rihand, Srisailam and Idduki are excellent examples of providing hydropower to the nation and have ushered the economic growth and prosperity to the region. 70 35

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Fig 7.1 Topographic Map of India.

The agricultural output is primarily governed by the availability of water making the country’s agrarian economy sensitive to the status of water resources and the monsoon in particular. As the monsoons serve not only as a sole provider of water to large areas of

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IMPLICATIONS FOR INDIA’S WATER RESOURCES

rainfed cultivation but also remain the primary source of water to recharge the ground water resources of the country. The demands on the water resources in the country, by the several sectors are not surprisingly dominated by the agriculture sector. In the year 1999, agriculture consumed 85.3% of the water, industry 1.2%, energy sector 0.3% and other sectors 6.4% whereas domestic consumption was 6.6% (GOI, 2000). The two sources of freshwater are ground water and surface water; of these the river basins represent the main source of freshwater in the Indian subcontinent. India is giftedwith a river system involving over 20 major rivers with many tributaries. The total annual discharge in the rivers that flow in various parts of the country amounts to 1,880 km3yr-1 (CWC, 1995). Many of these rivers are perennial though few are seasonal. The large rivers such as the Indus, the Ganges and the Brahmaputra have their origin in the Himalayas and flow throughout the year though their flows significantly reduce during the lean summer period (March to May). The Himalayan snow and ice supports three main river systems viz., Indus, Ganges and Brahmaputra having their average annual stream flow of 206 km2, 488 km2 and 510 km2 respectively. Thus, more than 50% of water resources of India are located in various tributaries of these three river systems (Fig. 7.2). Average water yield per unit area of the Himalayan Rivers is almost double that of South Peninsular river system indicating the importance of snow and glacier melt contribution from high mountains. The average intensity of mountain glaciations varies from 3.4% for Indus to 3.2% for Ganges and 1.3% for Brahmaputra. The tributaries of these river systems show maximum intensity of glaciations (2.5% to 10.8%) for Indus followed by Ganges (0.4% to 10%) and Brahmaputra (0.4% to 4%); the average annual and seasonal flows of these systems give a different picture thereby demonstrating that the rainfall contributions are greater in the Eastern region while the snow and glacier melt contributions are more important in the Western and Central Himalayan region. Most of the rivers in South Peninsular India like the Cauvery, the Narmada and the Mahanadi are fed through ground water discharges (base-flow) and are supplemented by the monsoon rains. Therefore, these rivers have very limited flow during the non-monsoon period. The importance of these rivers lies not just in the size of their basins but also on the quantity of water they can carry. The flow rate in these rivers is independent of the water source of the river and depends upon the precipitation rate in the region. Therefore, in spite of being smaller in size, the rivers flowing West have a higher flow rate due to higher precipitation over that region. Apart from the rivers, the Indian subcontinent is covered by a number of reservoirs, lakes, wetlands, mangroves and ponds. During lean season, these reservoirs are the key source of water. For example, a large dam in Mettur over Cauvery River has a 40 m high reservoir with a storage capacity of about 10 km3. The amount of water stored here during the monsoon season is released for irrigation under controlled conditions during the dry period. Even though various types of freshwater bodies are widely distributed across the Indian subcontinent, still the availability of drinking water suggests skewed distribution of actual supply. These water bodies regulate both the quantity and quality of water in addition to supporting the biota of various species. The importance of these water bodies is apparent from the fact that in the thirteen States, which experienced frequent floods and drought in the last few years, 50% of the areas of those States are prone to periodic droughts possibly due to the shrinking or vanishing of these water bodies. Many lakes in Rajasthan (including the largest lake in Udaipur) have been heavily silted and the water levels in the Krishnaraja reservoir in Tamil Nadu on the river Cauvery has gone down recently due to lack of water input from the upstream region. Table 7.1 presents an overview of the storage capacity of various reservoirs in India.

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Fig. 7.2 Major Rivers of India. Table 7.1 Water storage capacity of reservoirs in India

Reservoir’s storage at the end of monsoon 1998 1999 Reservoirs (number) Designed capacity (km3) Storage (km3) Average of last 10 years (km3) Current year’s storage as % of designed storage % of this year’s capacity to last 10 years

68 129 106 101

68 129 95 104

82

74

106

92

The ground water resources of the country are also vast. Ground water acts as a regulating mechanism for storing water during wet season and thus it complements surface storage, which being location-specific may not be available. The ground water level in the marshy and swampy Terai region of the Himalayas, the Northern most stretch of the Ganges basin, is only 2 m-3 m below the ground surface, but it goes down drastically to 15 m-30 m below the surface in certain parts of the river basin. The amount of freshwater that exists in

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IMPLICATIONS FOR INDIA’S WATER RESOURCES

this unconfined aquifer is massive and has not been brought into utilization in any systematic manner. In fact, a good part of the dry season flow in the river system is augmented by the flow back of the ground water from the unconfined aquifers in the area adjoining the Ganges and its tributaries. The deep artesian aquifers underlying millions of acres of alluvia and deltaic cropland in the Ganges basin are believed to be filled with freshwater to depths as great as 2,000 m. The total replenishable ground water resource available in India is currently estimated to be 45.22 million hectare meter/year (mha m/yr). Of this quantity, 6.933 mha m/yr may be used for drinking and industrial purposes while the rest can be used for irrigation. Interestingly, almost 80% of domestic water requirement in India today is met from ground water sources. However, the ground water resources in several States of India are fast getting depleted primarily due to over extraction and poor recharging facility. 7.2.2

THE NEED FOR SUSTAINABLE DEVELOPMENT OF WATER RESOURCES

Despite the presence of substantial reserves of water in India, the actual utilizable quantity is limited and water crisis is seen to be inevitable in the future. The annual quantity of freshwater including ground water available in India is currently about 1.88 km3 (CWC, 1995). This puts the per capita availability to be about 2,000 m3 i.e., 2x109 liters per person per year and this quantity is further expected to drop to 1,480 m3 in the next decade due to increase in population coupled with no further augmentation of water resources and also its consequent decrease over the same time due to consumption. India will reach a state of water stress before 2025 when the availability falls below 1,000 m3. This clearly indicates the ‘two sided’ effect on water resources - the rise in population will increase the demand of water leading to faster withdrawal of water and this in turn would reduce the recharging time of the water tables. As a result, availability of water is bound to reach critical levels sooner or later. In this regard the emerging disputes are already indicative of what can be expected in the future. Fights over water have already broken out in between States (Cauvery issue, Narmada problem, Krishna water disputes). Disputes between nations also already exist over sharing of river water between India and Bangladesh over the Ganges water and India and Pakistan over the Indus water. Water riots have also been reported in Bhavnagar and Rajkot in Gujarat (Ramakrishnan, 1998). This makes it imperative to draw out appropriate action plans and strategies to conserve our water resources and optimize utilization of water from the various water sources. 7.3

CLIMATE OF INDIA

7.3.1

PRESENT CLIMATE AND ITS SPATIAL DIVERSITY

India, a country of subcontinental size, is the largest peninsula in the world and is surrounded by seas on the three sides with an extensive coastline of about 6,000 km. Climatologically, India covers the tropical, sub-tropical and the temperate regimes. The country is divided into almost two equal halves by the Tropic of Cancer. The Northern half cutoff from the rest of the continent by the Himalayan range, experiences temperate type of climate whereas the extreme Southern part of the country falls within the tropical latitudes. The inner Himalayas present sub-polar conditions registering extremely low and even negative temperatures in winter due to the altitude effect while the presence of the seas on all three sides brings the Southern Peninsular India under direct maritime influence with low diurnal temperature differences and a very moderate climate. The interior of the

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country experiences a continental type of climate with extreme annual temperature swings. The summer temperatures over this region soar and often go beyond 40oC while the temperature in winter drops radically. India’s unique geographical configuration gives it the peculiar climate regime with four seasons. Winter season covering the months of December, January and February is followed by the summer (pre-monsoon) season extending from March to May. India comes under the sway of the Southwest monsoon season from June to September and then goes through post-monsoon season from October to November. The basic driving force behind the monsoons is the thermal contrast between the land and the sea. During the pre-monsoon, as the sun progresses Northwards, a simultaneous shift in the converging zone of the trade winds of the two hemispheres (ITCZ) occurs to the North of the geographical equator. The Southeasterly trades blowing in from the Southern Hemisphere get deflected to the right as they enter the Northern Hemisphere and blow into the subcontinent from the West Coast bringing with it moisture from the adjoining seas. This marks the advent of the Southwest monsoon over the subcontinent. The point of first entry of the monsoon in India is the Kerala Coast. These Southwesterlies bring rain throughout the country, mainly to the South of the monsoon trough. As the Southwest monsoon winds blow over peninsular India they collect more moisture from the Bay of Bengal and, on striking the Himalayan range in the North, get deflected Westward. These deflected Southeasterly trades bring rains to the Northern half of the country. As the summer monsoon enters from the Southwestern corner of the country, it moves progressively North and by 15th of July, it covers the entire Indian subcontinent (Fig. 7.3). The monsoon circulation over the subcontinent is associated with several synoptic scale events such as the development of the heat low over Rajasthan in the Northwest India during the pre-monsoon season, the Tibetan high occurring over the Tibet plateau, the Mascarene high off the coast of Madagascar and the weakening of the sub-tropical Westerlies over North India with the subsequent onset of the tropical Easterly jet stream over the peninsular India. Towards the end of the monsoon, as the sun begins its journey Southward the monsoon starts withdrawing. This event is heralded by the reinforcement of the sub-tropical Westerlies over North India. The Easterly jet disappears rapidly with the recession of the monsoons. As the Westerly jet stream re-establishes itself South of the Himalayas, winter rains start to the Southeast coast near Tamil Nadu in India. This is known as the Northeast or the winter monsoon. During the winter months, rain also occurs over North India due to the Southward shift of the polar fronts. Frontal or extratropical cyclones developing over West Asia and the Mediterranean Sea pass through North India during its passage Eastward. The presence of the Himalayas weakens these disturbances and the temperature contrast of the air masses is also somewhat reduced because of which the frontal characteristic of these extra-tropical cyclones is not clearly evident. Since these disturbances have their origin in the West, the rains which result over North India is said to be due to the Western disturbances. The long-term average annual rainfall for the country as a whole is 116 cm - the highest for a land of comparable size in the world. But this rainfall is highly variable both in time and space. The percentage areal distribution of annual rainfall over India is given in Table 7.2 below. The rainfall is highly variable in time as well. The maximum rainfall occurs in July and August during the four-month (June to September) Southwest monsoon season. There are considerable intra-seasonal and inter-seasonal variations as well. The summer monsoon rainfall oscillates between active spells with good monsoon (above normal) on all India basis and weak spells or the breaks in the monsoon rains when

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deficient to scanty (≤20%) rains occur on all India basis for a few days at a stretch. Weak and active spells of the summer monsoon is determined by the position of the monsoon trough extending from the Northwestern end over the Rajasthan desert to the head Bay of Bengal. The monsoon trough oscillates either South or North of this normal position over the Gangetic plains. When the trough is to the South or close to the normal position, active spells result and when it is near the foothills, weak monsoon conditions prevail. The average seasonal summer monsoon rainfall of India is about 85 cm with a standard deviation of about ±10%. Orissa, East Madhya Pradesh, West Bengal, and the Northeastern States of India, the Western coast and the Ghats receive more than 100 cm of rainfall during this season. The submontane region extending from North Bihar to Jammu also receives more than 100 cm of rainfall. The heavy rainfalls in the Northeastern States, West coast and the Ghats and the submontane regions are influenced by the orography. The peninsular India South of 15oN gets less than 50 cm rainfall. The lowest rainfall is received in the extreme Southeast Peninsula. The West and the Northwest regions of the country receive about 50 cm of rain in the season. The rainfall decreases rapidly to less than 10 cm in the West Rajasthan. Regions above 50 cm in the season are classified as wet and those less than 50% as dry parts of India.

Fig. 7.3 The onset and withdrawal dates of the Southwest monsoon.

The two monsoon seasons (the Southwest monsoon in June to September and the Northeast monsoon in November -December bring forth rains - many a times in intensities and amounts sufficient to cause serious floods creating hazardous (and often disastrous) situations. Moreover, cyclonic storms in the pre-monsoon months (April-May) and the

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post-monsoon months (October-November) cause large-scale inundation, destruction and deaths. In fact, floods and cyclones are the two major natural disasters, which visit India quite often. The adverse impacts of these two natural disasters cannot be assessed merely in economic terms based on destruction of crops, property and infrastructure because the toll of human misery in the form of death, disease, injury, loss of employment, psychological trauma, and above all the set-back to development are too difficult to evaluate. Table 7.2 Areal Distribution (%) of Annual Rainfall over India

Mean Annual Rainfall

Corresponding % Area

0 - 75 cm 75 - 125 cm 125 - 200 cm > 200 cm

30 % 42 % 20 % 8%

An annual mean global warming of 0.4°C to 0.8°C has been reported since the late 19th century (IPCC, 2001). Surface temperature records indicate that the 1990s have been the warmest decade of the millennium in the Northern Hemisphere and 1998 is the warmest year (Fig. 7.4). The observations also suggest that the atmospheric abundances of almost all greenhouse gases reached their highest values in recorded history during the 1990s (Nakicenovic et al., 2000). Anthropogenic CO2 emissions due to human activities are virtually certain to be the dominant factor causing the observed global warming. In India, the analysis of seasonal and annual surface air temperatures (Pant & Kumar, 1997), using the data for 1881-1997, has shown a significant warming trend of 0.57oC per hundred years (Fig. 7.5). The warming is found to be mainly contributed by the post-monsoon and winter seasons. The monsoon temperatures do not show a significant trend in any major part of the country except for a significant negative trend over Northwest India. Similar trends have also been noticed in Pakistan, Nepal, Sri Lanka and Bangladesh. The rainfall fluctuations in India have been largely random over a century, with no systematic change detectable on either annual or seasonal scale (Fig. 7.6). However, areas of increasing trend in the seasonal rainfall have been found along the West Coast, North Andhra Pradesh and Northwest India and those of decreasing trend over East Madhya Pradesh, Orissa and Northeast India during recent years (Fig. 7.7). The global warming threat is real and the consequences of the climate change phenomena are many, and alarming. The impact of future climatic change may be felt more severely in developing countries such as India whose economy is largely dependent on agriculture and is already under stress due to current population increase and associated demands for energy, freshwater and food. In spite of the uncertainties about the precise magnitude of climate change and its possible impacts particularly on regional scales, measures must be taken to anticipate, prevent or minimize the causes of climate change and mitigate its adverse effects. 7.3.2

IMPACT OF GLOBAL WARMING ON INDIA’S CLIMATE

Besides being the most important determinant of the economic welfare of the country, the monsoon is the predominant source of freshwater required for the rejuvenation of the water resources after the hot spell of the pre-monsoon season. The leading concern today

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1997 1998

1998 1997

1998 1997

1998 1997

1998 1997

1998

1998

1998 1997

1997

0.5

1997

0.6

1998

0.7

1991

0.8

1998 1990

0.9

1995

1998

1.0

1998 1988

Increase over 1880-1998 mean (oC)

is the probable impacts that climate change and global warming might have on the annual cycle of the monsoon and the precipitation pattern. A few of the currently available state-of-the-art Global Climate Models [CCSR/NIES (Japan), CSIRO (Australia), ECHAM (Germany) and UKMO (England) global climate models] have the ability to simulate the monsoon process realistically enough in order to be able to project the plausible regional climate change and its impacts on the long-term cycle of events including monsoons over the subcontinent (Lal & Harasawa, 2000). These models have been run with realistic forcing history for the 20th century and allow direct comparison of the model’s response to the observations. The combination of the warming effects on a global scale from increasing

0.4 0.3 0.2 0.1 0.0 Jan Feb Mar Apr May Jun

Jul

Aug Sep Oct Nov Dec

Month Fig. 7.4 Monthly global mean temperature anomalies in the year 1998 and the previous warmest year. 1.5 Linear Trend = 0.57⬚C/100 yrs

Temperature Anomaly (⬚C)

1

0.5

0

-0.5

-1

5-Point Gaussian Lowpass Filtered

-1.5 1881 1891 1901 1911 1921 1931

No. of stations 1881-1900 ------ 25 1901-1990 ------ 121 1991-1997 ------ 30

1941 1951 1961 1971 1981 1991

Fig. 7.5 All-India Mean Annual Surface Air Temperature Anomalies (1881-1997).

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CO2 and the regional cooling from the direct effect of sulfate aerosols produced a better agreement with observations of the time evolution of the globally averaged warming and the patterns of 20th century climate change. With the possible effects of future changes of anthropogenic aerosols as prescribed in the IS92a emission scenario (~1% per year compound increase of equivalent CO2), the coupled atmosphere-ocean global climate models (A-O GCMs) suggested a global and annual mean warming at 2100 relative to 1990 of between 1oC and 3.5oC (at an average rate of 0.3oC per decade). Rainfall Anomaly (% of Mean)

30

La Nina

20 10 0 -10 -20

El Niño -30 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Years Fig. 7.6 All-India Summer Monsoon Rainfall Anomalies (1871-1999). 100 % Departure from normal

83

80 60

20

45 42

44

40 17 12

37

32

2425 20

15

14

21

1511 15

7

6

2

0 -20

-13

-19-16

-12

-6 -9

Punjab

Haryana, Chandigarh & Delhi

-3

-10 -12-11 -25

-25

-40

-9 -15

Kerala

East Madhya Pradesh

-6 -18

-12 -15 -21 -28

North Eastern States

Fig. 7.7 Recent trends (1991-1998) in monsoon rainfall in selected regions of the Indian subcontinent.

Climate change scenarios for the Indian subcontinent based on an ensemble of results as inferred from the four A-O GCMs (which have demonstrated some skill in simulating the present-day climatology over Indian subcontinent) on annual and seasonal mean basis are presented in Table 7.3. Three future time periods centered around 2020s (2010-2039), 2050s (2040-2069) and 2080s (2070-2099) have been considered here for developing scenarios of changes in surface air temperature and precipitation relative to the baseline period of 1961-1990 over the Indian subcontinent. The projected area-averaged annual

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mean warming is about 2.7oC for the decade 2050s and about 3.8oC for the decade 2080s over the land regions of India as a consequence of increases in atmospheric concentration of greenhouse gases (Lal & Harasawa, 2001). Under the combined influence of greenhouse gas and sulfate aerosols, the surface warming is restricted to only 1.9oC and 3.0oC for the decade 2050s and 2080s, respectively. In general, the projected warming is found to be higher during winter than during monsoon. The A-O GCMs show high uncertainty in future projections of both winter and summer precipitation over the Indian subcontinent (with or without aerosol forcing). The magnitude as well as the sign of projected changes in monsoon rainfall over the region varies significantly among the models. This is largely attributed to complex feedbacks due to differences in treatment of ground hydrology and cloud-radiation interactions in these models. The likely magnitude of mean sea level rise along the Indian Coastline due to thermal expansion of seawater has also been calculated and is included in Table 7.3. Even though the aerosol forcing reduces the surface warming, its magnitude is still considerable and could substantially impact the Indian subcontinent. The inter-model differences over the tropics represent the primary source of uncertainty in regional projections of simulated precipitation changes in current A-O GCMs. In order to predict the changes in the seasonal as well as annual variability of the monsoons in response to increases in radiative forcing of the atmosphere, climate change scenarios over Indian subcontinent under the new SRES ‘Marker’ scenarios have also been developed based on the data generated in more recent numerical experiments with A-O GCM of the CCSR/NIES, Japan (Lal et al., 2001). The new set of emission scenarios covers a wide range of the main demographic, technological, and economic driving forces of future global emissions (Nakicenovic et al., 1998). Four ‘Marker’ scenarios (namely A1, A2, B1 and B2 scenarios) have been identified each of which describes a different world evolving through the 21st century and each of which may lead to quite different greenhouse gas emission trajectories. The scenario B1 projects the most conservative future emission of greenhouse gases while A2 scenario is characteristic of scenarios with higher rates of greenhouse gas emissions in combination with higher sulfur and other aerosol emissions. More recently, the A1 scenario family has been further divided into three groups that describe alternative directions of technological change in the energy system (Nakicenovic et al., 2000). The three A1 groups are distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all sources (A1B). The SRES scenarios exclude the effects of climate change and climate policies on society and the economy (‘non-intervention’). Most of the recent numerical experiments with A-O GCMs, however, have not included all the SRES scenarios as yet. The projections of regional climate change based on these newer sets of emission scenarios of greenhouse gases are likely to be more realistic than the IS92a emission scenario used earlier in transient experiments with A-O GCMs. Over land regions of the Indian subcontinent, the area-averaged annual mean surface temperature rise by 2080s is likely to range between 3.5oC and 5.5oC (least in B1 scenario and maximum in A2 scenario). The area-averaged surface temperature increase during the winter over India by 2080s would be at least 4oC (B1 scenario) and could reach even 6oC (A2 scenario). During summer monsoon, the warming may range between 2.9oC and 4.6oC (Table 7.4). The projected surface warming is more pronounced during winter than during summer monsoon season. The spatial distribution of surface warming as a consequence of increase in anthropogenic radiative forcing (with respect to 1981-1990) suggests that North India may experience an annual mean surface warming of 3oC or more by 2050s, depending upon the future trajectory of anthropogenic forcing. The spatial pattern of

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temperature change has a large seasonal dependency. The model simulates peak warming of about 3oC over North and Central India in winter. Over much of the Southern Peninsula, the warming is likely to be under 2oC during the winter season. The surface temperature rise would be more pronounced over the Northern and Eastern regions of India (~2oC) during the monsoon season. A marginal increase of about 7% to 10% in area-averaged annual mean precipitation is projected over the Indian subcontinent by 2080s (Table 7.4). A decline of between 5% to 25% in area-averaged winter precipitation is likely. During the monsoon season, an increase in area-averaged precipitation of about 10% to 15% over the land regions is projected. Contrary to earlier projections (Lal et al., 1994; 1995), the new simulation experiments suggest appreciable change in spatial pattern of winter as well as summer monsoon precipitation over land regions of the Indian subcontinent. This could be attributed to inclusion of more realistic estimates of regional aerosol concentrations as well as the indirect radiative forcing due to aerosols. A decrease of between 10% and 20% in winter precipitation over most parts of Central India is simulated for 2050s. During the monsoon season, the results suggest an increase of 30% or more in precipitation over Northwest India by 2050s. The Western semi-arid margins of India could receive higher than normal rainfall in a warmer atmosphere. In order to examine the likely changes in intra-seasonal and inter-annual variability in summer monsoon over Central India (land points only confined to latitudes 18oN and 30oN and longitudes 67oE to 90oE) in response to changes in anthropogenic forcing, we have analyzed the simulated daily data for rainfall from 1st May until 30th October (183 days) during each of the 30-year period corresponding to 1970s and 2050s. Figure 7.8 depicts the temporal variation of observed (based on daily rainfall data averaged for 10 Central Indian stations during the period 1966-1990) as well as simulated (1961-1990) daily values of total rainfall averaged over Central India from 1st May till 30th October for each years along with daily mean for the selected period (thick line). The rainfall maxima coincides with the peak monsoon activity over the region around mid-July. The seasonal total of simulated daily rainfall is marginally higher (by 4.9%) as compared to observed rainfall while the intensity of simulated daily rainfall is only two-thirds of the observed over the central plains of India. This could be attributed to far more number of rainy days in model simulation as against observations. The year-to-year variability in monsoon rainfall simulated by the model (as inferred from the standard deviation of area-averaged monsoon rainfall for 30-year period) is significantly low (only 42% of the observed) relative to observed rainfall variability. The temporal variations of simulated daily total rainfall averaged over Central India during the years 2036-2065 in each of the four SRES ‘Marker’ scenarios are depicted in Figure 7.9. A comparison of Figure 7.8 with Figure 7.9 reveals many aspects of plausible changes in Indian summer monsoon activity over the central plains of India. The standard deviation of future projections of area-averaged monsoon rainfall centered around 2050s is not significantly different in each of the four scenarios relative to that simulated for the present-day atmosphere. This implies that the year-to-year variability in Central India rainfall during the monsoon season may not significantly change in the future. More intense rainfall spells are, however, simulated over the land regions of the Indian subcontinent in the future (relative to that simulated for the present-day atmosphere) thus increasing the probability of extreme rainfall events in a warmer atmosphere. It is interesting to note here that there are no appreciable shifts in rainfall maxima during July-August (located at about 20oN) in the temporal variation of simulated monthly mean precipitation over the region in any of the four ‘Marker’ scenarios. The Northward

Annual Winter Monsoon Annual Winter Monsoon Annual Winter Monsoon

1.18 1.19 1.04 2.87 3.18 2.37 5.09 5.88 4.23

1.00 1.08 0.87 2.63 2.83 2.23 5.55 6.31 4.62

1.32 1.37 1.12 2.23 2.54 1.81 3.53 4.14 2.91

1.41 1.54 1.17 2.73 3.00 2.25 4.16 4.78 3.47

* Based on CCSR/NIES Model Experiments; Area-averaged for land regions only.

2080s

2050s

2020s

Scenarios:

Temperature Change (oC) A1 A2 B1 B2 2.29 0.39 1.81 9.34 3.22 10.52 9.90 -19.97 14.96

A1 2.16 -1.95 2.37 5.36 -9.22 7.18 9.07 -24.83 15.18

4.15 4.36 3.83 6.86 3.82 7.20 7.48 -4.50 11.12

Rainfall Change (%) A2 B1

Table 7.4 Climate Change Projections* for Indian subcontinent under the new SRES Marker Emission Scenarios

5.97 3.64 5.10 7.18 3.29 8.03 7.62 -10.36 10.10

B2

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advancement of monsoon rains over India with the progression of the season therefore seems quite robust. A detailed analysis of the daily rainfall data suggests that, under A1 and A2 scenarios, while the model still simulates the first spell of intense rainfall appearing over the Southern most part of India (5oN to 10oN) during the first week of June on an average, the spread of simulated onset date at 10oN (based on the criteria that rainfall at all grid points along 10oN in the region is 3 mm day-1 or more for at least three consecutive days) extends from 24th May to 11th June during the 30-year period centered around 2050s against between 29th May and 8th June during the 30-year period of the present day atmosphere. This implies the possibility of enhanced variability in the date of onset of summer monsoon over Central India in a warmer atmosphere.

Fig. 7.8 Temporal variation of observed as well as (1961-1990) daily values of total rainfall averaged over Central India from 1st May till 30th October. The thick line depicts daily mean for the selected period.

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Utility of precipitation primarily depends upon its spatial as well as its temporal distribution. Uniform precipitation over a larger area is more useful than its occurrence concentrating over a smaller region and also, precipitation occurring over a larger time period would be more effectively utilized rather than, when it occurs within a short time span. Therefore, the projected changes in the precipitation pattern over the Indian subcontinent as presented above come as bad news for the water resource sector. On the first count, the decrease in the winter precipitation would reduce the total seasonal precipitation being received during December, January and February implying a greater water stress. On the second count, intense rain occurring over fewer days, which other than implying increased frequency of floods will also mean that much of the rain would be lost as direct runoff resulting in reduced ground water recharging potential.

Fig. 7.9 The temporal variations of simulated daily total rainfall averaged over Central India during 2036-2065 in each of the four SRES ‘Marker’ scenarios.

7.4

FLOODS AND DROUGHTS

7.4.1

PERIODICITY AND OCCURENCE

Rain gauge records of the Indian monsoon are available for over a century. In 1910, Sir Gilbert Walker, the then Director General of the India Meteorological Department, used gauge records since 1840 to describe the variability of Indian monsoons. The analyses of the rainfall records of the monsoon trends have continued till this day. These analyses have

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yielded a 30-year cyclicity of the Indian monsoons. It was observed that drought as well as flood years occurred in runs rather than scattered randomly through the years. Walker in his study found two periods of greatest rainfall deficiency, 1843-1860 and 1895-1907. The latter period extended till about 1920. This period was then followed by a remarkably low frequency of droughts for the next 30 years or so. Droughts became once more common in the 1960s. Of the 14 major drought years in the 85-year record, 8 occurred in the first 30-year period (1891-1920) whereas there was only one in the second 30-year period (1921-1950). In the 25-year period from 1951-1981, five major drought years were recorded. In 1972 and 1979 deficient rainfall (about 25% below normal) was recorded in one-half to two-thirds of India’s plains. In 1994, monsoon rainfall was deficient (by between 20% and 43%) in 10 of the 31 meteorological subdivisions of India. Gujarat, West Rajasthan, Tamil Nadu and Kerala had deficient monsoon rainfall during the year 1999. Apart from the inherent 30-year cyclicity of the Indian monsoons, droughts have been found to be more frequent during the years following El Niño-Southern Oscillation (ENSO) events. At least half the severe failures of the Indian summer monsoon since 1871 have occurred during the El Niño years (Webster et al., 1998). In the event of enhanced anomalous warming of the Eastern Equatorial Pacific Ocean such as that observed during the 1997-1998 El Niño, the higher frequency of drought conditions is possible. Floods and cyclones are the natural disasters where excess of water (rains) creates the havoc in India. In case of floods, the swollen rivers with overflowing banks do the damage in floodplains. Of late, flooding or water logging is becoming a major problem in urban and metropolitan areas. Cyclonic storms pose a hazard mainly in coastal regions (more on the East Coast as compared to the West Coast) but no place in the country is free from floods (even Rajasthan suffers from floods and flooding) although floodplains of rivers and cyclone-affected coastal regions are most prone to floods. While cyclone is a natural disaster in the full sense of the term, flood problem (including flooding) has been seriously aggravated by human activities such as overgrazing, deforestation, soil erosion and siltation. On the average, the area actually affected by floods every year in India is of the order of 10 mha of which about half is cropland. In fact, the area prone to floods in India has been estimated to be of the order of 40 mha. Persistent occurrence of rainfall over an area already soaked with rain or intense rainfall often results in flood. Excess water in a river, due to heavy and/or persistent rains in the catchment area or the upper regions of the river system also create flood downstream. Absence or lack of adequate drainage in any area will aggravate the flooding. Flash floods occur due to high rate of water flow as also due to poor permeability of the soil. Areas with hardpan just below the surface of the soil are more prone to floods as water fails to seep down to the deeper layers. As is evident, floods and drought occurring in India are closely associated with the nature and extent of the summer monsoon. The inter-annual fluctuations in the summer monsoon rainfall over India are sufficiently large to cause devastating floods or serious droughts. Though floods are often caused by tropical depressions and cyclones, these cyclones are not a part of the monsoons per se. Severe tropical cyclones generally develop during the pre-monsoon or post-monsoon season (generally defined cyclone seasons are October-November and March-June). The Eastern Coast of India along Bengal, Orissa and Andhra Pradesh are prone to such tropical cyclones. These cyclones cause devastating coastal floods, which often take the proportion of national disasters. A case in point would be the super cyclone that hit the Orissa Coast on 29th October 1999 with wind speed of about 260 kmph and heavy rains causing severe floods. This cyclone ranked highest in the damage caused in terms of both life and property. As per the information received from the State Relief Commissioner’s Office in Bhubaneshwar (CDBI Special Issue No. 15, 1999),

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9,885 people lost their lives; 2,142 people were injured; 370,297 cattle heads perished and 1,617,000 hectares of paddy field and 33,000 hectares of other crops were damaged. Several villages had been completely wiped out and over a million made homeless with storm-surge of height 9 m above astronomical tide level at Paradip, which penetrated 35 km inland. Many of the tropical cyclones move inland and may even reach as far inland as Nepal though at a much reduced intensity. Sometimes these cyclones stagnate over a region as the Orissa super cyclone did (it was more or less stationary with slight Southward drift over the region after making landfall) and it is these cyclones that cause maximum damage to life and destruction to the existing infrastructure. 7.4.2

IMPACT OF GLOBAL WARMING ON FLOODS AND DROUGHTS

Several recent studies (Kitoh et al., 1997; Lal et al., 2000) suggest an increase in the inter-annual variability of daily precipitation in the Asian summer monsoon with increasing greenhouse gas concentrations in the atmosphere. An examination of the frequency distribution of daily monsoon rainfall over India in the model-simulated data has suggested (Lal et al., 2000) that the intensity of extreme rainfall events are likely to be higher in future as a consequence of increased convective activity during the summer monsoon period suggesting thereby the possibility of more frequent flash floods in parts of India, Nepal and Bangladesh. Some of the most pronounced year-to-year variability in climate features and the extreme weather events (such as cyclones) in many parts of Asia have been linked to ENSO events. The analysis of data generated in several A-O GCMs indicate that, as global temperatures increase due to increasing greenhouse gases, the Pacific climate will tend to more resemble an El Niño-like state (Meehl & Washington, 1996; Knutson & Manabe, 1998; Mitchell et al., 1995; Timmermann et al., 1999; Boer et al., 1999). Collins (1999) finds an increased frequency of ENSO events and a shift in their seasonal cycle in a warmer atmosphere, so that the maximum occurs between August and October rather than around January as currently observed. Meehl & Washington (1996) suggest that future seasonal precipitation extremes associated with a given ENSO event are likely to be more intense in Tropical Indian Ocean region; anomalously wet areas could become wetter and anomalously dry areas become drier during future ENSO events. During ENSO, a cyclone in tropical oceans has more than 40% chance of being a severe one (Lander, 1994). The role of sea surface temperature in the genesis and intensification of tropical cyclones has been well demonstrated, for example, by Gray (1979), Emanuel (1988) and Saunders & Harris (1997). One of the necessary (but not sufficient) conditions for tropical cyclone formation in the North Indian Ocean is that the sea surface should have a minimum temperature of about 28oC. Analysis of sea surface temperature in the Bay of Bengal during the period 1951-1997 suggests that the sea surface temperatures have been increasing here since 1951. Observational records suggest that, while there has been a rising trend in all-India mean surface air temperature, the numbers of monsoon depressions and tropical cyclones forming over the Bay of Bengal and Arabian Sea exhibits declining trends since 1970 (Fig. 7.10). There have been a number of studies that have considered likely changes in tropical cyclones (Knutson et al., 1998; Henderson-Sellers et al., 1998; Royer et al., 1998; Krishnamurti et al., 1998). Some of these studies suggest an increase in tropical storm intensities with CO2-induced warming though there is no conclusive evidence to suggest that cyclone frequencies or their preferred locations may change in the future. An increase in sea surface temperature will be accompanied by a corresponding increase in cyclone

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Fig. 7.10 Trends in all-India mean surface temperature anomaly and number of monsoon depressions and cyclones in Indian Seas. intensity (wind speed). The relationship between cyclone intensity (maximum sustained wind speed) and sea surface temperature is well discussed in literature (Emanuel, 1987; 1999). A possible increase in cyclone intensity of 10%-20% for a rise in sea surface temperature of 2oC to 4oC relative to the threshold temperature of 28oC is very likely. Thus, while there is no evidence that tropical cyclone frequency may change, the available data strongly suggests that an increase in its intensity is most probable. Storm-surges are generated by the winds and the atmospheric pressure changes associated with cyclones. At low latitude land-locked locations such as the Bay of Bengal, the tropical cyclones are the major cause of storm-surges. Any increase in sea surface temperature is likely to cause greater convective activity, leading to an increase in wind

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speed. The stress exerted by wind on water underneath is proportional to the square of the wind velocity. Amplification in storm-surge heights should result from the occurrence of stronger winds and low pressures associated with tropical storms. Thus, an increase in sea surface temperature due to climate change should lead to higher storm-surges and an enhanced risk of coastal disasters along the East Coast of India. 7.4.3

IMPACT OF FLOODS AND DROUGHTS ON HUMAN SOCIETY AND DEVELOPMENT

When drawing a comparison between the flood and drought events, it is seen that rural communities suffer less from floods than from droughts because good crops can be grown after the water recedes (depends on timing of flow and crop calendar). Flood deposits silt, thereby adding organic matter and nutrients to the soil. They also recharge the aquifers thereby improving the ground water availability. However, impacts of these events on human and animal populations vary according to the nature and severity of the calamity. Most problems relate to the availability of food, safe drinking water and shelter. The extent of the disasters was evident in the four episodes - (a) the 1977 typhoon in Andhra Pradesh claimed nearly 10,000 lives, (b) 1978-1979 floods in Uttar Pradesh, Bihar and West Bengal damaged 18 mha of cropped land, destroyed nearly 4 million hutments and took a toll of 2,800 human lives and about 200,000 cattles, (c) 1979-1980 drought in large areas of Northern and Eastern India that affected more than 38 mha of cropped areas and endangered the lives of 130 million cattles and more than 200 million people, and (d) 1999 super cyclone in Orissa which claimed nearly 10,000 human lives and damaged about 1,617,000 hectares of paddy field and 33,000 hectares of other crops. Looking into the flood damage scenario of the country as shown in Table 7.5, it is observed that the flood damage is mainly related to the damage of land and cropped area and shows an upward trend during the three decades starting from 1953. The most damaged areas belong mainly to the States falling within the Ganges and the Brahmaputra basin. Ranks have been assigned to the different States according to the magnitude of average annual damage to crops, population and land (Table 7.6). The States located in the mountainous regions in Jammu and Kashmir, Himachal Pradesh, Nagaland, Manipur and other hilly States are least affected by floods. The damage to land, cropped areas, population, property and livestock depends on the geomorphology of the area as well as population distribution. Damage to population is more in the areas where the population density of the floodplains is higher such as in the Gangetic plains. In order to assess drought conditions in the country, the area-averaged Southwest monsoon rainfall for the country as a whole and the percentage of the country receiving deficient rainfall during the monsoon season are considered. According to the intensity, drought in India may be declared as all India drought, severe all India drought and phenomenal all India drought. There have been 17 incidents of all India droughts in this century, 8 severe all India droughts and 3 phenomenal droughts. The drought of 1987 was declared as a phenomenal of all India’s droughts. The worst affected were the three meteorological subdivisions of Saurashtra, Kutch and Diu (departure from normal rainfall was -74%), West Rajasthan (departure from normal rainfall was -74%), Haryana and Delhi (departure from normal rainfall was -67%). As many as 18 subdivisions had rainfall departures between -20% and -60% during this year. Based on the data in the report of the National Commission on Agriculture and additional data from the Central Water Commission of the Government of India, Bagchi (1991) identified 100 districts in the 13 States in India as drought prone which are detailed in Table 7.7.

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Table 7.6 Ranking of the States as per Flood Damages*

River Basins

States Land

Ganges, Brahmaputra, Other River Basins and Coastal Areas

Uttar Pradesh Bihar West Bengal Rajasthan Madhya Pradesh Assam Andhra Pradesh Gujarat Orissa Tamil Nadu Kerala Haryana Punjab

Rank As Per Damage To Cropped Area Population

1 2 4 7 9 3 6 8 5 14 10 12 11

1 2 4 3 8 9 7 5 6 13 15 11 10

1 2 4 10 13 8 3 7 6 5 9 11 12

*Source: Goel, 1993.

The impacts of drought are mainly two types: (i) Impacts on the Environment - Moisture stress, shortage of drinking water, damage to natural vegetation and various ecosystems, increase in air pollution (increased dust) and water pollution (scarcity of surface and sub-surface water), and (ii) Impacts on Society - (a) Economic impacts such as decreased agricultural output, loss of livestock, fall in industrial production, and unemployment resulting in poor purchasing power and the shortage of essential goods; and (b) Social impacts such as malnutrition, poor hygiene, ill health, migration and social strife. Apart from these direct effects, droughts have a far reaching effect on other sectors as well. Figure 7.11 shows the ramifications of the various impacts of droughts in India. Environment and society together constitute an interactive system with climatic extremes such as droughts creating significant socio-economic impacts on society both in the short-term and long-term. Obviously, the society in general, and economy in particular, try to cope with the impacts of climatic extremes by a combination of individual and collective action both on governmental and non-governmental levels. When these actions or adjustments turn out to be inadequate or the impacts are swift and/or large, socio-economic stress and/or social conflicts occur leading to loss of opportunity, property and even lives. It is here that the proper planning and preparedness on the part of the society assumes a very significant importance. 7.5

WATER RESOURCES OF INDIA

7.5.1

POTENTIAL OF SURFACE WATER RESOURCES

India has a large and intricate network of river systems of which the most prominent are

IMPLICATIONS FOR INDIA’S WATER RESOURCES

Table 7.7 Drought Prone Districts in India*

178

*Source: Kulshrestha, 1997.

Table 7.7 Continued

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IMPLICATIONS FOR INDIA’S WATER RESOURCES

the Himalayan river systems draining the major plains of the country. Apart from this, the numerous water bodies present in the subcontinent make it one of the wettest places in the world after South America. The Central Water Commission has put the country’s water resources at 1,880 km3yr-1 (CWC, 1995). All the large Himalayan Rivers including Sutlej, Yamuna, Indus are perennial sources of freshwater though the flow is reduced during non-monsoon periods. The primary sources of water for these rivers are either dry (snow) or wet (rain) precipitation. The potential for water withdrawal from these rivers for variety of uses has not yet been fully achieved in spite of the many existing dams and diversion canals. The South Peninsular Rivers are however charged by ground water and their flows are reinforced by the seasonal rainfall. Therefore, during the lean summer season their flows become limited. Many rivers such as the desert rivers of Luni and Mahi flowing through Rajasthan are totally fed by the monsoon rains and cease to exist during the rest of the year. Dams constructed across these rivers help to hold water in the reservoirs during lean seasons. Yet the construction of dams poses their own set of problems. Smaller check dams built in the upper reaches of the river reduce supply downstream and water problems get exaggerated there. Due to poor monsoon rainfall in 1999, all the reservoirs in Gujarat contained only 50% of the installed capacity in that year. The situation is further aggravated if a back-to-back drought occurs. The potentials of the non-glacier fed rivers are strongly associated with the health of the monsoons. A confident projection of and better understanding on the impact of global warming and climate change on the monsoons is thus very crucial. Wetlands in the form of inland lakes, coastal lagoons, mangroves also form an important component of the freshwater resources and are often the main source of water in areas where rivers are absent. They also serve as sources of livelihood for the local people such as fishing, aquaculture etc. Yet these water bodies are fast shrinking and degrading due to land-use changes, siltation, over exploitation and reduced recharging facilities, higher evaporation rates due to increased temperature and pollution. The coastal water bodies such as the backwaters of Kerala, the Sundarbans in Bengal, the Chilka in Orissa are saline as they receive saline influxes from the sea. Most of these water bodies are also located in areas of high human activity and receive nutrients in the form of runoffs, sewage and effluents. They are sources of methane emission due to the low oxygen levels, salinity and high nutrients. The inland lakes such as the lakes in Rajasthan have small catchment drainage basins with minor rivers draining into them. These lakes are generally shallow and are sensitive to extreme maximum temperatures making them prone to evaporation as is evident in the lakes in Udaipur. Some of the lakes in Udaipur went totally dry in 1999 such that the bottoms of these lakes were visible for the first time in 300 years. This aspect of the inland lakes also makes them vulnerable to fluctuations in the prevailing monsoons and droughts radically affect the water levels in these water bodies. Today, many of the rivers in India face a variety of problems due to contaminated urban waste discharge. The Central Pollution Control Board (CPCB) that regulates water quality in India has laid down standards for different types of use based on certain parameters (CPCB, 1996). Water quality has been classified into five categories namely A, B, C, D and E according to utility based on the BOD, DO and the coliform count (Table 7.8). Water quality classified as Class A is fit for drinking after disinfections while water quality falling in Class B is only fit for bathing. Prescription for water belonging to Class A is the most stringent with a coliform count less than 50 ml in 100 ml and DO exceeding 6 mg/l and BOD less than or equal to 2 mg/l. Water in Class B may have a coliform count up to 500 ml in 100 ml, DO level of 5 mg/l or more and BOD of 3 mg/l. Water quality in Class C can be used for human consumption though only after

SOCIAL STRIFE

MIGRATION

STARVATION

PRICE RISE

REDUCTION/ CONCEALMENT OF STOCK

FOOD & FODDER SHORTAGE

CROP FAILURE POOR CROPS

LACK OF IRRIGATION

Fig. 7.11 Drought and its impact on human community.

DEATH

MALNUTRITION

ILL HEALTH

DISEASE

POOR HYGIENE

CHANGE IN LAND-USE

POOR PURCHASING POWER

LOSS OF EMPLOYMENT OPPURTUNITIES

FALL IN INDUSTRIAL ACTIVITY

REDUCED POWER GENERATION

REDUCTION IN WATER RESOURCE

DROUGHT

LOSS OF FARM LAND

SOIL EROSION DEGRADATION

ABANDONMENT OF LAND

LOSS OF LIVESTOCK

ANIMAL & PLANT DISEASES

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conventional treatments and disinfections and therefore the total coliform count is allowed to be as high as 5,000, DO of 4 mg/l or more and BOD of 3 mg/l or less. Water quality in the Class D is fit for wildlife. The main criterion in this category is that the free ammonia should be less than 1.2 mg/l. Water in Class E can only be used for irrigation and industry and must have electrical conductivity of less than 2,250 ìmhos/cm and a maximum sodium absorption ratio (SAR)1 of 26. The proper ratio of sodium ions to calcium and magnesium ions in irrigation water results in irrigated soil, which is granular in texture, easily worked, and permeable. With increasing proportions of sodium as the SAR increases, soil tends to become less permeable and more difficult to work. As per the standards laid down by CPCB, very few rivers in India meet the specifications of Class A except in the upper reaches of some rivers such as Beas. Most of the rivers at selective points only are suitable for bathing such as at Rishikesh on the Ganges and at Rangantithu on Cauvery. A number of water bodies such as the Sukhna Lake at Chandigarh and Ramgarh Lake at Udaipur come under Class C. Rivers such as Cauvery at Tiruchirappalli and the Narmada River at Bharuch also come under this category. Several water bodies in Bihar, Orissa, Maharashtra and Gujarat as well as the Rajmahal and Pichola Lake in Udaipur and certain locations along the Ganges such as Kanpur belong to Class D. Certain lakes in Pondicherry and the Ganges at Varanasi fall in the category of Class E. There are several water bodies that are totally unfit for use and are even below Class E such as Sabarmati at Ahmedabad, Asthamudi backwaters in Kerala, the creeks at the Elephant Caves. Water quality of the surface waters in India as a whole is of concern as today a large amount of this resource is unfit for one or the other type of uses. Being centers of intense human activity and also due to high population density in the catchment areas, most of the river basins especially the larger ones such as the Ganges and the Brahmaputra are polluted. Therefore, in spite of the presence of large perennial rivers, poor quality of water in the rivers restricts our options for utilizing these sources of water. Problems of water quality aggravate during the lean season due to poor dilution of pollutants and are intensifying due to reduced flows in the rivers. This can become more critical for the rainfed rivers in the years when the monsoon fails. The existence of promising potentials of freshwater lies in our rivers. However, due to lack of proper management of rivers in India, the country is already on the brink of water stress. The reintroduction and the recent emphasis which is being laid on the traditional ideas of rain water harvesting and augmenting recharging of ground water using defunct bore wells holds immense potential and will definitely assist in alleviating water stress. But sole reliance on these options may not completely solve the problem of water at the present or in the future as these options too have their drawbacks such as availability of large land areas and also there dependence on precipitation which is subject to large intra-seasonal and inter-annual fluctuations. Despite environmental and social disturbances caused by big dams, we may not be able to ensure food security and a reliable water supply of the country without implementation of large hydrological projects. The fall out of these projects can be managed and damages may be minimized and feasibility of such projects entertained through cost benefit analysis including environmental and social costs. The need for such projects are emphasized by the fact that that the entire water supply of Mumbai, Pune, Hyderabad and Warangal cities is dependent on a series of dams like Vaitarana, Tansa, Bhatsa, Khadakwasla, Panset, Majira, Singur and Sriramsagar dams. The cluster of thermal and super thermal power stations in Uttar Pradesh is entirely dependent on the 1

SAR = Na+/((Ca+2+Mg+2)/2)0.5

A

B

C

D

E

Drinking water source without conventional treatment but after disinfections

Outdoor bathing (organized sector)

Drinking water source after conventional treatment and disinfections

Propagation of wild life and fisheries

Irrigation, industrial cooling, controlled waste disposal

Criteria

1. pH between 6.0 to 8.5 2. Electrical Conductivity at 25°C micro mhos/cm Max 2250 3. Sodium absorption Ratio Max. 26 4. Boron Max. 2mg/l

1. pH between 6.5 to 8.5 2. Dissolved Oxygen 4mg/l or more 3. Free Ammonia (as N) 1.2 mg/l or less

1. Total Coliforms Organism MPN/100ml shall be 5000 or less 2. pH between 6 to 9 3. Dissolved Oxygen 4mg/l or more 4. Biochemical Oxygen Demand 20°C 3mg/l or less

1. Total Coliforms Organism MPN/100ml shall be 500 or less 2. pH between 6.5 and 8.5 3. Dissolved Oxygen 5mg/l or more 4. Biochemical Oxygen Demand 20°C 3mg/l or less

1. Total Coliforms Organism MPN/100ml shall be 50 or less 2. pH between 6.5 and 8 3. Dissolved Oxygen 6mg/l or more 4. Biochemical Oxygen Demand 20°C 2mg/l or less

*Source: Data from Central Pollution Control Board, 1996.

Class of Water

Designated Best-Use

Table 7.8 Water Quality Standards for different users*

1. Mahi at the confluence of River Chap Gujarat 2. Sabarmati at Miroli Village, Gujarat

1. Brahmani at Panposh, Orissa 2. Brahmaputra at Dibrugarh, Assam

1. Achankoli at Thumpaman, Kerala 2. Cauvery at Napokulu Barrage, Karnataka

1. Ganges at Reshikesh, Uttar Pradesh 2. Tapi at Nepanagar, Madhya Pradesh

1. Beas at Manali, Himachal Pradesh 2. Ravi at Madhopur, Himachal Pradesh

Some Examples from Rivers in India

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IMPLICATIONS FOR INDIA’S WATER RESOURCES

storage of Rihand Dam for their water supply. Therefore the need for such projects is urgent in order to meet the demand for the future and more so as the present utilizable water resources have already been exploited to the limit. The Himalayan river system holds immense potential as a future freshwater source. The Himalayan snow and ice supports three major main river systems viz., Indus, Ganges and the Brahmaputra having an average annual stream flow of 206 km3, 488 km3 and 510 km3 respectively. Thus, almost 50% of our water resources of our country are located in the various tributaries of these three river systems. Apart from monsoon rains, India has been using the Himalayan Rivers for over a century for its water resource development. In recent decades, the hydrological characteristics of the watersheds in this region seems to have undergone substantial change as a result of extensive land-use (e.g., deforestation, agricultural practices and urbanization) leading to more frequent hydrological disasters, enhanced variability in rainfall and runoff, extensive reservoir sedimentation and pollution of lakes, etc. The global warming and its impact on the hydrological cycle and nature of hydrological events have posed an additional threat to this mountainous region of the Indian subcontinent. Extreme precipitation events have geo-morphological significance in the Himalayas where they may cause widespread slope failures (Ives & Messerli, 1989). The issue of the response of hydrological systems, erosion processes and sedimentation in this region could alter significantly due to climate change. The Himalayas have nearly 1,500 glaciers and it is estimated that these cover an area of about 33,000 km2. These glaciers provide the snow and the glacier melt waters keep our rivers perennial throughout the year. The most useful facet of glacier runoff is the fact that glaciers release more water in a drought year and less water in a flood year and thus ensuring water supply even during the lean years. The snow line and glacier boundaries are sensitive to changes in climatic conditions. Almost 67% of the glaciers in the Himalayan mountain ranges have retreated in the past decade (Ageta & Kadota, 1992; Yamada et al., 1996; Fushimi, 2000). The mean equilibrium line altitude at which snow accumulation is equal to snow ablation for glacier is estimated to be about 50 m-80 m higher relative to the altitude during the first half of the 19th century (Pender, 1995). Available records suggest that Gangotri glacier is retreating about 30 m/yr. A warming is likely to increase the melting far more rapidly than the accumulation. Glacier melt is expected to increase under changed climate conditions, which would lead to increased summer flows in some river systems for a few decades, followed by a reduction in flow as the glaciers disappear (IPCC, 1996). The Ganges River basin is capable of providing sufficient quantity of freshwater to accommodate the future demands. The river basin is as huge as the combined area of France, Germany and Belgium. The maximum discharge of the river, often attained during the month of September, is close to 60,232 m3/sec; this drops to a minimum discharge of 1,743 m3/sec during the peak of the dry season. What is evident from this disparity in flow is that we can harvest Ganges water when its flow is high and then increase its minimum discharge during the dry season significantly and thereby manage the demand for a consistent water supply. This will also provide a sustainable supply of water for our agriculture, domestic and industrial needs. 7.5.2

GROUND WATER POTENTIALS

In many regions due to the absence of an alternative or reliable source, ground water is the main source of water. Ground water is the principle source of drinking water in the rural habitations of the country and almost 85% of the rural water supply is dependent on ground water. India on the whole has a potential of 45.22 mha m/yr of replenishable ground water.

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Unfortunately, due to rampant drawing of the sub-surface water, the water table in many regions of the country has dropped significantly in recent years resulting in threat to ground water sustainability. These regions mainly correspond to the States that have registered ground water development above the national average. The States include Gujarat, Punjab, Haryana, Tamil Nadu and Rajasthan. The situation in Gujarat, in particular, is critical. The water table in Ahmedabad is reported to be going down at the rate of 4 m to 5 m every year. In some localities of Delhi, the water table has fallen by over 10 m. Even in Kerala, where the intensity of monsoon rain is heavy, water table has been falling systematically in all parts of the State. Gujarat has developed 41% of its ground water resources as compared to the all India average of 32%. About 90% of the ground water is currently used for irrigation facilities. According to government sources, the water table has registered a net fall in the level of water table for the nation as a whole in the year 1999 (Table 7.9).

The water quality of sub-surface water is interlinked with quantity. Overexploitation of ground water has resulted in a drop in its level leading to ingress of seawater in coastal areas making the sub-surface water saline. India is especially susceptible to increasing salinity of its ground water as well as its surface water resources especially along the coast due to increase in sea level as a direct impact of global warming. Increase in sea level leads to intrusion of saline water far into the land mass through the rivers draining into the sea and it also increases ground water contamination by making water saline. Saline water

186

IMPLICATIONS FOR INDIA’S WATER RESOURCES

cannot be used for either agriculture or fishery development. Lower levels of water due to excess withdrawing have also led to deterioration of water quality. Several problems of arsenic and fluoride contamination in water have surfaced in certain parts of the country. High levels of fluoride in water has led to acute cases of flourosis in many villages of Andhra Pradesh, Ajmer in Rajasthan, Gurgaon District in Haryana, Salem in Tamil Nadu and some villages in Agra in Uttar Pradesh. Arsenic problem is rampant in West Bengal and has given rise to acute health problems in the State. More than 7,000 wells in several districts in West Bengal have high dissolved arsenic usually more than 50 µg/l today. The ground water resources may still have large potentials for the future. Apart from being a major source of sub-surface water, the vast body of water carried by the Ganges and the heavy rainfall experienced in the valley and the mountains in the North has created another source of water wealth. The ground water level in the marshy and swampy Terai region of the Himalayas and the Northern stretch of the Ganges basin is only 2 m-2.5 m below the ground surface, though it goes down drastically to 15 m-30 m below the surface in certain parts of the river basin. The amount of water that exists in this confined aquifer is massive and has not been brought into utilization in any systematic manner. In fact, a good part of the dry season flows in the river area adjoining the Ganges and its tributaries is augmented by the flow back of ground water from this aquifer. In the Terai region, the aquifer never drains and it has already become a huge deep aquifer. A study by the World Bank (Chitale, 1992) shows that the ground water resources available for the development in South Asia beneath the alluvia and deltaic plains, in deeper and regionally extensive confined aquifers is so large that it cannot be accounted for accurately. These artesian aquifers are filled with freshwater to a depth as great as 2,500 m. Water in deep aquifers is naturally replenished from the surface sources. These deep aquifers are artesian and they often produce water through wells without pumping. This would decrease at least the direct operational cost and would also cost significantly less than the surface irrigation projects to supply water to the same area. Moreover, the drinking water of deep aquifers is less likely to be contaminated by agricultural chemicals and other pollutants as water in these aquifers have accumulated there after passing through thick sediment column, which is perhaps the most effective filtering system. Therefore, sustainable use of this huge resource is a feasible option. 7.5.3

POTENTIAL OF THE MONSOONS TO SUPPLEMENT WATER SUPPLY

Over the last one hundred years or so, we have seen two paradigmatic shifts in water management in India. One is that individuals and communities have steadily given over their role almost completely to the States. This dependence on the State has meant cost recovery being poor the financial sustainability of water schemes has run aground; and, repairs and maintenance is abysmal. With people having no interest in using water carefully, the sustainability of water resources has itself become a question mark. As a result, there are serious problems with government’s drinking water supply schemes. Despite all the government efforts, the number of ‘problem villages’ does not seem to go down. The second is that the simple technology of using rainwater has declined. Instead exploitation of rivers and ground water through dams and tube wells has become the key source of water. As water in rivers and aquifers is only a small portion of the total rainwater availability, there is an inevitable growing and, in many cases, unbearable stress on these sources. Keeping in view the huge demand on the water resources and the present state of our water sources, alternatives must be devised to supplement the present reserves of

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freshwater and reduce over exploitation such that the system of extraction is sustainable. Micro-watershed development is the most viable method of harnessing water by cheaper, quicker and safer means. This is basically an approach to conserve land and water in which natural and human resources can be dovetailed and deployed to ensure food, fodder, fuel, fruits and fiber. In watershed planning, the basic principle is using land according to its capability and water according to its availability. In this scheme effective moisture conservation holds the key. The three parameters are rainfall, runoff and recharge. The vegetation cover of grass and trees has a vital role in retarding the runoff and allowing maximum recharge. In order to sustain the available water resources in a region, the key factors that must be taken into consideration include (i) water availability, (ii) favorable topography, (iii) physiography and hydrogeological setup, (iv) infiltration and percolation characteristics of vadose zone, (v) hydrologic characteristics of the aquifers such as capacity to store, transmit and yield water, and (vi) techno-economic feasibility. Various technological options for ground water recharge keeping in view the local conditions must also be explored. Today the major problem seems to be the paucity of drinking water in almost all urban centers of India. In order to supplement the domestic water requirement, harvesting of the water received, as rain is a wise option. India receives 400 mha meters of precipitation every year and it is estimated that nearly 70 mha meter of this water evaporates immediately from the soil and about 105 mha meter flow out to the sea. After allowing for all the hydrological processes, the total utilizable potential is about 105 mha meter though less optimistic estimates put it at 86 mha meter-92 mha meter. The use of the traditional systems of water harvesting to catch and store water is a feasible option but it may not be adequate to meet the rising demand of water for industrial, agricultural and energy purposes. However, the rooftop water harvesting would be a good supplementary source of water for domestic purposes primarily to meet the demand of water for cooking and drinking. The rooftop harvesting approach could also enhance the recharging potentials of the aquifers by allowing water to sink into the ground through the new bore wells and through the use of defunct bore wells as well. However, the effectiveness of these options is subject to the eccentricities of the climatic conditions and a poor monsoon could seriously affect the potentials of these options. The climate change will affect the spatial and temporal distribution of surface water, soil moisture and ground water resources. Precipitation and temperature are the two key variables affecting the availability and demand for water. However, future projections of likely changes in monsoon rainfall and its spatial and temporal variability over India as presented above have rather low confidence due to limitations of the currently available global climate models. None-the-less, it has been suggested that there is no village in India that cannot meet its basic drinking and cooking needs through rainwater harvesting. For example, the average population is about 1,200 in an Indian village today. India’s average annual rainfall is about 1,100 mm. If even only half this water can be captured, an average Indian village needs 1.2 hectares of land to capture 6.57 million liters of water it can use in a year for cooking and drinking. In case of a drought or poor monsoon even if the rainfall levels dip to half the normal, the land required would rise to a mere 2.4 hectares. Thus, a mass movement for rainwater harvesting could perhaps provide a lasting relief against droughts in many States of India. Community-based rainwater harvesting in India - the paradigm of the past - has in it as much strength today as it ever did before.

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IMPLICATIONS FOR INDIA’S WATER RESOURCES

7.6

FUTURE DEMAND AND SUPPLY OF WATER

7.6.1

WATER DEMAND

At present, available statistics on water demand shows that the agriculture sector is the largest consumer of water. About 85% of the available water is used for agriculture alone. The quantity of water required for agriculture has increased progressively through the years as more and more area was brought under irrigation. In 1950, a total area of 25 x 106 hectares was under irrigation and this has increased over 3 folds in 5 decades. The contribution of surface and ground water resources for irrigation has played a significant role in India attaining self-sufficiency in food production during the past 3 decades and is likely to become more critical in future in the context of national food security. According to available estimates, the demand on water in this sector is projected to decrease to about 74% by the year 2050 though agriculture will still remain the largest consumer. In order to meet this demand, augmentation of existing water resources by development of additional sources of water or conservation of the existing resources through impounding more water in the existing water bodies and its conjunctive use will be needed. Water demand in the other sectors is also expected to increase significantly (Table 7.10). The water demand for the energy sector is likely to increase dramatically, almost 70 times by the year 2050. This may be associated with the rapid growth in industry and urbanization. The projected share of demand by the energy sector would most likely be about 8.9% in the year 2050 as against the present share of 0.3%. The demand of water in the industry sector could increase 8 folds during the same period. The water demand can be expected to increase to 4.3% in the year 2050 as against 1.2% for this sector, which is poised for rapid expansion in the decades to come. Table 7.10 Water availability and demand in next 50 years

Sector 2000

Year 2025

2050

Domestic Irrigation Industry Energy Other

42 541 8 2 41

73 910 22 15 72

102 1,072 63 130 80

Total

634

1,092

1,447

As the population increases, there will also be a corresponding increase in the demand for water in the domestic sector. The demand for domestic water is likely to increase from the present-day 6.6% to 7.0% in 2050. Even at present, at least part of the urban populations in many States do not have access to drinking water. In certain States like Assam, the percentage of urban population with an access to drinking water is only 10% and in Tamil Nadu it is 50%. The other States that are currently facing problems of inadequate drinking water include Kerala, Andhra Pradesh, Bihar, Goa, Orissa, West Bengal, Punjab and the Northeastern States of Meghalaya, Mizoram, Manipur, Tripura and Sikkim. Based on available statistics, the per capita daily water availability in India is expected to drop from 600 liters per day in 1990 to a critical level of 300 liters per day by

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2030. The per capita water available in the capital city of Delhi is nearing the critical level (Zerah, 2000) while the actual amount for Delhi is already below the critical level. The fall in per capita water availability will, however, not be uniform throughout the country. For instance, based on the data on the per capita availability of water for the last fifty years, projections have been made for the next fifty years. The national average in the year 2000 has been projected to be 2.5 x 103 m3/yr/person. The corresponding availability of water for the Northeastern region was reported as 18.4 x 103 m3/yr/person which is much higher than the national average. The per capita availability in the Southern part of the country and Tamil Nadu, in particular, is however lower than the national average and is about 0.4 x 106 m3/yr/person (Suresh, 2000). Projections made on the global scale suggests that with the increase in population and potential climate change the per capita water availability is likely to decrease by 34% in 2025 to 48% in 2080 relative to the present availability of water (Jones et al., 1999). 7.6.2

LONG-TERM WATER SUPPLY PROSPECTS

In order to fulfill such demands in the future, we will need to rationalize on the various means of capturing and storing water. Harvesting of rainwater should contribute to meeting the future water requirements sustainability in India. But the increase in the corresponding demand in the energy sector, industrial as well as increased demand in irrigation will require more water than can be harvested from rainfall alone. The inter-annual variability of the monsoons is expected to increase in the future making the monsoons less reliable as an assured source of water. Therefore, efforts are needed for more efficient ground water recharge and harvesting of rainwater through identification, adoption and adaptation of technological options. Some of the structural activities such as Nalla bunds, contour bunds, contour trenches, gully plugs, check dams, pits and shafts, basin percolation tanks, surface channels, ground water dams, injection wells, connector wells, storage tanks, dug well recharge, bore hole flooding, ditch and furrow, stream augmentation, de-silting of existing tanks and inter-watershed transfer should be tried depending on local conditions. Restoration, revival, revitalization and upgrading of existing/traditional rainwater harvesting structures would ensure sustainability of water resources. Much of the future demand will need to be met from the ground water resources which may have immense potential. The water potential of the Ganges valley can irrigate an additional 200 mha of land which can sustain rice productivity of about 4 tons per hectare and can produce another 80 million tons of rice that can sustain another 350 million-400 million people (Singh, 1995). The excess water requirement in the future can, however, only be made through properly planned and precise management. Studies carried out for the Ganges basin need to be conducted for all major river basins in the country in order to discover additional potential sources of water such as deep artesian aquifers. 7.7

GOVERNMENT POLICY AND LEGISLATIVE TOOLS

The increasing demand of water and its reduced availability is a growing national concern. Projections of water stress in the near future are rapidly turning into stark reality. Yet, all said and done, India till date has not been able to adequately handle the problem of water quantity and quality. As a result, water shortage is being felt all over the country. A number of legislative tools to protect the national water resources do exist but these have had no

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significant impacts due to poor implementation. The Water Pollution Act of 1972 was primarily introduced to prevent the deterioration of water quality. Its implementation as with all other Environmental Acts has been lax and pollution of water sources is going on unhindered. The National Water Policy of 2002 declared water to be a scarce and a precious natural resource to be developed and conserved on an integrated and environmentally sound basis. The emphasis has been appropriately laid on the end use of water. Efforts are underway to increase the efficiency of utilization of water by the several sectors. There also exists a Supplementary Act to the Water Pollution Act of 1972, known as the ‘Water Cess Act’ by which industries consuming above a certain quantity of water are to pay higher taxes on the water consumed by them. These laws are in keeping with the water policy of the country but the cost of water has not been accurately assessed due to which water has been grossly under priced and the very purpose of the Act is defeated. It is economically more feasible for the industries to pay the amount rather than to install and operate recycling or treatment plants. Water in many parts of the country is still almost free of cost. Due to the low price of water there is rampant abuse of this resource. Water supplied for irrigation is a case in point. The negligible amount charged for irrigation has resulted in widespread overuse resulting in a perpetual flooding condition of croplands and the loss of fertile land. A World Bank study on subsidies offered by governments on water has reported that it fails to benefit the poorer sections of the population. In fact the poor end up paying a higher price for water as they do not have access to the government supply and are forced to purchase water from commercial vendors. Due to lack of sufficient incentives, the industries have failed to respond to the growing need of reuse and recycling of wastewater. There also exists a lacunae in national policy to involve individuals in conserving water. Though several NGOs of the country are encouraging the people to take the initiative to harvest rainwater and promoting the cause of water conservation at the grass root level, the government has not yet adequately moved in this direction. Major initiatives need to be taken by the government to plan and implement water resource conservation programmes. Keeping in mind the plausible impacts of global warming on our water resources, the government has to come up with appropriate guidelines and action plans for water conservation for the future. Without such initiatives, the water crises in India are likely to increase in the future. 7.8

COPING WITH CLIMATE CHANGE AND ADAPTATION

Climate change is just one of a number of factors influencing the hydrological system and water resources. Population growth, changes in land-use, restructuring of the industrial sector, and demands for ecosystem protection and restoration are all occurring simultaneously. Current policies affecting water use, management, and development are often contradictory, inefficient, or unresponsive to changing conditions. In the absence of explicit efforts to address these issues, the societal impacts of water scarcity in India are likely to rise as competition for water use grows and supply and demand conditions change. A change in drought or flood risks is one of the potential effects of climate change with the greatest implications for human well-being. Few studies have looked explicitly at the implications of climate change for drought or flood frequency, in large part because of the lack of detailed regional precipitation information from global climate models. Higher average or a greater range of flows of water could reduce pollutant concentrations or increase erosion of land surfaces and stream channels, leading to more

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sediment and greater chemical and nutrient loads in rivers and coastal deltas. Lower average flows could reduce dissolved oxygen concentrations, reduce the dilution of pollutants and reduce erosion. For almost all water bodies, land-use and agricultural practices have a significant impact on water quality. Changes in these practices, together with technical and regulatory actions to protect water quality, can be critical to future water conditions. Key Messages for Water Resource Planners Climate is not static and assumptions made about the future based on the climate of the past may be inappropriate. Assumptions about the probability, frequency, and severity of extreme events used for planning should be carefully re-evaluated. Climate changes will be imposed on top of current and future non-climate stresses. In some cases, these changes will be larger than those expected from population growth, land-use changes, economic growth, and other non-climate factors. Certain threshold events may become more probable and non-linear changes and surprises should be anticipated, even if they cannot be predicted with a high degree of confidence. The time lags between identifying the nature of the problems, understanding them, prescribing remedies, and implementing them are long. Waiting for relative certainty about the nature of climate change before taking actions to reduce climate change related risks may prove far more costly than taking certain pro-active management and planning steps now. Methods must be used that explicitly incorporate uncertainty into the decision process. Expensive and long-lived new infrastructures should consider a wider range of climate variability than provided by the historical record into infrastructure designs.

There are many opportunities to reduce the risks of climate variability and change for India’s water resources. Past efforts have focused on minimizing the risks of natural variability. Many of the approaches for effectively dealing with climate change are different than the approaches already available to manage risks associated with existing variability. Tools for reducing these risks have traditionally included supply-side options such as new dams, reservoirs, and more recently improving efficiency. This is largely independent of the issue of climate change, which will have important implications for the ultimate severity of future water stresses. Sole reliance on traditional management responses should be avoided. First, climate change is likely to produce - in some places and at sometimes - hydrologic conditions and extremes of a different nature than current systems were designed to manage; second, climate change may produce similar kinds of variability but outside of the range for which current infrastructure was designed and built; third, relying solely on traditional methods assumes that sufficient time and information will be available before the onset of large or irreversible climate impacts to permit managers to respond appropriately; and fourth, this approach assumes that no special efforts or plans are required to protect against surprises or uncertainties.

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The first situation could require that completely new approaches or technologies be developed. The second could require that efforts above and beyond those currently planned or anticipated be taken. Complacency on the part of water managers, represented by the third and fourth assumptions, may lead to severe impacts that could have been prevented by cost-effective actions taken now. As a result, we make the following observations and recommendations: •

• •

•

•

• • •

•

Prudent planning requires that a strong national climate and water monitoring and research program should be developed, that decisions about future water planning and management be flexible, and that expensive and irreversible actions be avoided in climate-sensitive areas. Better methods of planning under climate uncertainty should be developed and applied. Decision makers at all levels should re-evaluate technical, and economic approaches for managing water resources in view of potential climate changes. The government should ask all States managing national water systems to begin assessing both climate impacts and the effectiveness of different operation and management options. Improvements in the efficiency of end uses and the management of water demands must now be considered major tools for meeting future water needs, particularly in water-scarce regions. Water demand management and institutional adaptation are the primary components for increasing system flexibility to meet uncertainties of climate change. Water managers should begin a systematic re-examination of engineering designs, operating rules, contingency plans, and water allocation policies under a wider range of climate conditions and extremes than have been used traditionally. For example, the standard engineering practice of designing for the worst case in the historical observational record may no longer be adequate. Cooperation between water agencies and leading scientific organizations can facilitate the exchange of information on the state-of-the-art thinking about climate change and impacts on water resources. The timely flows of information among the climate change scientists and the water-management community are valuable. Such lines of communication need to be developed. Traditional and alternative forms of water supply can play a role in addressing changes in both demands and supplies caused by climate changes and variability. Options to be considered include wastewater reclamation and reuse, rainwater harvesting and even limited desalination where less costly alternatives are not available. None of these alternatives, however, is likely to alter the trend toward higher water demand in the future. Prices and markets are increasingly important for balancing supply and demand. Because new construction and projects can be expensive, environmentally damaging, and politically controversial, the proper application of economics and water management can provide incentives to use less and produce more. Among the new tools that need to be explored are water banking and conjunctive use of ground water.

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193

RESEARCH NEEDS

Records of past climate and hydrological conditions are no longer considered to be reliable guides to the future. The design and management of both structural and non-structural water-resource systems should allow for the possible effects of climate change, but little professional guidance is available in this area. Further research by hydrologists, civil engineers, water planners, and water managers is needed to fill this gap, as is broader training of scientists in the universities. • • •

• • • • • •

7.10

More work is needed to improve the ability of global and regional climate models to provide information on water-resources availability, to evaluate overall hydrologic impacts, and to identify regional impacts. Substantial improvements in methods to downscale climate information are needed to improve our understanding of small-scale processes that affect water resources and water systems. Information about how our summer monsoon will be affected due to climate change is vitally important for determining impacts on water and water systems, yet such information is still not reliably available. More research on how the severity of cyclones and other extreme hydrologic events might change is necessary. Increased and widespread hydrologic monitoring systems are needed. There should be a systematic re-examination of engineering design criteria and operating rules of existing dams and reservoirs under conditions of climate change. Information on economic sectors most susceptible to climate change is extremely weak, as is information on the socioeconomic costs of both impacts and responses in the water sector. More work is needed to evaluate the relative costs and benefits of non-structural management options, such as demand management and water-use efficiency in the context of a changing climate. Research is needed on the implications of climate change for international water treaties and agreements with Nepal and Bangladesh. Little information is available on how climate changes might affect ground water aquifers, including quality, recharge rates, and flow dynamics. New studies on these issues are needed. CONCLUDING REMARKS

There are two distinct but complementary approaches to address the problem of water resources in a holistic manner. They are: (i) (ii)

Augmenting and enhancing the present reserves Taming the end demand

Sustainable use of water resource gets increasingly difficult as the demand for water far exceeds the availability, and the discounting rates for the future tends to increase under such circumstances. Therefore, in order to make our water utilization more sustainable both these approaches will have to be followed. Though India is endowed with extensive sources of water, the utilizable quantity is less as the full potential of our rivers has not yet been assessed accurately (as is evident

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from the study of water potential of the Ganges basin) neither has there been proper river management programme. The present water availability is further restricted due to water pollution as well as increasing salinity which has rendered several of our river water as well as ground water unfit for one or the other type of use. In order to meet the future demand of water in a sustainable manner, emphasis has appropriately been laid in recent years on the implementation of rainwater harvesting as well as rooftop harvesting of rainwater. But these options are limited in their capacity to meet the burgeoning national demand of water as they are subject to the variability of the monsoon, which is projected to increase due to global warming. These steps would be most effective at the grass root level in meeting the demands of the rural population without having to depend on the government for the required infrastructures and would help in supplementing the main water supply. In spite of the increase in the anti-dam sentiments in the country, it is considered irrational to rule out the hydrological projects completely. It would be otherwise impossible to ensure food security and supply of water for energy and industrial sectors. These projects would also increasingly reduce our dependence on the monsoons as the precipitation patterns over the country become more erratic due to climate change. So we will have to rationalize on the options available to us and make the best use of our resources. The other end of the water problem is to increase the efficiency of the end use of water. The water policy of the country has been rightly oriented in this regard but implementation of the policy has been far from satisfactory. The water demand has to be tamed through an appropriate conjunction of economic as well as legislative mechanisms.

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Fushimi, H.: Recent Changes in Glacier Phenomena in the Nepalese Himalayas. In A. Domoto, K. Iwatsuki, T. Kawamichi and J. McNeely (eds.). A Threat to Life: The Impact of Climate Change on Japan’s Biodiversity, Tsukiji-Shokan Pub. Co., Ltd., Japan and The World Conservation Union (IUCN), Gland, Switzerland and Cambridge, U.K., 2000, pp.42-45. Gray, W. M.: Hurricanes: Their Formation, Structure and Likely Role in the Tropical Circulation, In Shaw D. B. (ed), Meteorology over Tropical Oceans, Royal Meteorological Society, Bracknell, 1979, pp.155-218. GOI (Government of India): Compendium of Environmental Statistics, Ministry of Statistics and Programme Implementation, New Delhi, 1999, p.228. GOI (Government of India): Yearbook, Published by Ministry of Information and Publication, New Delhi, 2000. Goel, R. S. (ed.): Environmental Impacts of Water Resources Development, Proc. of the National Round Table Discussion in New Delhi, June 4-5, Tata McGraw-Hill Publishing Company Ltd., 1993, p.359. Henderson-Sellers, A., Zhang, H., Berz, G., Emanuel, K., Gray, W., Landsea, C., Holland, G., Lighthill, J., Shieh, S-L, Webster, P. and McGuffie, K.: Tropical Cyclones and Global Climate Change: A Post IPCC Assessment. Bull. Amer. Meteorol. Soc. 79 (1998), pp.19-38. IPCC (Intergovernmental Panel for Climate Change): Climate Change 1995: The Science of Climate Change. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change. In J. J. Houghton, L. G. Meiro Filho, B. A. Callander, N. Harris, A. Kattenberg and K. Maskell (eds.), Cambridge University Press, Cambridge and New York, 1996, p.572. IPCC (Intergovernmental Panel for Climate Change): Climate Change 2001: The Scientific BasicsSummary for Policymakers and Technical Summary of the Working Group I Report, IPCC, Geneva, 2001. Ives, J. D. and Messerli, B.: The Himalayan Dilemma: Reconciling Development and Conservation, Routledge, London, 1989. Jones, R. N., Hennessy, K. J., Page, C. M., Pittock, A. B., Suppiah, R., Walsh, K. J. E. and Whetton, P. H.: An Analysis on the Effects of the Kyoto Protocol on Pacific Island Countries, Part Two: Regional Climate Change Scenarios and Risk Assessment Methods. CSIRO Atmospheric Research Report to the South Pacific Regional Environment Programme, 1999, p.69. Kitoh, A., Yukimoto, S., Noda, A. and Motoi, T.: Simulated Changes in the Asian Summer Monsoon at Times of Increased Atmospheric CO2. Jr. Meteor. Soc. Japan 75 (1997), pp.1019-1031. Knutson, T. R. and Manabe, S.: Model Assessment of Decadal Variability and Trends in the Tropical Pacific Ocean. J. Climate (1998), pp.2273-2296. Knutson, T. R., Tuleya, R. E. and Kurihara, Y.: Simulated Increase of Hurricane Intensities in a CO2Warmed Climate. Science 279 (1998), pp.1018-1020. Krishnamurti, T. N., Correa-Torres, R., Latif, M. and Daughenbaugh, G.: The Impact of Current and Possibly Future SST Anomalies on the Frequency of Atlantic Hurricanes. Tellus 50A (1998), pp.186-210. Kulshrestha, S. M.: Drought Management in India and Potential Contribution of Climate Prediction, Joint COLA/CARE Technical Report No. 1, Maryland, USA, 1997, p.105. Lal, M., Cubasch, U. and Santer, B.D.: Effect of Global Warming on Indian Monsoon Simulated with a Coupled Ocean-Atmosphere General Circulation Model. Current Science 66(6) (1994), pp.430-438. Lal, M., Cubasch, U., Voss, R. and Waszkewitz, J.: Effect of Transient Increases in Greenhouse Gases and Sulphate Aerosols on Monsoon Climate, Current Science 69(9) (1995), pp.752-763. Lal, M. and Harasawa, H.: Comparison of the Present-Day Climate Simulation Over Asia in Selected Coupled Atmosphere-Ocean Global Climate Models. Jr. Meteor. Soc. Japan 78(6) (2000), pp.871-879. Lal, M. and Harasawa, H.: Future Climate Change Scenarios for Asia as Inferred from Selected Coupled Atmosphere-Ocean Global Climate Models. Jr. Meteor. Soc. Japan 79(1) (2001) . Lal, M., Meehl, G. A. and Arblaster, Julie M.: Simulation of Indian Summer Monsoon Rainfall and Its Intra-Seasonal Variability. Regional Environmental Change 1(3 & 4) (2000), pp.163-179.

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Lal, M., Nozawa, T., Emori, S., Harasawa, H., Takahashi, K., Kimoto, M., Abe-Ouchi, A., Nakajima, T., Takemura, T. and Numaguti, A.: Future Climate Change: Implications for Indian Summer Monsoon and its Variability, Current Science (2001) (Communicated). Lander, M.: An Exploratory Analysis of the Relationship Between Tropical Storm Formation in the Western North Pacific and ENSO. Mon. Wea. Rev. 122 (1994), pp.636-651. Meehl, G. A. and Washington, W. M.: El Niño-Like Climate Change in a Model with Increased Atmospheric CO2 Concentrations. Nature 382 (1996), pp.56-60. Mitchell, J. F. B., Johns, T. C., Gregory, J. M. and Tett, S. F. B.: Climate Response to Increasing Levels of Greenhouse Gases and Sulphate Aerosols. Nature 376 (1995), pp.501-504. Nakicenovic, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J., Gaffin, S., Gregory, K., Grubler, A., Jung, T. Y., Kram, T., La Rovere, E. L., Michaelis, L., Mori, S., Morita, T., Pepper, W., Pitcher, H., Price, L., Raihi, K., Roehrl, A., Rogner, H. H., Sankovski, A., Schlesinger, M., Shukla, P., Smith, S., Swart, R., van Rooijen, S., Victor, N. and Dadi, Z.: Emissions Scenarios, A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 2000, p.599. Nakicenovic, N., Victor, N. and Morita, T.: Emissions Scenarios Database and Review of Scenarios. Mitigations and Adaptation Strategies for Global Change 3 (1998), pp.95-120. Pant, G. B. and Kumar, K. R.: Climates of South Asia, John Wiley & Sons Ltd., West Sussex, U.K., 1997, p.320. Pender, M.: Recent Retreat of the Terminus of Rika Samba Glacier, Hidden Valley, Nepal. In C. P. Wake (ed.), Himalayan Climate Expedition - Final Report, Glacier Research Group, University of New Hampshire, 1995, pp.32-39. Ramakrishnan, S.: Ground Water, Published by S. Ramakrishnan, Chennai, 1998, p.471. Royer, J. F., Chauvin, F., Timbal, B., Araspin, P. and Grimal, D.: A GCM Study of the Impact of Greenhouse Gas Increase on the Frequency of Occurrence of Tropical Cyclones. Climatic Change 38 (1998), pp.307-343. Saunders, M. A. and Harris, A. R.: Statistical Evidence Links Exceptional 1995 Atlantic Hurricane Season to Record Sea Warming, Gephys. Res. Lett. 24 (1997), pp.1255-1258. Singh, T.: Drought Disaster and Agricultural Development in India, Peoples Publishing House, New Delhi, 1995. Suresh, V.: Sustainable Development of Water Resources in Urban Areas, Proc. 10th National Symposium on Hydrology, July 18-19, CSMRS, New Delhi, 2000. Timmermann, A., Oberhuber, J., Bacher, A., Esch, M., Latif, M. and Roeckner, E.: Increased El Niño Frequency in a Climate Model Forced by Future Greenhouse Warming. Nature 398 (1999), pp.694-696. Webster, P. J., Magana, B. O., Palmer, T. N., Shukla, J., Thomas, R. A., Yanagi, M. and Yasunari, T.: Monsoons: Processes, Predictability and the Prospects for Predication. Journal of Geophysical Research 103(C7) (1998), pp.14451-14510. Yamada, T., Fushimi, H., Aryal, R., Kadota, T., Fujita, K., Seko, K. and Yasunari, T.: Report of Avalanches Accident at Pangka, Khumbu Region, Nepal in 1995. Japanese Soc. of Snow and Ice 58(2) (1996), pp.145-155. Zerah, M. H.: Water’s Unreliable Supply in Delhi, Manohar Publishers and Distributors, New Delhi, 2000, p.168.

8 Climate Change and Water Resources Management in Pakistan ASAD SARWAR QURESHI

8.1

INTRODUCTION

8.1.1

PHYSIOGRAPHY

Pakistan is located between latitudes 24oN and 37oN and longitudes 61oE to 76oE. It is bordered by India in the East, China in the Northeast, Afghanistan in the North, Iran in the Southwest and the Arabian Sea to the South (Fig. 8.1). Pakistan is divided into three major geographic areas: the Northern Highlands, the Indus River Plain, and the Balochistan Plateau. The Northern highlands include parts of the Hindu Kush, the Karakoram Range, and the Himalayas. This area includes famous mountainous peaks such as K2 and Nanga Parbat. The Indus Plain, stretches from the Salt range to the Arabian Sea. This flat plain is largely made up of 300 m deep alluvium, deposited by the Indus River and its tributaries. The Balochistan Plateau is in the Southwest of the country; with an average altitude of about 600 m. Dry hills run across the plateau from Northeast to Southwest. A large part of the Northwest is desert. Administratively, Pakistan is divided into four provinces, namely the Punjab, Sindh, Northwest Frontier Province (NWFP) and Balochistan and several areas with special status. These include the State of Azad Jammu and Kashmir, Federally Administered Tribal Areas (FATA) and Federally Administered Northern Areas (FANA). Pakistan has a federal government system and the provinces enjoy fair degree of autonomy. The land area of Punjab is 25.8%, Sindh 17.8%, NWFP 12.8% and Balochistan 43.6% of the total land area of Pakistan. The current population of Pakistan is 145 million with the population growth rate is estimated at 2.7%. Punjab has 56.5%, Sindh 22.6%, NWFP 15.8% and Balochistan has only 5.1% of the total population. The overall population density is 166 persons per sq. km. About 32% of the total population lives in urban areas, while 68% lives in rural areas. The total geographical area of Pakistan is about 80 million hectares (mha), out of which about 27% is currently under cultivation. The forest and desert cover about 4.8% and 14%, respectively. The forest resource is meager but it plays an important role in Pakistan’s economy by employing half a million people, providing about 3.5 million cubic meters (m3) of wood and one-third of the national energy need. Forests and rangelands support about 30 million herds of livestock, which contribute more than US$ 400 million to foreign exchange earnings. In recent years, forest cover has reduced due to continuous demands for fuel wood and illegal logging.

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Fig. 8.1 Map of Pakistan.

8.1.2

AGRICULTURE AND RURAL ECONOMY

Agriculture is the single largest sector of Pakistan’s economy, although its share to GDP has been steadily decreasing over the years as other sectors have expanded. Agriculture accounts for a large share in GDP, contributing about 25% in 1999/2000. It employs about 44% of the labor force, supports about 75% of the population and accounts for more than 60% of foreign exchange earnings. Over the last decade, agriculture grew at an average annual rate of 4.5% with some fluctuation in growth mainly on account of weather conditions. The arable agricultural resources base of Pakistan is about 22 mha, which is 27% of the total land area. About 17 mha are irrigated and 5 mha rainfed. The irrigated area produces almost 90% of all agricultural productions. The irrigated land is usually located in the river basins of the Indus. Though there are irrigated lands in the Northwest, Northeast to Southwest parts of the country, the proportion is small as compared to that in the Northern areas. The principal crops include wheat, rice, cotton, sugarcane, oilseeds, fruits, vegetables and pulses. There have been significant increases in the gross production and yields of major crops including wheat, cotton, rice and sugarcane over the last three decades. However, the overall yield per hectare of most crops is still far below their demonstrated potential. Table 8.1 gives the detail of the cultivated area, production and yields of major cereal crops in 2000.

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Table 8.1 Cultivated area, production and yields of major crops in Pakistan

Crops

Wheat Rice Cotton Sugarcane

Area (mha)

Production (million tons)

Yield (tons/ha)

8.4 2.1 2.6 0.9

18.7 3.9 9.7 35.0

2.3 2.0 0.7 46.9

Despite the marked geographical differences in wealth generating capacities across the country, there is a similarity to village society irrespective of location or agro-ecological zone. This cross-section covers small landholders, landowners, sharecroppers and landless tenants. The average holding is 3.8 hectares (ha). The vast majority of landholding falls in the range of 0.3 ha to 12.7 ha. Holding under 20 ha accounted for 14% of land ownership and those over 60 ha for 10%. Distribution of farm size is given in Table 8.2. Table 8.2 Farm size distribution in Pakistan

Farm Size (ha)

Farms (%)

Average Size of Farm Area (ha)

<2.0 2-5 5-10 10-20 20-60 >60

12 26 22 16 14 10

0.8 3.1 6.6 12.7 28.5 126.0

It is generally accepted that families with less than 0.5 ha of irrigated land have great difficulty in earning their livings solely from agricultural production in most parts of Pakistan. Therefore, off-farm income generating activities serve as an integral part to achieve a modest living. The majority of women in Pakistan work in agriculture. They constitute a large portion of the agricultural labor force. Livestock production plays important roles both in contributing to the national economy and livelihood for a large number of people living in rural and urban areas. Livestock sub-sector contributes to 35%-40% to the total agricultural sector, which is almost equivalent to the contributions of major crops. Livestock production contributes about 10% of total export earnings of the country (GOP, 1999). Total livestock population, including cattle, buffalo, sheep, goats, camels, horses, asses and mules, is estimated at 55 million heads. About 23 million or nearly 42% are found in Balochistan province, while NWFP hosts 15 million, Punjab 12 million and Sindh nearly 5 million. The structure of Pakistan rural society is based on numerous settlements many of which have very limited access to basic needs of life. Poverty is an overriding social problem. Although average per capita food availability of 2,700 calories is adequate, malnutrition is widespread, with a reported 8 million malnourished children (ABD, 2002). Average life expectancy is comparable with other South Asian nations, but infant mortality

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rate is above the regional average. Gender inequality is substantial. For example, literacy rate for males is 59% as compared to 35% for females. Majority of the population in Pakistan do not have access to safe drinking water and sanitation. The coverage of sanitation in Pakistan is lower than the water supply i.e., only 13.5% in rural areas (PWP, 2001). In most of the cities in Pakistan, the wastewater from the municipal areas as well as the effluents from the industries are disposed of untreated to the natural surface water bodies. In the country about 2,122 million gallons per day of sewerage is being thrown in the water bodies. Access to water for domestic purposes in the urban areas is limited to about 83% of the population. About 57% of the people have piped supply to their homes whereas in other mainly poor areas people get water either from community taps, hand pumps, wells or pay heavy cost to the water vendors. The present water use for municipal and industrial supplies in the urban sector is of the order of 5.3 BCM. Most urban water is supplied from ground water except for the cities of Karachi and Hyderabad and part of the supply to Islamabad. The demand is expected to increase to about 14.9 BCM by the year 2025. The present domestic water use in rural areas is estimated at 1.0 BCM. Most rural water is supplied from ground water except in saline ground water areas where irrigation canals are the main source of domestic water. Only about 53% of the rural population has access to drinking water from public water supply sources. The remaining population gets their drinking water supply from streams, canals, ponds or springs, etc that is untreated and unsafe for human consumption. 8.1.3

OBJECTIVES OF THE CHAPTER

Pakistan is fast turning into a water scare country. The gap between demand and supply of water has increased to the levels creating unrest among the federating units. The extended drought during recent years exacerbated the problem. Future change in climate may impact water resources availability in Pakistan and that may lead to additional stress on the water dependent sectors especially the agriculture sector. This chapter has three broad objectives. First, it provides a comprehensive review of the present water resources of Pakistan and challenges faced by the irrigated agriculture; second, possible impacts on the water resources availability are discussed; and third, it highlights on the strategies to overcome these problems are to ensure sustainability of irrigated agriculture. 8.2

WATER RESOURCES IN PAKISTAN

8.2.1

SURFACE WATER RESOURCES

Surface water resources of Pakistan are based on the flows of the Indus River and its tributaries (Jhelum, Chenab, Ravi, Sutlej, Beas on the East and Kabul River on the West). The Indus River has a total length of 2,900 km and a drainage area of about 966,000 sq. km. The inflow to these rivers is mainly derived from snow and glaciers melt and rainfall in the catchment areas. Outside the Indus basin most of the rivers are ephemeral streams, which only flow during the rainy season and do not contribute significantly to the surface water resources. After the Indus Basin Treaty of 1960 between India and Pakistan, Pakistan was allowed exclusive use of three Western Rivers (Indus, Jhelum and Chenab) and India was entitled to three Eastern Rivers (Ravi, Sutlej and Beas). This treaty also provided provision for the construction of a number of link canals, barrages and dams on the Indus and its two tributaries. The Indus basin has now developed into the largest contiguous irrigation

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201

system. The existing surface water system which is now all weir controlled, consists of 4 storage reservoirs (Warsak, Chasma, Mangla and Tarbela), 16 barrages, 12 inter-river link canals, 2 siphons, 44 canal commands (23 in Punjab, 14 in Sindh, 5 in NWFP and 2 in Balochistan), 59,000 km long irrigation canals and 107,000 km long watercourses. The Irrigation System commands a gross irrigable area of 16.85 mha, of which 14 mha is culturable command area (CCA) to which water is allocated. The perennial canal supply is available to 8.6 mha while the remaining area is entitled to irrigation supplies only during the summer (Kharif) season. In terms of the quantum of the surface water resources, the flows of Indus and its tributaries, available to Pakistan, are the most significant. The meager and highly variable flows of all other streams; offer only, a limited potential for adding to the “stock” of water. The Indus River and its tributaries, on an average, bring 190 BCM of water annually. This includes 179 BCM from the three Western Rivers (Indus, Chenab and Jehlum) and 11 BCM from the Eastern Rivers (Rave, Beas and Sutlej). Most of this, about 129 BCM, is diverted for irrigation. Fifty BCM flows to the sea and about 11 BCM are consumed by the system losses, which include evaporation, seepage and spills during floods (Zuberi, 1997). Although the surface flows of the Indus River and its tributaries available to Pakistan are quite significant, these are characterized by a great variation. Against the average annual inflow of 170 BCM, the historic data from 1922-1997 indicates a high of 230 BCM (34% higher than the average in 1960) and a low of 120 BCM (30% lower than the average in 1975). About 65% of the total river flows comes from the Indus alone, while the share of Jhelum and Chenab is 17% and 19%, respectively. Apart from the large annual fluctuations, there are large seasonal variations in these flows. The average inflow during the six months of summer cropping season is 142 BCM whereas the flow in the remaining six months of winter season is only 27 BCM. Table 8.3 gives history of seasonal variation in the inflow at Rim Stations, diversion and outflow to sea. The water quality of Indus River and its tributaries is generally considered excellent for irrigation purposes. The total dissolved solids (TDS) ranges from 100 ppm-200 ppm in the upper reaches to 350 ppm in the lower reaches of the Indus, which are reasonable levels for irrigated agriculture and also for domestic purposes. The disposal of saline drainage effluents has been a major factor in increased TDS quantity in the lower reaches of the rivers in Punjab. The BOD values of Jhelum, Chenab, Sutlej and Indus range between 2 mg/l to 5 mg/l. The p ollution in river Ravi is the highest of all the rivers in Pakistan. The river presently receives 47% of the total municipal and industrial pollution load discharged into the rivers of Pakistan. The BOD in the river is estimated to be 77 mg/l on the basis of mean annual flow. The DO contents in all rivers are above the acceptable levels of 4 mg/l (Halcrow, 2001; Haq, 1998). 8.2.2

GROUND WATER RESOURCES

The Indus basin is underlain by an extensive unconfined aquifer covering about 16 mha of surface area, of which 6 mha are fresh and the remaining 10 mha are saline (Haider et al., 1999). The aquifer has been built due to direct recharge from natural precipitation, river flow, and the continued seepage from the unlined canals, distributaries and watercourses and application losses from the irrigated fields. The safe ground water yield is estimated to be about 68 BCM, whereas the extraction from agriculture, domestic and industrial sectors is of the order of about 59 BCM. Thus the remaining ground water potential is about 9 BCM (PWP, 2001). The ground water table in most of the fresh ground water areas is falling therefore the potential of further ground water exploitation is very limited.

202

WATER RESOURCES DEVELOPMENT IN PAKISTAN

The use of ground water for irrigation in Pakistan has a long history. In early days, the ground water abstraction was done by means of open wells with rope and bucket, Persian wheels, karezes, reciprocating pumps and hand pumps. The massive development of ground water from the Indus basin aquifer started in 1960s with the launching of Salinity Control and Reclamation Projects (SCARPs). Under this program, some 15,000 public tube wells of large capacity (60 l/s-150 l/s) were installed to lower the ground water table. This demonstration also led to the proliferation of private tube wells with a capacity of 30 l/s-60 l/s. Subsidized power supply and introduction of country made diesel engines provided an impetus for dramatic increase in the number of private tube wells from 10,000 in 1960 to over half a million in 2002. Category-wise distribution of private tube wells in different provinces of Pakistan is given in Table 8.4. State-wise distribution of tube wells is shown in Figure 8.2. Investment on the private tube wells is of the order of state-wise Rs. 25 billion (Pak Rs. 59 = US$ 1) whereas the annual benefits in the form of agricultural production are to the tune of Rs. 150 billion. The number of users is over 2.5 million farmers, who exploit ground water directly or hire the services of tube wells from their neighbors. Their behavioral patterns are highly variable and they understand little about any adverse interaction, which is likely to result due to unsystematic and erratic nature of ground water exploitation. Due to decreasing surface water supplies and occurrence of drought conditions, the population of private tube wells has taken a quantum jump over the last decade. The density of private tube wells per 1,000 ha in Punjab has increased to 46 in 2002 as compared to only 3 in 1965 (Fig. 8.3). The average utilization factor of private tube wells in Pakistan varies from 9% to 18% (Qureshi and Mujeeb, 2002). This variation in utilization factor is due to factors like cropping patterns, cropping intensity, agro-climatic zones, ground water quality, tube well types and growing seasons. The utilization factor of electric tube wells is almost double than the diesel tube wells. The main reason for higher utilization factor of electric tube wells is due to low operating and maintenance cost as compare to diesel tube wells in Pakistan. 8.2.3

GROUND WATER QUALITY

The ground water quality ranges from fresh (salinity less than 625 ppm near the major rivers to highly saline farther away, with salinity more than 1,800 ppm. About 79% (4 mha) of the area in Punjab province has access to fresh ground water. Saline waters are mostly encountered in Central Doab Areas. Cholistan area in the Southern Punjab is well known for highly brackish waters, which cannot be used for drinking purposes. In some parts of Punjab, there are also reports of high fluoride content (7 mg/l-12 mg/l) and high concentrations of arsenic (50 µg/l) in the ground water. Ground water quality in the Indus basin is shown in Figure 8.4. In the Sindh province, about 28% of the area has access to fresh ground water. Large areas are underlain by poor quality of ground water. Indiscriminate pumping has resulted in contamination of the aquifer at different places where salinity of tube well water has increased. The situation is further complicated by the occurrence of high fluoride in some areas. In NWFP and Balochistan provinces, abstraction in excess of recharge in certain areas has lowered the ground water table and resulted in the contamination from underlying saline water. The ground water quality in Pakistan is generally poor and is becoming one of the major water resources issues.

147.1

Average

179.3

82.1

79.8 87.8 76.6 85.1 77.5 83.3

Kharif

46.7

45.4 47.8 45.4 50.3 48.8 44.0

Diversion Rabi

128.8

125.1 135.6 121.9 135.4 126.3 127.3

Annual

47.4

46.6 23.1 13.5 47.1 77.1 41.4

Kharif

3.2

1.9 17.8 0.1 5.1 1.7 5.6

50.6

48.4 24.8 13.5 52.2 78.8 47.0

Outflow to Sea Rabi Annual

1

A rim station, in the context of the Indus Basin Irrigation System, is a control structure (reservoir, barrage, etc.) on the river just when it enters into the Pakistani territory or upstream of the canal-irrigated Indus Plains of Punjab and Sindh provinces. The rim stations for the Indus System Rivers are the Kalabagh Barrage (or sometimes Tarbela Reservoir) for the main Indus River, Mangla Reservoir for the Jehlum River, Marala Barrage for the Chenab River and Balloki and Sulemanki Barrages for the Ravi and Sutlej Rivers.

32.3

172.1 168.3 145.2 204.9 196.0 175.5

143.5 135.5 113.0 161.6 160.1 135.9

1975-1976 1980-1981 1985-1986 1990-1991 1995-1996 1997-1998

28.6 32.8 32.1 43.4 35.9 39.6

Rim Station1 Inflow Kharif Rabi Annual

Year

Table 8.3 Inflows of the Indus and its tributaries (BCM)

A. S. QURESHI 203

204

WATER RESOURCES DEVELOPMENT IN PAKISTAN

Table 8.4 Category-wise number of tube wells in Pakistan

Electric Tube Wells

Province

Diesel Tube Wells Peter Engine Tractor Operated

Total

65,354 2,993 5,538 11,913

379,728 25,086 4,209 10,364

121,364 1,330 -

566,446 28,079 11,077 22,277

Total

85,798

419,387

122,694

627,879

Number of Tube Wells ('000)

Punjab Sindh NWFP Balochistan

700 600 500 400 300 200 100 0 1965

1970

Punjab

1975

Sindh

1980

1985

1990

NWFP

1995

2000

Balochistan

2002 Total

Fig. 8.2 Historical development of tube wells in four provinces of Pakistan.

No. of TWs/1000 ha

50 40 30 20 10 0 1965 Punjab

1970

1975 Sindh

1980

1985

1990

NWFP

Fig. 8.3 Increase in tube well density in four provinces of Pakistan.

1995

2000

Balochistan

2002

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205

Fig. 8.4 Ground water quality of the Indus basin.

8.2.4

RAINFALL AND TEMPERATURE

Rainfall in Pakistan is markedly variable in magnitude, time of occurrence and its aerial distribution. However, almost two-thirds of the rainfall is concentrated in the three summer months of July to September. The mean annual precipitation ranges from less than 100 mm in parts of the Lower Indus Plain to over 750 mm near the foothills in the Upper Indus Plain (Bhatti and Akhtar, 2002). There are two major sources of rainfall in Pakistan: the Monsoons and the Western Disturbances. The Monsoons originate in the Bay of Bengal and usually reach Pakistan, after passing over India, in early July. They continue until September. The Indus Plains receive most of their rainfall from the monsoons. The relative contribution of rainfall in most of the canal commands is low when compared with the two other sources of irrigation water i.e., canal water and ground water. More than 60% of the Kharif season rainfall is concentrated in the month of July for almost all of the canal commands. The winter rains are generally widespread. The contribution of rainfall in agriculture sector is of the order of about 30 BCM (Hussain, 2002). The rainfall varies from the North and Northeast to the South of the country. It is only the canal command areas in the North-West Frontier Province (NWFP) and the Northern most canal commands of the Punjab Province that receives some appreciable amount of rainfall during the summer as well as the winter season. The canal commands upstream of the rim stations (i.e., in the NWFP) receive almost 55% of their annual rainfall during the Kharif season. The canal commands in the Upper and Lower Indus Plains receive 75% and 85%-90% of the annual rainfall respectively, during the Kharif season. Located in the North of the tropic of cancer, Pakistan possesses a great range of climatic diversity, from some of the hottest in the world in Jacobabad and Sibi Districts to the snowy cold parts of Balochistan and Northern mountain areas. Along the coastal belt, the climate is modified by sea breezes. In plains, the minimum temperature in the month of

206

WATER RESOURCES DEVELOPMENT IN PAKISTAN

January varies from 4°C to 15°C and in June/July from 30°C to 39°C. The maximum temperature in January varies from 17°C to 24°C and in June/July from 37°C to 52°C. Based on 10-year (1990-1999) data the average annual rainfall in some major cities of Pakistan is given in Figure 8.5. 1400

1320

1200

Rainfall (mm)

1000 724

800

528

600 322

310

400

147

130

155

Hyderabad

Jacoabad

Karachi

183

200

346

276

247

0 Zhob

Rawalpindi

Quetta

Peshawar

Multan

Lahore

Khuzdar

D.I.Khan

Bahawalpur

Fig. 8.5 Average 10-years (1990-1999) rainfall data of the 12 major cities of Pakistan.

8.2.5

SMALL-SCALE WATER DEVELOPMENT THROUGH FLOODS

Apart from the major surface water development based on the flows of Indus and its tributaries, the infrequent flows in the smaller streams have been developed through traditional means such as Rod Kohi, hill torrents and more recently attention has been paid to the construction of small dams in the upland areas for local uses. All these developments are typical of water scarce environments and are subject to the extreme variability in precipitation. Spring and floods contribute about 40 BCM as an additional source of surface water for agricultural purposes (Hussain, 2002). Hill torrents in the hilly areas of the country especially in NWFP and Balochistan provide another source of surface water, which has not been developed to its full potential. There are about 14 distinguishable hill torrents areas in all the four provinces of Pakistan offering a total potential of about 23 BCM at about 1,200 sites. Out of this almost 60% can be developed for crop production. This water offers excellent opportunity to irrigate about 2.5 mha of culturable wasteland in the hill torrent area (Halcrow, 2001). Flood irrigation (locally called as Rod Kohi) is widely practiced particularly in areas of hill torrents. Small dams, recharge dams and delay action dams have constructed by small communities to meet their local irrigation needs on a number of small streams. Rod Kohi is of immense value to small settlements and such irrigation has been serving as the main occupation and source of food needs. Floods are also a natural means of irrigation for riverine forests and other ecosystems with rich bio-diversity such as Manchar Lake, Haleji Lake and riverine ecosystems.

A. S. QURESHI

8.3

MAJOR CHALLENGES

8.3.1

LOSS OF STORAGE CAPACITY DUE TO SILTATION

207

The main source of surface water in Pakistan is the Indus River and its tributaries, all of which are perennial and have their origins in the mountains. The sources of supply of water to these rivers are snowmelt, seepage from geological formations and runoff generated by seasonal rains in the watershed areas. The runoff generated by rain erodes the soils and picks up the sediment and transports it into the reservoirs and dams. The scale of Indus and its potential effects are quite significant because of its size. The Indus River has the 7th largest delta in the world, and the 12th largest drainage area. Its annual water runoff places it 10th, and its annual sediment discharge places it 6th in the world. It is estimated that the Indus and its tributaries carry about 0.435 BCM of sediment annually of which nearly 60% remain in the system where it deposits in reservoirs, canals and irrigation fields. In 1990, the Indus River carried the 5th largest sediment load of the world, estimated over the whole basin of 16 mha at 4.5 tons of silt per hectare. In the Tarbela catchments area, it has been estimated that 167 m3/km2 per annum of silt are produced (PWP, 2001). The Mangla, Tarbela and Chashma Dams/Reservoirs play an important role in the economy of the country. Not only do they provide water for irrigation, but also generate cheap hydroelectric power. As in the case with most reservoirs, both the Mangla and Tarbela Reservoirs are facing the problems of sedimentation. According to recent investigations, the rate of loss in live storage capacity is 0.14 BCM per year for Tarbela and 0.031 BCM per year for Mangla Dam (PWP, 2001). Sedimentation in the three major reservoirs Tarbela, Chashma and Mangla is going to decrease their storage capacities and it will be 32% by the end of the year 2020. Table 8.5 gives the detail of the estimated loss of the storage capacity of the three major reservoirs until 2020 (WAPDA, 1999; Warsi, 1991; PWP, 2001). There was no provision of silt exclusion in the design of Warsak Dam, which resulted in silting up of the reservoir after three years of operation of the dam. Currently, the riverbed on the upstream side of Warsak Dam has been raised to the extent that the dam is essentially run-of-the-river, with no capacity for storage. 8.3.2

INCREASING GAP BETWEEN WATER AVAILABILITY AND DEMAND

Pakistan is also one of the countries that could face severe food and water crises in the 21st century. Continuous population growth with limited land and water resources has put an enormous pressure on the economy of the country. These pressures are the result of the increasing demand for food and ever limited possibilities for the extension of irrigation to other areas due to scarcity of water and costs of development. The population in Pakistan is increasing at a rate of 2.8% per year and has reached about 140 million. It is projected that the population will increase to 250 million in 2025. The percentage of urban population will increase from the current 35% to 52% by 2025. Compared to irrigation the current demand for domestic and industrial use is minimal. Because of continuous rise in population, water demand for domestic, industrial and non-agricultural uses will increase by about 8% and is expected to reach 10% of the total available water resources by the year 2025 (Bhutta, 1999). Water availability per capita will reduce to less than 600 cubic meters per capita in year 2025 (Fig. 8.6). This is roughly the value below which water availability becomes a primary constraint to life (Engelman and Leroy, 1993).

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WATER RESOURCES DEVELOPMENT IN PAKISTAN

Table 8.5 Capacity loss of on-line storage reservoirs

Reservoir

Year Live storage capacity (BCM) Decrease (%) Commissioned Initial 1997 2000 2010 2020 1997 2000 2010 2020

Mangla Chashma Tarbela

1967 1971 1974

6.5 0.9 11.9

Total

5.7 0.5 10

5.5 0.4 9.8

5.2 0.2 9

4.9 0.1 8.1

12 44 16

15 56 18

20 78 24

25 89 32

19.3 16.2 15.7 14.4 13.1

16

19

25

32

In Pakistan, irrigation dominates water use and it is expected to continue as the major use of both surface and ground water resources in the future. The water requirements for irrigation are estimated at 250 BCM in 2025. The total water availability in the Indus basin is estimated at 185 BCM by the year 2025. Even by exploiting the full ground water resources, the water availability will not be more than 190 BCM. Considering the reduction in present storage capacities and non-availability of additional storage facilities, the shortfall of water requirements would be about 32% by the year 2025 (ADB, 2002). This shortfall of water will result in serious food shortages in the years to come and will severely hurt the national economy. 1600 1400 1200

-1

Water/person

1000

150

800 600

100

400 50

Population

3

200

yr

-1

250

m water person

Population (millions)

300

200

0

0 1961

1968

1978

1987

2000

2013

2025

Years Fig. 8.6 Population growth and water availability per capita per year in Pakistan.

Due to increase in life expectancy and migration from rural to urban areas, the demographic profile of the country will undergo major changes in the next 25 years. Therefore the need of the population for agricultural products especially food grains, edible oil, milk, meat, fruits and vegetables will also increase. Estimated requirements of the agricultural commodities for the project population in 2025 are given in Table 8.6.

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Table 8.6 Agricultural requirements and projected productions and shortfalls of different crops for the year 2025 (million tons)

8.3.3

Crops

Requirement

Production

Shortfall

Food-Grains Sugarcane Cotton (lint) Pulses Oilseed Vegetables Fruits

50 82 3.5 1.9 3.3 14.3 16.1

31.5 46.4 2.7 1.4 1.5 9.0 9.0

18.5 35.4 0.8 0.5 1.8 5.3 7.1

Total

171

102.8

69.4

WATER LOGGING AND SOIL SALINIZATION

The introduction of large-scale irrigation without adequate drainage altered the hydrological balance in the Indus basin. At the time of construction of irrigation canals about a century ago, the ground water table depth in different canal command areas ranged between 20 m to 30 m below the soil surface. Therefore the need for provision of sub-surface drainage as a part of irrigation system was not felt. Persistent seepage over the years from unlined earthen canals and from a large network of distributing channels and percolation losses from irrigated fields, increased the ground water recharge substantially. In the absence of drainage in the canal command areas, the ground water table rose rapidly in vast irrigated areas to within 1.5 m of the soil surface. This created water logging and, consequently, soil salinity. These problems are more serious in saline ground water areas. The rise of the ground water table after the introduction of the irrigation system in Punjab is shown in Figure 8.7. The ground water table in the Indus basin fluctuates seasonally. In general, ground water tables are deepest at the end of the dry season (May-June) and shallowest immediately after the wet season (September). It is presently estimated that after the monsoon season, about 4.7 mha (30% of the irrigated area) have a ground water table within 1.5 m of the soil surface (severely waterlogged). Prior to monsoon, this area is reduced to about 2 mha i.e. 13% of the irrigated area (Tarar, 1995). The Punjab province has about 25% of its irrigated area severely waterlogged and Sindh has about 60% in the same category. Due to the presence of this shallow and saline ground water, about 40,000 ha are annually abandoned within the Indus basin due to secondary salinization. Figure 8.8 shows that about 46% of the irrigated land has ground water tables deeper than 3 m and this proportion is not affected by the season. To arrest and reverse the process of water logging and associated salinity problem, the Salinity Control and Reclamation Projects (SCARPs) were launched in 1960. Under this program, 57 SCARPs were planned and 15,000 public tube wells (with a capacity of 60 l/s-150 l/s) were installed. These SCARPs tube wells were pumping about 12 BCM of ground water per year. This scheme brought green revolution in 1970s and area under crop cultivation was doubled. However in late 1980s the performance of SCARPs started declining due to improper operation and maintenance, as well as, the ground water table started rising again. Since then the extent of problem keeps on changing and the present

210

WATER RESOURCES DEVELOPMENT IN PAKISTAN

estimates show that about 13% of the area in the Indus basin has ground water table within 1.5 m from the surface and about 50% within 3 m from the surface (Fig. 8.9).

Fig. 8.7 Rise of the ground water table after the introduction of canal irrigation in the Punjab, Pakistan (after Sarwar, 2000). The ground water profiles are shown for the years 1920 and 1960.

Percent of Irrigated Area

50 40

Before monsoon After monsoon

30 20 10 0 0-1.5 1.5-3.0 >3.0 Ground Water Table Depth Range (m)

Fig. 8.8 Seasonal effects on ground water table depths in the Indus basin.

The Indus basin is faced with a considerable salt balance problem. The salts are brought in by the rivers and their tributaries. The average annual salt inflow by the Indus River water is estimated to be 33 million tons while the outflow to the sea is only 16.4 million tons. This means an annual average addition of some 16.6 million tons to the salt storage in the Indus basin. Out of this only 2.2 million tons is deposited in a series of evaporation ponds and the remainder of salts accumulates in the soil profiles in the irrigated lands and its underlying strata and aquifer (Nespak/MMI, 1993). This implies that, annually, an average of one ton of salts is added to each hectare of irrigated land. This salt

A. S. QURESHI

211

accumulation is mainly causing salinization of the land. Therefore about 35% to 40% of the irrigated areas are affected by salinity. Out of this, 8% is severely affected and 6% moderately affected by salinity. Of course, the scale of the problem of salt accumulation in the root zone is even greater if saline ground water is used for irrigation. 60 50 40 30 20 10 0 1978 1982 1986 1988 1990 1992 1993 1994 1995 1996 1997 1998 Years Percent of area of CCA less than 1.5 m

Percent of area of CCA greater than 3.0 m

Fig. 8.9 Historical trends of waterlogged area in Pakistan.

Most of the soil salinity in the Indus basin is inherent, as it was produced during the process of soil formation. The secondary salinization associated with the shallow ground water tables and use of poor quality ground water for irrigation has further compounded the problem. Therefore salt-affected soils have become an important ecological entity in the Indus basin of Pakistan. It is estimated that nearly 6 mha are already afflicted with this menace, of which about half is in irrigated areas. An estimated 2 mha are abandoned due to severe salinity (Wolters and Bhutta, 1997). The extent keeps on changing due to dynamic nature of the problem. The problems of soils in the Indus basin are not only of salinity but also of sodicity. About 70% of the tube wells in the Indus basin pump sodic water, which contain high concentrations of carbonate and bicarbonate. Application of this quality of water for irrigation turn the soils to saline-sodic affecting soil structure and infiltration rates thereby restricting the growth of conventional crops. Salt-affected soils of the Indus basin are usually classified into four types (Qureshi and Barret-Lennard, 1998). The area affected and the characteristics of these four soil types are given in Table 8.7. The above facts indicate that the agricultural sector suffers deeply from both water logging and salinity. About 75% of the population and about half of the Gross National Product (GNP) are directly or indirectly related to the agricultural sector. This shows that the problems of water logging and salinity are not just agricultural problems, but that they do affect the country as a whole and ultimately the social fabric of Pakistani society. Water logging and salinity have very adverse social and economical effects on communities in Pakistan, causing poor living standards in affected areas and health problems for humans and animals. This situation has forced the local population to migrate to other areas.

Area Affected (million ha) 0.7 1.9 1.1 2.3

Classification of Salt-Affected Soils

Slightly saline-sodic

Porous saline-sodic

Severely saline-sodic

Soils with sodic tube well water

Severely sodic due to application of sodic tube well water. Contain high concentrations of carbonates and bicarbonates. Almost impervious.

Have high ground water tables, dense and nearly impervious to water.

Saline-sodic throughout the root zone, porous and pervious to water.

Slight salinity-sodicity problem, occurring as patches (about 20% of the area) in cultivated fields.

Characteristics

Table 8.7 Classification of salt-affected soils in the Indus basin (after Qureshi and Barret-Lennard, 1998)

212 WATER RESOURCES DEVELOPMENT IN PAKISTAN

A. S. QURESHI

8.3.4

213

POOR GROUND WATER MANAGEMENT

Development of ground water has been a key contributor in enhancing agricultural productivity and drought mitigation. Ground water irrigation has largely supported employment generation, rural development and poverty alleviation. The flexibility provided by ground water has largely helped in providing confidence in the farmers and they are enjoying the benefits of ground water use. However, due to unsystematic and erratic nature of pumping, a number of problems have emerged in many areas. Under the prevailing circumstances the excessive use in certain areas has resulted in mining of the aquifers and/or deterioration of the ground water quality. There are, however, areas where the ground water development has lagged behind or where new technologies may be needed for skimming the shallow useable ground water. It has been estimated that by adopting appropriate technologies, 14 BCM of additional ground water can be exploited. Thus, there is a need for control on ground water pumpage in some areas, whereas in some areas, its use has to be encouraged. In Pakistan, ground water is considered as a God’s gift and there is no restriction or control on its abstraction. There is neither any mechanism for allocating ground water rights nor for regulating its use. Anybody can install a tube well anywhere in his land and extract any amount of water at any time without consideration of detrimental effects of his action on the resource and on others. Ground water abstraction from 1965 to 2000 has increased from 10 BCM to 69 BCM. Over 80% of ground water exploitation taking place in Pakistan is in the private sector. This unplanned pumpage of ground water is creating an array of management and equity problems. Due to continuous lowering of water table, the ground water has become inaccessible to the small poor farmers, which has questioned the sustainability of irrigated agriculture. Already 5% area in Punjab and about 15% in Balochistan have gone out of the reach of poor farmers (Qureshi and Mujeeb, 2002; 2003). This area is likely to increase to 15% in Punjab and 20% in Balochistan in the next decade. Excessive lowering of the ground water tables is making pumping more expensive and wells are going out of production. These problems have questioned the sustainability of this resource for various uses especially for agriculture. The average ground water table in the Indus basin has declined from 3.6 m in 1988 to over 7 m in 1996; and is declining at a rate of 1.5 m per year in some areas. Analysis of ground water table data of the Indus basin has shown that water tables are falling in 26 canal commands out of 45 (PWP, 2001). Depletion of ground water is occurring in those canal commands where water-allowance is low and crops are heavily dependent on ground water. Figure 8.10 shows the ground water depletion in the Indus basin during the last decade. 8.3.5

LOW SYSTEM EFFICIENCY AND CROP PRODUCTIVITY

The contiguous Indus Basin Irrigation System was designed about a century ago. The major objective of irrigation development at that time was to prevent crop failure and avoid famine (Jurriens and Mollinga, 1996). Another design feature was the low management and operational requirements, which is an advantage, with an inherent disadvantage of inflexibility. Increasing demand for food to cope with the ever-increasing population has caused the annual cropping intensities to rise to about 150%. Moreover, many canals can even no longer convey their official design capacity, due to siltation and erosion of banks. From the scarcity by design and the intensified farmer practices, over time canal water availability per unit of irrigated land has become even more limited.

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WATER RESOURCES DEVELOPMENT IN PAKISTAN

Fig. 8.10 Ground water depletion in the Indus basin during the last decade.

The Indus Basin Irrigation System consists of the perennial rivers, a network of unlined canals, distributaries and watercourses therefore an appreciable amount of water is lost through evaporation and seepage. Due to age and poor maintenance, the delivery efficiency of irrigation system is low, ranging from 35% to 40% from canal head to the crop root zone (Tarar, 1995). In practical terms, therefore, much surface water is currently lost enroute, which, if salvaged, could be profitably used by the farmers. The principal crops grown in Pakistan include wheat, rice, cotton, sugarcane and maize. There have been noteworthy improvements in gross production and yields of these major crops during the last three decades (Table 8.8). The growth in yields has ranged from

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0.9% per annum for rice to 3.4% for cotton. Increases in production and yield result from the introduction of new agronomic practices and technologies, improved crop management, higher use of agro-chemicals and increases in the availability of irrigation water. However, increases in gross production are mainly due to increase in area rather than crop yields. Although the yields of most crops have increased since the advent of green revolution in 1965, the overall per hectare yield for most crops except cotton are, however, still far below their demonstrated potential and yield in other countries. Table 8.8 Increase in production (million tons) and yield (tons/ha) of major crops

Year

Wheat Prod.

Yield

Rice Prod.

Yield

Sugarcane Prod. Yield

Cotton (lint) Prod. Yield

1966 2000

3.92 21.08

0.76 2.50

1.32 5.16

0.94 2.05

22.31 46.33

37.37 45.9

0.41 1.92

0.26 0.46

% increase

538

328

391

218

207

123

468

177

Table 8.9 shows a comparison of major crop yields in several countries of the world. Under almost similar agricultural conditions, the average wheat yield in Egypt is more than double than in the Indus basin of Pakistan. Major reasons for these low crop yields are uncertain policies in marketing and pricing, poor dissemination of technologies to the farmers, inefficient post harvest processing and storage. Improvements in these areas can bring a major economic gain to the farmers in a relatively short period. Table 8.9 Comparison of different crop yields in (tons/ha) different countries

Country

Wheat

Rice

Sugarcane

Cotton

Pakistan India Egypt China USA World

2.5 4.9 5.9 3.8 6.5 4.8

3.07 2.97 8.49 6.34 6.69 3.83

47.8 69.0 107.4 64.2 64.7

1.92 0.85 2.26 2.87 1.79 1.62

Despite the shortage of water, the overuse of water in irrigation is a major problem in Pakistan. The impact of this is not only the wastage of water, which could be directed to other sectors or expansion of agriculture, but it also leads to water logging and salinity. This, in turn, reduces crop yields (a reduction of 25% overall and a high of 40% in Sindh). Irrigated agriculture yields can be increased through the use of improved technology and better management of the highly complex agricultural management system. It is estimated that to meet the food requirements of the country, cultivated area of wheat would need to increase by 46% at present yield levels. Similarly areas for other crops will need to be increased. However, given the present situation water resources, it will not be possible. Therefore the only way to achieve this food target is to increase the water productivity. The productivity of water in Pakistan is amount the lowest in the world. For

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WATER RESOURCES DEVELOPMENT IN PAKISTAN

wheat for example it is 0.5 kg/m3 as compared to 1.0 kg/m3 in India and 1.5 kg/m3 in California (IWMI, 2000). The maize yields in Pakistan are very low and there is a tremendous scope for substantial improvements in the maize yields. In terms of water productivity, maize has a factor of nine between lowest in Pakistan (0.3 kg/m3) and highest in Argentina (2.7 kg/m3). This reveals that there is a substantial scope for increasing water productivity in Pakistan. 8.3.6

DROUGHT

Due to global climatic changes, frequency of droughts has increased in recent years. The drought phenomenon (dry year) has been observed to occur in 4 out of 10 years instead of 3 out of 10 years. The precipitation during 1997-2000 has been exceptionally low i.e. 50% of the normal. This resulted in low river flows and not only precious human lives were lost but also thousands of livestock heads died due to shortage of fodder crops. According to one estimate, only during 1999-2000, 143 humans and 2.48 million livestock died due to severe drought conditions. The drought of the year 2001 has been termed as one of the worst in the history of the country and can be judged from the fact that it was the major cause behind the low economic growth rate of 2.6% of last year. Agricultural growth has, however, suffered a severe setback during 2000/2001 due to the unprecedented drought situation and shortage of irrigation water, causing a decline of 2.5% as against an impressive growth of 6.1% last year (PWP, 2001). This drought caused a loss of Rs. 25 billion to the national exchequer in the year 2000-2001 (UN, 1999). The loss of livestock to drought was about 43% in Punjab, 40% in Balochistan and NWFP, and 66% in Sindh. The cumulative loss, in the last three drought years, is estimated at 43% of the country’s livestock population. Heavy direct losses due to animal mortality, production losses and distress sales of animals have been widely reported. If the productivity levels can be restored to levels similar to the rest of the region, then Pakistan should be able to resolve medium to long-term food security concerns. Meteorologists, who blame the prolonged drought on the La Nina weather phenomenon, warn that the country has entered a dry cycle and can expect drought-like conditions to return every 3 to 4 years. Experts predict that with the prevailing consumption rates and a population growth of 4 million people a year, 1 out of 3 people in Pakistan will face critical shortages of water, “threatening their very survival”. Environmental experts suggest that underground aquifers of Balochistan province are dropping at 3.5 m annually, and will run out in 15 years. Massive internal displacement is expected. According to the United Nation (1999) the provinces of Balochistan and Sindh continue to be under the stress of drought along with Cholistan region in Punjab and the Southern Districts of NWFP. In the province of Balochistan, for the last three years, the monsoon rains, which generally occur in the months of July-September, have touched only Eastern areas of the province with limited and scattered precipitation whereas large parts of the Central and Western areas received no rain. The Sindh province and rainfed regions of Punjab and NWFP are suffering equally from the shortage of water due to the below normal monsoon of last year and nearly 40% below normal current winter rains. Figure 8.11 shows the severely drought hit areas of Balochistan and Sindh provinces.

8.3.7

DISPOSAL OF SALINE DRAINAGE EFFLUENT

The Indus Plain is characterized by a lack of any well-defined natural surface drainage and differences in micro-relief define the pathways for surface runoff during the monsoon

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Fig. 8.11 Map of severely drought hit areas of Pakistan.

season. The surface drainage problems are further aggravated by the construction of infrastructures like roads, railways, flood embankments and the irrigation system. Due to the flat nature of the Indus basin, natural sub-surface drainage through down valley movement of ground water is also restricted. Therefore, ponding of agricultural lands following intense rainstorms, with consequent crop and property damages, has become a recurrent phenomenon in many parts of the Indus Plains. The need for surface drainage of agricultural lands has long been recognized and measures were taken to construct surface drains in areas prone to severe damage. Even though about 15,000 km of surface drains have been constructed to date, crop losses because of rain flooding remain excessive, especially in the Punjab and Sindh provinces (Afzal, 1992). The Indus River and its tributaries is the only natural drainage outlet to the sea and also the major source of irrigation water supply. The capacity of the river system to accept saline drainage effluent is therefore limited and depends upon the water quality standards adopted both for irrigation and other uses. The Left Bank Outfall Drain (LBOD) takes the drainage water from the areas on the left bank of the Indus River in Sindh. Construction of the Right Bank Outfall Drain (RBOD) is planned to take the drainage water from the areas on the right bank of the Indus River in Sindh to the sea. Economic disposal arrangements of saline effluent to the sea are possible if the drainage system is designed and regulated in the same way as the canal system. The ultimate drainage requirements of saline drainage effluent are 13.5 BCM i.e. 3.63 BCM from Punjab and 9.82 BCM from Sindh and Balochistan. The requirements of drainage in Sindh are high due to extent of saline ground water as well as relatively high water allowances for the crop grown, particularly rice (Halcrow, 2001).

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WATER RESOURCES DEVELOPMENT IN PAKISTAN

8.4

CLIMATE CHANGE IMPACTS ON WATER RESOURCES: THE WAY FORWARD

8.4.1

CLIMATE CHANGE SCENARIOS

CICERO (2000) has estimated 0.9oC increase in temperature by 2020, doubling to 1.8oC by 2050. There is an uncertainty about precipitation, which may change by ±3% by 2020, and 6% by 2050. Scenarios for sea level may be 20 cm by 2020 and 30 cm by 2050. These synthetic scenarios are consistent with the results from climate models. The CSIRO9 model predicts a 17% increase in wet (summer) season rainfall in South Asia for doubling of CO2. When the CSIRO9 scenarios have been scaled to correspond to the low, medium, and high IS92 emissions scenarios of the IPCC, the range lies between 5% and 50%. These results are supported by some other climate models, including the CCC (now CCCma), UKMOH and GFDLH, which project 20%, 24%, and 59% increases, respectively. Temporal changes in extreme precipitation events are also expected. The CSIRO9 model projects that the average time period between heavy rainfall events may be reduced by more than one-half (Watson et al., 1998). CICERO (2000) has suggested that the impacts of climate change should consider the differences between the projected socio-economic conditions without climate change (the reference case) and those that are projected with climate change. The socio-economic conditions are significant determinants for reduced vulnerability and adaptive capacity. 8.4.2

IMPACTS ON WATER AND OTHER ASSOCIATED SECTORS

8.4.2.1

IMPACTS ON THE INDUS RIVER BASIN

The small changes in the climate could have a significant effect on water availability, as experienced in recent years, in some parts of the world. Climate changes could have major effects on precipitation and runoff. With increase in temperature, evaporation is expected to increase. Wigley and Jones (1985) showed that a modest change in precipitation in the river basins with low precipitation could have proportionally large impact on water supplies. This is particularly important for the arid and semi-arid regions like Pakistan. Masood and Ullah (1991) examined impacts of future climate change on water availability in the Indus River basin. They used a 30-year historical discharge data for comparing the results. The UBC-Mongla watershed model was used to forecast inflows to the Mongla reservoir. The model requires daily temperature and precipitation data as inputs. Three arbitrary climate change scenarios were used for the assessment. Results are presented in Tables 8.10, 8.11, 8.12 and 8.13 and they demonstrate few observations. • • • •

Negative changes occur in the Kharif season, and positive changes occur in the rabi season. Nevertheless, without a change in precipitation, the net change is negative in both 2020 and 2050. Based on water requirements for crops and current yields, efficiency of both water and land-use has to be doubled by 2020. Evaporation rates from lakes, ponds, ground water, flowing water and water supply systems are expected to increase as a result of rise in temperature (Table 8.13). It is estimated that an additional 1.476 mha-m and 1.845 mha-m must be added to the system by 2020 and 2050, respectively to meet increasing water demand of the key economic sectors.

With base%

Flow Inflow Change With base% Month Inflow Change

With base%

Flow Inflow Change With base% Month Inflow Change

2 0.40 0.009 -2.28

1 0.664 0.077 -1.41

2 0.394 0.153 -4.56

Main 15.99 -0.11 -0.679

1 0.67 0.004 -0.70

Main 16.04 -0.05 -0.34

0.91

4 0.37 0.003 2.41

5 0.40 0.009

o

3.14

6 0.76 0.023

3 0.375 0.123 -3.88 1.81

4 0.377 0.055 4.83

5 0.413 0.155 6.29

6 0.779 0.375

8 1.67 0.002 -0.17

9 2.67 0.059 -2.18 -2.84

10 3.74 0.109

4.00

7 1.138 0.356

8 1.669 0.047 -0.34

9 2.618 0.972 -4.37

10 3.632 1.778 -5.68

PPT per decade) Rabi Kharif 3.00 14.76 0.028 -0.221 0.954 -1.474

2.00

7 1.12 0.021

Main scenario-2050 (0.3 C and o% Eastern Others 1.21 0.571 -0.054 -0.028 -4.341 -4.750

3 0.38 0.007 -1.94

Main scenario-2020 (0.3 C and o% PPT per decade) Eastern Others Rabi Kharif 1.23 0.59 2.99 14.87 --0.02 -0.01 0.01 -0.11 -2.17 -2.37 0.48 -0.74

o

Table 8.10 Summary of regional, seasonal and monthly inflows (in mha-m) and variations

0.92

12 1.81 0.016

1.21

11 3.874 0.377

1.84

12 1.827 0.268

Total 17.76 -0.193 -1.07

0.60

11 3.85 0.023

Total 17.86 -0.07 -0.54

A. S. QURESHI 219

6.19 10.27 6.03 5.85 3.63 0.25 0.82 4.68 -4.58 3.15 -0.30 2.42 2.21

-0.54

Total

2020 o 0.3 C & +1%PPT

0.91 2.41 3.14 2.00 -0.17 -2.18 -2.84 0.60 0.92 -0.70 -2.28 -1.94

o

0.3 C & 0%PPT

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Year Month

-3.35

-2.25 -1.88 -0.78 -1.92 -2.95 -4.71 -5.49 -1.99 -1.90 -3.12 -4.68 -4.59

o

0.3 C & -1%PPT

Table 8.11 Monthly change (%) from baseline for the three climate scenarios

-1.07

1.81 4.83 6.29 4.00 -0.34 -4.37 -5.68 1.21 1.84 -1.41 -4.56 -3.88

o

0.3 C & 0%PPT

8.47

15.29 24.11 18.03 22.76 9.91 0.64 2.00 11.05 12.28 7.82 0.87 7.14

2050 o 0.3 C & +1%PPT

-6.69

-4.50 -3.75 -1.56 -3.85 -5.89 -9.42 -10.98 -3.98 -3.80 -6.24 -9.35 -9.18

o

0.3 C & -1%PPT

220 WATER RESOURCES DEVELOPMENT IN PAKISTAN

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Table 8.12 Total water availability

Base Source

Total

Rabi

Kharif

Available at rim stations Rainfall in the canal command areas Total inflow 1 Required below Kotri Evaporation @10% 2 Seepage losses@30% Total surface water 3 Ground water Total available Total water availability in 2020* o 0.3 C and 0%PPT/decade o 0.3 C and 1%PPT/decade o 0.3 C and -1%PPT/decade Total water availability in 2050* o 0.3 C and 0%PPT/decade o 0.3 C and 1%PPT/decade o 0.3 C and -1%PPT/decade

17.96 3.08 21.03 1.23 1.98 5.35 12.48 5.41 17.89

2.98 0.58 3.56 0.62 0.30 0.80 1.86 1.35 3.21

14.98 2.49 17.47 0.62 1.69 4.55 10.62 4.06 14.68

17.43 18.03 17.07

3.13 3.25 3.06

14.32 14.79 14.01

17.03 18.21 16.28

3.06 3.29 2.92

13.96 14.92 13.37

Nazir (1993), 1water accord, 2assumed 30% safe potential by Tarar (1997), 3evaporation from ground, @ general mean evaporation. Table 8.13 System evaporation

Year Growth Rate Dams Lakes/ponds From ground From flowing water Water supply @15% Net losses

o

o

Base 1990

0.9 C 2020** High Low

1.8 C 2050** High Low

0.33 0.52 1.23 3.69

0.36 0.54 1.30 3.87 0.20 6.27

0.369 0.5781 1.3776 4.0713 0.3075 6.7035

5.77

0.36 0.54 1.30 3.87 0.20 6.29

0.37 0.58 1.38 4.07 0.38 6.78

*Based on existing rate of evaporation in the system (Nazir, 1993), ** mean evaporation for climatic scenarios. Water supply served to 50% of the population in 2020 and 75% in 2050. 1.23 ha-m storage added to the system in 2020.

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WATER RESOURCES DEVELOPMENT IN PAKISTAN

•

8.4.2.2

Increases in precipitation may result in higher inflow. This may open an oppportunity of construction of small dams. However, higher precipitation may also result in increased silt loads, which may affect the structural stability of the dams. A decrease in precipitation would increase evaporation losses, and shallow ponds would dry more quickly. HYDROPOWER GENERATION

Climate change may have some effects on the hydropower generation. Currently hydropower contributes 28% to the country’s total generation of 17,651 MW. The share of hydropower may increase in future as the government of Pakistan is planning to reduce the overall cost of generation. Note that the cost of generation has increased due to fossil fuel based generation, which constitutes 72% of the supply (MOE, 2003). The MOE (2003) estimated the likely effect of climate change on the hydropower with some arbitrary scenarios (Table 8.14). In the 2020, under no change in precipitation scenario, the increase in hydropower is 1.5 MW. However, with only 1% increase in precipitation, the generation may increase by 50 MW. With a drought scenario, the generation may be reduced by as much as 200 MW. Therefore any reduction in precipitation will have a significant impact on the hydropower generation (MOE, 2003). Table 8.14 Changes in hydropower generation at main dams

Scenarios 0.3oC + 0% PPT* 0.3oC + 1% PPT 0.3oC - 1% PPT

2000

Changes (in %) 2010 2020

2050

0 0.02 -0.01

0.04 0.86 -0.83

0.22 4.32 -3.85

0.03 1.98 -1.46

* These scenarios are at the low end. GOP/UNEP (1998) constructed climate change scenarios for Pakistan, which showed +0.9oC temperature increase and ±3% increase in precipitation for 2020.

8.4.2.3

IMPACTS ON THE COASTAL REGION

Pakistan, with its thousand kilometers long coast is particularly vulnerable to the effects of sea level rise. Karachi, the largest city of Pakistan where almost 10% of the total population lives, and about 40% of all manufacturing units is situated on the coast. Analysis of tide gauge data processed at the National Institute of Oceanography (NIO) shows that along the coast, sea level rise is approximately 1.1 mm/year. This is similar to the global mean sea level rise of 0.1 m-0.2 m reported by the IPCC (2001). Therefore, Pakistan may experience a sea level rise similar to the maximum global projected rise of 90 cm by 2100 (MOE, 2003). The primary impacts of sea level rise impacts may include increased risk of erosion, coastal flooding, permanent inundation and displacement of wetlands and lowlands and degradation of water quality through salinization of ground and surface water (MOE, 2003). Increased risk of occurrence of severe cyclones and storm-surges is also expected due to changes in air and sea surface temperatures. Cyclones are associated with high-pressure gradients and consequent strong winds and heavy rains, while a storm-surge is an abnormal rise of sea level near the coast caused by a severe cyclone. As a result seawater inundates vast stretches of low lying coastal area and cause extensive

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damage to life and property by drowning human beings and livestock, eroding beaches and embankments, destroying vegetation and reducing soil quality and fertility. 8.4.2.4

COASTAL EROSION

Severe erosion has been reported in the islands at the approaches of the creeks in the Indus Delta (MOE, 2003). The creeks which are near the present outfall of the Indus River, at the concave bulge of the delta are facing erosion due to natural hydraulic forces which include reduction in the supply of sediments by the river and wave actions in the comparatively recently formed delta together with the arid condition of the delta itself. On the West (Makran) Coast, erosion already threatens coastal property, coastal agriculture land and habitats, and a further sea level rise may intensify such damaging effects (MOE, 2003). 8.4.2.5

IMPACTS ON THE INDUS DELTAIC COAST

Until now, it is not clear whether the combined effects of climate change and water development will yield more or less water for the Indus Delta (MOE, 2003). If freshwater inflows to the delta were reduced by climate change and upstream water development, the historical processes of economic and ecological degradation would continue. Establishment of minimum flows from Kotri Barrage to the Indus Delta is an urgent policy issue to offset the likely effect of climate change. 8.4.2.6

INUNDATION OF COASTAL AREAS

Sea level rise can cause significant flooding impacts in the coastal zone, particularly in the low-lying deltaic regions. These areas would become more vulnerable to flooding because a higher sea level provides a higher base for storm-surges to build upon. For example, the shoreline of Karachi has retreated in recent decades. More severe monsoons and a rise in sea level may inundate city’s street for longer periods by reducing coastal drainage. The Indus Delta, South of Karachi, already retreating because of a sharply reduced silt load, could lose up to 25% of its area due to sea level rise (climate.org, 2004). 8.4.2.7

SALINIZATION OF SURFACE AND GROUND WATER

Because of sea level rise, saltwater will penetrate further upstream and inland, as was the case in the lower Indus Plain (Fig. 8.12). This effect would be particularly evident during a drought. Sea level rise would also enable saltwater to ingress farther inland, and upstream into the rivers, wetlands, and aquifers, which would be harmful to aquatic flora and fauna, and would threaten human uses of water (MOE, 2003). Increased salinity has already been reported in most of the coastal areas especially in the lower deltaic plain region. A rising sea level, combined with decreased river flow and sediments dispersal, would result in a land ward penetration of the saltwater wedge within the columns of ground water aquifers. This would have significant implications for communities living in the coastal regions. 8.4.2.8

CYCLONES AND STORM-SURGES

The Indus deltaic creeks are critically located on the path of cyclones of the Arabian Sea (MOE, 2003). One cyclone/year is usually generated in the Arabian Sea. About 75% of

224

WATER RESOURCES DEVELOPMENT IN PAKISTAN

these cyclones lands at the Omani Coast on the Western Arabian Sea and the remaining 25% move clockwise and cross the coast near the Rann of Kutch. Sometimes the cyclones cross the Indus deltaic coast. The frequency of cyclones in the Arabian Sea was 0.86 per year for the period 1891 to 1960 and 1.25 per year for the period 1967 to 1970 (MOE, 2003). Analysis of five years’ data (1992-1996) shows that the frequency has increased significantly to 2.2 cyclones per year. In the last seven years (1996-2003), two cyclones have crossed the Indus deltaic coast, whereas in the period from 1891 to 1960, only six cyclones crossed the Indus deltaic coast. Climate change is postulated to increase the frequency and severity of cyclones and storm-surges along the coast of some areas (IPCC, 2001). Table 8.15 gives the maximum surges at different sites. The model was calibrated using this basic data.

Fig. 8.12 Seawater Intrusion Observed from Satellite (Source: MOE, 2003). Table 8.15 Maximum surge at selected sites

Location Gwadar Pasni Ormara Sonmiani Karachi PQA Keti Bander

Coastal Surge Height (m) 0.82 1.40 1.00 1.70 2.56 2.40 2.01

The problems of water resources management in Pakistan are complex and a straightforward solution seems impossible. In order to increase agricultural production and sustainability of irrigated agriculture, the overall strategy should be to increase water capital and make better use of water. For quick recovery of water sector, increase in crop

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production and improvement in water use efficiency and environmental sustainability, following steps may be identified. 8.4.3

IMPROVEMENTS IN THE WATER USE AND SYSTEM EFFICIENCIES

Future prosperity will depend to a considerable extent on how well we harness our freshwater resources and how efficiently we use them. The way water is being used will have to change significantly if sustainable development is to be achieved in Pakistan. Despite the overall shortage, the overuse of water in irrigation is a major problem in Pakistan. Farmers are ignorant of actual crop water requirements and irrigation practices are still largely based on the maximum amount of water a farmer can capture. Therefore present irrigation practices of farmers include a tendency to over-irrigate, whereas the opposite should be accomplished. The impact of this is not only the wastage of water, which could be directed to other sectors or expansion of agriculture, but it also leads to water logging and salinity. This situation is directly related to the low efficiency of irrigation system and poor irrigation management at farm and system levels. This, in turn, has led to a reduction in crop yields (a reduction of 25% overall and a high of 40%-60% in Sindh) lower overall agricultural productivity and loss of cultivable land. It is estimated that about 50% of the water is lost from canal heads to the root zone. Increasing irrigation efficiency, therefore, will result in improved crop yield and overall agricultural productivity as well as reduced water use. There is also a need to reduce the water losses from the water supply systems. The water use efficiency both in irrigation and water supply sub-sectors in Pakistan are very low. Therefore water conservation is critical to meet the needs of all water sub-sectors. This will require a concerted effort in watershed management to reduce degradation of upper catchments so that runoff is moderated and sedimentation is minimized. The greatest effort in water conservation should be made in the irrigated agriculture sub-sector because this is by far the greatest user of water. Even relatively modest improvements in irrigation efficiency will result in significant reductions in water use, which can then be reallocated to other uses, primarily urban and rural domestic water supplies. Improved water management through institutional strengthening and increasing participation of water users in water management will likely have the greatest impact. 8.4.4

SUSTAINABLE GROUND WATER MANAGEMENT

Increasing demand for water has put enormous pressure on the ground water resources. Consumption of ground water has reached the upper limit in most parts of Pakistan. The ground water tables in most of the freshwater areas are falling and therefore the potential of further ground water exploitation is very limited. In Balochistan, ground water tables are falling at a very fast rate and it is estimated that deficit in Quetta sub-basin is about 26 million cubic meter (mcm) per year. With this frequency, aquifer storage will be exhausted in 20 years. The overexploitation of this resource has caused devastating impacts on drinking water supplies for urban and rural population. For the preservation of this future resource, the government needs to develop appropriate policies to effectively manage and monitor ground water development and use. Steps should be taken for the revision and enforcement of ground water regulatory laws. Communities should be directly involved in the campaign of artificially recharging the aquifers and in the conjunctive use and management of surface and ground water resources.

226

8.4.5

WATER RESOURCES DEVELOPMENT IN PAKISTAN

ADJUSTMENTS IN THE LAND-USE PATTERNS

The types of crops grown need to be rationalized to ensure that the crops grown are efficient in terms of water use and economic productivity. The traditional cropping pattern of rice and wheat has benefited from increased irrigation supplies. Since rice is a water-intensive crop, it is essential to review whether Pakistan should continue to grow rice for export or instead use this water for other crops where the country has a comparative advantage. A review of the past five years of agriculture in all four provinces clearly demonstrates that the traditional cropping patterns are economically taxing. Modern research has shown several alternative cropping patterns that can raise productivity of existing farm systems. In the intensive agriculture systems of Punjab, Sindh and NWFP there are ample opportunities to increase farmers’ income from technologies such as zero tillage, introduction of high value crops like sunflower, pulses, vegetables and orchards, etc. 8.4.6

INTRODUCTION OF IMPROVED IRRIGATION AND CULTURAL PRACTICES

The major concerns regarding performance of irrigated agriculture in Pakistan are low crop yields and low water use efficiencies at the farm level. The overall irrigation application efficiency in Pakistan is only about 60%. Farmers in Pakistan use basin or flooding method irrigation. The distribution of water in the field is also not uniform due to inadequate land leveling and irrigation application practices. This uneven distribution of irrigation water produces patches of low and high infiltration rates, which in turn produces patches of low and high salinity within the same field. Farmers usually do not have enough knowledge of crop water requirements and their irrigation amounts are based on estimates. Therefore efforts should be made to educate farmers through extension services about the exact amount of irrigation water required for optimizing crop production. Farmers should be encouraged to adopt water conservation measures. Adaptation of water conservation strategies can save up to 25% of the water resources without compromising on crop yields (Sarwar and Bastiaanssen, 2001). The water saved through conservation measures can be used to bring more areas under irrigation. Improved cultural practices such as precision land leveling, zero tillage, bed and furrow planting can also help a great deal for on-farm water saving. Furrow-bed method of irrigation can save up to 40% of irrigation water as compared to basin irrigation method (Qureshi et al., 2002). Farmers should also be encouraged to use water efficient irrigation techniques such as sprinkler and drip irrigation systems. These techniques have been very successful in saving considerable amounts of irrigation water. The sprinkler irrigation for crops and drip irrigation for fruits/forests plants provide an alternate option for farming and resource conservation in these areas. Therefore, there is a need to introduce these systems with operations that are cost effective and adaptable to farmers, crops and physical local conditions. Now a days small and cost-effective pressurized irrigation systems are available. Considering the costs involved in the land development, the investments required for the installation of these systems is feasible. 8.4.7

DROUGHT FORECASTING AND MANAGEMENT

Pakistan has a history of droughts of varying severity and will continue to experience in the coming years due to climatic changes, which are occurring in the region. Given the

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country’s variable climatic conditions and vulnerability to drought, water availability for agriculture is likely to be a subject of debate both for rainfed as well as for irrigated agriculture. Therefore the efficient use of water must be the foundation for a fully productive agriculture sector. Traditional coping and mitigating strategies have been broken down under growing population pressures and the collapse of the rural economy. For poverty alleviation, farmers should be provided with the opportunities to generate off-farm incomes. 8.4.8

PROMOTION OF RAINWATER HARVESTING TECHNOLOGIES

Farmers in the rainfed areas should be encouraged to use water harvesting and watershed management, including more water storage structures both small and large. Farmers should be introduced and trained in the use of modern water saving technologies and crop varieties, which has proven successful in other arid environments similar to Pakistan. One mode of rain harvesting is to channelise rainwater from rooftops through drainpipes into a pit. The terraces and roofs of houses and building complexes can be converted into catchment areas for rainwater by this simple technique. Rainwater harvesting can also be introduced in public and community wells situated near slums and in villages, draining water from nearly rooftops and streets into them. Connecting storm water drain lines to tanks and rivers can greatly improve the water position of a city with little effort and maintenance. 8.4.9

USE OF WASTEWATER FOR AGRICULTURE

The total annual quantity of wastewater produced in Pakistan is estimated at 4.5 BCM. This amount of wastewater can effectively be used for augmenting the water resources. South Africa is currently supplementing its water supplies by the reuse of 4.6 BCM per year of wastewater. Similar approach can be used in Pakistan. The use of wastewater will not only increase the water availability but also reduce the requirement of reducing these pollutants to environmentally acceptable limits. The lack of treatment of wastewater is caused by the lack of investment in this sector and the non-functioning of plants is mainly related both institutional problems and inadequate maintenance. Major industries responsible for the generation of wastewater should be forced to treat it before disposing it into the main water bodies. 8.4.10

USE OF SALINE WATER FOR AGRICULTURE

Presently, the use of saline water is restricted to growing salt resistant crops. Such crops as grasses for fodder, bushes and trees such as eucalyptus have proved in providing a reasonable economic return to the farmers of the saline areas. While this may not have a widespread benefit, there is likely a potential for local improvements in farmer income. There is a strong need to develop techniques for using saline lands and brackish water for major crops. Techniques like sequential biological concentrations have proved to be very successful in growing valuable crops using highly saline waters by providing extra leaching through the installation of efficient drainage systems. These technologies should be tested for local conditions. 8.4.11

IMPROVEMENT OF INSTITUTIONAL ARRANGEMENTS

Lack of coordination between inter-departments at the provincial and federal level has

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been one of the major bottlenecks in successful and effective implementation of various water management strategies. In Pakistan, water resources are managed by different organizations therefore appropriate institutional arrangements should be made for proper coordination of different ministries and line agencies involved in the management of water resources. The roles and responsibilities of these organizations should be clearly defined to avoid overlapping and to ensure effective management of water resources at all levels. 8.5

CONCLUDING REMARKS

Pakistan, once a water surplus country due to extensive water resources of the Indus River and its tributaries, is now fast turning into a water scarce country. It is estimated that to feed the increasing population, 40% more food would be required by the year 2025. On the other hand, due to reduction in present storage capacities and lack of compatible development of water resources, per capita water availability will be reduced to 600 m3 by that time. The scope of expansion in irrigated area will also be limited due to shortage of land and water resources. The problems of irrigated agriculture in Pakistan are complex and no straightforward solution is possible. Large tracts of irrigated lands are salinized and others are under threat. Areas where proper drainage facilities are rare. Due to an overall shortage of canal water, use of poor quality ground water for irrigation has become a necessity. This practice is adding huge amounts of salts in root zone, which are not only aggravating the problem of soil salinization but also reducing crop yields. Therefore Pakistan has to fight at many fronts at the same time. In order to increase sustainability of irrigated agriculture to ensure future food security, the following potential solutions can be suggested: • • • • • •

Improve irrigation efficiencies to save more water for irrigation; Conserve water at all levels and increase productivity of water; Minimize drainage requirements to reduce disposal problems; Evacuate salts from the root zone to arrest soil salinization; Manage water quality to maintain acceptable salt balance at field and system levels; Improve irrigation water distribution to increase reliability of water supply at farm.

The absence of institutional arrangements is perhaps the greatest barrier for the formulation and evaluation of strategic options and monitoring the implementation of national policies for public water sector. Therefore in addition to technical solutions, strong linkages between different organizations involved in the management of land and water resources need to be developed. This will help for consistent planning, coordination and monitoring of policy implementations.

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REFERENCES Asian Development Bank (ADB): Water Resources Strategy Study, Draft Report Vol. 1, Islamabad, Pakistan, 2002. Afzal, M.: Economics of Drainage and Reclamation Measures: A Case Study from Pakistan. Proceedings of 5th International Drainage Workshop, Lahore, Pakistan, ICID-CIID, IWASRI, Vol. III, 6.185-6.194, 1992. Bhutta, M. N: Vision on Water for Food and Agriculture: Pakistan Perspective: Regional South Asia Meeting on Water for Food and Rural Development, New Delhi, June 1-3, 1999. Bhatti, M. A. and Akhtar, M. J. U.: Increasing Irrigated Agriculture Productivity for Poverty Reduction in Pakistan. Proceedings of the 2nd South Asia Water Forum, Islamabad, Pakistan, December 14-16, 2002. Center for International Climate and Environmental Research - Olso (CICERO): Developing Strategies for Climate Change: The UNEP Country Studies on Climate Change Impacts and Adaptations Assessment, Report 2000:2, CICERO, Oslo, Norway. Climate.org: Pakistan Country Report (www.climate.org/pubs/climate_alert/articles/7.4/ pakistan.shtml), 2004. Engelman, R. and Leroy, P.: Sustaining Water: Population and Future for Renewable Water Supplies. Population and Environmental Program, Washington, D.C.: Population Action International, 1993. Government of Pakistan (GOP): Agricultural Statistics of Pakistan 1997-1998. Government of Pakistan, Ministry of Food, Agriculture 7 Livestock, Food, Agriculture & Livestock Division (Economic Wing), Islamabad, 1999. Government of Pakistan (GOP)/United Nations Environment Programme (UNEP): Pakistan Country Case Study on Climate Change Impacts and Adaptation Assessment, UNEP, Nairobi, 1998. Halcrow, S. W.: Pakistan National Water Sector Profile. Report Submitted to Asian Development Bank Under Water Resources Strategy Study-ADB TA 3030 Pak, 2001. Hussain, M.: Water Conservation and Role of Youth. Proceedings of the 2nd South Asia Water Forum, Islamabad, Pakistan, December 14-16, 2002. Haider, G.; Prathapar, S. A.; Afzal, M. and Qureshi, A. S.: Water for Environment in Pakistan. Paper Presented in the Global Water Partnership Workshop Held on April 11, Islamabad, Pakistan, 1999. Haq, A. U.: Case Study of the Punjab Irrigation Department. Pakistan National Program, International Irrigation Management Institute, Lahore, Pakistan, Report No. C-12, 1998. Intergovernmental Panel on Climate Change (IPCC): Climate Change 2001: The Scientific BasisSummary for Policy Makers and Technical Summary of the Working Group I Report, IPCC, Geneva, 2001. International Water Management Institute (IWMI): Water Issues for 2025, A Research Perspective. Research Contribution to the World Water Vision, Colombo, Sri Lanka, International Water Management Institute, 2000. Jurriens, R. and Mollinga, P. P.: Scarcity by Design: Protective Irrigation in India and Pakistan. ICID Journal 45(2) (1996), pp.31-45. Khan, S. R. and Iqbal, F. Y.: Pakistan. In: Confronting Climate Change: Economic Priorities and Climate Protection in Developing Nations (B. Biagini Editor), National Environmental Trust, Washington D.C., 2000, pp.63-92. Ministry of Environment, Local Government and Rural Development (MELGRD)/United Nations Environment Programme (UNEP)/Global Environment Facility (GEF): Study on Climate Change Impact Assessment and Adaptation Strategies for Pakistan, 1998. Ministry of Environment (MOE): Pakistan’s Initial National Communication on Climate Change, MOE, Islamabad, 2003. Nazir, A.: Water Resources of Pakistan, Gulberg, Lahore, 1993. Nespak/MMI: Feasibility Study National Drainage Program I, Executive Summary, NESPAK and Mott MacDonald, Islamic Republic of Pakistan, 1993.

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Pakistan Water Partnership (PWP): Supplement to the Framework For Action (FFA) For Achieving the Pakistan Water Vision 2025, 2001. Qureshi, A. S.; Akhtar, M.; Masih, I. and Bilal, M.: Sustaining Ground Water Boom: Protecting Food Security and Small Holder Livelihooh in Punjab, Pakistan. Proceeding of the 2nd South Asia Water Forum, Islamabad, Pakistan, 2002. Qureshi, A. S. and Barret-Lennard, E. G.: Saline Agriculture for Irrigated Lands in Pakistan: A Handbook. ACIAR Monograph 50 (1998), p.142. Qureshi, A. S. and Mujeeb, A.: The Impact of Utilization Factor on the Estimation of Ground Water Pumpage, Journal of Irrigation & Drainage, PARC, Islamabad, Pakistan (in press), 2002. Qureshi, A. S. and Mujeeb, A.: Ground Water Economy of Pakistan. Paper Presented at the Workshop on “Water, Livelihoods and Environment in India: Frontline Issues in Water and Land Management and Policy”. Annual Partner’s Research Workshop-IWMI-Tata Water Policy Program, Institute of Rural Management Anand, Gujrat, INDIA, January 26-29, 2003. Sarwar, A.: A Transient Model Approach to Improve On-Farm Irrigation and Drainage in Semi-Arid Zones. Ph.D Dissertation, Wageningen University and Research Center, Wageningen, The Netherlands, 2000, pp.147. Sarwar, A. and Bastiaanssen, W. G. M.: Long-Term Effects of Irrigation Water Conservation on Crop Production and Environment in Semi-Arid Areas. ASCE Irrigation and Drainage Engineering, Vol. 127, No. 6:331-338, 2001. Tarar, R. N.: Drainage System in Indus Plains - An Overview. In: Proceedings of the National Workshop on Drainage System Performance in Indus Plains and Future Strategies, Tandojam, Pakistan, January 28-29, 1995, Vol. II, pp.1-45. Tarar, R. N.: Surface Water Scenarios in the 21st Century and Needed Actions. Proceedings of the International Symposium of Water for the 21st Century: Demand, Supply, Development and Socio-Environmental Issues. Center of Excellence in Water Resources Engineering, UET, Lahore, Pakistan, June 17-19, 1997. United Nations (UN): World Population Prospect: 1998 Revision, New York: UN Department of Policy Coordination and Sustainable Development, 1999. Warsi, M.: Indus and Other River Basin of Pakistan, Stream Flow Records. Case Study Report, WAPDA, 1991. Water and Power Development Authority (WAPDA): Water Sector Investment Planning Study. Federal Planning Cell/Consultants, Lahore-Pakistan, 1999. Wigley, T. M. L. and Jones, P. D.: Influences of Precipitation Changes and Direct CO2 Effects on Stream Flow. Nature 314 (1985), pp.149-152. Wolter, W. and Bhutta, M. N.: Need for Integrated Irrigation and Drainage Management, Example of Pakistan. Proceedings of the ILRI Symposium. Towards Integrated Irrigation and Drainage Management, Wageningen, The Netherlands, 1997. Zuberi, F. A.: Integrated Surface and Ground Water Management Programme for Pakistan-Ground Water Resources, Interim Report, IWASRI, 1997.

9 Climate Change and Water Resources Management in Bangladesh HOSSAIN SHAHID MOZADDAD FARUQUE1 MD. LIAKATH ALI2

9.1

INTRODUCTION

9.1.1

LOCATION

Geologically major part of Bangladesh is occupied by one of the largest deltas of the world, formed by the Ganges-Brahmaputra-Meghna River system (Fig. 9.1). The quaternary deposits cover more than 85% of the country and the rest is by the folded tertiary sedimentary rocks. Unique geographic and tectonic position and geomorphologic conditions have made Bangladesh meeting place of natural hazards especially most vulnerable to climate change and sea level rise (SLR). Himalayas in the North, close to the subduction zone and Bay of Bengal in the South have made Bangladesh vulnerable to climate changes. Cyclones, storm-surges, floods, droughts, river and coastal erosion, are common in Bangladesh (Alam, 1997). Some part of the country is within the seismic zone. The country has a very low and flat topography, except the Northeast and Southwest regions. It is almost entirely an alluvial deltaic plain with hills on the Northeast coast and Southeast margins. About 10% of the country covering 14,000 km2 is hardly 1 m above the mean sea level (MSL). One-third of Bangladesh is under tidal influence. Generally Bangladesh is blessed with a sub-tropical monsoon climate. There are three prominent seasons in a year namely winter, summer (pre-monsoon) and monsoon. Winter, which is quite pleasant, begins in November and ends in February. Usually in winter there is not much fluctuation in temperature. The normal winter temperature ranges from a minimum of 7°C to a maximum of 31°C. The winter season receives a negligible amount of rainfall and is characterized by low temperature, low humidity and high solar radiation. The summer begins from March through May, with a mean temperature of about 30°C and occasionally a rise above 40°C. The hot summer (pre-monsoon) season receives some rainfall in occasional heavy thunderstorms and hailstorms. The summer is characterized by its highest temperature and evaporation rates. The monsoon (rainy) season begins in June 1

Director General, Water Resources Planning Organization (WARPO), Ministry of Water Resources, Government of Bangladesh. 2 Senior National Expert, Program Development Office for Integrated Coastal Zone Management Plan.

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and continues up to October with maximum temperature usually around 30°C with high humidity and low solar radiation. Mean annual temperature throughout the country is about 26°C but extreme temperatures range from about 5°C to about 43°C (Bangladesh National Committee of the International Commission on Irrigation and Drainage, 1995). The average overall annual rainfall is about 2,300 mm. About 81% of the rainfall in Bangladesh occurs in the wet monsoon period (June-September) (BANCID, 1995).

BASIN BASIN

BASIN

Farakka Barrage Commissioned 1975 Fig. 9.1 Location and major river basins of Bangladesh.

The major water related problems of the country are - floods, droughts, river erosion, land degradation, arsenic contamination in ground water, river sedimentation, low flow in rivers, cyclone, storm-surges, river pollution, etc. To support its agro-economy Bangladesh, ensuring people’s safety and mitigating economic damage, in the past water resources development was focused on flood control, drainage and irrigation activities. 9.2

WATER RESOURCES PROBLEMS AND THEIR MANAGEMENT

9.2.1

FLOODS

Almost every year floods occur in Bangladesh. But the intensity and the magnitude of the floods vary from year to year. In some years, floods occur locally and in others it encompasses vast areas of the country. Floods of 1987, 1988 and 1998 were extensive in a real extent and colossal in terms of destruction (Mirza, 2003). As much as three-fourths of the country was affected in 1998. Floods cause enormous economic loss to the country destroying its infrastructures, standing crops, livestock and also human lives. Natural floods: About one-fifth to one-third of the country is flooded to varying degrees each year during June through September when about two-thirds of the food grain (mainly rice) are produced. The following natural floods are encountered: • • • • •

River flood; Rainfall flood; Flash flood; Tidal flood; Storm-surge flood.

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Table 9.1 shows the flooded area of Bangladesh for different return periods.

The available flood damage information is not always complete. Flood damage assessments are generally prepared by various organizations, which are often not systematic and well coordinated. There is a need for a unified and consistent method of collecting data. One of the best available sources of flood damage information is the Ministry of Disaster Management and Relief. The assessments by various institutions are compiled together into an overall flood damage assessment. The flood control embankment itself suffers substantial damage. Flood damage to embankment has a strong correlation with the magnitude of flood. The properties and infrastructure suffer substantial damage during large and medium floods. It is noted that in addition to damages, there are consequential effects such as reduced employment, industrial production loss, reduced consumer demand, reduced economic activities due to disruption to daily life of poor people, etc. When converted into monetary term, it is found that flood damage to infrastructure and property outweighs the damage to crops. But the misery of the people however cannot be translated into economic indices. The 1988 flood caused over 1,517 deaths and damages variously estimated at about $1,200 million. The lower death toll in the 1998 flood of less than 1,000 and a considerable reduction in livestock deaths (down from 350,000 in 1988 to 26,564 in 1998) reflect improvements in flood preparedness over the intervening period. The 1998 flood forced over a million people out of their homes, damaged 16,000 km of roads and 4,500 km of embankment, and destroyed crops of over 500,000 ha of land. 9.2.2

EROSION

Morphological behavior of the Bangladesh rivers are very unpredictable and in some cases unstable. This has placed them at constant risk from erosion but is most marked along the major rivers and their estuaries. The Jamuna is highly unstable and has occupied its present course for approximately only the last 200 years. Although over the last 25 years there is a negligible net Westward migration overall, both banks are eroding at a rate of about 70 m/year that shows no sign of abating (Nizamuddin, 2001). The Ganges and Padma Rivers erode their banks locally, while the lower Meghna is extremely active at Chandpur, in the region of the Tetulia channels, and in the entire area of Bhola, Hattiya and Sandwip Islands. The impact of erosion can be sudden and dramatic, even when the riverbank is protected. Border river erosion is also an essential issue for Bangladesh. River training and erosion control can be very expensive in human terms because of the amount of land acquisition required. The amount of land needed varies but often a broad strip of riverside land is required. The Flood Action Plan (FAP) 21/22 investigations of river training on the Jamuna required 42 hectares of riverbank land to be procured, so dislodging and disenfranchising many poor families from along the riverbank chars, often with inadequate compensation for land and livelihood lost. Future compensation for land acquired and livelihoods lost, on the basis of the Bhairab Bridge model would be expected to be much more extensive and thus act as a disincentive to large-scale land acquisition.

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River training can also cause social conflict, especially if access to the river for navigation and port facilities, fishing, irrigation, and livestock washing is disrupted. Facilities for mooring are still highly limited and are inadequate under some existing riverbank protection schemes. Many people have been displaced and outstanding cases and claims arising from previous water sector projects have yet to be resolved. Policy stresses the adoption of socially sensitive, multi-purpose schemes and adherence to participatory planning as ways of rectifying these fundamental points of conflict. The popular structural options practicing in Bangladesh for erosion control are: • • 9.2.3

Hard points; Continuous revetments. DROUGHTS

Drought occurs when rainfall is absent for a prolonged period of time, causing earth to parch, wells to dry, underground water level to fall, crops to wither leading to crop failure and scarcity in fodder for livestock. Because of meager supply of water, food and fodder both humans and livestock suffer untold miseries. In some places women had to wake up at midnight to track 5 km to a well to fetch a pitcher full of drinking water (NWMP, 2001). In other areas the affected people relentlessly dug in dry riverbeds and ultimately ended up with a pitcher full of foul smelling, muddy, brackish water unfit for human consumption. Water supplies, the environment, crops and navigation all fall under threat during droughts. In contrast to the high rainfall brought by the Southwest monsoon from May to October, there are months without rain in the dry season. This can bring hardship to people living in areas with poor access to surface water and ground water resources. There were severe droughts in Bangladesh in 1979, 1981, 1982 and 1989 and between November of 1998 and April of 1999; there was a period of 150 days with almost no rain in Bangladesh. A GoB report of the Task Force on Drought on what was seen as an impending drought in 1995 noted that the rainfall in the monsoon had been below average in the NW, SW, NE, SE and SC Regions (Fig. 9.2) by 35%, 20%, 25%, 30% and 15% respectively. Corresponding reduction in surface water availability was expected to be 20%, 20%, 5%, 10% and 5%. Overall, it was expected that the areas under Low Lift Pump (LLP) irrigation would reduce by 55,000 ha. They also expected that GW level would fall by 0.5 m to 3 m, and that 90,000 Shallow Tube Wells (STW) would be affected. The report stated that aman crop production in the 1994 season was reduced by 377,000 tons due to the effects of the drought. The effect of drought is more marked now that irrigated boro (dry season) rice has become the major rice crop. Streams and water bodies used for LLPs dry up, and STWs reach their suction limit of 7 m. Farmers using LLP start abstracting water reserved for environmental needs. STW can be lowered 2 m in pits to reach more water, but deeper setting is difficult. When farmers draw the water down, there is a corresponding fall in the village hand pumps, which are also suction mode pumps, set generally on higher land and consequently more vulnerable. Women seek water from contaminated surface water sources as a result, with corresponding risks to public health and welfare. Thus water supplies, the environment, crops and navigation are all under threat during droughts. Drought monitoring and contingency plans will be prepared for regions that experience recurrent seasonal shortages of water. These will include action to limit the use of ground water to human needs if necessary. Obviously, human needs must take precedence over non-consumptive needs. The government can empower Local

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Government, or any local body it deems fit, to exercise its right to allocate water in scarcity zones during periods of severe drought. This will clearly need to be planned judiciously to avoid bias in allocation to one sector at the expense of another.

Fig. 9.2 Map of 8 hydrological regions as in NWMP, 2004.

9.2.4

LOW FLOW SITUATION

There are 57 international rivers flowing over Bangladesh with 54 of them entering from India and 3 are from Myanmar. There have been disputes over sharing the water of the international rivers. There is only one Treaty signed in 1996 with India to share waters of the Ganges River in the dry season (January-May). Bangladesh needs these water sharing treaties/agreements on all other international rivers to estimate the magnitude of cross-border inflows in order to facilitate its water management plans. Presently especially in dry season due to upstream withdrawal/control most of the river flows fall dramatically to a very low level on which Bangladesh has no control.

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WATER RESOURCES MANAGEMENT IN BANGLADESH

WATER MANAGEMENT

During the past decades since 1960s huge investments (an average annual development allocation from the financial year 1990-1991 to 2000-2001 was 173 million dollars) have been made in flood management, drainage and irrigation schemes and to reclaim and develop many polder areas. In these areas a careful water management is required to get optimal results from the investments in the physical infrastructure and enable the farmers to have a reasonable living. However, although in the initial year after completion of the polder projects it gave remarkable results but now over the years the actual water management in the Flood Control and Drainage (FCD/FCDI) schemes of coastal polders has been below expectation, resulting in lower yields than were envisaged during the feasibility, design and construction stages. Water management in FCD schemes is complex and fundamentally different from traditional water management in irrigation systems. A distinct characteristic of water management in FCD schemes is that there are many different stakeholders, each with different, often-conflicting water management demands. The stakeholder’s occupation or the location of the land owns determine his level of interest in water control. So, participation of stakeholders in the context of FCD schemes in Bangladesh is crucial for the planning and design of sustainable water management schemes. 9.2.6

CLIMATE VARIABILITY AND CHANGE

The IPCC (2001) in its Third Assessment Report concluded that there was new and stronger evidence that most of the warming observed over the second half of the last century was attributable to emissions resulting from human activities. It further observed that it was very likely that the 20th century warming has contributed significantly to the observed sea level rise, through thermal expansion of seawater and widespread loss of land ice. Projected temperature, precipitation, extreme weather events and sea level rise have been summarized in Box 9.1. In Bangladesh and the adjacent region, mean observed temperature change in the last century was 0.4°C, which is comparable to the observed global mean temperature change (0.6 ± 0.2°C). No discernible changes in precipitation were observed in the same period. The mean tidal level at Hiron Point (in the Sundarbans) has shown an increasing trend about 4.00 mm/year. Similarly near Meghna Estuary and near Cox’s Bazaar it has registered a positive trend of 6.00 mm/year and 7.8 mm/year respectively. The increment in SLR along the Bangladesh coast is much more pronounced as compared to the global rate (SMRC, 2000).

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Bangladesh, one of the most densely populated countries in the world, is a victim of frequent natural calamities like tropical cyclones, storm-surges, tornadoes, floods and droughts. In the wake of global climate change, the SLR has emerged as a new threat. The SLR is likely to have greater impact on Bangladesh due to its low and flat topography and a vast floodplain. Since 21% of the population lives in the low coastal belt, any increase in sea level will be a problem of ominous proportion for Bangladesh (SMRC, 2000). Sea level and temperature rise, increased evaporation, changes in precipitation and resultant changes in cross boundary river flows are identified as the agents of change, which cause the most threatening impacts in the natural, social and economic systems of the country. Climate change in the future may compound water resources management problems in Bangladesh. People in Bangladesh are generally adapted to natural climate variability. However, there is a necessity of a greater focus on adaptation in the context of climate change when serious socio-economic damages are expected to occur. In the water resources sector, the following key areas have been identified for adaptation: drainage congestion, reduced freshwater availability, disturbance of morphologic processes and increased intensity and duration of flooding associated with river erosion and disasters. This chapter addresses possible impacts, key water management issues and adaptation measures for the water resources sector in the context of future climate change. Section 9.3 illustrates present water management practices. Section 9.4 describes major studies carried out on the complex water systems in Bangladesh and states salient findings. 9.3

WATER MANAGEMENT PRACTICES

An important characteristic for classifying Flood Control and Drainage (FCD) schemes is the type of flooding they are subjected to. This classification ties in with the four different types of floods in Bangladesh, namely: • • • •

Rainfall floods; River floods; Tidal or coastal flooding; Flash floods.

It is possible to classify FCD schemes as drainage-only schemes, high level of protection against river flood schemes, protection against tidal flooding (coastal polders) schemes and protection against flash flood (Haors - partial protection by submersible embankment) schemes. Flash floods may occur in the Eastern, Northern and the Northeastern areas of the country at anytime during the wet season. A flash flood is characterized by a sharp rise followed by a comparatively rapid recession. The duration of high flood stages may be for a few days only. A rapid rise in river stage and associated high velocity may cause large damage to crops and properties. The tidal floods, is typical for the coastal zone. Coastal areas consist of large estuarine channels, extensive tidal flats, and low-lying islands. High tides regularly inundate large tract of these areas. During extreme monsoon storms freshwater runoff from the big rivers, combined with wind and wave set-up caused by strong Southern winds, inclines the sea surface on the Bay of Bengal. Therefore the maximum water levels are higher than the predicted tides. Tide levels determine the inland tidal flooding with saline tidal waters, causes damage to standing crops. Wet season floods and water scarcity during the dry season are major challenges for water resources development and water management in Bangladesh. Cropping patterns

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and crop yields in the floodplains are strongly affected by floods, as are fisheries and transportation. In coastal areas, salinity and cyclones are additional factors influencing farming systems. Over the years the primary objective of water management activities focused heavily to increase agricultural production through the provision of one, or any combination, of the following measures: flood control, drainage, reduction of salt intrusion, and irrigation. The most commonly used structural options for flood control measures practiced in Bangladesh are: a) river embankments, b) construction of dams, c) reforestation, d) network as drainage channels, and e) pump drainage. Flood Control and Drainage (FCD) schemes are located in the floodplains of the rivers in Bangladesh and also in the coastal areas. Embankments along the periphery provide protection against river, or sea floods, or against salt intrusion. Where necessary, sluices are placed in the embankments to drain natural khals (natural channels which connect the low-lying area and the rivers). Many inland FCD schemes have field depressions that contain water during most or all of the year, called beels, in their interior. They are often connected to rivers through a network of khals or man-made canals and can only be drained when river levels permit. In most FCD schemes there are nowadays three distinct cropping seasons, namely: Kharif-I (mid-April to mid-July), Kharif-II (mid-July to mid-November) and Rabi (mid-November to April). From an agricultural perspective the FCD schemes are designed to: • Protect standing aus against early river floods (Kharif-I); • Expand the area under aman by excluding flood from the schemes (Kharif-II); • Retain water in the system during the post-wet season (Rabi). Water management is a dominant feature of life in rural Bangladesh. It has many forms and is regulated by many institutions, including customary rights, traditions and social norms, as well as more formal types of organizations. Every farmer, every fisherman and the villagers who are not a farmer or a fisherman manage water, both individually and collectively. In addition, there are specialized groups whose whole livelihood depends on their ability to manage these resources: professional fishing communities, boatmen, net makers, shrimp farmers, salt producers, irrigation pump owners and many others. Water management in FCD schemes is the control of water surpluses, shortages and quality by adequate operation and maintenance of system elements as canals, sluices, and embankments to obtain optimal conditions for activities within the boundaries of the FCD scheme. 9.3.1

WATER MANAGEMENT CONFLICTS

All the stakeholders of a polder do not have the same interest. So, conflicts are found in almost all types of FCD schemes. These conflicts prevail between: • • • • •

Large farmers and small or marginal farmers; Highland and lowland farmers; Farmers and fishermen; Farmers and boatmen; and Protected versus unprotected neighborhoods.

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For various reasons operation and maintenance of the FCD schemes is not up to the mark. Lot of effort is still needed to improve this situation. 9.3.2

ROLE OF FCD/FCDI SCHEMES

The rural economy of Bangladesh is changing under the influence of many other factors than water control interventions. New crops and cropping techniques now emerged, new markets developed, transport patterns changed, population pressure increased, new investments are made, technologies for managing land and water improved and many other developments imply that rural areas are experiencing dynamic transformations. FCD schemes created a physical environment for intensive cultivation with High Yielding Varieties (HYV) crops. They helped in two ways first by saving crops for damages, second created opportunity for irrigation practices. In most cases FCD schemes are justified on the grounds that they will improve conditions for agriculture. More particularly, depending on the scheme, it is argued that (Flood Plan Coordination Organization, 1992): • • • •

9.3.3

Reductions in normal wet season water levels, duration, and rates of rise in water level will encourage farmers to adopt more productive crops (rice varieties) which cannot tolerate unmanaged wet season conditions; Damages due to unusual floods will be reduced, resulting in higher average yields for a given crop; Reduced variation in wet season conditions will reduce the risks faced by farmers, who are then encouraged to adopt HYV technology (which would otherwise entail high losses in flood years, while the costs of production are higher); Irrigation makes a change possible from low yielding rabi crops to more profitable and productive HYV boro in the dry season. IMPACTS ON AGRICULTURE

The primary objective of the FCD schemes of increasing food production has largely been achieved. The farmers are ready to invest in more input required by High Yielding Varieties (HYV), partly because there are fewer losses due to flooding (Flood Plan Coordination Organization, 1992). In some of the schemes, the targeted agricultural growth rates were exceeded considerably. In others, the targets seem to have been too high. Most of the gain in production has come from the shift to improved varieties of rice, which shows the confidence of the farmers in the performance of the schemes. Drainage congestion is the major constraint to further growth in rice production. Improvements in design and maintenance would result in further increase, especially if the drainage problem can be dealt with adequately. In a floodplain setting, some drainage congestion is probably unavoidable and a costly solution may not be feasible. The agricultural performances in the four selected FCD schemes are summarized in Table 9.2. 9.3.4

IRRIGATION AND GROUND WATER

In Bangladesh irrigation is categorized as either minor or major irrigation. Minor irrigation comprises mostly of farmer-operated and owned tube wells and low-lift pumps, and also a small area irrigated by government owned DTWs and small Local Government Engineering Department (LGED) projects (85%). Major irrigation comprises the Bangladesh

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Water Development Board (BWDB) surface irrigation projects and accounts for 8% of the total. Rest was covered by traditional irrigation practices. Most of these BWDB projects have flood control and drainage (FCD) component. Irrigation in Bangladesh is therefore largely in the hands of the private sector. Ground water is the most important source for domestic, industrial and irrigation supplies at present. Present trends in ground water development for irrigation have shown the alluvial aquifers of Bangladesh to be amongst the most productive in the world. The aquifer is recharged through rainfall and flooding, and replenishes every year, except beneath Dhaka City where ground water abstraction now exceeds recharge. The estimate of potential recharge is particularly sensitive to the deep percolation characteristics of the sub-soil, monsoon rainfall depth and the extent of flooding during the monsoon. Expansion of ground water irrigation nevertheless causes seasonal water levels to decline further, although in those areas where irrigation is already highly developed, this results in a small change from the current levels. Increased seasonal draw down is of significance both to rural water supply planning, as well as to the types of technologies required for irrigation abstraction. Fully understanding the sustainable limits of GW use and the impacts that quality has on its utility, and the long-term strategic implications. 9.3.5

FISHERIES SITUATION

Fishing is the main source of income for the professional fishing communities found throughout Bangladesh. These communities are increasingly facing problems, with declining fish stocks and reduced access to open water bodies. These processes are aggravated further by hydraulic structures, through interrupting fish migration and excluding floodwaters. The farmers on the other hand require more secure water management conditions by expanding both irrigation and farming areas. The present availability and future sustainability of both subsistence and professional fishing is a key aspect of water management in the polder areas. Fisheries and FCD development are in principle in conflict with each other. The sweet water capture fisheries resources are dependent on inland water bodies. The annual flooding and post-flood standing water in the floodplains has a significant role in fish production. In the wet season, floodplains play the primary role of re-population and increase of biomass in open water fishery production systems. However within the flood controlled areas the culture fisheries have expanded dramatically in the recent years (BWDB, 2000). 9.3.6

ENVIRONMENTAL IMPACTS

Flood protection schemes bring about overall improvements, through the reduction of flood depth to ensure more secure environments for living as well as for agriculture. However, they can also bring about drastic changes in the natural water regime, which may result in an imbalance in aquatic environments and ecosystems. For example, structural interventions disrupt the free flowing environment of the floodplains. Moreover, continued congestion, or stagnation can prevent natural flushing and lead to the spread of water-borne diseases that may threaten public health. Total elimination of floodwater can also severely impact ground water recharge. Cultivation, land settlement, vegetation clearance, hunting and fishing, all have increased in the scheme areas as population density has increased at an alarming rate over the last few decades. The FCD schemes undoubtedly contribute to the loss of bio-diversity particularly of aquatic, birds and vegetation species.

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Analyzing the description presented above it is clear that the water development in FCD schemes have a wide diversity in demands and interests of the stakeholders. It shows a wide scope for integration among the various aspects of water development, especially environmental, agricultural and institutional aspects. In the past no legal framework and no water rights for stakeholders participation in the development and management of FCD schemes were defined. Recently this has been outlined for new reclamations, as well as for improvements in the existing schemes. Conceptual understanding and recognition of the importance of operation and maintenance in FCD schemes need to be improved. Now it has been mandatory to conduct Environmental Impact Assessment (EIA) for all types of FCD/I projects. 9.3.7

OPERATION AND MAINTENANCE

FCD schemes need to develop sustainable means for the operation and maintenance of hydraulic structures and watercourses. This requires a sound and sustainable financial base. This has never been the case within any responsible agency, specially the Bangladesh Water Development Board, which continually suffers from inadequate Government budget. Which demands for the need for alternative financing. This need resulted in the proposal that the stakeholders should pay for, or contribute to the services they receive. Measures would have to be taken to transfer at least a part of the responsibilities and financial contribution to operation and maintenance of irrigation and drainage infrastructure from the public sector to the stakeholders. Legal provisions for collection of water fees have been brought in place for some years. Application of these rules has been inadequate and the collection of fees is minimal. 9.4

MAJOR STUDIES, POLICIES AND PLANS

Bangladesh has suffered from the twin problems of ‘floods and droughts’ for centuries. After the country had suffered from unprecedented floods in two consecutive years 1954 and 1955, a flood commission was constituted in December of 1955 by the government to look into the problems and to advise on remedial measures (East Pakistan Water and Power Development Authority, 1964). Subsequently, a UN Technical Assistance Mission popularly known as the Krug Mission reviewed the gigantic problems associated with the floodings and submitted a report in 1957. Based on the recommendations of the Krug Mission, the East Pakistan Water and Power Development Authority (EPWAPDA) was created in 1959 for the unified and coordinated development of the water and power resources in the present Bangladesh. In the context of the increased need for agricultural development, in 1961, the East Pakistan Agricultural Development Corporation (EPADC), presently the Bangladesh Agricultural Development Corporation (BADC) was created to supply seed, fertilizers, pumps and other production inputs to farmers. 9.4.1

EPWAPDA MASTER PLAN (1964)

The EPWAPDA, with the help of the International Engineering Company Inc. (IECO), prepared a Master Plan for water resources development in 1964 (EPWAPDA, 1964). This plan marked the beginning of the formulation of an integrated plan for flood control and development of water resources of the country. The Master Plan organized the limited available hydrological data and recommended emphasis on systematic and scientific

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hydrological data collection and processing. The Master Plan included a portfolio of 58 land and water development projects including 3 barrages on major rivers for implementation spread over 20 years, beginning in 1965. These projects envisaged flood protection for 5.8 mha of land. Not all the identified projects were taken up for implementation mainly due to shortage of funds. Irrigation within the flood-protected areas was foreseen, but emphasis was given to flood control through a system of dykes and polders. Three alternative options were proposed: • • • 9.4.2

Flood embankments with gravity drainage; Flood embankments with tidal sluice drainage; Flood embankments with pump drainage. IBRD & IDA REPORT (1972)

The emphasis shifted from large-scale projects for high-level flood control to quick yielding smaller irrigation projects following the IBRD’s Land and Resources Sector Study in 1972. The development of minor irrigation through low lift pumps (LLP) and tube wells, to some extent supported by complementary low cost FCD schemes, was advocated. 9.4.3

FLOOD ACTION PLAN

The disastrous floods that struck Bangladesh in 1987 and 1988 attracted worldwide attention and resulted in a concentrated international effort to find a long-term solution to the persisting flooding problem. As a result the Flood Action Plan (FAP) was initiated in 1989, which was coordinated by the World Bank. The Government of Bangladesh setup the Flood Plan Coordination Organization (FPCO) in 1990 to supervise, coordinate and monitor the FAP activities. Under FAP project, 26 studies were conducted. The FAP studies culminated in the Bangladesh Water and Flood Management Strategy (BWFMS) report (FPCO, 1995). The report noted the limitations of earlier master plans, which had focused too heavily on agriculture development without adequate consideration of the needs of the other sectors. A widespread criticism of earlier plans was that the social and environmental impacts of land and water development were not being addressed. Responding to this, the BWFMS recommended that the government should formulate a national water policy that addressed these issues and that a comprehensive National Water Management Plan (NWMP) should be prepared within this framework. 9.4.4

GUIDELINES FOR PEOPLES PARTICIPATION (GPP)

In August of 1994 the Ministry of Water Resources issued Guidelines for Peoples Participation in Water Development Projects. Through the approval of these guidelines in June of 1995 the Government of Bangladesh expressed its commitment to participatory water management in FCD schemes. In April of 1998 the Bangladesh Water Development Board came out with the revised guidelines. In view of too many guidelines already formulated and more than one agency being involved in the process, in May of 1999, an inter-agency taskforce committee was constituted to integrate all approaches in this regard for developing the guidelines for participatory water management. The committee finalized a guideline in 2001.

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9.4.5

NATIONAL WATER POLICY

Past experiences in the water sector development showed the necessity of a good water policy in Bangladesh. The National Water Policy was finalized in 1999 (Ministry of Water Resources, 1999). In this policy it was noted, among others, that the lack of inter-agency coordination among the various organizations (government and non-government organizations). The National Water Policy will be reviewed periodically and revised as necessary. Objectives of the National Water Policy are broadly: • • • • • •

9.4.6

To address issues related to the harnessing and development of all forms of surface water and ground water and management of these resources in an efficient and equitable manner; To ensure the availability of water to all elements of society, including the poor and the underprivileged, and to take into account the particular needs of women and children; To accelerate the development of sustainable public and private water delivery systems with appropriate legal and financial measures and incentives, including delineation of water rights and water pricing; To bring institutional changes that will help decentralize the management of water resources and enhance the role of women in water management; To develop a legal and regulatory environment that will help the process of decentralization, sound environmental management, and improve the investment climate for the private sector in water development and management; To develop a state of knowledge and capability that will enable the country to design future water resources management plans by itself with economic efficiency, gender equity, social justice and environmental awareness to facilitate achievement of the water management objectives through broad public participation. NATIONAL WATER MANAGEMENT PLAN (NWMP)

One of the main proposals of the Bangladesh Water and Flood Management Strategy was the preparation of a broad-based National Water Management Plan (multi-sectoral). The emphasis is on year round water management, social and environmental considerations, full participation of stakeholders, particularly affected people, in the planning process and institutional development. The Water Resources Planning Organization started the preparation of the plan in March of 1998. They have already published the draft development strategy for the plan. Now the plan is in the final stage and waiting for approval. 9.5

CLIMATE CHANGE AND WATER RESOURCES SECTOR IN BANGLADESH

The population of Bangladesh is already severely affected by storm-surges. Catastrophic events in the past have caused damage up to 100 km inland. It is hard to imagine to what extent these catastrophes would be with accelerated sea level rise. In Figure 9.3, digital terrain modeling techniques have been used to display the impact of sea level rise in Bangladesh. A three-dimensional view of the country has been overlaid with the current coastline and major rivers and potential future sea levels at 1.5 m. Since

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this scenario was calculated in 1989, the expected rate of sea level rise has been modified. At present expected rates, this stage can occur in about 150 years from now.

Dhaka

Dhaka

Fig. 9.3 Potential impact of SLR on Bangladesh (UNEP).

Even a very cautious projection of 10 cm sea level rise, which may likely happen well before 2030, would inundate 2,500 km2, about 2% of the total land area. Patuakhali, Khulna and Barisal regions are most vulnerable. The probable effects of global climate change have been examined in general by the Intergovernmental Panel for Climate Change (IPCC) and are regularly reviewed. Warrick, BCAS, Stratus and Huq have studied the findings for Bangladesh in detail. The most recent projections set out the changes anticipated in Bangladesh, which are (NWMP, 2001): • •

A rise in sea level in the order of 30 cm by the year 2030 and 70 cm by 2075. This suggests a rise of 25 cm by 2025, at the rate of 1 cm/year. An increase in monsoon rainfall of about 10% by the year 2030 and 25% by 2050. Dry season rainfall is projected to reduce in the long-term.

Climate change will also affect flows in the Transboundary Rivers. Temperature changes would affect the timing and rate of snowmelt in the upper Himalayan reaches, which would alter the flow regime in the rivers that rise in the Himalayas. Lower dry season rainfall and increased water demands due to higher temperatures would increase abstractions from rivers upstream and reduce the flow reaching Bangladesh. 9.5.1

CONSEQUENCES OF CLIMATE CHANGE

The key consequences of climate change for Bangladesh are:

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In the monsoon; • •

•

Increased flooding due to increased monsoon season rainfall. Worsening drainage congestion, water logging and flooding due to higher sea levels and a consequential rise in riverbed levels. Higher sea levels will increase the tide-locked period for tidal drainage sluices and reduce their drainage capacity. Marginally increased coastal erosion is anticipated due to greater foreshore depths and corresponding wave depths.

In the dry season: • • • •

•

Changes in the balance of GW recharge and demand on aquifers due to changes in climate parameters. Increase in demand on surface resources. Reduced transboundary surface water inflows into Bangladesh Disturbance of existing morphological processes by the changed balance between wet and dry season flows and changes in sediment transport and deposition caused by changes in flows and water levels. This will affect riverbank erosion and channel sedimentation. Increased incidence of cyclones making landfall due to reduced energy losses over warmer seas in the Bay of Bengal.

Impacts on the water supply and demand situation calculations have been made for the key meteorological stations in Bangladesh of the changes in rainfall and potential evapo-transpiration. The average for the country are shown in the Figures 9.4 and 9.5 (NWMP, 2000). In general, the combination of increased rainfall and unchanged evapo-transpiration in the monsoon results in increased GW recharge. By contrast, the combination of unchanged rainfall and increased evapo-transpiration in the dry season results in increased demands. Because of its very low elevation and exposure to various water related hazards, Bangladesh is at great risk from global climate change. Although the magnitude of the changes in climate may appear to be small, they could substantially increase the magnitude of existing climatic events (floods, droughts, cyclones), and decrease their return period. For example, a 10% increase in precipitation may increase runoff depth by one-fifth, and the probability of an extremely wet year by 700%. The likely climate change scenarios for Bangladesh are provided in Table 9.3. The SLR is likely to have greater impact on low-lying coastal areas. The loss of land through inundation and coastal erosion, increased frequency of coastal flood with a rising base level are one of the impacts of SLR on the coastal areas of the world. 9.6

FUTURE FRAMEWORK OF MANAGEMENT

Perceptions about vulnerability context vary according to socio-economic condition of the people. For poorer men, (lack of) employment is a major vulnerability issue, while cyclone has been perceived as a major issue among the relatively rich. Women of all strata perceive (availability of) domestic water as a prominent issue, while women of richer households consider employment as an important issue. Poorer women also consider sanitation and health as important issues. Law and order has surfaced as another important issue among the richer households.

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Fig. 9.4 Changes in rainfall due to GCC.

Fig. 9.5 Changes in potential evapo-transpiration due to GCC.

Shocks, trends and seasonality could be of natural origin or having accelerated and/or aggravated through human induced activities. As such the context has been elaborated under natural hazards and man-made hazards.

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CYCLONES AND STORM-SURGES

Storm-surges associated with tropical cyclones have the most damaging effect on loss of human lives, livestock and properties in the coastal area of Bangladesh. Cyclonic storms with winds of more than 120 km/hr occur with the advent of the monsoon season. These are particularly severe just before and after the monsoon. Winds of over 160 km/hr, heavy downpours and tidal surges of over 6 m above the normal level have brought devastation to life and property. The predicted surge heights at the coast corresponding to return periods of 20 years, 50 years and 100 years for five coastal regions are presented in Table 9.4. The average frequency of tropical cyclones over the Bay of Bengal is about 6 per year, but not all of them strike Bangladesh. The country is also periodically affected by cyclonic storms in the coastal districts. The country has over 700 km of coastline on the mainland and several offshore islands in the Bay of Bengal. During the last 125 years, over 42 cyclones have hit the coastal belt; 14 occurred during the last 25 years. Cyclones often take a heavy toll in human life, livestock, crops, properties and physical infrastructure (World Bank, 1995). 9.6.2

WATER LEVELS, INUNDATIONS AND WATER LOGGING

Any rise of the sea level will propagate upstream into the river system. In Bangladesh, this backwater effect will be pronounced because of the morphologically dynamic rivers, which will adapt their bed levels in a relatively short time period (Huq et al., 1996). This whole process will lead to decreased river gradients, increased flood risks and increased drainage congestion. Since most of the coastal plains are within 3 m to 5 m from MSL, it was previously thought that a significant part of the coastal areas (as high as 18% of the country) would be completely inundated by rising seawater levels (Huq et al., 1995; Houghton, 1996). This speculation was based upon two major approximations: (a) the coastal plains are not protected, and (b) the seawater front will follow the contour line. In reality, however, it is found that most of the coastal plains in the central regions are protected. Due to the backwater effect, embankments further land inwards may be topped and areas flooded. This could still turn most of the seaward polders into islands. Drainage congestion may become an even more serious threat than higher flood risks. Due to siltation and poor maintenance of the drainage channel network in many parts of the coastal zone, drainage congestion is already a grave problem (EGIS, 1998), and the problem is to increase considerably. Proper emphasis should be given to the fact that protection measures against inundation by embankments interrupt the natural processes of land sedimentation and delta formation. This implies that subsidence and sea level rise will not be compensated by sedimentation, and the effect of inundation and drainage congestion will be even greater in the future. These amplifying effects are particularly alarming, and indicate that quite a new approach may be required to face the problems in especially the seaward parts of Bangladesh. Unlike the densely populated seafront areas, the Sundarbans is not protected and is heavily influenced by tidal effects. A rise in sea level will tend to inundate the mudflats of the forest and reduce the land area of the forest. The forest floor, however, may be experiencing a natural uplift at a rate similar to the anticipated rate of sea level change. Whether natural uplift is strong enough to counterbalance sea level rise is very uncertain, and present research continues to emphasize the vulnerability aspects of the Sundarbans.

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Saline water intrusion is highly seasonal in Bangladesh; it is at its minimum during the monsoon (June-October) when the 3 major rivers discharge about 80% of the annual freshwater flow. In winter months the saline front begins to penetrate inland, and the affected areas rise sharply from 10% in the monsoon to over 40%. Climate change would increase salt intrusion through several means: • • • • • •

Directly pushing the saline/fresh waterfront in the rivers through higher sea levels; Decreased river flows from upstream increase the pushing effect from the sea; Upward pressure on the saline/freshwater interface in the ground water aquifers (every cm of sea level rise will result in a thirty-fold rise of the interface because of the hydrostatic pressure balance); Percolation from the increased saline surface waters into the ground water systems; Increasing evaporation rate in winter, leading to enhanced capillary action and subsequent salinization of coastal soils; and Increasing storm surges that carry seawater.

All these effects would have significant adverse impacts in the coastal areas. Climate change induced extreme weather events, especially low flow conditions in winter, will accentuate the salinity intrusion in the coastal areas (Habibullah et al., 1998). Agricultural activities in Bangladesh will suffer greatly from impacts of climate change. Increased salinity levels will reduce freshwater availability for irrigation, while growing drainage congestion problems will result in longer periods of flood inundation. This will reduce the areas suitable for rice production. In addition, increased coastal morphological dynamics will contribute to the existing problem of loss of valuable agricultural land due to erosion. In recent years, over 2,000 cyclone shelters have been built in the coastal areas to save human lives. But no such infrastructure has been built for livestock, food grains and other perishable items. These resources will increasingly experience the threat destruction. Specific recommendations for adaptation are proposed (World Bank, 2000) in relation to most of the climate change impacts. However, coastal defense against sea level rise by physical interventions will be expensive. Huq et al. (1995) estimated that the cost of defending against a 1 m sea level rise would be $1 billion. This does not include the associated costs of impacts of hard coastal defenses, such as sea walls, on e.g. tourism and bio-diversity. 9.6.3

NON-STRUCTURAL FLOOD MANAGEMENT

Disaster management is largely about non-structural measures that have little impact upon the environment. Indeed, the essence of the strategy is to allow nature to take its course, and help people accommodate and adjust to the impacts. In comparison with a “do-nothing” scenario, it is obviously necessary to keep people aware and help to take precautionary measure as well as measures after the disaster. Structural measures such as embankments and training works have adverse environmental impacts in some cases, which has to be minimized in a suitable way. On environmental grounds therefore, flood-proofing measures have been proposed. But these measures are suitable only for local or smaller area.

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FLOOD FORECASTING AND WARNING

Flood forecasting is part of the overall national flood preparedness strategy. Presently the Flood Forecasting and Warning Center can accurately forecast flood levels at a limited number of locations for up to 48 hours ahead, though this has little applicability in the local context as a mechanism for public awareness-raising. Work is underway to improve the technical aspects of flood preparedness. Improved communications networks and the effective use of mass and electronic media (including the Internet and E-mail) as well as hand radios and sirens are expected to improve locally based mass warning systems. Invigorated community mobilization program for men and women, paramedical training, wealth-creation campaigns which would permit greater access for the remote communities to amenities like transistor radios, and access for the elite to better weather forecasting and warnings through cable television, would contribute to increased understanding and knowledge of disasters before they strike. Inter-personal networks may still be required to target fishermen and other vulnerable groups. At the same time, communities have indicated that auditory media such as drums are effective in signaling community-wide alerts and should be re-introduced as a warning mechanism. 9.6.5

MANAGEMENT IN THE FUTURE

Overall, the incidence of flood, erosion and drought events is likely to increase rather than decrease in the future, and little can be done within Bangladesh to prevent them from happening. This is recognized in the National Water Policy, which underlines the importance of implementing effective non-structural measures to reduce the impact on people of material losses and damage, and suggests changes in agriculture and land-use practices, flood preparedness and disaster management. Some flood control measures (structural) have to be taken at the existing ones are to be strengthened, at least to protect the country from worsening situation i.e. to maintain the present day situation. Proposals of creation of reservoir in Nepal may be looked into, which will reduce flood situation as well as improve low water flow or drought situation. The polders in coastal areas may have to be strengthened and raised. The country should focus on the non-structural measures in an integrated way. General options for non-structural measures are: • • • • •

Zoning (both horizontal and vertical); Warning systems; Hazard preparedness; Improved communications; Relief and rehabilitation.

Options for managing erosion: • •

Low cost erosion control measures; Erosion forecasting and warning system.

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Options for managing droughts: • • •

Regulation; Drought proofing; Drought forecasting and warning system.

Future improvement possibilities are: • • • • • 9.7

Financing (loan, insurance etc. for the affected people); Workable institutional arrangements; Risks and uncertainties; Regionality; Integrated approach for the improvement. CONCLUDING REMARKS

Possible change in climate will complicate water management problem in Bangladesh. In order to minimize the potential risks, studies have been undertaken regarding adaptation to climate change in Bangladesh and there appears to be consensus that the country is too vulnerable to be able to ignore the anticipated effects in current and future planning. Many of the proposed strategies, which are those, needed even without climate change effects in order to accommodate the needs of the rising population. The basic strategies identified for accommodating the effects of climate change are: (a) Physical measures to reduce drainage congestion (or at least avoid worsening the present situation). (b) Pumped or other natural energy based (wind or tidal current) drainage may be required. (c) Land filling using natural or artificial methods to prevent, or at least reduce, inundation and promote drainage. (d) Increased tree and mangrove planting on accreted lands and in coastal belts. (e) Measures for the improvement of livelihood condition of the coastal people. (f) Encourage more efficient use of water resources. Strategic adaptation to climate change should produce a coordinated response, supported by all stakeholders, on three different levels: • • •

Planning and natural resources management, including the participation of different stakeholders in the decision-making process; Information needs, management and dissemination; International positioning and representation.

It is recommended that the next steps to reduce vulnerability due to impacts of climate change and SLR must concentrate on the adaptation mechanisms of planning, information management and international actions. Here, the National Water Management Plan (NWMP) that is currently being developed and the Integrated Coastal Zone Management Policy (ICZMP) under preparation offer key opportunities.

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The following specific actions are recommended: • •

• • •

Establish an operational structure or committee to coordinate climate change activities (planning and design) in Bangladesh. Strengthen the existing structure and ongoing processes to develop and implement integrated water resources management and strengthen integrated coastal zone management, focusing on protection, land-use and water management. Alternative crops, livelihoods attention to water management and access to local coping and adaptation. Establish, manage and execute a coordinated research agenda on climate change impacts and develop and operate a shared climate change knowledge base. Promote, structure and support international activities. Two types of international activities have been identified: (i) international debates on effects, mitigation and adaptation, and (ii) water sharing negotiations with neighboring countries.

REFERENCES Alam, A. K. M. K.: An Approach to Natural Hazards in the Eastern Asia, Report of Eastern Asia Natural Hazards Mapping Project (EANHMP), Geological Survey of Bangladesh, 1997. Ali, M. L.: An Integrated Approach for the Improvement of Flood Control and Drainage Schemes in the Coastal Belt of Bangladesh, A PhD Thesis. Wageningen University and International Institute for Infrastructural, Hydraulic and Environmental Engineering (IHE), The Netherlands, 2002. Bangladesh National Committee of the International Commission on Irrigation and Drainage: Non-Structural Aspects of Flood Management in Bangladesh, International Commission on Irrigation and Drainage (ICID), Dhaka, Bangladesh, 1995. Bangladesh Water Development Board (BWDB): Water Management Study Report, Polder 43/2A, Integrated Planning for Sustainable Water Management, Directorate of Planning-III, Dhaka, Bangladesh, 2000. Datta, A.: Planning and Management of Water Resources - Lessons from Two Decades of Early Implementation Projects in Bangladesh, University Press Limited, Dhaka, 1999. Environment and Geographic Information System (EGIS): Environmental and Social Impact Assessment of Khulna-Jessore Drainage Rehabilitation Project, Environmental GIS project (EGIS), Ministry of Water Resources, Government of the People’s Republic of Bangladesh, Dhaka, 1998. East Pakistan Water and Power Development Authority (EPWAPDA): Master Plan, Volume I, International Engineering Company Inc., San Francisco, USA, 1964. Flood Plan Coordination Organization (FPCO): Flood Action Plan 12, FCD/I Agricultural Study, Volume 1, Main Report, Ministry of Irrigation, Water Development and Flood Control, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh, 1992. Flood Plan and Coordination Organization (FPCO): Bangladesh Water and Flood Management Strategy, Ministry of Water Resources, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh, 1995. Habibullah, M., Ahmed, A. U. and Karim, Z.: Assessment of Food Grain Production Loss Due to Climate Induced Enhanced Soil Salinity. In S. Huq, Z. Karim, M. Asaduzaman and F. Mahtab (eds.), Vulnerability and Adaptation to Climate Change for Bangladesh, Kluwer Academic Publishers, Dordrecht, 1998, pp.55-70.

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Houghton, R. A.: Land-Use Change and Terrestrial Carbon: The Temporal Record. In: MJ APPS and DT Price (ed.), Forest Ecosystems, Forest Management and the Global Carbon Cycle, NATO ASI Series I, Volume 40, Springer Velag, Berlin, Germany, 1996, pp.117-134. Huq, S., Ahmed, A. U. and Koudstaal, R.: Vulnerability of Bangladesh to Climate Change and Sea- Level Rise. In T. E. Downing (ed.), Climate Change and World Food Security, NATO ASI Series I37, Springer Verlag, Berlin, Hiedelberg, 1996, pp.347-379. Huq, S., Ali, S. I. and Rahman, A. A.: Sea Level Rise and Bangladesh: A Preliminary Analysis, Journal of Coastal Research Special Issue, 1995. Intergovernmental Panel on Climate Change (IPCC):Climate Change 2001: The Scientific Basis, Cambridge University Press, U.K., 2001. Ministry of Water Resources (MWR): National Water Policy, MWR, Bangladesh, Dhaka, 1999. Mirza, M. M. Q.: Three Recent Extreme Floods in Bangladesh: A Hydro-Meteorological Analysis. In: Flood Problem and Management in South Asia (M. M. Q. Mirza, A. Dixit and A. Nishat Eds.), Kluwer Academic Publishers, Dordrecht, The Netherlands, 2003, pp.35-64. National Water Management Plan (NWMP): Draft Development Strategy, Ministry of Water Resources, Water Resources Planning Organization, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh, 2000. National Water Management Plan (NWMP): Draft, Ministry of Water Resources, Water Resources Planning Organization, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh, 2001. National Water Management Plan (NWMP): Ministry of Water Resources, Water Resources Planning Organization, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh, 2004. Nizamuddin, K.: Disaster in Bangladesh. Selected Readings, Disaster Research Training and Management Center, Department of Geography and Environment, Science Annex Building, University of Dhaka, Bangladesh, 2001. SAARC Meteorological Research Center (SMRC): Recent Climatic Changes in Bangladesh (SMRC Publication No. 4), Dhaka, September, 2000. SAARC Meteorological Research Center (SMRC): The Vulnerability Assessment of the SAARC Coastal Region Due to Sea Level Rise: Bangladesh Case (SMRC Publication No. 3), Dhaka, July, 2000. The World Bank: Coastal Embankment Rehabilitation Project, Agriculture Operations Division, Country Department 1, South Asia Region, Dhaka, Bangladesh, 1995. The World Bank: Bangladesh - Climate Change & Sustainable Development, Report No. 21104 BD, 2000. The World Bank and Bangladesh Center for Advanced Studies: Bangladesh 2020 - A Long-Run Perspective Study, University Press Limited, Dhaka, Bangladesh, 1998. Water Resources Planning Organization: (Final), National Water Management Plan, Ministry of Water Resources, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh, 2001.

10 Adaptation Options for Managing Water-Related Extreme Events Under Climate Change Regime: Bangladesh Perspectives AHSAN UDDIN AHMED

10.1

INTRODUCTION

Owing to a number of natural and man-made factors, Bangladesh is highly vulnerable to water-related extreme events (Ahmad et al., 1994; Ahmad et al., 2000). In the wake of global warming and its associated changes in biophysical environmental conditions, it is feared that the country would most likely to be severely affected. The anticipated changes in the hydrological regime would accentuate high intensity climatic events. Unfortunately, livelihoods of majority of the people depend directly on prevailing precarious state of environmental conditions, which is likely to be perturbed significantly due to anticipated impacts of climate change. Despite high incidence of poverty, high population density and unemployment, the country has made significant stride to sustainable development in the recent decades. Adverse impacts of climate change would be detrimental, especially when the hydrological extreme events are likely to be exacerbated under the climate change regime. The resolute population of the country has been coping with extreme water-related events since ages. The focus of all response measures, however, has been on survival coping. People’s survival coping strategies have so far been effective, although not robust. Resilience of both human and natural systems of the country can be enhanced if adaptation options for management of the water resource sector, especially those addressing long-term climate change - induced adverse impacts, are designed and implemented throughout the country. 10.2

WATER-RELATED EXTREME EVENTS AND CLIMATE VARIABILITY

In terms of annual per capita endowment of water, Bangladesh ranks amongst the top in the world. Unfortunately, however, the country faces serious problems as the water availability is characterized by wide temporal variability - too much of water in the wet season and too little in the dry season. Such an acute temporal distribution can be attributed to very strong influence of the monsoon circulation, which causes about 80% of rainfall to occur between June and October. Furthermore, the country being the lowest riparian in the combined catchment areas occupying only about 7% of the

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Ganges-Brahmaputra-Meghna (GBM) system (Fig. 10.1) and over three-fourths of the water generated in the basins passes through Bangladesh while the river system discharging waters from a flat terrain into the Bay of Bengal making the country highly prone to annual flooding (Ahmad et al., 1994; Ahmad et al., 2000). Simultaneously, having too little water in the dry season results in low flows in the river systems, which fails to avoid gradual ingress of salinity along the coastal rivers. Meanwhile, diminishing rainfall in the dry season cannot offset increasing evapo-transpiration, especially in the Western parts of the country, leading to phenological drought conditions. Climatic variability results into a number of water-related extreme weather events in Bangladesh: floods, droughts, salinity ingress, and cyclonic storm-surges. 10.2.1

FLOODS

The country that shares the vast Bengal Delta is globally known as a land, which has one of the highest susceptibility to floods. An analysis of past flood events suggest that about a quarter of the country’s landmass is subject to annual flooding and overall, about 68% is at risk of floods with varied intensity (Ahmed and Mirza, 2000). According to the government sources, two high intensity floods occurring in 1998 and 1988 inundated about 100,000 km2 and 89,000 km2, respectively. People and the economy of the country suffer a lot due to the occurrences of severe (or high intensity) floods. Floods cause considerable damages in the basin in the economic sectors - agricultural, housing, industry, fisheries, transportation infrastructures are particularly affected. Mirza (2002) revealed that, the 1988 flood has displaced and affected about 31 million people in 52 districts of Bangladesh, while an estimated 2.4 million houses were completely or partially damaged.

Fig. 10.1 The Ganges, Brahmaputra and Meghna basins. Source: Mirza, 2003.

In Bangladesh, generally four types of flooding occur: (a) flash floods, which are caused by overflowing of hilly rivers of Eastern and Northern Bangladesh in April-May and September-November; (b) rainfall-induced floods, triggered by heavy rainfall and subsequent drainage congestion; (c) riverine floods in the floodplains of major rivers, often

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caused by excess water flowing from the upstream areas of the GBM basins during monsoon (i.e., June-September); and (d) coastal saline floods caused by storm-surges. An analysis of the distribution of the flood areas shows that the frequency of occurrences of high intensity flood events has increased since 1974 and the return period of the one severe flood episode from the preceding one of similar high intensity is becoming shorter (Choudhury et al., 2003). On the other hand, the area inundation increased with time since 1974. The duration of the floods has also been increasing gradually - duration exceeding 67 days for the 1998 flood. Since late 1960s, major efforts have been made by the Government of Bangladesh (GoB) to reduce the country’s flood vulnerability: predominantly by erecting embankments. Due to poor management, many of these flood protection embankments have failed to live up to their promises. Moreover, unplanned road construction, without having adequate drainage infrastructure, often impede drainage and increase flood vulnerability to many areas along the major rivers. Despite the fact that embankments allowed many risk-free economic activities even within flood vulnerable zones of the country, man-made complexities nullified the potential benefits accruable from these infrastructures to a large extent. 10.2.2

DROUGHTS

Droughts are caused due to lack of precipitation during a length of period, which affects the human lives, crops and other economic activities. Drought affects lives of people along with economic and industrial activities of the country. During a prolonged dry spell, often manifested as an extreme event under climate variability, strong evapo-transpiration causes loss of topsoil moisture that aggravates drought conditions. Moisture deficit is generally compensated by application of irrigation. Moisture stress condition may grow worse, if inadequate compensation occurs due either to reduction in surface flows consequent upon human interventions in the upstream parts of the rivers, or to lack of application and/or availability of ground water resource. During a drought year, ground water recharge is often slow and inadequate owing to reduced precipitation and lower runoff through the rivers. In the past, Bangladesh experienced droughts quite often. Very severe droughts hit the country in 1951, 1957, 1961, 1972, 1976, 1979, 1986, 1989 and 1997. Droughts in the recent past have typically affected about 47% of the country (Task Force, 1991) (Fig. 10.2). An analysis of temporal and spatial variability of rainfall and temperature (Karim et al., 1990) suggests that the Western part of Bangladesh is more dry and prone to droughts, particularly in the dry period (winter through pre-summer months). Severe droughts mostly occur in the pre-monsoon period, although occurrence of post-monsoon severe droughts is also observed. Pre-monsoon severe droughts affect standing winter rice, i.e., boro, and to a lesser extent, wheat. Occurrence of drought in pre-monsoon is so obvious in the Western parts of the country that the farmers do not even perceive cultivation of rainfed boro as an economic activity. The pre-monsoon droughts are occasionally found to extend throughout the monsoon period due to late onset of monsoon and weak monsoon activities. Droughts occurring in the monsoon period may also severely affect production of the rice crop. Agricultural droughts are presently being tackled through irrigation, primarily using ground water. However, rice cultivation in the monsoon season is mostly dependent on rainfall and surface water available from normal flooding conditions. The crops severely suffer if the precipitation is much lower than required and the normal flooding does not take place due to low water condition of the rivers.

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Fig. 10.2 Distribution of drought prone areas in Bangladesh. Severe drought prone areas are located in the Ganges basin.

In the past, post-monsoon droughts have been tackled by offering supplementary irrigation from surface water sources. Historically, there had been no mechanism to tackle large-scale pre-monsoon (i.e., dry season) drought conditions until 1980 when the mechanized extensive-scale irrigation became operational exploiting both the surface and

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ground water resources. During 1980s and 1990s, several irrigation projects have become operational, which helped to solve the drought problems in the winter and pre-monsoon periods. Due to the expansion of the mechanized irrigation practices, not only winter rice production has increased significantly, the overall cropping intensity has also increased. Since high intensity floods severely affect production of monsoon rice, farmers opted for extensive cultivation of winter rice (i.e., boro) aided by irrigation. 10.2.3

SALINITY INGRESS

Ingress of salinity along the coastal rivers is common in Bangladesh. Salinity from the brackish water zones penetrates inland (towards the North) due to low flow in the dry season, which is triggered primarily by temporal climate variability. When the rivers discharge huge quantum of freshwater, especially during peak flood season, salinity is at its minimum. Of course, withdrawal of dry season flows due to anthropogenic reasons may have compounding effects on salinity, as has been observed in the river systems criss-crossing the entire Southwestern parts of Bangladesh. According to published sources, surface water salinity is extended over an area of about 0.4 mha to 0.5 mha, while soil salinity of varying degree affects crop production in about 3.1 mha land (Halcrow et al., 2001a; Karim and Iqbal, 2001). Increasing salinity in surface water systems easily causes salinization of topsoils as well as shallow ground water aquifers along the coastal regions of Bangladesh. In addition to adversely affecting standing crops, ingress of salinity affects surface as well as ground water systems that are generally used as sources of drinking water. According to unofficial sources, in absence of freshwater alternatives, an estimated 15 million to 17 million people have been forced to drink salt-laden water in the coastal areas of the country. 10.2.4

OTHER WATER-RELATED HAZARDS

Bangladesh is frequently visited by tropical cyclones and related tidal surges. Cyclones originate in the deep Indian Ocean when sea-surface-temperature exceeds the threshold of 27°C. Cyclones track through the Bay of Bengal where the shallow waters contribute to huge tidal surges when cyclones make landfall. The risks associated with high intensity wind are not generally perceived to be big. However, it is the tidal surge, which cause significant damages to lives and properties (Haider et al., 1991). Existing literature records storm-surges in the range of 1.5 m to 9 m, however there may be exaggeration towards recording the upper limit. Given that over half of the country is less than 10 m above sea level and densely populated, storm-surges contribute to flooding and loss of life and livelihoods far beyond the coast. The intense precipitation that usually accompanies the cyclone only adds to the damage through inland and riverine flooding. A cyclone in 1970 resulted in close to 300,000 deaths, and another, in 1991 led to the loss of 138,000 lives, although in recent years greater success in disaster management has significantly reduced the lives lost (Ahmed, 2000b; World Bank, 2000). Nevertheless, the potential damages for economy and infrastructure remain very significant. Riverbank erosion is another water-related hazard, which has grave impacts on people’s well-being and societal ramification. High volumes of water passing through the river systems in monsoon often causes scouring along the rivers, which ultimately triggers land erosion along the banks of scouring rivers. The Brahmaputra and Meghna are by far the

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two most erosion-prone rivers of the country. Although monsoon discharge triggers scouring and resultant land erosion, the effect is observed in other seasons as well. Chandpur, Sirajgang and Rajshahi are the most erosion-vulnerable urban townships, where a host of other areas, primarily along the Southern coastal districts, are also erosion vulnerable. Since erosion often devours large chunks of lands along the major rivers, it inflicts the process of pauperization and forces rural poor to out-migrate to the urban areas. 10.3

CLIMATE CHANGE AND ITS IMPLICATIONS FOR WATER RESOURCES

10.3.1

CLIMATE CHANGE SCENARIOS

In order to understand the implications of water sector under the climate change regime, it is necessary to define one or more projected climate change scenarios. Scenarios are generally obtained through climate simulation analysis, often driven by Atmosphere-Ocean General Circulation Models, in combination with socio-economic scenarios. GCMs can provide information regarding extent of changes in climate parameters such as temperature, precipitation (rainfall, snow conditions etc.), wind speed, solar radiation, humidity, etc. In recent years, however, the simulation algorithms are better resolved with the advent of Regional Climate Models (RCMs), which can resolve the equations in a much finer grid (Giorgi et al., 2003; Terray et al., 2003; Giorgi et al., 1998). For Bangladesh, GCM-resolved coarse climate change model outputs have been published by Ahmed and Alam (1998). Later, Mirza et al. (2003) provided results from an ensemble of GCM outputs. Recently, an improved GCM-ensemble analysis is provided by OECD (2003). For this present study, scenarios provided by OECD have been considered. 10.3.1.1 TEMPERATURE AND PRECIPITATION (RAINFALL) As indicated above, a climate scenario tool MAGICC/SCENGEN was used, based upon over a dozen recent GCMs, to estimate projected changes in area averaged temperature and precipitation over Bangladesh. In the first step, results obtained from 17 GCMs for Bangladesh were examined. Next, 11 of 17 models which best simulate current climate over Bangladesh were selected for the study. The models were run with the IPCC B2 SRES scenario1 . In order to provide an estimate of the degree of agreement across various models, an ensemble of results are considered. The results of the MAGICC/SCENGEN analysis for Bangladesh are shown in Table 10.1. As revealed by the modeling exercise, all the climate models estimate a steady increase in temperature for Bangladesh, with little inter-model variance. In general, warming in winter will be higher than in the monsoon. Precipitation shows a marked annual distribution, in addition to already existing climate variability. Most of the climate models estimate that precipitation will increase during the summer monsoon because they estimate that air over land will warm more than air over oceans in the summer. This will

1

The IPCC SRES (Special Report on Emission Scenarios) B2 scenario assumes a world of moderate population growth and intermediate level of economic development and technological change (IPCC, 2001). SCENGEN estimates a global mean temperature increase of 0.8°C by 2030, 1.2°C by 2050, and 2°C by 2100 for the B2 scenario.

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Note: DJF represents the months of December, January and February, usually the winter months. JJA represents the months of June, July and August, the monsoon months. Modified from OECD, 2003.

deepen the low-pressure system over land in the summer and will enhance the monsoon2 . The estimated increase in monsoon precipitation appears to be highly significant; it is larger than the standard deviation across models. OECD argues that this does not necessarily ascertain increased monsoon activity, but signifies increasing confidence in stating that it is likely to happen (OECD, 2003). As presented in Table 10.1, the climate models also tend to show small decrease of rainfall in the winter months (December through February), on top of already very low rainfall distribution for the same season. Given very low winter rainfall, the change is not statistically significant. However, with higher temperatures combined with a small decrease in precipitation will tend to increase evapo-transpiration, leading to increased intensity for moisture stress, even droughts. These results may be compared with previous results provided by Ahmed and Alam (1998). It was projected3 that, temperature would rise 1.3°C by 2030 (over mid-20th century levels) and 2.6°C by 2070. The study revealed that winter warming would be greater than summer warming. The study also estimated little change in winter precipitation and an increase in precipitation during the monsoon. This is slightly higher than what is projected in Table 10.1 and may reflect lower climate sensitivity in more recent climate models. The core findings however are consistent with the analysis presented above. 10.3.1.2 SEA LEVEL CHANGE Since most of the coastal plains of the delta are within meters from the sea level, a change in sea level can have catastrophic impacts and increase vulnerability significantly. Since the 2 If, however, aerosols increase sufficiently, as a result of pollution and other causes, then it is possible they will exert a differential cooling effect over land. This is because pollution sources that are the source of the aerosols are found over land. Aerosols over land could therefore partially offset the warming over land, and it is possible that the air over land will warm less than air over oceans. This would weaken the low pressure system and the monsoon. 3 By the GFDL01, a transient GCM with 1% increase in emissions.

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GBM Delta is morphologically highly dynamic, there is no specific regional scenario for net sea level change. It is evident from literature that the coastal lands are simultaneously subject to accretion and tectonic subsidence (Huq et al., 1996; Allison, 1998). Compaction of sediment may also play a role in defining net change in sea level along the coastal zone due to the fact that the landform is constituted by sediment decomposition. Lacking more specific information, if one assumes that sediment loading cancels out the effects of compaction and subsidence, then the net sea level rise can be assumed to be close to the global average as projected by the IPCC. Asaduzzaman et al., (1997) put the range at 30 cm-100 cm by 2100, while the IPCC 3rd Assessment gives a global average range with slightly lower values of 11 cm to 77 cm. Higher mean sea levels are likely to compound the enhanced storm-surges expected to result from cyclones with higher intensity. Even in non-cyclone situations, higher mean sea levels are going to increase problems of coastal inundation and salinization in the low lying deltaic coast (OECD, 2003). 10.3.1.3 FREQUENCY AND INTENSITY OF CYCLONES AND STORM-SURGE In view of the current vulnerability to cyclonic storm-surge, a critical question is whether (and how) climate change might affect cyclone patterns and intensity in the Bay of Bengal. The latest IPCC findings reveal that, current climate models do not perform a good job of resolving the influence of climate change on cyclones owing to their relatively small spatial extent. Further, the historical record has large decadal variability, which makes any trend analysis based upon only a limited time-series data difficult to interpret conclusively (IPCC, 2001). Based on emerging insights from some climate model experiments as well as the empirical record IPCC states, “… there is some evidence that regional frequencies of tropical cyclones may change but none that their locations will change. There is also evidence that the peak intensity may increase by 5% to 10% and precipitation rates may increase by 20% to 30%” (IPCC 2001). Despite the fact that the IPCC assessment on cyclone is tentative, the results have several major implications for Bangladesh. First, projection on no-change in cyclone tracks under climate change means that Bangladesh is likely to remain vulnerable to cyclonic hazards with perhaps a higher possibility of formation of cyclones in a warmer world. Moreover, the fact that peak intensities may increase by 5%-10% has serious implications for a country already very vulnerable to storm-surges. Finally, an increase in 20%-30% in the associated precipitation could only make the concerns even more serious, particularly in the coastal embanked areas where heavy rainfall can instantaneously inundate otherwise protected agricultural lands. 10.3.2

IMPLICATIONS OF CLIMATE CHANGE FOR WATER RESOURCES

10.3.2.1 REGIONAL CONTEXT Since hydrological conditions, even extreme events occurring within the boundaries of Bangladesh do not necessarily depend only on climate variability over the country, assessment of implications of climate change for water resources of the country therefore should consider regional aspects. It would be intriguing to find out whether the indications available from model simulation are still valid for the whole of the GBM catchment and how that would sensitize hydrological aspects of Bangladesh, given the spatial and temporal distribution. Two important issues deserve special mention: (a) glacier melt and

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subsequent threats from Glacier Lake Outburst Floods (GLOFs), and (b) precipitation in the GBM basins (details on these issues are discussed in Chapter 4 and Chapter 5]. It is now known that the Himalayan glaciers are melting, predominantly because of gradual increase in terrestrial temperature (Bahadur, 1998; Pradhan et al., 2002). Satellite imageries provide ample evidence that there have been a gradual thawing and subsequent retreat of glaciers in the Himalayas. Under climate change, it is expected that the rate of glacier melt will only increase leading to increase runoff into the GBM river systems. However, the increase in runoff will be significantly lower compared to the effects produced by monsoon rainfall throughout the GBM basins. In shorter time-scale, the increased runoff will only be effectively experienced during pre-monsoon and summer months. With retreating snow-laden areas, it may be speculated that the overall albedo response to the radioactive budget over the Himalayas will be changed in the long-run, which might change rainfall distribution patterns in the area. No experimental results could be provided at this stage in order to substantiate this argument. However, the major impact of climate change will be observed through changes in rainfall distribution patterns in the region. The tendency of the models to show increasing rainfall during monsoon means that, the discharge volume will increase in the GBM Rivers without increase in discharge capacity. Nevertheless, continued sedimentation will lead to decrease discharge capacity by gradually raising the riverbeds (Ahmed et al., 1998a). Consequently, there will be frequent over bank spillage and incidence of flood. The relationship between increasing temperature and precipitation with flood intensity has been examined by Mirza and Dixit (1997). It is reported that a 2°C warming with a 10% increase in precipitation4 would increase runoff in the Ganges, Brahmaputra and Meghna Rivers by 19%, 13% and 11%, respectively. A 10% increase in monsoon precipitation could increase runoff depth by 18% to 22%, resulting into a sevenfold increase in probability of an extreme wet year (Qureshi and Hobbie, 1994). Alam et al. (1998) reported that, by the year 2030 an additional 14.3% of the country would become extremely vulnerable to floods, while the already flood-vulnerable areas will face higher levels of flooding. It is also reported that, even if the banks of the major rivers are embanked, more non-flooded areas will undergo flooding in the year 2075. Simultaneously, decreasing winter rainfall throughout the GBM catchment areas would accentuate low flow conditions in rivers, resulting in ingress of salinity along the coastal rivers of Bangladesh. At present, salinity of varying intensity in both soil and water restricts agricultural production in about 3.1 million hectares of coastal lands (Karim and Iqbal, 2001). Moreover, About 15 million people are currently forced to drink saline laden water, mostly due to increasing salinity in the dry season. The consequence of low flow in the dry season has been dramatic on the well-being of the Sundarbans mangrove forest a World Heritage Site declared by UNESCO. It is reported that, increasing salinity would be translated into reduced high-value timber production along the Western zones of the forest, while freshwater loving tree species will gradually be replaced by bushy species (Ahmed et al., 1998b). Therefore, increasing low flow conditions would further reduce economic potential as well as human and ecosystem well-being of the coastal areas of Bangladesh. In order to run industries and thermal power generation plants, water is an extremely essential resource. The current industrial water demand is low due to the fact that extensive industrialization is still in its nascent state. In retrospect of past two decades of 4

This scenario closely matches with GCM projections for monsoon months June, July and August for 2100.

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industrialization, it is likely that an increasing number of industries will be setup in the country and therefore, industrial water demand will be rising rapidly. However, climate change-induced low flow driven scarcity of freshwater, in conjunction with regionally manipulated flow regime in major transboundary freshwater sources would affect these sectors. The effect of low flow is evident in the Southwestern parts of the country where existing industries are facing acute shortage of process water during the dry season. In future, increased low flow will discourage industrial ventures in those areas. 10.3.2.2 NATIONAL CONTEXT The observed data on flooded areas suggest that high intensity floods are on the increase: 3 of the 5 very high intensity floods during the past century have occurred during the past three decades. In addition to regional context for the occurrence of very high intensity floods, one can easily establish changing drainage conditions within the country (Ahmed and Mirza, 2000). Continued heavy siltation in the channels and subsequent rise in riverbed levels has significantly reduced the conveyance capacity of the rivers. Providing flood-safety to embanked areas has been translated into increased flood vulnerability to non-embanked areas, especially during peak flood season. Poorly designed infrastructure without proper and/or inoperable drainage facilities has impeded drainage and increased both extent and duration of floods. Given that, the situation might not be changed in the near future and climate change-induced peak-flood water volume will pass over the floodplains of the country, flood vulnerability is bound to increase in terms of both extent and frequency. Simultaneously, due to the rise in sea-stage and increased backwater effect at the confluences between (a) Meghna-Padma (Ganges) and (b) Ganges-Brahmaputra, there would be increased impediments in discharge behavior. All these will lead to high intensity floods, as it has been observed in the cases of 1988 and 1998. It is speculated that, increased rainfall runoff in the vast GBM region, comprising a total catchment area of 1.75 mkm2, will also contribute to enhanced sediment flows along the GBM river systems. It is likely that the process of sedimentation in the lower parts of the delta (i.e., in Bangladesh) will be enhanced leading to increased rate of bed level rise in the channels and also in the floodplains. Subsequently, it is expected that the conveyance capacity of the rivers will be reduced and intensity of the subsequent floods will increase. Moreover, in case of deposition of silt instead of sediment, the productivity of the topsoils will be diminished. Climate change triggered and rainfall induced higher sedimentation rates will, therefore, have serious social and economic implications in the future. Increased monsoon activity and subsequent increased sedimentation rates, particularly during climate change induced monsoon would result in rise in bed levels of rivers. During low flows, however, water levels would decline which in combination with already elevated bed levels would result in decreased navigability in many medium to small rivers across the country. However, due to paucity of information, it is not possible to ascertain navigability of which parts would be more vulnerable than the others. Other than floods, drought is becoming an increasing concern, particularly in the Western parts of the country. Since the winter average temperature will increase, while already very low rainfall will be further diminished under climate change, the resulting effect will be an increase in evapo-transpiration. Currently, due to insufficient water flows in the river in the dry season, people compensate moisture deficit with ground water irrigation. However, in parts of the country such as Nawabganj, Rajshahi, Khustia and Natore, there have been reports of lowering of piezometric surface of ground water aquifers (Halcrow et al., 2001b). It is argued that, under climate change there will be

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increasing pressure on ground water resources and in many Western sub-districts (Thanas), the shallow tube wells can no longer be utilized for drawing ground water to offset the moisture deficit. Consequently, the most preferred dry season crop (i.e., boro) (Fig. 10.3) can no longer be cultivated profitably, resulting in further hardships (Ahmed, 2000a).

Fig. 10.3 Various seasons and crop calendar of Bangladesh.

Karim et al. (1998) studied the effect of climate change-induced moisture stress and resulting phenological drought impacts. A geographical distribution of drought-prone areas under climate change scenarios shows that the Western parts of the country will be at greatest risk of droughts, both during the Kharif and pre-Kharif seasons. Due to moisture stress, production of aus, boro and wheat will be decreased significantly. Under a severe climate change scenario (with 60% moisture stress) yield of boro might be reduced by 55% and 62%. It is reported that, under a moderate climate change scenario aus production would decline by 27% while wheat production would be reduced up to 61% (Karim et al., 1998). It is feared that moisture stress might force the Bangladeshi farmers to reduce the area for boro cultivation. Unfortunately, boro is currently the most important crop that contributes towards attainment of food grain self-sufficiency of the country. A reduction in potential for boro would significantly affect the country’s food security. Under climate change, irrigation demand will increase significantly. Currently, 7.6 mha out of the total cultivable land of 9.7 mha used in agriculture are suitable for irrigation, and about 4.1 mha are irrigated. A study suggests that the estimated irrigated area would reach 6.9 mha by 2020 (WB-BCAS, 1998). Changes in climate may affect irrigation requirements for all the three cropping seasons: Rabi, Kharif-I, and Kharif-II. The increase in temperature will lead to escalating irrigation demands by 200 Mm3 for March only (Warrick and Ahmad, 1996). It is interesting to note that, under moderate climate change, which may be expected within the next few decades, due to increasing atmospheric concentration of CO2, there might be a slight increase in crop production. With increasing temperature, as projected

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for the latter half of the century, the fertilization effect will be diminished and moisture stress will tend to reduce yield of crops (Karim et al., 1998). Water resources availability, therefore, will play a vital role in maintaining food security of the country. In recent years, the fisheries sector of the country has been thriving, mostly due to rapid expansion of culture fisheries. Simultaneously, due to rapid fragmentation of wetland habitats, open-water fisheries have been suffering severely. Climate change is likely to increase hardship for the fish due primarily to increasing low flow conditions during the winter and pre-monsoon months, and indirectly, by increasing demand on remainder of the surface water for irrigation to offset increased moisture stress. Moreover, increasing low flow would further complicate already precarious water quality condition in winter months, inflicting further hardship upon the fish to grow. Increasing salinity would also reduce habitat for fish. However, the potential for shrimp-rice co-production along the coastal areas is likely to increase under climate change regime. 10.4

COPING WITH CLIMATE VARIABILITY

There is no denying the fact that floodplains are prone to be inundated frequently as part of a natural drainage system. In the wake of every monsoon people living in frequently flooded areas behave differently compared to people living in less frequently flooded areas. Maintaining livelihoods in a floodplain therefore requires special skills - particular responses to specific problems in relation to rise and fall of water levels on an inundated floodplain. Such coping skills may be developed, nurtured and adopted over thousands of years of practices; validated and sharpened through local-level knowledge sharing with peers, and relentless learning by doing. Depending on risk factors prior to, during, and following a flood, there are always some coping activities that enable people in the flood vulnerable areas to at least modify the perceived risks. The application of ancestral knowledge in tandem with appropriate technologies generally enriches coping practices. At one point, ancestral behavior becomes part of ‘adaptation culture’, while infusion of technologies only make it robust and enhance its resilience. Application of technologies depends on a number of factors including: (a) availability of technologies, (b) their efficacy towards risk minimization, (c) cost of their application, and (d) their social acceptance. In the floodplains of Bangladesh people tend to apply many such coping methods, satisfying one or the other or any combination of the above mentioned factors. However, the overall effectiveness of any ‘adaptation’ depends on how the risk is perceived. In Bangladesh, annual flooding for a few days over any floodplain is not perceived as a risk and known as ‘barsha’, while commonly known high intensity floods are easily recognized by all as ‘bonna’ and considered to be very risky. There are floods of catastrophic proportion, which are also recognized by any common people in a floodplain and are known to be ‘plabons’. An adaptation or a coping practice known to work in a barsha may not be as effective during a bonna. Local people can easily differentiate the extent of flooding and accordingly apply a few adaptation mechanisms (Ahmed, 2003). The most common mechanism to cope with flood is to build the homestead on raised lands, even within the floodplain. In the flood susceptible areas of rural Bangladesh, every homestead is found to have an adjacent pond testifying that earth excavation had been carried out in the past in order to raise the land level above flood risk levels (Ahmed, 2003). Recognizing the fact that floods can disrupt movement over inundated lands, well-to-do households often own a boat - just to maintain communication during floods. Raised earthen platform and a boat are obvious signs of preliminary coping with floods.

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However, local religious centers, educational institutions, and markets are also built on raised lands in floodplains. Choudhury et al. (1993) reported that, people tend to build houses on stilts where flood vulnerability is higher, allowing floodwaters to pass through the homestead5 . In those areas, the well-to-do families even build their cattle as well as poultry sheds on sufficiently raised lands. Most of the national highways and railway tracks are also laid above certain level, which corresponds to water levels only attainable in a catastrophic event. These are, of course, planned coping mechanisms. In anticipation of a flood, people tend to prepare themselves. They wrap dry food items, molasses, medicine etc. in plastic sheets and hang it on the roof. Seeds are placed in earthen pots and also hung from the roof to avoid inundation. If water levels keep rising and people perceive incidence of a bonna, they often make temporary platforms inside the household, or sometimes even on top of a tall tree as an alternate shelter. These are reactive coping, as opposed to planned coping. When people perceive that, the risks from rising water levels would be sufficiently high, they tend to take shelter in a neighboring school building and/or on the highway, or take refuge to relatives living in flood-free highlands. People’s coping is greatly facilitated by kinship and grass roots initiatives - often community-based efforts come into play (Ahmed, 2003). Rescuing a marooned family, relocating someone to a neighboring safer place, providing emergency services in various forms to poor households - many such services are provided during floods by local youths, collectively as an organized body or individually. Their efforts in the past have been very successful, especially during the deluges of 1988 and 1998, when thousands of temporary flood shelters were built and services provided, ensuring supply and distribution of food and drinking water, emergency health care, safe sanitation, conflict resolution and peacekeeping etc. When such a local initiative flourishes, often the activities are facilitated by supports from the local elite as well as the government. Government agencies often come forward with relief to meet the demand of the poor flood victims. Before the late 1950s, local-level initiatives have been the major modalities to facilitate grass roots coping. The institutional facilitation has taken a big stride in the 1960s by modifying the risks from flooding. Since then, the government started to build flood protection embankments in many flood vulnerable areas. By 2002, over 5,000 km of flood protection embankments have been built along the floodplains of the country. Although there have been criticisms regarding the fact that such structures have actually increased flood vulnerability outside the protected areas, it is undeniable that such a coping to climate variability enabled farmers to grow HYV aman varieties, increasing the food production significantly. The Bangladesh Water Development Board (BWDB) has been the implementing agency for the flood protecting embankments. Taking cognizance of the mixed-response to such structures, the government has given emphasis on non-structural measures, and flood forecasting and warning has been seen as the most preferred modality. During the 1990s, the BWDB have developed a commendable capacity to forecast floods and issue flood warnings. It houses a Flood Forecasting and Warning Center (FFWC) that is capable of processing data collected from a number of stations spreading across the country. The data is then extrapolated to forecast flood ability for over 40 stations across the country. Unfortunately, the effectiveness of issuance of flood forecasts is of little significance to facilitate coping of flood vulnerable people. Ahmed (2000c) reported two limiting factors: (a) forecast is disseminated poorly which often fails to reach flood vulnerable people, and (b) the lead 5

The century-old technique is now termed as ‘flood proofing’ of homesteads.

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time for the forecasts are so short that, even if it reaches the people at the grass roots, people do not find any substantial time to take precautionary measures. The government has been trying to import real-time data from co-riparian neighbors, with very little success to date (Ahmad and Ahmed, 2003). In case of a bonna or plabon, post-flood rehabilitation often becomes a Herculean task. Despite that the poor are the worst sufferers; they have very limited resources to invest during post-flood rehabilitation. Loss in monsoon crop may further aggravate their misery. In such a situation, institutional coping bails them out of extreme hardships. Both the government organizations and NGOs continue to offer services during the post-flood rehabilitation. Restoration and reconstruction of damaged houses, decontamination and re-sinking of tube wells, restoration of physical infrastructure, continuation of health care services - all require gigantic efforts on the part of both government agencies and NGOs. Continuation of relief operations by the then government following the deluge of 1998, especially giving the poor food grains as relief 6 successfully averted an anticipated famine - it is now regarded as a major example of post-disaster institutional coping (Ahmed, 2003). The coping mechanisms mentioned above for flood can only be marked as elements of a survival strategy, these are not at all sufficient to ensure well-being of affected population. Nevertheless, these coping mechanisms are most likely to be followed by people as well as communities and the government. The anticipated adverse impacts consequent upon increase in extent and frequency of high intensity flood events under climate change, however, cannot be completely counteracted by these observed coping mechanisms. People must try to enhance adaptability to extreme water-related events. Coping with drought (i.e., moisture stress) is a question of resource availability: (a) water for irrigation and (b) financial and technological means to draw water from its source(s) and distribution over moisture-stressed soils. Unavailability of water simply leaves no room for coping. Once water is available, then comes the question of its utilization. Timing of application of irrigation can be very important, which is a function of the crop being produced. Climate change-induced increased drought susceptibility would result in depletion of ground water resource, particularly in the Western parts of the country. Due to constraints in ground water availability in some parts of Nawabganj, Rajshahi, Natore and Kushtia Districts, the cost of irrigation might be a limiting factor for the poor farmers to cope with drought vulnerability. Increase in salinity will also add to constraints in resource availability, particularly in the Southwestern zones. In order to maintain dry season flow in major distributaries of the Ganges, the government has come forward to increase conveyance capacity of a few rivers such as the Gorai and Kobadak (GRRP, 2001). The government has also engaged in a long-term Treaty with neighboring India and signed the Ganges Water Sharing Treaty, which was the culmination of a long-discussed official negotiation aided by unofficial facilitations (Ahmad and Ahmed, 2003). The National Water Policy clearly given mandate to enhancing efforts for ‘water diplomacy’ with co-riparian countries in order to ensure augmentation of lean season flows in major transboundary rivers (MOWR, 1999). The past such adaptations, even if not totally effective, have surely paved the way for future adaptations. Bangladesh has a considerable experience in managing water-related disasters. Although the institutional approaches to disaster management have been introduced fairly recently, mostly since the late 1950s, the small-scale flood and drought management 6 Vulnerable Group Defense (VGD) cards were distributed in large numbers to offer food relief for months following the flood.

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techniques are relatively well developed at the grass roots levels. Ancestral behavior and learning by doing approach have taught local affected people a lot. However, such approaches to disaster management focuses predominantly on reactive management and not on anticipatory measures. It is often argued that, large-scale damages and losses cannot be minimized by adopting reactive measures. The need for disaster oriented anticipatory measures and the required focus on long-term changes creates special institutional and technical problems, which needs to be approached through institutional adaptations. At farmers’ level, a few adaptation techniques may be applicable to reduce risks of increasing salinity (Habibullah et al., 1998). In order to implement those proposed adaptations, the farmers might require hands on training, which needs to be arranged by the relevant government agency such as the Department of Agricultural Extension. 10.5

TOWARDS A FRAMEWORK OF FUTURE ADAPTATIONS

Adaptation is defined as any adjustment of physical infrastructure, natural systems, social and economic activities or institutional arrangements, that (i) reduces the vulnerability to climate change, or (ii) enhances the opportunities these changes offer. Adaptation measures aim to provide one or more pragmatic ways to reduce society’s vulnerability to potential adverse impacts of climate change. Focus will thus be on anticipatory, rather than on reactive measures. It is also expected that the focus will primarily be on planned adaptations, by means of preparedness for long-term gradual changes. Since people of the country have been relentlessly facing adverse climate variability and resulting impacts, it is generally believed that the flood-affected people have a variety of response measures to cope with adversaries. However, a majority of these coping measures are considered for survival coping and are reactive in nature. Effectiveness of these measures is questionable in the wake of climate change-induced increased extreme events. It may be argued that the extent of biophysical changes might not allow people to effectively utilize such century old coping strategies. Rather, a new set of response measures might be required either to modify the extent of biophysical changes and risks and/or to successfully avoid loss burden so that people’s livelihoods might be maintained, if not enhanced. If it is possible to estimate changes in biophysical parameters and subsequent risk factors, a series of strategic measures may be envisaged, planned and gradually implemented - the latter two are functions of societal ability to master technological solutions, financial and institutional strengths. In anticipation of an adverse biophysical condition, it is also possible to plan response measures well ahead of time. In case of Bangladesh’s water-related vulnerability, the existing coping measures are inadequate, while the recent developments in water management suggest that there exists a considerable potential for planned adaptation in water-related sectors (World Bank, 2000). Reducing flood vulnerability in a floodplain would warrant unimpeded flow in the major rivers, particularly during the peak flood period. Unfortunately, due to past dramatic changes in hydraulic conditions of the rivers and their spill channels, conveyance capacity of many of the rivers has decreased significantly. Adaptation for reducing floods would therefore require an enhancement of drainage capacity. The National Water Management Plan (draft) has considered specific measures to increase drainage capacity of major rivers (Halcrow et al., 2000). During the late 1990s, the Bangladesh Water Development Board (BWDB) started dredging of the Gorai River, a major distributary of the Ganges supplying

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freshwater in the Southwestern Bangladesh. In 2003, the BWDB started another program to dredge the silted bed of the Kobadak River. It is expected that, implementation of the relevant elements of the plan would greatly facilitate adaptation in terms of increasing discharge capacity of the river systems. A large majority of the physical infrastructure in the country, especially the road network, does not have adequate drainage facility, which consequently impedes drainage of monsoon flows. A thorough analysis of flow patterns of the rivers and drainage requirements is a prerequisite to identify appropriate ‘drainage infrastructure’ for the roads and highways. In order to augment drainage, it would be of great help if the existing physical infrastructures are properly equipped with drainage infrastructure. Considering that flood will continue to affect the floodplains of the country, it would be useful to create permanent facilities, which would enhance coping in future. Creation of flood shelters may be a modality for long-term adaptation to floods. During past deluges, people in flood vulnerable areas sought temporary shelter primarily in educational institutions, which were transformed by local people into flood shelters. A significant proportion of such educational institutions are located in relatively highlands (i.e., non-flooded lands), some are even built strong enough to avoid flood damage. These structures must be identified and their structures strengthened sufficiently enough to avoid rising waters, and arrangements must be made to temporarily convert them into flood shelters to accommodate flood victims. When such structures are built and/or existing structures refurbished, local people must be informed about its functions and use. The Local Government Institutions would then be given responsibility to run it involving the community. Controlling flood by means of increasing structural measures is another option for adaptation. The past experience of the BWDB and the recent policy regime practically discourage this options, unless it is meant to safeguard high-value assets - primarily in urban areas. While pronouncing its National Water Policy (MWR, 1999), the government clearly states that the future emphasis for flood protection will be on ‘vulnerable major urban areas’ and not on agricultural lands. Recognizing the fact that many medium to small urban centers will automatically grow with increasing urbanization within the next decade or so, one must contemplate regulated accumulation of wealth in urban growth centers that are located in flood vulnerable zone. In order to discourage growth in highly flood vulnerable urban centers, tax disincentives could be applied, while urbanization in flood-free growth centers can be offered tax incentives. In the longer run, the apparent loss in revenue may be greatly compensated by not investing huge sums for implementing full flood protection schemes. In this regard, the experiences of protecting Serajganj, Chandpur and Rajshahi towns may be revisited. For the protection of agricultural lands, especially in natural depression areas such as haors, submergible dykes can be good response to flash floods. There are existing dykes in the Northeastern parts of Bangladesh, which might require proper operation and maintenance (O&M). Efforts must be made to enhance O&M activities in all the flood protection embankments, otherwise people within the embanked areas might face increased flood vulnerability due to breach in embankments. Instead of trying to control floods, a less-expensive ‘controlled flooding’ option may have increased potential. The country has limited experience in controlled flooding in combination with compartmentalization. Technical feasibility and social response to such an adaptation needs to be revisited in order to find its relevance in flood vulnerable areas of the country. The possibility of creating ‘green rivers’ - an offset full flood protection

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embankment and a strip of land around the riverbed left for seasonal flooding - might not be a techno-economically feasible solution for Bangladesh (World Bank, 2000). As an alternative to expensive structure-biased flood control measures, a host of non-structural measures such as forecasting, preparedness, flood proofing etc. may be promoted. The National Water Policy has clear preference for non-structural flood management measures over flood control measures. The effectiveness of the current flood forecasting can greatly be enhanced if the government engages into regional cooperation, particularly with immediate upstream neighbor, India. It is mentioned time and again that the modeling capacity can be tremendously effective if real-time upstream water-related information can be obtained from India (Ahmad and Ahmed, 2003). However, the current difficulties in dissemination of the flood warning need to be overcome. Flood affected people must be able to understand, in their own terms, what risks they are about to face. The reactive adaptations on the part of the flood victims can only be effective if the risk perception is clear to them. Droughts, unlike floods, do not directly affect households and physical infrastructure. Crops and vegetation are the victims of droughts. The major response to increased drought is to offset deficit of moisture at the root level and irrigation is the most widely used response to droughts. Unfortunately, the need for offsetting moisture stress occurs only in the dry season when the surface water systems gradually become drier. Irrigation requirement is at its peak, during March-April to support boro and wheat cultivation, when surface flows are at its lowest and there is minimal rainfall over the region. No wonder that about four-fifths of the irrigation is now being done exploiting ground water resources. Bangladesh has a considerable ground water resource. Unfortunately, due to overexploitation in the resource to offset current level of droughts, particularly in the central Western districts, there has been a gradual decline in piezometric surface of the ground water aquifer. To avoid future complications arising from further decline in exploitable ground water resources, it is great importance to make farmers aware of more efficient utilization of irrigation water. The wasteful irrigation culture should be discouraged by application of economic disincentives. The century old rainwater harvesting in ponds and simultaneous expansion of culture fisheries needs to be promoted and supplemented with institutional facilitation. Rural electrification has significantly helped expansion of irrigation. Efforts must be continued to increase rural electrification network, especially in drought vulnerable areas. It would be useful to develop and promote an alternative cropping sequence for the most drought vulnerable areas, especially with crops that consume less irrigation water. It would reduce the overall demand for irrigation, help maintain livelihoods of farmers and rural economy, and help achieve crop diversification7 . The recent thrust for providing technologies and inputs to promote non-cereal crops, particularly oilseeds and lentils, may become increasingly important. In addition, efforts must be made to develop cereal varieties which would have lesser evapo-transpiration under climate change. Adaptation to climate change for freshwater would require responsive activities towards increasing the recharge of ground water, the regulation of surface water and halting coastal erosion. In addition the creation of operation and maintenance capacity and the strengthening of integrated coastal zone management will greatly contribute to reducing Bangladesh’s vulnerability to climate change. Adapting to climate change also 7

Crop diversification has been given high priority in the National Agricultural Policy (1999), while the IPRSP considers it as a means to alleviate rural poverty.

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involves maintaining, and if necessary, modifying current infrastructure that is sensitive to climate changes. Adaptation to climate change can also be sought in modifying the design requirements of current infrastructure sensitive to climate variability and change. This adaptation would facilitate drainage, including coastal drainage, and arrest tidal overflow in the coastal embankments. 10.5.1

ADAPTATION IN AGRICULTURE

The agriculture sector of Bangladesh is highly sensitive to water resources availability and therefore, to climate change. Prolonged inundation of land from sea level rise, increased drought, salinity and loss of land due to erosion are the climate change induced water- related enhanced risks facing agriculture. Since agriculture is the mainstay for the economy of the country and its poor population, this sector should receive high attention in adaptation. Key adaptations encompass research into development of varieties and agricultural practices less vulnerable to droughts for the dry periods, less vulnerable to inundation in the wet periods and less vulnerable to salinity during winter and pre-monsoon periods. Dissemination of knowledge concerning best cultivation practices about these varieties is an important adaptation. According to grass root farmers, the current extension services are largely inadequate (Ahmed and Karim, 2004). Enhancing quality of dissemination of agriculture related information should be considered as an adaptation. Changing agricultural practices as per given climatic regime may also be considered an adaptation. Given the fact that, experience with new crops and agricultural practices has to be built up and shared, the agricultural sector is not as flexible to climate change as is widely believed. 10.5.2

ADAPTATION FOR MAINTAINING ECOSYSTEM AND BIO-DIVERSITY

It is believed that, ecosystems and bio-diversity are perhaps at greatest risk of all sectors sensitive to water resources, and therefore, to climate change. Whereas adaptation to reduce vulnerability of the other sectors can be addressed as part of existing programs, the management of ecosystems is still relatively weak in its institutional realization and the institutions that are involved lack the capacity. Most at threat from climate change are the Sundarbans (Fig. 10.4), the haor wetland, the beel wetland and fish and other aquatic life. Sea level rise may inundate a good portion of the Sundarbans, especially the low-lying mudflats. In addition, ecosystems are threatened by salinization of surface and ground water. Higher water temperatures, loss of brackish waters and reduced flows could harm fisheries. In order to address vulnerability of the Sundarbans, its ecosystem, and bio-diversity, the most important adaptation would be to increase the dry season flow regime of the major distributaries of the Ganges. The National Water Management Plan (NWMP) has described a number of options to tackle low flows in rivers supplying freshwater to the forest, which needs to be prioritized and subsequently implemented. A concerted effort to protect bio-diversity in Bangladesh may require significant infusions of capital and training from development partners. Restoration of some of the dried up key wetlands is critical for safeguarding habitats for aquatic bio-diversity. Adaptations to decrease threats to ecosystems and bio-diversity are difficult because these changes are directly linked to the exogenous climate changes and sea level rises. Another key adaptation is the strengthening of institutions that are involved in realizing conservation and management of ecosystems (OECD, 2003).

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Fig. 10.4 The Sundarbans and Southwest region of Bangladesh.

10.5.3

CROSS-CUTTING ADAPTATION

There are a number of climate change adaptations which are not specific to water resources sector, but having potential benefits for other sectors. They address monitoring and projections of climate change as well as planning and coordination of adaptation. The country has a long experience in disaster mitigation and is in a continuous process to improve on its capacity to mitigate the impacts of such disasters as cyclones and riverine floods (Ahmed, 2000b; Ahmad et al., 2000). The country also lacks adequate capacity

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(viz. financial, institutional, technological, legal, etc.) and mechanisms to account for long-term changes. There is no denying the fact that traditional planning techniques are inadequate. Planning that would promote adaptation to climate change faces at least two methodological problems. First, impacts are understood through a process of sarcastic (what-if mode) analysis, the results of which may not occur within the next decades, and the recommendations are often beyond the normally adapted planning horizons for intervention. Second, sarcastic analyses leave room for uncertainties, allowing skeptics to avoid making timely decisions in favor of anticipatory adaptation. These are, perhaps, realities across the globe. Awareness building at the decision-making level may therefore be considered as an adaptation. Preparing and, more importantly, investing for long-term preparedness warrant political will at the highest possible level. Capacity building at the highest level should, therefore, be approached hand in hand with capacity building at the grass roots levels. A different type of adaptation measure is mitigation of the causes of the impacts in the natural, social and economic system. They are not under the exclusive control of the planners and policy makers in Bangladesh, but have to be addressed in an international effort. Examples are agreements on the maintenance of transboundary river flow and the global emission of greenhouse gasses. A number of sectoral policies have so far been pronounced by the government. A few of these policies have elements that would promote adaptation. 10.5.3.1 POLICY DIMENSIONS There is no specific policy regime on adaptation to climate change. However, Ahmed (2002) identified a number of policy elements in relevant sectoral policy pronouncements of the government, which would greatly facilitate adaptation in water resources sector and in other sectors dependent on water resources (more on the adaptation policies are discussed in Chapter 11). Generally, towards defining development pathways the focus is on project planning instead of resource planning. Lack of integration toward efficient use and management of resources is a paramount problem for the country. Optimization of resource often calls for integration, both through the various tiers of the government and also at all sectoral spheres. Since long-term changes mainly affect the availability of resources to properly account for them requires integrated management, coordinating all management agencies involved (World Bank, 2000). Although a number of policies have been pronounced by the GoB for sustainable resource management, developments in the direction of proper resource planning are only in an initial stage. Integration of various sectoral concerns incorporated in the National Water Management Plan has, however, given a ray of hope. Physical planning should be the basis for any resource-oriented planning as it defines and depends on the spatial distribution (and intensity) of the use of the land and water resources. The National Land-Use Policy called for land-use zoning for the coastal zone, which offers huge potential for adaptation to climate change. The system of planning and management is strongly centralized in the country. Adaptations to long-term changes will require a combination of measures on a national level and changes in behavioral patterns on a local level. The need for this “vertical” integration is widely recognized. In recent years, steps are gradually being taken to establish sustainable local institutional arrangements. Establishment of a people-oriented good governance process is vital for successful operation of any major adaptation activity.

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The experience of the Cyclone Preparedness Program taught a major lesson towards the planning and implementation of any future large-scale adaptation activity. On the field cooperation and coordination among various elements of national and local institutions could greatly benefit implementation of climate change related policies. Given the overriding importance of the coastal and water resources in Bangladesh for its development, climate change is considered a major threat to the country’s potential for sustainable development (Ahmad and Ahmed, 2002). In order to maintain the usual process of development, the country must invest on adaptation in all possible ways: physical adaptation, technological adaptation, social adaptation, institutional adaptation etc. It should be emphasized that rather than being mutually exclusive, adapting to climate change can thus be part of and complementary to a sustainable development strategy. There are many opportunities that would help accrue benefits even if climate change does not occur. Investment for those adaptations could also be sought and implemented. Individual’s preparedness can only lead to survival coping. However, suffering starts at individual level. To facilitate adaptation for the people, local level initiatives need to be strengthened. There are many collective activities, which a flood vulnerable community can undertake in each stage of a flood event: before incidence, during the flood, and following the event (Ahmed and Karim, 2004). The local government can facilitate these Community Based Flood Management activities. The government, through the pronouncement of National Water Policy, pledged that people should be involved in the planning and implementation of projects. In the overall water governance framework, local level micro-planning and initiatives must be paid sufficient attention in order to facilitate adaptation to climate change-induced floods. Moreover, the government may contemplate supporting resilience-building activities instead of focusing on post-flood relief. Offering soft-term credits for the poor to enhance resilience of their otherwise makeshift house, particularly for flood-proofing, can greatly help adapt to increased risks from flooding. 10.6

CONCLUDING REMARKS

Bangladesh is already highly prone to water-related extreme events. Any significant change in the climate system would exacerbate water-related problems. Vulnerability due to monsoon floods will be increased in terms of both extent and frequency, while moisture stress due to high evapo-transpiration will put additional constraints to crop production, particularly during the Rabi season. Potential for reduction of surface flows in rivers during dry season will cause salinity ingress throughout the coastal areas. Current vulnerability due to cyclonic storm-surge and riverbank erosion is also likely to increase. Climate change will have far reaching impacts on biophysical environment of the country, people’s livelihood, and national economy. Over the past millennia, people of Bangladesh have been showing indomitable courage to cope with extreme events driven by climate variability. Indigenous survival coping strategies have been key to overcome adverse situations, which have been complemented in recent decades with application of technologies and institutional response measures. To face anticipated high intensity events under climate change, one cannot rely only on ‘survival coping strategies’. Time has come to re-evaluate both available and potential ways and means to cope with extreme events, make people aware of anticipated adverse climatic events, facilitate their preparedness responses, and simultaneously, try to implement institutionally supported appropriate response measures so that the threat to human and ecological security is minimized, if not totally eliminated. People-centric

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anticipatory and planned adaptation measures, implemented phase-wise through institutional facilitation and supplemented by conducive policy and regulatory regime will be the keys to reduce vulnerability to climate change in the water resource sector for Bangladesh.

REFERENCES Ahmad, Q. K., Ahmad, N. and Rasheed, K. B. S. (eds.): Resources, Environment and Development in Bangladesh with Particular Reference to the Ganges, Brahmaputra and Meghna Basins, Academic Publishers, Dhaka, 1994. Ahmad Q. K. and Ahmed, A. U. (eds.): Bangladesh: Citizens’ Perspectives on Sustainable Development, Bangladesh Unnayan Parishad (BUP), Dhaka, August, 2002. Ahmad, Q. K. and Ahmed, A. U.: Regional Cooperation in Flood Management in the GangesBrahmaputra-Meghna Region: Bangladesh Perspectives. In M. M. Q. Mirza, A. Dixit and A. Nishat (eds.), Flood Problem and Management in South Asia, Kluwer Academic Publishers, Dordrecht, 2003, pp.181-198. Ahmad, Q. K., Chowdhury, A. K. A., Imam, S. H. and Sarker, M. (eds.): Perspectives on Flood 1998, The University Press Limited (UPL), Dhaka, 2000. Ahmed, A. U.: Adaptability of Bangladesh’s Crop Agriculture to Climate Change: Possibilities and Limitations, Asia Pacific Journal on Environment and Development, Volume 7, No. 1, 2000a, pp.71-93. Ahmed, A. U.: Cyclonic Storm-Surge Related Emergency Management: A Bangladesh Case Study. Paper Presented at the Third International Emergency Management Conference, Florida, May, 2000b. Ahmed, A. U.: The Role of GBM Regional Information Sharing Towards Flood Mitigation in Bangladesh. Proceedings of the International Conference on Sustainable Development of Water Resources: Socio-Economic, Institutional and Environmental Aspects, Held in New Delhi, India, November 27th-30th, Institute for Resource Management and Economic Development, 2000c, pp.9-16. Ahmed, A. U.: Reviewing Policy Regime in Relation to Water Resources Vulnerability to Climate Change. Presented in the Dialogue on Water and Climate, Dhaka, December, 2002. Ahmed, A. U.: Climate Variability and Flood: Observed Coping Mechanisms in Bangladesh. In SIWI, Drainage Basin Security - Balancing Production, Trade and Water Use, 13th Stockholm Water Symposium Proceedings, Stockholm International Water Institute (SIWI), Stockholm, 2003, pp.187-189. Ahmed, A. U. and Alam, M.: Development of Climate Change Scenarios with General Circulation Models in Vulnerability and Adaptation to Climate Change for Bangladesh, S. Huq, Z. Karim, M. Asaduzzaman and F. Mahtab (eds.), Kluwer Academic Publishers, Dordrecht, 1998, pp.13-20. Ahmed, A. U., Alam, M. and Rahman, A. A.: Adaptation to Climate Change in Bangladesh: Future Outlook. In S. Huq, Z. Karim, M. Asaduzaman and F. Mahtab (eds.), Vulnerability and Adaptation to Climate Change for Bangladesh, Kluwer Academic Publishers, Dordrecht, 1998a, pp.125-143. Ahmed, A. U. and Karim, Z.: A Manual for Facilitating Community Based Approaches to Flood Management in Bangladesh, Final Draft, Mimeo, Bangladesh Unnayan Parishad (BUP) and World Meteorological Organization (WMO), Dhaka, 2004. Ahmed, A. U. and Mirza, M. M. Q.: Review of Causes and Dimensions of Floods with Particular Reference to Flood ’98: National Perspectives. In Q. K. Ahmad, A. K. A. Chowdhury, S. H. Imam, M. Sarker (eds.), Perspectives on Flood 1998, The University Press Limited, 2000.

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Ahmed, A. U., Siddiqi, N. A. and Choudhury, R. A.: Vulnerability of Forest Ecosystems of Bangladesh to Climate Change’ in Vulnerability and Adaptation to Climate Change for Bangladesh, S. Huq, Z. Karim, M. Asaduzzaman and F. Mahtab (eds.), Kluwer Academic Publishers, Dordrecht, 1998b, pp.93-113. Alam, M., Nishat, A. and Siddiqui, S. M.: Water Resources Vulnerability to Climate Change with Special Reference to Inundation. In S. Huq, Z. Karim, M. Asaduzzaman and F. Mahtab (eds.), Vulnerability and Adaptation to Climate Change for Bangladesh, Kluwer Academic Publishers, Dordrecht, 1998, pp.21-38. Allison, M. A.: Historical Changes in the Ganges-Brahmaputra Delta Front. Journal of Coastal Research 14(4) (1998), pp.1269-1275. Asaduzzaman, M., Reazuddin, M. and Ahmed, A. U. (eds.): Global Climate Change: Bangladesh Episode, Department of Environment, Ministry of Environment and Forests, Government of the People’s Republic of Bangladesh, Dhaka, 1997. Bahadur, J.: Himalayan Glaciers, Vigyan Prasar, New Delhi, 1998. Choudhury, A. M., Quadir, D. A., Neelormi, S. and Ahmed, A. U.: Climate Change and Its Impacts on Water Resources of Bangladesh. In: A. Muhammed (Ed.), Climate Change and Water Resources in South Asia, Asianics Agro-Dev International, Islamabad, 2003, pp.21-60. Disaster Management Bureau (DMB): Standing Orders on Disaster. Disaster Management Bureau (DMB), Ministry of Disaster Management and Relief, Government of the People’s Republic of Bangladesh, Dhaka, 1999. Giorgi, F., Francisco, R. and Pal, J. S.: Modeling the Effects of Surface Sub-Grid Scale Variability on the Hydrologic Cycle Over Regions of Complex Terrain. Journal of Hydrometeorology 4 (2003), pp.317-333. Giorgi, F., Meehl, G. A., Kattenberg, A., Grassl, H., Mitchell, J. F. B., Stouffer, R. J., Tokioka, T., Weaver, A. J. and Wigley, T. M. L.: ‘Simulation of Climate Change with Global Coupled Climate Models and Regional Modeling Techniques’. In R. T. Watson, M. C. Zinyowera and R. H. Moss (eds.), Annex B to The Regional Impacts of Climate Change: An Assessment of Vulnerability. A Special Report of IPCC Working Group II, Cambridge University Press, New York, 1998, pp.427-437. Gorai River Restoration Project (GRRP): Draft Final Report, Volume on Main Report, Ministry of Water Resources, Government of the People’s Republic of Bangladesh, Dhaka, 2001. Habibullah, M., Ahmed, A. U. and Karim, Z.: Assessment of Food Grain Production Loss Due to Climate Induced Enhanced Soil Salinity. In S. Huq, Z. Karim, M. Asaduzaman and F. Mahtab (eds.), Vulnerability and Adaptation to Climate Change for Bangladesh, Kluwer Academic Publishers, Dordrecht, 1998, pp.55-70. Haider, R., Rahman, A. A. and Huq, S. (eds.): Cyclone ’91: An Environmental and Perceptional Study, Bangladesh Center for Advanced Studies, Dhaka, 1991. Halcrow and Associates: National Water Management Plan (Draft), Ministry of Water Resources, People’s Republic of Bangladesh, Dhaka, 2000. Halcrow and Associates: Options for Ganges Dependent Area. Main Report, Sir William Halcrow and Associates, Water Resources Planning Organization (WARPO), Ministry of Water Resources, Government of the People’s Republic of Bangladesh, Dhaka, 2001a. Halcrow and Associates: Options for Ganges Dependent Area. Volume 2, Annexes, Sir William Halcrow and Associates, Water Resources Planning Organization (WARPO), Ministry of Water Resources, Government of the People’s Republic of Bangladesh, Dhaka, 2001b. Huq, S., Ahmed, A. U. and Koudstaal, R.: Vulnerability of Bangladesh to Climate Change and Sea Level Rise. In T. E. Downing (ed.), Climate Change and World Food Security, NATO ASI Series I(37), Springer Verlag, Berlin, Hiedelberg, 1996, pp.347-379. Intergovernmental Panel on Climate Change (IPCC): Climate Change 2001. Scientific BasisSummary for the Policy Makers. Intergovernmental Panel on Climate Change (IPCC), WMO, UNEP, Geneva, Switzerland, 2001. Karim, Z., Hussain, S. G. and Ahmed, A. U.: “Climate Change Vulnerability of Crop Agriculture”. In S. Huq, Z. Karim, M. Asaduzaman and F. Mahtab (eds.), Vulnerability and Adaptation to Climate Change for Bangladesh, Kluwer Academic Publishers, Dordrecht, 1998, pp.39-54.

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Karim, Z., Hussain, S. G. and Ahmed, M.: Salinity Problems and Crop Intensification in the Coastal Regions of Bangladesh, Bangladesh Agriculture Research Council (BARC), Dhaka, 1990. Karim, Z., Ibrahim, A. M., Iqbal, A. and Ahmed, M.: Drought in Bangladesh Agriculture and Irrigation Schedules for Major Crops. Soils Publication No. 34, Bangladesh Agriculture Research Council (BARC), Dhaka, 1990. Karim Z., and Iqbal, A. (eds.): Impact of Land Degradation in Bangladesh: Changing Scenario in Agricultural Land-Use. Soils Division, Bangladesh Agricultural Research Council (BARC), Dhaka, 2001. Mirza, M. M. Q.: Global Warming and Changes in the Probability of Occurrence of Floods in Bangladesh and Implications. Global Environmental Change 12 (2002), pp.127-138. Mirza, M. M. Q. and Dixit, A.: Climate Change and Water Resources in the GBM Basins. Water Nepal 5(1) (1997), pp.71-100. Mirza, M. M. Q., Warrick, R. A. and Ericksen, N. J.: The Implications of Climate Change on Floods of the Ganges, Brahmaputra and Meghna Rivers in Bangladesh. Climatic Change 57 (2003), pp.287-318. Ministry of Water Resources (MWR): National Water Policy, The Government of the People’s Republic of Bangladesh, Dhaka, 1999. Organization for Economic Co-operation and Development (OECD): Development and Climate Change in Bangladesh: Focus on Coastal Flooding and the Sundarbans, Paris, 2003, p.70. Pradhan, P., Mool, P., Bajracharya, S. and Joshi, S.: Experiences in Inventorying Glaciers and Glacier Lakes of Hindu Kush Himalayas, ICIMOD, Kathmandu, 2002. Qureshi, A. and Hobbie, D.: Climate Change in Asia, Asian Development Bank (ADB), Manila, 1994. Task Force: Report of the Task Force on Bangladesh Development Strategies for the 1990s, Vol. I-IV, University Press Limited, Dhaka, 1991. Terray, P., Delecluse, P., Terray, L. and Labattu, S.: Sea Surface Temperature Forcing of the Indian Summer Monsoon’. Climate Dynamics 21 (2003), pp.593-618. Warrick, R. A. and Ahmad, Q. K.: 1996. The Implications of Climate and Sea Level Change for Bangladesh, Kluwer Academic Publishers, Dordrecht, 1996. World Bank-Bangladesh Center for Advanced Studies (WB-BCAS): Bangladesh 2020: A Long-Run Perspective Study, University Press Limited, Dhaka, 1998. World Bank: Bangladesh: Climate Change and Sustainable Development, Rural Development Unit, South Asia Region, the World Bank, Dhaka, 2000.

11 Using the Adaptation Policy Framework to Assess Climate Risks and Response Measures in South Asia: The Case of Floods and Droughts in Bangladesh and India M. MONIRUL QADER MIRZA IAN BURTON

11.1

INTRODUCTION

South Asia is noted for climate and hydrological extremes such as floods, droughts, heat waves, and cyclones. The climate of South Asia is highly influenced by the Southwest monsoon (see Chapter 1). More than three-quarters of the annual precipitation occurs in the monsoon months (June-September). The onset and departure of the monsoon is spatially highly variable, so the precipitation is also. The failure of the monsoon and high summer temperatures leads to drought in many parts of Bangladesh, India and Pakistan. In the Eastern Coast of India and in the coastal region of Bangladesh disastrous cyclones are regular visitors. Glacier Lake Outburst Floods (GLOFs) in Bhutan, Nepal and Pakistan cause disasters to life and property downstream, resulting in serious death tolls as well as the destruction of valuable forests, farms and costly mountain infrastructure. In Nepal and Bhutan, 44 glacier lakes have been identified as potentially dangerous and which may result in GLOF (ICIMOD, 2001). In South Asia, particularly in the Himalayan region, the frequency of the occurrence of GLOF events increased in the second half of the 20th century. The 1990s was the warmest decade of the last century and several extreme climate events occurred in the South Asia region. In July of 1993, the Tistung station in Nepal registered 540 mm rain over a 24-hour period triggering a severe flood. Severe droughts occurred over large regions in India and Pakistan in 2000. Bangladesh experienced the worst flood in recent history in 1998 which engulfed about 70% of the country. It appears that extreme climate events are increasing in frequency and magnitude, causing more deaths, injury, disability and disease, economic and social impacts in the impoverished nations of South Asia (Table 11.1). The Intergovernmental Panel on Climate Change (IPCC) (2001) concluded that there would be likely increases in intense precipitation events, droughts, tropical cyclone peak wind intensities and tropical cyclone mean and peak precipitation intensities in the future due to climate change. Therefore, a dramatic increase in damage is also expected.

Bangladesh

* On average 21.5% is inundated. *Inundation may increase to 70%. * During 1953-2000, 15,678 people died. * On average 475 persons died per event. * Extreme flood can cause economic damage of US$ 3 to 5 billion.

Extreme Event

Flood * 40 million ha area are flood vulnerable. * Average flooding event affected 34 million people. * 1,595 people/event. * US$ 250 million economic loss/event.

India

Table 11.1 Extreme climate events and damages in selected countries in South Asia

* During 1983-2000, 5,935 people died. * On average 330 people died per event. * In July of 1993, one single flood event killed 1,336 people. * Economic loss of 1993 flood was Rs. 4,904 million * Dig Tsho GLOF in 1985, destroyed the Namche small hydropower project that built at the cost of US$ 1.5 million.

Nepal

* Floods affect urban areas severely.

Pakistan

280 ADAPTATION POLICY FRAMEWORK TO ASSESS CLIMATE RISKS

Bangladesh

* Past droughts typically affected 47% of the country. * Affected 53% of the population. * On average, loss crop in drought is equal to a flood. * In 1979, drought created almost a famine like situation.

* Cyclones affect Bangladesh severely. * The 1970 cyclone killed 250,000 people. * The 1991 cyclone killed 138,000 people.

Extreme Event

Drought

Cyclone

Table 11.1 Continued

* India is one of the worst cyclone affected countries. * In 1999, the Orissa super cyclone killed 10,000 people.

* 68% of the landmass is vulnerable to droughts. * Severe droughts occur once in every 9 years. * The 1987 drought affected 60% of the cropped area and 285 million people.

India

Not vulnerable

* Droughts occur occasionally but effects are not significant.

Nepal

Not vulnerable

* Droughts affect agriculture severely. * In 2000-2001, due to drought GDP growth rate dropped to 2.6% as against targeted 5%.

Pakistan

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Climate change poses a considerable risk to the future sustainable development of countries in South Asia. How might the countries of the region best respond to these risks? The diverse character and widespread nature of the risks is described as above. From these we have selected urban floods in Dhaka, Bangladesh and droughts in Gujarat, India as specific case studies. We have selectively drawn upon some of the concepts and methods in the Adaptation Policy Framework (APF) (Fig. 11.2) (UNDP, 2004) and applied them to the two case studies. In doing so we recognize that present adaptation falls short of what is necessary to prevent the further growth of vulnerability and damage potential. There is in fact a current adaptation deficit in coping with climate variability and extremes even without taking into account the added risk associated with climate change (Burton, 2004). For this reason we recognize two types of adaptation. Type I Adaptation refers to current adaptation strategy, policy, and measures without considering climate change. Most of the adaptation measures are in practice belong to Type I. Type II Adaptation is the additional adaptation that is required to cope with climate change. Because climate change risks have still not been factored into many development decisions, and because awareness of the need for adaptation has still not been well incorporated into the work of development agencies/ministries in the developing countries and because adaptive capacity is lacking, not much Type II adaptation has taken place. In this regard, the APF has been designed to help factor climate change risks in to development decisions in order to reduce vulnerability and facilitate sustainable development. The APF approach is briefly described in Section 11.2 and some of the major concepts are described in Section 11.3. We then use the APF as a means of formulating an analysis in the two case studies (Section 11.4). Finally, we discuss opportunities and challenges associated with the APF with particular reference to the two case studies.

Damage (in million Rs. x 10)

3500 3000 2500 2000 1500

5-year moving average

1000 500 0

1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 Year

Fig. 11.1 Damage due to floods/heavy rains in India during 1953-2000. Figures for 1999 and 2000 are tentative. Data source: Singh, 2001.

11.2

ADAPTATION POLICY FRAMEWORK

While substantial literature exists (Carter et al., 1994; IPCC, 2001; US Country Study Program, 1996; and Feenstra et al., 1998) regarding climate change impacts, information on

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TP 7 – MEASURING AND ENHANCING ADAPTIVE CAPACITY

TP 2 – STAKEHOLDER ENGAGEMENT

adaptation policies and strategies is limited. Burton et al. (2002) commented that effective adaptation policy had to be responsive to a wide variety of economic, social, political, and environmental circumstances. In order to do so, a common framework of concepts, linked together in a flexible manner is required. Therefore the development of the Adaptation Policy Framework (APF) has been motivated by the lack of practical guidance on adaptation to climate change. The driving concern underlying the APF was that discussions about climate change adaptation had not progressed significantly beyond the identification of possible adaptation measures (UNDP, 2004). The goal of the APF is to help narrow a wide range of policy options and measures into site-specific policies for particular climate risks. Since the potential effects of climate change are pervasive, adaptation can include a wide range of responses and policies in all economic sectors and all regions (UNDP, 2001). The framework is intended to integrate short-, medium- and long-term threats to national economic development planning, as well as the relevant current policies and measures. In designing the APF, coping with present climate variability is seen as an effective way to reduce long-term vulnerability to climate change. Project scope and design

Assessing current vulnerability

Characterizing future climate risks

TP 1 – PROJECT SCOPE AND DESIGN TP 3 – VULNERABILITY ASSESSMENT TP 4 – CURRENT CLIMATE RISKS TP 5 – FUTURE CLIMATE RISKS TP 6 – SOCIO-ECONOMIC CONDITIONS

Developing an adaptation strategy

Continuing the adaptation process

TP 8 – ADAPTATION STRATEGY

TP 9 – CONTINUING ADAPTATION

Fig. 11.2 Outline of the Adaptation Policy Framework (APF) process (UNDP, 2004).

Countries in South Asia have now conducted some studies (ADB Country Study Program, UNFCCC National Communications, US Country Study Program, etc.) under Stage I Adaptation (Box 11.1). However, it is recognized that more work is needed to progress to the next step and to prepare for Stage II Adaptation (Box 11.1), towards which the APF is specifically directed. Over the long-term, this framework is critical for preparing the ground for detailed analysis in Stage III Adaptation (Box 11.1). The APF has five major steps (Fig. 11.2) compared to the seven steps of the “first generation” of impact and vulnerability assessment method (Carter et al., 1994). The APF (UNDP, 2004) is more robust and flexible and its “first generation” counterpart and designed to fit present and future requirement in terms of climate variability and change. The five-step analysis is supported by 9 Technical Papers (TPs) which are: APF Project Scope and Design, Stakeholder Engagement in the Adaptation Process, Vulnerability Assessment for Climate Adaptation, Vulnerability Assessment for Climate Adaptation,

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Box 11.1 Initial Guidance from the Conference of the Parties on Adaptation (Decision 11/CP.1)

Stage I: “Planning, which includes studies of possible impacts of climate change to identify particularly vulnerable countries or regions and policy options for adaptation and appropriate capacity building”. Stage II: “Measures, including further capacity building which may be taken to prepare for adaptation as envisaged in Article 4.1(e)”. Stage III: “Measures to facilitate adequate adaptation, including insurance and other adaptation measures as envisaged by articles 4.1(b) and 4.4”.

Assessing Current Climate Risks, Assessing Future Climate Risks, Socio-Economic Conditions, Measuring and Enhancing Adaptive Capacity, Formulation of an Adaptive Strategy and Continuing the Adaptation Process. The APF and TPs can be downloaded from http://www.undp.org/cc/apf_outline.htm. 11.3

VULNERABILITY AND ADAPTATION: A BRIEF SYNTHESIS

11.3.1

VULNERABILITY

The concept of vulnerability has gone through a comprehensive evolution process in the last few decades. Generally it is defined from three perspectives: natural hazard, climate change and variability and entitlement. From a natural hazards perspective Blaikie et al. (1994) defined vulnerability as “…the characteristics of a person or group in terms of their capacity to anticipate, cope with, resist and recover from the impact of a natural hazard (p.57)”. It is focused on only human systems and three temporal situations in terms of natural extreme events that cause hazards are taken into account: pre-event and post-event and during the event. The authors also argue that vulnerability “…is a measure of a person or group’s exposure to the effects of a natural hazard, including the degree to which they can recover from the impact of that event (p.57)”. The exposure refers to physical, economic and human well-being and recovery is related to adaptive capacity and resiliency. Kelly and Adger (2000) widened the definition of vulnerability as “…the ability or inability of individuals or social groupings to respond to, in the sense of cope with, recover from or adapt to, any external stress placed on their livelihoods and well-being (p.300).” Their approach focuses on existing “wounds” (or prior damage), which might limit capacity to respond to stresses and are independent of future threats. The Intergovernmental Panel on Climate Change (IPCC) (2001) broadened natural hazard perspective based definition by focusing on the future as well as incorporating natural systems in addition to human system. It defines vulnerability as “…the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude and rate of climate change and variation to which a system is exposed, its sensitivity, and its adaptive capacity (p.18).” Many authors (e.g., Liverman, 1994; Adger and Kelly, 1999) have argued for the use of a political economy framework, often using the “entitlements approach” which begins

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at household level (developed by Sen (1981, 1987), in analyses of vulnerability. A household’s food entitlement consists of the food that the household can obtain through production, exchange, or extra-legal legitimate conventions - such as reciprocal relations or kinship obligations (Drèze and Sen, 1989). Ribot (1996) argues this approach introduces a household perspective on vulnerability, one that replaces “eco-centric” approaches to environmental change. The main contribution of this approach lies, perhaps, in its focus on the vulnerability of individuals and social groups. Within this framework vulnerability is understood as being determined by access to resources-specifically, by individuals’ “entitlement” to call on these resources. Watts and Bohle (1993), using Drèze and Sen’s (1989) analysis of entitlements, argue that vulnerability is configured by the mutually constituted triad of entitlements, empowerment and political economy. Here empowerment is the ability to shape the political economy that in turn shapes entitlement. The Food and Agriculture Organization (FAO) of the United Nations (1999) defines vulnerability from the food security perspective as “the presence of factors that place people at risk of becoming food insecure or malnourished.” This definition focuses on causes of food insecurity due to human interventions, such as political decisions, armed conflicts and international economic embargo. Inappropriate political decisions often cause hunger in Sub-Saharan Africa and Asia; armed conflicts either do not allow food distribution or purchase of food due to diversion of resources for buying military hardware/software; and international economic embargoes often lead to hunger by limiting a country or government’s spending power or accumulation of economic resources. 11.3.2

ADAPTATION

A number of definitions of adaptation can be found in the literature. IPCC (2001) defined adaptation as an adjustment in natural or human systems in response to actual or expected climate stimuli and their effects or impacts, which moderates harm or exploits beneficial opportunities. It refers to changes in processes, practices and structures to moderate potential damages or to benefit from opportunities associated with climate change. Smithers and Smit (1997) describe adaptation as involving “change in a system in response to some force or perturbation”. Pielke (1998) refers adaptation “to adjustment in individual, group and institutional behavior in order to reduce society’s vulnerabilities to climate. Adger (2001) views adaptation as a dynamic social process and believes that the ability of a society to act collectively determines its ability to adapt. 11.3.2.1 ADAPT TO WHAT? Adaptation occurs in both natural and socio-economic systems (Burton et al., 1998). People generally adapt and practice measures to adapt to the variability of natural climate and extreme weather events. Human intervention modifies the threat of natural variability. However, human action can cause irreversible damage to systems and their natural resiliency may be lost. Burton et al. (1993) pointed out that human activities are not always as well adapted to climate as they might be. The mounting losses from great natural disasters are in substantial part associated with extreme weather events. Therefore, in a situation where natural climate and hydrologic systems have been modified by human intervention, even efficiently designed corrective measures might be proven to be either partially effective or ineffective.

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11.3.2.2 ADAPTIVE CAPACITY IPCC (2001) defined adaptive capacity as the potential, capability, or ability of a system to adapt to climate change stimuli or their effects or impacts. Adaptive capacity depends on a number of determinants that include: socio-economic wealth, governance, technology, information and skills, infrastructure, institutions and equity. Among these determinants, socio-economic factors are the most important determinants that help develop adaptive capacity. Socio-economic factors affect the ability of a system to absorb (robustness) or respond to changes that occur to natural system due to natural causes or human interventions (Smith et al., 1998). In South Asia, socio-economic conditions of various economic groups, location and living conditions, inequality between rural and urban population (including their intra inequality) and gender broadly defines exposure of these groups to extreme weather events or human interventions. 11.3.2.3 ADAPTATION TYPES Various types of adaptation include anticipatory and reactive adaptation, private and public adaptation, and autonomous and planned adaptation. Salient features of various types of adaptation are presented in Table 11.2.

11.3.2.4 ADAPTATION MEASURES There are many potential adaptation measures that may be adopted in response to climate change and variability. Burton et al. (1993) divided them into the following eight categories depending on the individual’s choice of options. The choice typology has been extended to include the role of community structures, institutional arrangements, and public policies (also see Fig. 11.3). Table 11.3 summarizes the measures.

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1 Share the Loss

Adaptation/ Response Options

2 Bear the Loss

Structural, Technological

3 Modify the Events

Legislative, Regulatory, Financial

4 Prevent the Effects

Institutional, Administrative

5 Change Use

Market Based

6 Change Location

On-Site Operations

7 Research 8 Education, Behavioral

Fig. 11.3 Classification of adaptation options (Burton et al., 1993). Table 11.3 Classification of adaptation measures

Classification Bear the cost Share the losses Modify the events Prevent the events or their effects Change use occurs when an economic activity is impossible or extremely risky Change location Research

Education for behavioral change

Examples Accept the cost because there is no other choice. Use insurance or government relief, or community or family sharing. Modify the actual physical events themselves (e.g. flood control, coastal surge protection). Preventing drought by cloud seeding but very few success stories. Change human use activities (e.g. regulate floodplain land-use; use drought-tolerant crops).

Relocating major crops and farming regions, shifting human settlement and livestock population. Development of salt tolerant crops for coastal region, rice varieties that can remain underwater for a longer period, etc. Saving water to reduce climate driven water demand; conservation of energy to reduce cooling demand, etc.

11.4

PRESENT VULNERABILITY AND ADAPTATION MEASURES AND POLICIES IN SOUTH ASIA: URBAN FLOODING IN DHAKA

11.4.1

URBAN FLOODS IN DHAKA, BANGLADESH

Bangladesh acts as the drainage outlet for the three large rivers: the Ganges, Brahmaputra and Meghna (GBM). Huge rainfall in the basins during the monsoon, geographical

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proximity and flat terrain of Bangladesh make it highly vulnerable to recurring flooding. On average about 20.5% of Bangladesh gets inundated annually. In extreme flooding years, the extent of inundation may be as much as 70%. Figure 11.4 shows year to year extent of flooding in Bangladesh. Four types of floods commonly occur in Bangladesh: flash, riverine, rainfall and storm-surge floods (Box 11.2).

Fig. 11.4 Extent of flooded area (%) in Bangladesh from 1954 to 2001. Source: Flood Forecasting and Warning Center (FFWC), Dhaka.

Box 11.2 Bangladesh flood types

The Northern, Northeastern and Southeast parts of Bangladesh are vulnerable to flash floods. They usually occur due to a heavy rainfall in the neighboring hills and mountains in India as well as in Bangladesh. The normal period of flash flooding is late April to early May. Riverine floods are caused by over bank spillage of monsoon flows in the major rivers and their distributaries. Riverine floods may occur several times depending on timing and magnitude of rainfall in the basins and may prolong for months in the monsoon (June-September). Rainfall floods occur when high local rainfall generates huge volume of runoff in the rivers and streams exceeding the drainage capacity. Occurrences of such floods are common when the three major rivers are at high stages. Storm-surge floods occur during October-December and April-May in the low-lying coastal areas of Bangladesh. Tropical cyclones generate storm-surges that bring tidal bores often 9 m high (Ahmed and Mirza, 2000).

In the 1980s and 1990s, three extreme floods in 1987, 1988 and 1998 engulfed 36%, 63% and 69% of the country, respectively and caused human, environmental and economic devastation in Bangladesh. During the flood of 1988, Dhaka City - the capital of Bangladesh was severely affected. Again in 1998, a catastrophic flood engulfed the greater Dhaka area in the months of August and September. Due to the flooding, about 56% of the greater Dhaka was submerged, and affected about 1.9 million people (30% of the population).

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11.4.2

289

VULNERABILITY OF DHAKA TO FLOOD HAZARDS

Dhaka City is highly vulnerable to flooding and subsequent hazards (Fig. 11.5) for a number of reasons. First, the city is surrounded by the distributaries of the two major rivers, the Brahmaputra and Meghna. The surrounding rivers are Buriganga to the South, Turag to the West, Tongi Khal to the North and Balu to the East. The combined area of Dhaka East and Dhaka West known as Greater Dhaka covers an area approximately 275 km2 (JICA, 1991). Dhaka is situated on a flat terrain which makes it vulnerable to flooding. The elevation of Greater Dhaka is only 2 m-13 m above mean sea level (MSL) and most of the urbanized areas are at 6 m-8 m above msl. About 62% area of Greater Dhaka is below 6 m (JICA, 1987). This is consistent with the overall elevation of Bangladesh where 80% of land area is below 12 m above MSL. Second, Dhaka’s population has been growing at a very fast rate. Urbanization in Bangladesh is poverty driven-caused by an unsustainable rural economy characterized by extreme entitlement contraction among the majority of marginalized peasantry (Barkat et al., 1997). Other causes include riverbank erosion, flooding, droughts and cyclones. The present population of the Dhaka Metropolitan Area is more than 10 million. The last decadal growth rate was about 70%, though the population growth rate was even higher. In the decade 1981-1991, population doubled. Population statistics of Dhaka City show that the annual growth rate was 2.9% (1951-1961), 10.2% (1961-1974) and 8.1% (1974-1981) (Table 11.4). Third, poverty is another important factor that makes the poor sections of Dhaka more vulnerable to flooding. About 30% of Dhaka’s population is classified as a hardcore poor (per capita monthly income ≤US$ 43) and 50% as poor (per capita monthly income ≤US$ 65). Altogether about 3 million or nearly one-third of Dhaka’s population live in 2,100 slums and squatter settlements (Rahman and Tariquzzaman, 2001). In particular, Dhaka suffers from shortage of basic infrastructure and services such as water supply, sanitation, solid waste disposal and transport. The problem is not only shortage, but also unequal distribution of service, with much of the impact absorbed by low income and poorer section of Dhaka. Fourth, loss of internal water bodies increases the vulnerability to flooding. Dhaka used to have a number of canals (Dhoali Khal, Begunbari Khal, etc.) connected to the surrounding rivers and large water bodies. Most of these canals and water bodies have disappeared over the last 3-4 decades mainly due to private and public encroachments. Therefore, drainage congestion is a regular event and flooding from drainage overflow is a severe problem even after a moderate shower. The water depth in some areas may be as high as 40 cm-60 cm, which results in large infrastructure problems for the city, economic losses in production, and damage to existing property and goods (Huq and Alam, 2003). Fifth, loss of carrying capacity of surrounding rivers increases vulnerability. Dhaka is surrounded by four rivers: Buriganga, Turag, Tongi Khal and Balu. Over the years, the water carrying capacity of these rivers has been lost due to siltation and illegal encroachments. Therefore, floodwater quickly overtops the bank and inundates the surrounding urban area. Recession of floodwaters also takes longer time for the same reason. Sixth, rapid urbanization and built-up areas lead to shortening of the runoff concentration time and an increase of the peak flow. In the last three decades rapid urbanization has occurred in Dhaka. Therefore a substantial increase in development of residential and commercial areas has taken place to accommodate rapid growth of population at the initiatives of private land developers, real state business and public

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sector. These actions resulted in substantial growth of impervious areas, created obstruction to natural drainage patterns, and reduced detention basins. Bari and Hasan (2001) investigated the effects of urbanization on runoff concentration time and peak flow with the aid of NAM1 conceptual model and the Rational Formula2 (Kuichling, 1889) and found an increased volume of runoff with the increase of built-up areas in Dhaka City. Table 11.4 Population of Dhaka in various decades

Year

Area (km2)

Population (million)

Source

1951 1961 1971 1974 1981 1991 2001

28 40 40 155.4 1,530

0.34 0.55 1.5 1.6 3.44 6.95 10.6

Census of Pakistan, 1951 Census of Pakistan, 1951 Census of Bangladesh, 1974 Census of Bangladesh, 1974 Census of Bangladesh, 1981 Census of Bangladesh, 1991 Census of Bangladesh, 2001

Dhaka City was also severely affected by the 1998 flood (Fig. 11.6). The water levels in the rivers surrounding Dhaka approached the respective danger levels in the second half of July and crossed the danger levels in mid-August. Peak floods usually occur in the last week of August and the first two weeks of September and floodwater recedes in the last ten days of September. The floods of 1987 and 1988 followed this pattern. However, during the flood of 1998, the water levels crossed the danger levels almost a month earlier and stayed there until the last week of September (Faisal et al., 2003). Peak water levels, return period and days above the danger level3 of the Buriganga River are given in Table 11.5. Seventy out of ninety, Dhaka City corporations were under water (Jahan, 2000). The flood affected almost all aspects of human life including income, health and occupation. People of various income and occupation suffered in varying degrees, and there were also significant spatial variations in the impact of the flood (Jahan, 2000). 11.4.3

ADAPTATION AND COPING MECHANISMS

The location of the Dhaka City has made it particularly vulnerable to floods. It is surrounded by the Buriganga to the South, Turag to the West, Tongi Khal to the North, and Balu to the East. Dhaka City and the adjoining areas are composed of alluvial terraces of the Southern part of the Madhupur tract and low-lying areas of doab of the rivers Meghna and Lakhya. The city suffered from flooding mainly due to the spillage of the 1 The NAM conceptual lumped hydrologic model was developed by the Technical University of Denmark in 1973 and is widely applied in Bangladesh. 2 Kuichling (1889) first applied the Rational Method to estimate peak discharge. The formula is Qp = FCIA, where F = unit conversion factor, C = runoff coefficient, I = intensity of rainfall (mm/hr) and A = drainage area (km2). 3 In Bangladesh danger level at a river location is the level above which it is likely that the flood may cause damages to nearby crops and homesteads. In a river having no embankment, danger level is about annual average flood level. In an embanked river danger level is fixed slightly below design flood level of the embankment.

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surrounding rivers. Local rainfall often complicates the flooding situation. Although the city was periodically flooded, adaptation and coping mechanisms are not well documented but some initiatives were taken in the wake of disastrous flooding of 1988 and 1998 (Huq and Alam, 2003; Hye, 1999; Faisal et al., 1999; and Jahan, 2000).

Fig. 11.6 Flooded area in Dhaka during 1998 floods. Source: Faisal et al., 2003. Reprinted with the permission of Kluwer Academic Publishers, the Netherlands. Table 11.5 Water levels in the Buriganga River and their return periods

River/Station

Flood Year

Danger Level

Peak Level

Return Period

Days Above Danger Level

Buriganga/ Dhaka

1954 1955 1974 1987 1988 1998

6.00

7.06 7.09 6.61 6.64 7.58 6.70

29.5 31.5 13.5 11.7 94.0 13.5

46 31 24 17 23 36

Dhaka Flood Protection Embankment: The first flood protection embankment was the Buckland Flood Protection Embankment along the Buriganga River constructed during the early period of the British rule. It was the first attempt to mitigate flood damage

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in Dhaka City (Huq and Alam, 2003). In the wake of the 1988 floods, construction of an embankment encircling Dhaka City was commenced under a “crash programme”. Under Phase I of the project, a flood protection embankment for the Western part of the city was completed. However, the Eastern part of the city remains unprotected (Faisal et al., 1999). As a result, almost the entire Eastern block was inundated during floods in 1998. Although the embankment provides some protection, it has increased vulnerability to flooding. Unplanned urbanization is taking place in the low-lying areas adjacent to the Western part of the embankment. In 1998, about 20% of the Western block was also inundated by floods. Non-Structural Measures: Important non-structural measures include flood forecasting and warning, retention ponds, natural water bodies and drainage network, land-use planning and relief and rehabilitation. Other practiced non-structural measures are summarized in Table 11.6. The FFWC of Bangladesh Water Development Board (BWDB) administers flood forecasting and warning in collaboration with the Institute of Water Modeling (IWM), Bangladesh Meteorological Department (BMD) and Space Research and Remote Sensing Organization (SPARRSO). Three hydrologic forecasting techniques-MIKE 11 Simulation Model, Muskingum/Cunge Flood Routing Method and Gauge-to-Gauge correlation used. The FFWC provides flood and river forecasts for 16 locations for 24-h and 48-h periods. In addition to this, it also provides daily river level and rainfall data for 50 rivers and 49 rainfall stations. Although these warnings are useful, a forecast in terms of inundation area would be more useful in making people understand the danger of floods. Apart from radio, TV and newspapers, there is no community-based mechanism to communicate flood forecasting and warning to the city dwellers. Dhaka City used to have many natural water bodies, which functioned as a buffer for floodwaters. Over the years, the natural water bodied dwindled significantly due to public encroachments for land development. Virtually no natural water bodies left in the old part of the city. Encroachments are continuing even in the new upscale residential areas of Gulshan, Banani and Baridhara. The minimum standard for a retention pond is 12% of the urban area whereas the present area is estimated to be less than 4% (RAJUK, 1995). The government has recently issued a decree banning the filling in of any wetland for urban development (Huq and Alam, 2003). Jahan (2000) investigated socio-economic coping mechanisms in Dhaka City during floods in 1998. The poorer sections of the society were hard-hit as the duration of the flood was more than two months. Many used up their savings and in addition, borrowed money to survive (Fig. 11.7). More than 35% of the credit came from relatives followed by shopkeepers and neighbors. Some people sold assets and mortgaged properties to buy food and other daily necessities. Help and assistance also came from various public and private organizations in terms of food, clothing, housing materials, medicine, water purification tablets, money, etc. (Table 11.7). Among the respondents, 44% and 40% said that they partly and completely recovered from the flood. The remaining 16% could not recover at all. 11.4.4

DROUGHTS IN INDIA: CASE STUDY OF GUJARAT

High drought prone areas in India are located mainly in the Western part of the country with arid and semi-arid climate (Fig. 11.8). However, occasionally other parts of the country are also vulnerable to droughts. The Planning Commission of India identified 54 drought prone districts distributed over 13 states (Kulshrestha, 1997). In India a drought is

Vulnerable group feeding, food for work, supply of building materials, soft or interest free loans for business and agriculture

Recovery and reconstruction

Note: Modified from Faisal et al. (1999).

Flood shelter

Flood fighting

Flood proofing

Medical care, potable water, food, candle, clothing, temporary housing, shelter Raising plinth level of the house, building on tall pillars, flood walls along properties, raising important roads and some power stations above the 1988 flood level, special embankment for the Zia International Airport Temporary flood wall made by brick or sand bags, water pumping, moving assets to upper floors, roof or elevated high grounds such as roads and embankments Community centers, educational institutions, public buildings, roads, embankments

Description

Emergency services

Activity

Table 11.6 Non-structural activities practiced by various groups

No specific urban flood shelters, other facilities used as shelters are either not designed as such or have insufficient capacity Affected group specially the poor people has limited access to such help. Misappropriation of relief and rehabilitation material or fund is common

People fight with floods until unbearable

Minimum ground elevation proposed for houses for the eastern part of the city

Widely practiced but limited to accessible places

Remarks

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considered to be “moderate” if the deficiency is between 26%-50% of the normal rainfall and “severe” if the rainfall deficiency is greater than 50% of the normal (IMD, 1971; GOI, 1976). The largest number of droughts occurred in the first quarter of the last century. The second quarters of both centuries experienced comparatively small number of droughts.

Fig. 11.7 Distribution of households by sources of credit. Source: Jahan, 2000. Table 11.7 Percentage distribution of households by types and sources of assistance

Types of Assistance

Govt. Agencies

Non-Govt. Agencies

Voluntary Agencies

Other

Food Clothing Housing materials Medicine Water purification tablets Money Other

29.80 0.96 0.96 6.73 6.73 0 0.96

23.08 1.92 0 10.57 8.65 4.81 1.92

43.27 2.88 0.96 15.38 13.46 8.65 0.96

11.54 1.92 0 4.81 2.88 0.96 1.92

Source: Jahan, 2000.

In 2000, Gujarat and some other Indian states were severely hit by a drought. It affected 25 million people in 17 out of 25 districts. Most severely impacted districts were: Kutch, Jamnagar, Junagadh, Rajkot, Amreli, Bhavnagar, Surendranagar, Mehsana, Banaskantha, Sabarkantha, Panchmahals, Vadodara, Bharuch and Saurastra. In 1987, another severe drought hit India. Rainfall in seven meteorological subdivisions was significantly lower than normal: Saurastra, Kutch and Diu (-74%), West Rajasthan (-67%), Haryana and Delhi (-67%), Punjab (-58%), Himachal Pradesh (-51%), Plains of West Uttar Pradesh (-51%) and East Rajasthan (-50%). The drought was caused

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by a record delay in the progression of the monsoon. Although the monsoon onset at Kerala was on time (2 June) - it slowly reached Delhi on 26 July - a full four weeks delay. The drought problem in 1987 was compounded by the weak monsoons in the two preceding years of 1986 and 1985 (Kulshrestha, 1997). Food grain production of the Kharif crop fell by 5.6 million tons.

Fig. 11.8 Mean annual rainfall in India. Source: Kulshrestha, 1997.

11.5

VULNERABILITY OF GUJARAT TO DROUGHT HAZARD

11.5.1

ANNUAL RAINFALL

The main cause of the vulnerability of Gujarat to recurrent drought is low rainfall. Annual rainfall in the state varies from 330 mm-1520 mm. The rainfall in the Southern highlands of Saurashtra and the Gulf of Cambay is approximately 630 mm while the other parts of Saurashtra have annual rainfall of less than 630 mm. The semi-arid area of Kutch has a very low rainfall. Long-term departure of rainfall from the mean (= 431 mm for the period 1871-1994) is shown in Figure 11.9. In 1999, the India Meteorological Department (IMD) noted that Saurastra and Kutch in Gujarat had received 58% deficient rainfall in the monsoon (Mirza, 2000). Potential evapo-transpiration (PET) in the state is also high (Table 11.8). In some areas, water deficit weeks exceeded the number of moist weeks.

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800 600 400 200

1997

1990

1983

1976

1969

1962

1955

1948

1941

1934

1927

1920

1913

1906

1899

1892

1885

1878

-200

1871

0

-400 -600

Figure 11.9 Mean annual precipitation in Saurashtra and Kutch in Gujarat. Data source: IITM, Pune, India.

11.5.2

LOW RIVER RUNOFF

With the exception of the Narmada and Tapi Rivers, there are few year-round water resources to sustain agricultural production in the region. Though Kutch has many rivers, they are small and do not have much water. Those flowing North disappear in the desert, while those flowing in other directions join the sea. Most of the rivers of Saurashtra and Kutch dry up in the summer. 11.5.3

SENSITIVITY OF CROP AND VEGETATION TO AMOUNT OF RAINFALL

Crops and vegetation in the region are highly sensitive to the amount of rainfall. The Kutch region, once covered with 1.5 m tall green grasses, has now been reduced to dusty plains. Most of the cattle grazers are battling the drought for their livelihood (Nathan, 2001). The

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available water is inadequate to support agriculture. Table 11.9 shows the water availability period for Gujarat and Saurashtra regions. In 1986 and 1987 in Gujarat, against a target of 2 million tons, rabi production fell by 0.9 million tons (Nathan, 2001). In 2000, food grain production declined by 30%.

11.5.4

FORECASTING

Forecasting and warning of monsoon in advance can save crops and reduce human misery to a great extent. India Meteorological Department (IMD) has a 16-parameter model for monsoon forecasting. The 16 parameters include regional and global scale temperature, wind pressure and snow related meteorological variables. Based on favorable characters of the parameters a “good” or “bad” monsoon is predicted. The model is not capable of determining the “inter-spell duration” which is very important for crop agriculture. In the past, the model successfully made predictions of monsoon but there have been instances of failure too (for example, in the year 2002). 11.5.5

OVEREXPLOITATION OF GROUND WATER

Over the years, due to drought and many other socio-economic reasons, ground water in the region has been significantly depleted. Ground water resources are overexploited in the state, with the water table going down nearly 4 m per year, particularly in the pre-monsoon season. In the drought years of 1999-2000, water levels began to dip drastically. The immediate response of the people to this depletion was the deepening of existing wells, drilling boreholes and drilling radial boreholes in the already deepened wells. Overdraft of ground water has led to the problem of seawater ingress, particularly along the Saurashtra Coast (Nathan, 2001). 11.5.6

GENDER INEQUALITY

Children and women are most vulnerable to the aftermath of natural hazards in South Asia. As a nation, India is committed to a policy of increasing women rights and freedom, but gender inequality is deeply rooted like many other similar global situations. During sudden

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or prolonged disasters, the patterns of inequality are translated into lack of economic assets (which include savings, credit, land, tools and training), personal safety and nutrition, health care, social security, etc. In Gujarat, due to social class differences, which cross-cut caste identities, women from 23 villages (62%) observed that low income women were most hurt by intersecting effects of embedded disasters (Enarson, 2001). 11.5.7

ADAPTATION AND COPING MECHANISMS

Water scarcity was the most crucial problem in the drought-affected areas. Gujarat state government and relief agencies spent Rs. 20,000 million for relief and rehabilitation operation. About half of the resources went to supply drinking water by tankers and piped water system. These coping mechanisms are ad-hoc and not sustainable in the long-run. A number of options were suggested. First, policy-level recognition of traditional sources of water such as talavs (lakes), virdas (shallow holes into which ground water seeps and is collected for drinking) and vavs (stepped well) is required. In addition to this, check dams and storage dams to for harvesting water at the village level will reduce the scarcity of water (Vabadam, 2001). Second, decentralization of water management systems need to be in place. It was argued that the people themselves can very well point out decentralized solutions that might yield benefits much earlier and more cheaply than mega water project such as the Narmada (Sangvai, 2000). Third, modifying the present structure of property rights over ground water. Ground water is not presently considered to be a common resource. According to the law, it belongs to the owners of the land in which it is located. This law has resulted in landowners trying to withdraw as much ground water possible regardless of the extent of their needs (Vabadam, 2001). Agriculture was also hard hit due to lack of rain, irrigation water and soil dryness. Somewhere between 190 mm-250 mm of rainfall fell in a span of 60 days to 80 days of crop growth is the requirement of dry land agriculture. Crops fail if the amount of rainfall is less than 115 mm-150 mm. Therefore, at least this amount of irrigation is required to avoid crop failure. Canal irrigation in Gujarat is dependent on the water available in the dams, which is also a function of rainfall. It was found that crop productivity is directly proportional to the filling of dam with water. If a dam is less than 50% full, water is usually not supplied for canal irrigation but is conserved for future need. Another alternative is large-scale recharge and decentralized ways of water harvesting. Sangvai (2000) suggested that the recharging of 200,000 wells would raise the ground water level throughout Saurastra. In the past such a campaign was found to be successful. During 1995-1998, farmers recharged thousands of wells. The endeavor does not involve big budget, bureaucratic and unwieldy planning. Peasants can implement this speedily without complicated technology at a cheaper cost. Altering pricing policy for agriculture is an option to reduce water demand. The pricing policy should be formulated in such a way that people are encouraged to grow other crops. Groundnut, a water intensive crop, is widely cultivated in Saurashtra, is the world’s largest supplier of this cash crop. Any attempt to make farmers switch to other crops will be resisted by the powerful exporters and vested quarters. 11.6

STAKEHOLDERS’ PARTICIPATION

11.6.1

BANGLADESH

Stakeholders’ participation in flood control/mitigation measures and disaster management

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has undergone a long evolutionary process. Flood control/mitigation received major attention after successive disastrous floods in 1954 and 1955. Stakeholders’ participation in planning, design and implementation of the water sector in Bangladesh can be divided into three major phases. Phase I (1955-1990): After the successive floods in the mid-1950s, the then government of Pakistan undertook a massive flood control program for East Pakistan (now Bangladesh). The highly bureaucratic East Pakistan Water and Power Development Authority (EPWAPDA) was created in 1959 and was administered mainly by the engineers. Up to 1991, all public sector water projects were driven by a Master Plan developed in 1964. The approach to development was centrally driven and planned. All the administrators and technicians had been trained primarily in Pakistan and were not able to adjust to the reality in Bangladesh. The orientation of the EPWAPDA was like a military administration where information was controlled in a military way. For example, maps were restricted and office of the Surveyor General of East Pakistan was under the Ministry of Defense (Pittman, 1994). This kind of management without the participation of various other levels of stakeholders created conflicts between farmers, fishers, and tradesmen with different interests in the project area. First, “public cuts” are one of such problems during a flood when people inside and outside the project area cut an embankment to reduce the threat. Second, unnecessary projects were implemented at the wish of politicians and engineers. These projects created more problems than well-being. Third, operation and maintenance are also affected. Projects are usually imposed from the top upon the landscape. Therefore the structures quickly dry up, wash out, or silt up because of lack of local level participation in their maintenance. Eventually the projects tend to run down and fail (Pittman, 1994). Fourth, lack of participation worsened environmental hazards. Many flood control projects (e.g., the Chandpur Irrigation Project) created environmental hazards such as the depletion of floodplain fisheries, employment, reduced supply of protein, water logging, soil salinity and agriculture pollution (Mirza and Ericksen, 1996). The top level bureaucracy was further expanded with the creation of National Water Council (NWC) in 1986 headed by the head of the government. One positive aspect was that several experts outside the government were rotationally chosen as members of the council. After the disastrous floods of 1987 and 1988, the government decided to re-examine the flood problem in Bangladesh. The Flood Action Plan (FAP) with 27 components supported by 15 donors was launched. In order to oversee the activities of the FAP, the Flood Plan Coordination Organization (FPCO) was created. The FPCO was under the Ministry of Water Resources and was independent from the BWDB. But the majority of the manpower of FPCO with ‘old school of thought’ was drawn from the BWDB. Initially a broad-based stakeholder participation was not in the statute of the FPCO. Phase II (1991-1998): Under the pressure from the non-government organizations (NGOs) and Civil Society, the plan was gradually changed from a structurally-oriented plan in 1990 to a plan with more emphasis on the environment and public participation. Special components for public participation were built into the FAP (Pittman, 1994). The Compartmentalization Pilot Project (CPP), Tangail planned and executed for the first time from a multi-disciplinary approach by taking into account the needs of fisheries, navigation and agriculture. Guidelines for public participation were produced and the government approved them. In the 2nd and 3rd national FAP conferences, many professionals outside of government as well as grass roots representatives were allowed to attend and raise questions. The FAP was completed in 1995 and the FPCO was renamed “Water Resources

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Planning Organization (WARPO)”. Phase III (1999-): A National Water Policy was formulated in 1999, recognizing that the participation of stakeholders was an integral part of water resources management. Special attention has been given to stakeholder participation intended to elicit direct input from people at all levels of engagement. It stated that the “Guidelines for People’s Participation (GPP) in Water Development Projects be adhered to as part of the project planning by all institutions and agencies involved in public sector management of water resources”. It also emphasized exploring opportunities and undertaking efforts to ensure participation of the landless and other disadvantaged groups (MWR, 1999). During preparation of the National Water Plan (1998-2002), the WARPO developed and implemented a People’s Participation and Consultation Programme (PPCP) targeting a wide cross-section of stakeholders. It was conducted through programmes of village, union, thana and district meetings, national and regional workshops and special group meetings with government agencies. 11.6.2

INDIA

In India two tiers of administrations the central and state governments, conduct water resources management. Therefore, the participatory model of stakeholders is rather complex. Figure 11.10 shows institutional arrangements in a top-down approach for the water sector in India. The central Ministry of Water Resources is responsible for policy guidelines and programs for the development and regulation of country’s water resources. One of its main functions is to provide technical guidance, clearance and monitoring of the irrigation, flood control and multipurpose projects (major/medium). The Ministry’s other major function is the operation of the central network for flood forecasting and warning on inter-state rivers. The state government has also responsibility of water management. The central Ministry has 17 organizations, which are involved with water resources research, development and management. The Ministry of Agriculture and Department of Rural Development also have watershed development programs, but the inter-ministerial and inter-departmental coordination is rather weak. Unlike Bangladesh, there are no exclusive guidelines for stakeholders’ participation in water management projects although the Indian National Water Policy 2002 states “Efforts should be made to involve farmers progressively in various aspects of management of irrigation systems, particularly in water distribution and collection of water rates. Assistance of voluntary agencies should be enlisted in educating the farmers in efficient water use and water management”(MWR, 2002). Although water resources management including extreme events like floods and droughts are dominated by top-down approach, some bottom-up approach is taking place. Successive droughts in Gujarat and Andhra Pradesh (AP) in 2000 and 2001 compelled the state governments to launch a local level participatory water conservation programme involving NGOs who have very strong grass roots level networks. One such programme is Sardar Patel Participatory Water Conservation Programme (SPPWCP) in Gujarat launched in January 2000; and the AP government launched the Neeru Meeru (Water and You) programme in May 2000. Regarding the SPPWCP, Down to Earth (2000) made two important observations. First, through launching the SPPWCP, the Gujarat government learned from its past mistakes and also from the successes of villages led by civil society. Second, the SPPWCP was formulated in a way that reduces bureaucratic wrangling. The people responded with enthusiasm and submitted proposals for more than 25,000 check dams.

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Ministries of Agriculture Water Resources Rural Development Forest & Envir.

Government of India

Departments of Agriculture Water Resources Rural Development Forest & Envir.

State Government

District Rural Development Agency

District Advisory Committee (MPs/MLAs/ Panchayat Leaders/Civil Servants)

Land Development Corporation

Irrigation Department

Forest Department

Projects

User Groups

Projects

User Groups

Contractors

User Groups

Fig. 11.10 Stakeholders arrangements in a top-down approach for water sector in India. Source: Enarth, 2002.

A bottom-up stakeholders’ participatory model in water management may run into trouble because of conflicts of interests of various groups. Enarth (2002) discussed such a case in Thalota, Meshna, Gujarat, India. Thalota, a village located at the tail end of a medium sized irrigation project served by a reservoir located about 110 km upstream. The majority of the population belongs to Patels and Thakores and Muslims, Brahmins, Banias (traders) and Harjans (untouchables) are a minority. Rich upstream farmers used most of the water flowing through the irrigation canal. An NGO negotiated with the upstream farmers and government department about the scarce water situation of Thalota. It also convinced the water users at Thalota to share the maintenance cost of the irrigation canal. An MOU was signed in 1996, and for the first time in more than 15 years, farmers saw water flow from the canal not just into the village but to the last plot of the farmland along the water course. However, the expansion of this Participatory Irrigation Management (PIM) was eventually stalled due to fear of NGO control over resources, a feeling of subordination of the concerned government officials of the irrigation department and a local politician member of the legislative assembly (MLA). In 1999, under intense political pressure the NGO had to abandon their community building process in many of these villages.

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PRESENT ADAPTATION POLICIES

11.7.1

BANGLADESH

303

There are no exclusive ‘adaptation policies’ for the water sector in the countries of South Asia. Control of extreme weather events (floods and droughts) primarily focused on ‘reduction of crop loss’ and ‘maximizing agriculture production’. In Bangladesh, for three decades (1960-1990) the control of floods was the prioritized policy. This policy focused on ‘structural solutions’ through the construction of flood control embankments to protect mainly agriculture and human settlements from flooding. The planning and design of the projects were based on 100-year and 20-year return period floods along the main rivers (Ganges, Brahmaputra and Meghna) and the minor rivers, respectively. This policy was implemented through the creation of a large engineering organization the Bangladesh Water Development Board (BWDB) (formerly EPWAPDA). The success of such structural solutions is questionable, as in many areas failure and over-topping of embankments are regular phenomena during severe floods (for example, the floods of 1998 and 1988). In the early years of flood control initiatives, less importance was given to non-structural measures that include flood modeling, flood forecasting and warning, evacuation, flood shelters, etc. Flood modeling received attention during the preparation of the 2nd Water Master Plan during 1983-1986 when the Danish Hydrologic Model ‘NAM’ was introduced. Subsequently, more sophisticated MIKE 11 and MIKE 21 hydrodynamic models were also introduced, calibrated and validated. The Surface Water Modeling Center (SWMC) (now IWM) was created in the late 1980s. The BWDB created flood forecasting and warning center (FFWC) in 1972. It received technical and financial assistance from the United Nations during 1981-1986 and 1989-1992. The Center now uses hourly rainfall, discharge and water level data collected from selected stations for simulating floods with the aid of MIKE 11-GIS model, and disseminating the warnings in electronic and print media and to several government departments. The forecasting and warning information is also available on the internet (www.ffwc.net). During the 1998 flood, forecasting and warning was found to be effective in reducing loss of lives and property (Chowdhury, 2000). Drought forecasting and management policies are rather neglected although its not a lesser ‘menace’ than floods. In the period 1973-1986, the average loss of crops due to droughts was as same as for floods (Mirza, 2002). There is no effective mechanism in place in Bangladesh for drought forecasting. Drought occurs for three main reasons: low residual soil moisture as a result of inadequate rainfall in the monsoon; low or no rainfall in summer together with high evapo-transpiration; and low summer flow in the rivers/streams unable to meet irrigation water demand. In addition, in many areas, low monsoon recharge can cause very high draw downs of water tables in summer leading to crop loss. Departments responsible for agriculture development do not prepare projected soil moisture maps. The BWDB do not forecast river flows for the summer months or estimate the amount of recharge that occurred in the monsoon or prepare maps of vulnerable areas. The meteorological department does not have any long-range weather forecasting model. Although the Ministry of Agriculture drafted ‘drought codes’ in 1980, they are focused at ‘reactive’ rehabilitation rather than ‘anticipatory’. As the delivery and implementation of the measures suggested in the codes are heavily top-down, by the time they reach at the grass roots level, the damage is already done.

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11.7.2

ADAPTATION POLICY FRAMEWORK TO ASSESS CLIMATE RISKS

INDIA

Flood control/management policy in India is also skewed to structural solutions (embankments, dams, reservoir, etc.). Not withstanding flood policy and flood control schemes, flood damage is increasing, with larger populations subjected to distress in increasing flood-prone areas. Menon and Kadvi (2003) report that the locus of flood damage has shifted away from the Gangetic belt. They also reported widespread flood damage in Andhra Pradesh, Karnataka, Kerala and Tamil Nadu in the South, Maharastra, Gujarat, and Rajasthan in the West, Uttar Pradesh in the North, and Bihar and West Bengal in the East. Gupta et al. (2003) concluded that despite more flood-prone area being protected, people in the affected areas were still highly vulnerable to floods. Over the years, flood policy in India evolved and several committees/commissions were constituted and policy documents were prepared including Policy Statement 1954; High Level Committee on Floods-1957; Policy Statement of 1958; Ministerial Committee on Flood Control-1964; Ministers’ Committee on Floods and Flood Relief-1972; Working Group on Flood Control for Five Year Plans; Rashtriya Barh Ayog (RBA)-1980; National Water Policy-1987; National Commission for Integrated Water Resource Development Plan-1996 and National Water Policy-2002. The evolution of policy observed a shift from only ‘structural solution’ to a mix of structural and non-structural solutions. Despite this shift, the maximum federal funding is allocated for large-scale structures most of which are incomplete and carried over from one five-year plan to another. Emphasis on establishing cooperative programme on precipitation and flood data sharing for transnational rivers is another significant shift in flood policy. However, flood forecasting and warning employing sophisticated hydrodynamic model is still neglected. Reactive adaptation measures such as disaster management programmes have also improved compared to the early years of independence. Now in addition to government efforts, NGOs take an active role in post-hazard disaster management. Like flood policy, drought policy is mostly reactive type. It is highly focused on post-hazard disaster management. Lack of resources for disaster management is a very regular affair. Management does not focus on all the elements created by a chain reaction triggered by the failure of the monsoon or very low flow in rivers and wells. Drying up of water sources and soil moisture usually leads to crop losses, loss of jobs, increasing levels of indebtedness, the necessity of sale of cattle and other assets, increasing out migration, a sharp drop in purchasing power, malnourishment and possibility of long-term illness. Disaster management ceases as soon as rainfall occurs or the next crop is harvested. However, the distress created by a drought goes beyond that which is missing in the drought management policies. Inequity is the most critical issue, which needs to be addressed in the drought policy. It can be of various forms and magnitudes. In India, the Dalits, religious minorities and tribal populations are often the victims of discrimination. In addition, women, children, the elderly and the physically challenged bear the brunt of adverse economic changes (Menon, 2000). Discrimination in the distribution of water is also observed. The arid districts of Kutch, Saurashtra and North Gujarat constitute around 80% of the State’s landmass, but receive only 30% of available water. A fair share of the State’s water goes to the more prosperous South and Central Gujarat (Frontline Team, 2002). Over the years, the India Meteorological Department (IMD) improved its capability in forecasting medium and long-range weather but their model is weak in forecasting the special distribution and inter-spell duration of rain. Proposals for creating grain and seed banks and ‘drought code’ and alternative cropping strategy were ignored in the past (Frontline Team, 2002). Rainwater harvesting policy brought changes in drought management in

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Gujarat and Andhra Pradesh (AP). The governments in these two states planned and executed two programmes - Sarder Patel Participatory Water Conservation Programme (SPPWCP) in Gujarat and Neeru Meeru (Water and You) in AP. 11.8

FUTURE CLIMATE CHANGE, RISKS AND ADAPTATION

11.8.1

CLIMATE CHANGE AND RISKS

Discussions in the preceding section demonstrate that the South Asia region is at substantial risk of climate and hydrological hazards. Risks associated with floods and droughts are shown in Figure 11.11. In the natural hazard based approach, risk is a combination of two factors: the probability that an adverse event will occur and the consequences of that adverse event (USPCC RARM, 1997). The combination can be expressed as: Risk = probability * consequence The probability of an adverse event can be expressed as the likelihood of a given climate hazard. The consequences of that adverse event are measured in social terms and can be characterized as vulnerability. In the vulnerability-based approach risk is assessed by determining the likelihood of exceeding a critical threshold of any climatic or hydrologic events. Mathematically the probability R, called risk that flood magnitude F will occur at least once in n successive years is:

R = 1−(1−

1 n ) T

where T is return period of the flood event which is expressed as :

T=

1 P( F )

In order to assess the present risks of climate hazard, the droughts in 2000 and 2001 in Gujarat, India can be taken as case studies. Figure 11.12 shows the probability density function for monsoon rainfall in Gujarat and Saurashtra and Kutch. In 1999 and 2000, the amount of rainfall was 181 mm and 241 mm, respectively and the corresponding return period was 10.3 (p = 0.097) and 6.1 (p = 0.16) years. Therefore, the likelihood of exceedence is 9.7% and 16%, respectively every year considered to be quite high. Future risks of floods and droughts will depend on the magnitude and intensity of precipitation. Model results show that a likelihood of increased intensity of precipitation events in a future climate with increased greenhouse gases. This remains a consistent result in a number of regions that includes South Asia (Hennessy et al., 1997; Yonetani and Gordon, 2001). In addition, changes in precipitation intensity have a geographical dependence. Bhaskaran and Mitchell (1998) note that the range of precipitation intensity over the South Asia monsoon region broadens in a future climate experiment with GHG forcing, with decreases prevalent in the West (relatively drier part) and increases widespread in the East (wetter part).

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Fig. 11.11 Risks associated with floods and droughts.

Lal et al. (1998) surveyed the results on the Indian subcontinent of 17 climate change experiments including both equilibrium 2 x CO2 and transient AIGCM simulations with and without sulphate aerosol forcing. In the simulations forced only by GHG increases, most models show wet season (June, July and August) rainfall increases over the region of less than 5% per degree of global warming. A minority of experiments shows rainfall decreases. The experiments, which included scenarios of increasing sulphate forcing all showed reduced rainfall increases or stronger rainfall decreases, than their corresponding GHG only experiments. Mirza (2002) used the climate change scenarios CSIRO9, GFDL, HadCM2 and LLNL GCMs to assess changes in the probability of occurrence of 20-year floods for the Ganges, Brahmaputra and Meghna Rivers in Bangladesh. The analysis indicates substantial changes in probability of occurrence floods even with a 2oC change in global mean temperature (Table 11.10). For the Ganges River, with the CSRIO9 model, the magnitude of future mean flood exceeds the current 20-year flood (67,984 m3/sec) with a 6oC rise in temperature. The largest change in the probability of a current 20-year flood

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(86,687 m3/sec) for the Brahmaputra River is associated with the GFDL model, followed by the HadCM2 model. For the Meghna River for both HadCM2 and LLNL models, the future mean flood may exceed the current 20-year flood (18,996 m3/sec) with a 6oC rise in temperature.

0.002 Probab ility

Probab ility 0.0015

1999

(a)

2000

0.001 0.0005 0 0

500

1000

1500

2000

Rainfall To tal (mm)

Probab ility

0.0025 Probab ility

0.002

2000

0.0015

(b)

1999

0.001 0.0005 0 0

200

400

600

800

1000

1200

Rain fa ll T otal (mm)

Fig. 11.12 Probability density function for monsoon rainfall in (a) Gujarat, (b) Saurashtra and Kutch. Data source: Indian Institute of Tropical Meteorology (IITM), Pune.

11.9

ADAPTATION POLICY FRAMEWORK: OPPORTUNITIES AND CHALLENGES

11.9.1

OPPORTUNITIES

UNDP (2003) has listed a number of benefits that the APF offers. In this section we will discuss the benefits of the APF with some examples. The APF will: • Increase the robustness of infrastructure designs and long-term investments. Presently infrastructures are designed based on available historical data and the economic life of a project. The economic life varies from project to project depending on purpose, size and the amount of investment. Usually for a large project (dam, reservoir, etc.), the economic life is 75 years and for a medium project (flood control embankment) 30 years-50 years. In most cases, at present, designs are done once and implemented accordingly. Maintenance works are done according to the original design specifications. One of the underlying assumptions of the APF is that adaptation

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is a continued process. Therefore, in the APF, there is room for continued revision of designs and investments of infrastructure. For example, a flood control embankment may be designed in such a way to accommodate future climate risk. A dam/reservoir may be designed to accommodate flexible operation rules. • Increase the flexibility and resilience of managed natural systems and social systems. Adaptation measures designed under a policy framework increase the flexibility and resilience of both managed natural and social systems. Flexible systems and policies are those that allow self-adjustments or mid-core corrections as needed without major economic or social disruption. For example, flexible systems can be fine tuned to cope with hot and dry weather as well as more intense rainstorms. Flexibility also takes into account future adjustments. Building a dam at a site is a less flexible policy than water conservation. Under a detailed assessment, if it is found that water conservation for the next 25 years-50 years is less expensive than building an expensive dam then it is the best option for now and the near future. However, the policy also allows room for building a dam in the future (80 years-100 years from now) when demand for water will be greater and conservation measures may be inadequate.

Integrating some adaptation measures, a system can be made more robust. In this context, sedimentation of the managed multi-purpose reservoirs in South Asia is a good example. Many of these reservoirs are losing their designed life by excessive sedimentation. Indian reservoirs on an average are losing storage capacity at the rate of 0.23 ha-m/km2/year. The life of Kaptai reservoirs was estimated to be 300 years in 1960, but today it is merely 180 years. The Kulekhani reservoir in Nepal which had design life of 100 years, lost 1 million m3 or 8% of its gross storage capacity in just 12 years since its impoundment in 1981. The reservoir had a designed dead storage of 12 million m3 (Mirza and Dixit, 1997). There is no effective sediment management plan for the watersheds of

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these reservoirs. The plan must address dynamic process of sediment generation, transportation and deposition (rate and amount) in the reservoirs. Plantation in the watersheds, grass cover in the areas vulnerable to erosion and sediment detention basins can effectively reduce the inflow of sediment to the reservoirs. If the capacity of the reservoirs can be maintained by halting or reducing sedimentation, they can tolerate a wide range of climate conditions and therefore will be less vulnerable to climate change extremes. In terms of social systems, for example, pro-active adaptation policies for drought management can reduce pressure on social systems such as poverty, migration and health. • Enhance the adaptability of vulnerable natural systems. Mangroves in Bangladesh and the Eastern Coast of India act as barriers against cyclones and storm-surges, saving lives and property in the coastal area as well as providing a variety of services. In 1988, a killer cyclone that hit the Sundarbans mangroves which could have killed thousands of people had there been no forest cover. Destruction of mangroves in the coast of Orissa is blamed for the loss of huge number of lives during a severe cyclone in November, 1999. In recent years, the Sundarbans in Bangladesh is facing degradation due to reduced freshwater supplies from upstream in India and human interventions. An effective adaptation policy under a framework towards increasing freshwater supplies and integrating local people with the forest management can rejuvenate the Sundarbans which would save lives and property from coastal flooding originating from storm-surges and high tides. In future, the likelihood of inundation will increase due to possible increases in sea level. • Reverse trends that increase vulnerability (also termed “maladaptation”). There are instances of adaptation measures that increased the vulnerability to climate variability. For example, in Bangladesh flood control projects along the smaller rivers (e.g. Gumti) increased flood vulnerability and higher risk of damage. This has occurred because sedimentation in the outer channel has increased the level of the channel. Therefore, risk of flooding is now greater even for the designed flood. In the past, floodwater breached and overtopped the embankment. Allowing controlled flooding inside a project area can rectify this kind of problem. • Improve societal awareness and preparedness for future climate change. Design and implementation of an APF will improve societal awareness and preparedness for future climate change in a number of ways. For example, one way of training one or more persons in drought- or flood-vulnerable villages as “climate managers” can create awareness about climate variability and change, and will also help in the adoption of measures to reduce crop and other social losses. Involving stakeholders (grass roots people, water managers, policy makers, politicians, etc.) at various levels of the APF will also enhance awareness and preparedness for the future. 11.9.2

CHALLENGES

Stepwise analysis of the APF for urban floods in Bangladesh and drought in Gujarat has been carried out. It demonstrates that most of the information is available to apply the APF. However, a few challenges remain which could be overcome with some efforts. First, although huge socio-economic information is available in both countries at the macro-level, for regional level application of the APF requires micro-level information which must be translated into adaptive capacity for grass roots level people. After the

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severe drought that occurred in Gujarat, the UNDP carried out a socio-economic survey, which showed that $850 per year household (4-5 member family) income was sufficient to absorb the shock. Second, there is a significant disconnect among the stakeholders of the water sector in South Asia. The system is too bureaucratic and a top-down planning approach is dominant but a change is slowly taking place in the two-century old bureaucratic culture. A bottom-up approach is receiving priority in developing any flood or drought management project. Third, hydro-meteorological data are maintained by several organizations. There are different protocols about data sharing and dissemination, which often create problems. More problems exist when two or more countries share a basin. For example, Bangladesh and India share 54 river basins and any effective flood management plan requires upstream hydro-meteorological data from India. However, the process of getting access to such data is complicated and in many cases impossible. Fourth, climate change and socio-economic scenarios are not readily available. Some institutions have the capability to construct coarse scale climate scenarios with the aid of GCM data. Facilities for downscaling scenarios at the station or regional level are still in their infancy. In addition, analytical scope to assess uncertainties attached to scenarios and at various levels of application (impact and adaptation assessment) is limited. 11.10

CONCLUDING REMARKS

Regardless of the incremental risks of climate change, the tasks of managing current climate variability and extremes (as illustrated in Bangladesh and Gujarat) will continue to pose a significant challenge to the hazard management capacity of the public agencies and the private sector as well as the vulnerable communities themselves. Many adaptation measures were implemented in the past to reduce vulnerability to climate and hydrological extremes. In many cases these measures were found to be inadequate. Under a climate change regime, vulnerability may substantially increase in future. A first step in dealing with climate change is to address the adaptation gap in current policies and measures. This alone will not be sufficient. Enhanced capacity is needed to cope with the new and stronger challenges of climate change. The UNDP’s Adaptation Policy Framework (APF) is primarily designed to help achieve these objectives. Analyses of two case studies (urban floods in Dhaka, Bangladesh and droughts in Gujarat, India) show that application of the APF is feasible. However, some challenges remain. Micro-level socio-economic data are insufficient but can be generated by conducting surveys. In the APF process, identification of gaps in the stakeholders’ participation in the project planning, design, implementation and monitoring is a formidable task. High resolution climate change scenarios at local levels are not readily available. There is a great need to invest in climate change and socio-economic scenario generation.

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Index

A Adaptability 133, 268, 276, 309 Adaptation -anticipatory 274, 275, 286 -autonomous 286 -planned 147, 269, 275, 286 -private 286 -public 286 -reactive 286 Adaptation policy 89, 93, 99, 101, 279, 282, 283, 303, 307, 309-311, 313 Adaptive capacity 89, 93-95, 97, 99, 101, 218, 283, 284, 286, 309, 310 Agriculture 5-7, 16, 18, 19, 21, 60, 61, 68, 100, 103, 105, 131, 133, 144, 155-158, 163, 175, 184, 186, 188, 198-201, 205, 213, 215, 223-230, 239, 241, 243, 251, 254, 265, 272, 276-278, 281, 285, 294, 298-303, 312 Annual -discharge 36, 55, 57, 63, 64, 107, 110-112, 114-121, 158 -runoff 52, 53, 57, 74, 103, 107, 115, 135, 207 APF-Adaptation Policy Framework 89, 93, 99, 101, 279, 282-284, 307-310, 313

B Bangladesh 2, 5, 6, 8-13, 16-21, 23, 35, 36, 39, 41, 49-51, 53, 55-68, 73-75, 77, 78, 80-83, 89, 99, 100, 103-108, 110-117, 122, 123, 127, 129, 131-135, 144, 147, 160, 163, 173, 193, 159, 231-239, 241-246, 248, 250-281, 287-289, 291, 299-301, 303, 306, 309-313 Base flow 26, 28, 29, 33 Bhutan 2, 5, 7, 12, 13, 18, 35, 49, 56, 69, 73, 80, 103-105, 111-114, 137-139, 143, 145, 147, 149-154, 159, 232, 256, 279 Brahmaputra 1-6, 12, 13, 18, 35-38, 41, 43, 45, 49, 50, 53, 55-59, 63-67, 73-75, 77, 80, 82, 83, 86, 96, 100, 103-106, 108-126, 132-134, 144, 158, 159, 175, 177, 182-184, 231, 232, 256, 259, 263, 264, 276, 277, 287, 289, 303, 306-308

C Catastrophic 59, 61, 137-139, 150, 152, 153, 244, 261, 266, 267, 288 Cauvery 3, 45, 158, 160, 182, 183

316

INDEX

Climate change 1, 5, 8, 9, 13, 15-17, 19-21, 23, 26, 27, 32, 34-37, 39, 41, 46, 48-54, 74, 77, 78, 80, 81, 83, 84, 86, 87, 89, 91, 95, 100, 101, 103, 106-108, 113, 115, 117, 119, 121-123, 126, 128, 130-135, 137, 139, 147, 151, 153, 155, 156, 163-167, 169, 175, 180, 184, 187, 189-197, 218, 222-224, 229, 231, 237, 244-247, 250, 252-255, 260, 262-266, 268, 269, 271-279, 282-286, 305, 306, 308-313 Climate variability 16, 53, 77, 81, 89, 93, 101, 191, 236, 237, 255, 257, 259, 260, 262, 266, 267, 269, 272, 275, 276, 283, 284, 309, 310, 313 Coastal -region 17, 172, 222, 223, 248, 254, 259, 278, 279, 287 -rivers 1, 256, 259, 263 Conceptual lumped-parameter model 23, 24, 28, 35, 48 Coping 77, 89, 94, 96, 100, 101, 153, 190, 227, 253, 255, 266-269, 275, 276, 283, 291-293, 299, 312 Coping capacity 89, 94, 100, 101 Cotton 198, 199, 209, 214, 215 Cultural practices 226 Cyclones 161, 163, 172-174, 193, 195, 196, 222-224, 231, 236-238, 246, 248, 253, 259, 262, 273, 279, 281, 288, 289, 309

D Dams 16, 17, 19, 89, 138, 140, 143, 144, 153, 180, 182, 184, 186, 189, 191, 193, 200, 206, 207, 221, 222, 238, 299, 302, 304 Danger level 291, 292 Deccan Rivers 1 Desert 1, 4, 156, 162, 180, 197, 297 Dhaka City 241, 288, 289, 291, 293, 311-313 DEM-Digital Elevation Model 40, 45, 46 Dig Tsho Lake 139, 141, 143 Discharge 6, 16, 23, 25-27, 29, 32, 35-37, 39-41, 43, 45, 50-53, 55-57, 59, 60, 62-67, 73-75, 83, 85, 103, 107, 108, 110-129, 131-134, 137, 138, 143, 158, 180, 184, 201, 207, 218, 250, 259, 260, 263, 264, 270, 291, 303, 312 Distributed model 108 Downstream 10, 18, 25, 48, 59, 65-67, 74, 105, 126, 134, 137, 139, 142, 144, 145, 150, 151, 172, 180, 279 Drainage -basin 1, 54, 180, 276 -channel 131, 238, 248 Drought -forecasting 226, 252, 303

E El Niño 1, 13, 103, 155, 165, 172, 173, 196 Empirical model 23-25, 32, 35-37, 39, 40, 48, 49, 83, 108, 110, 115, 117 Energy 4, 14, 18, 21, 25-27, 51, 52, 74, 75, 99, 139, 144, 151, 153, 158, 163, 166, 187-189, 194, 197, 246, 252, 287

INDEX

317

Environmental -impacts 27, 74, 195, 241-243, 250, 253 -pollution 189 Erosion 7, 17, 18, 28, 54, 59, 103, 154, 172, 173, 181, 184, 190, 191, 213, 222, 223, 231-234, 237, 246, 250, 251, 259, 260, 271, 272, 275, 289, 309 Evapo-transpiration 8, 10, 25-29, 30, 32, 46, 49, 52, 53, 80, 84, 85, 108, 246, 247, 256, 257, 261, 264, 271, 275, 296, 303 Extreme Events 23, 150, 191, 236, 250, 255, 257, 262, 268, 269, 275, 279-281, 284-286, 301, 303

F Fisheries 156, 183, 238, 241, 256, 266, 271, 272, 300, 313 Flood -damage 55, 59-63, 67-69, 135, 175-177, 233, 270, 280, 282, 292, 304 -depth 23, 24, 35, 39, 40, 82, 108, 115, 124, 126, 132, 241 -embankments 217, 243, 257, 267, 270, 271, 292-294, 303, 307, 313 -forecasting 23, 24, 251, 267, 271, 288, 293, 301, 303, 304, 311, 313 -frequency 11, 77, 78, 80-83, 89, 91, 155, 158, 171, 190, 246, 257, 266 -rain 105, 107, 217, 288, 290 -riverine 105, 107, 256, 259, 273, 288, 290 -urban 280, 282, 287, 294, 309 Food security 16, 20, 21, 100, 144, 151, 182, 188, 194, 216, 228, 230, 265, 266, 277, 285 Freshwater 5, 17, 19, 85, 155, 158-160, 163, 180, 182, 184, 186, 187, 223, 225, 237, 250, 259, 263, 264, 270-272, 309

G Ganges 1-6, 12, 13, 15, 17-19, 20, 35-38, 41, 43, 45, 49, 50, 53, 55-59, 63-67, 74, 75, 77, 80, 82, 83, 86, 96, 100, 103-106, 108-121, 123-126, 132, 134, 158-160, 175, 177, 182-184, 186, 189, 194, 231-233, 235, 255, 256, 263, 264, 268, 269, 272, 276, 277, 287, 303, 306, 308 GCM-Global Climate Model 8, 11, 15, 21, 32, 35, 37, 41, 90, 107, 108, 113, 115, 117-123, 125, 126, 128, 132, 165-167, 196, 260, 261, 263, 306, 308, 310, 312 GDP-Gross Domestic Product 5, 60, 105, 156, 198, 281 Glacier 1, 4, 5, 7, 11, 15, 19, 47-49, 52, 57, 103, 137-143, 145, 147-154, 158, 180, 184, 194-196, 200, 263, 277-279, 312 Glacier retreat 140, 144, 147, 149-151, 263 Global warming 11, 15, 115, 118, 138, 144, 149-151, 154, 163, 164, 173, 180, 184, 185, 190, 194, 195, 255, 278, 306, 313 GLOFs-Glacier Lake Outburst Floods 10, 15, 59, 137-141, 143-147, 150-152, 263, 279, 280, 312 Godavari 1-3, 6, 45, 159 Government policy 189, 275 Greenhouse gas 8-11, 20, 78, 86, 163, 166, 173, 194-196, 274, 305, 312 Ground water -quality 202, 205, 211, 213 Gujarat 12, 16, 17, 160, 172, 176-178, 180, 182, 183, 185, 282, 293, 295-299, 301, 302, 304, 305, 307, 309-311, 313

318

INDEX

H High Yielding Variety (HYV) 127, 132, 133, 239, 240, 265, 267 Himalayas 1, 4, 5, 15, 18, 57, 59, 74, 137, 138, 141, 144, 147, 149-154, 156, 158-161, 184, 186, 194, 195, 197, 231, 245, 263, 278 Hydrologic cycle 24, 106, 155, 182, 277 Hydrologic model 23, 24, 30, 32, 33, 35, 36, 45, 48, 49, 51, 53, 78, 80, 83, 84, 87, 89, 99, 101, 291, 303 Hydrology 8, 10, 19, 25, 27, 32, 50-53, 73, 74, 82, 100, 101, 135, 138, 147, 152, 153, 166, 196 Hydro-meteorology 21, 36, 47, 57, 69, 100, 134, 142, 144, 145, 151, 310 Hydropower 8, 18, 20, 144-146, 148, 150, 152, 153, 157, 222, 280

I Impacts 19, 20, 23, 27, 32, 39, 46-48, 54, 74, 83, 89, 93, 107, 134, 163, 164, 175, 177, 190-193, 195, 200, 218, 222, 223, 225, 229, 237, 239, 241, 243, 245, 246, 250, 252, 253, 255, 259, 261, 265, 268, 269, 273-275, 277, 279, 282, 284-286, 306, 311, 312 Impacts assessment 23, 24, 46, 229, 311 India 1-13, 17-20, 31, 35, 39, 45, 46, 48, 49, 51-53, 55-69, 79, 80, 103-105, 111-113, 115-117, 134, 143-145, 147, 149, 155, 156, 158-166, 168, 170-173, 175-178, 180, 183-185, 188, 191, 193-195, 197, 200, 205, 215, 216, 229, 230, 232, 235, 236, 256, 258, 268, 271, 273, 276, 278-282, 288, 293, 295-298, 301, 302, 304, 305, 309-313 Indus -Basin Treaty 200 Industrial growth 5, 7, 156 Industrial water demand 7, 8, 207, 263, 264 Infiltration 8, 28, 29, 52, 53, 187, 211, 226 Infrastructure 60, 61, 68, 103, 105, 133, 139, 145, 150, 163, 173, 191, 194, 217, 232, 233, 236, 242, 248, 250, 256, 257, 259, 264, 268-272, 279, 286, 289, 306-308 Inundation 6, 16, 17, 39, 40, 67, 89, 96, 126-133, 163, 222, 223, 246, 248, 250, 252, 257, 262, 267, 272, 277, 280, 288, 290, 293, 309 IPCC 8, 9, 14, 15, 17, 20, 37, 52, 106, 107, 134, 150, 163, 184, 195, 218, 222, 224, 229, 236, 245, 254, 260, 262, 277, 279, 282, 284-286, 310-312 Irrigation -systems 108, 200, 201, 203, 209, 213, 214, 217, 225, 226, 236, 301

K Kaptai 17, 308 Kharif 16, 132, 201, 203, 205, 218, 219, 221, 238, 265, 296 Kulekhani 17, 18, 20, 21, 308

L Land-use 8, 23, 24, 30, 31, 33, 36, 45, 46, 49, 59, 74, 95, 96, 104, 107, 134, 180, 181, 184, 190, 191, 218, 226, 251, 253, 254, 274, 278, 287, 293 Legal 47, 242, 244, 274, 285 Legislative tools 189

INDEX

319

Low flow 33, 35, 232, 235, 250, 256, 259, 263, 264, 266, 272, 304, 306 Lumped-parameter model 23, 24, 28, 35, 48, 29, 33, 34, 36

M Mahanadi 1-3, 45, 158, 159 Mangroves 156, 158, 180, 263, 309 Mean annual discharge 55, 110, 111, 115, 117-121 Meghna 1, 3, 12, 13, 35-38, 41, 43, 44, 49, 50, 53, 55-58, 63-67, 74, 77, 80, 82, 83, 96, 100, 103-106, 108-116, 119-120, 122, 123, 125, 126, 128, 132-134, 231-233, 236, 256, 259, 263, 264, 276, 287, 289, 291, 303, 306-308 MIKE 11-GIS model 35, 36, 39, 40, 42, 82, 108, 115, 123, 124, 293 MODSIM 47 Monsoon 1, 2, 4, 8-11, 13, 18, 20, 40, 55, 59-61, 67, 74, 80, 103-105, 107, 115, 131, 132, 137, 150, 154-159, 161-166, 168-175, 180, 182, 187, 189, 193-196, 205, 209, 216, 223, 231, 232, 234, 237, 241, 245-248, 250, 255, 257-261, 263, 264, 266, 268, 270, 272, 275, 277, 279, 287, 288, 296, 298, 303-305, 307 Morphology 53, 55, 143

N Narmada 1-3, 6, 31, 35, 158, 160, 182, 297, 299 National Water Policy -Bangladesh 243, 244, 251, 268, 270, 271, 275, 278, 301, 313 -India 190, 301, 304, 313 Nepal 2, 5, 7, 10, 12, 17-21, 35, 39, 55-57, 59-65, 68, 69, 73, 74, 80, 81, 103-105, 108, 111-113, 115, 134, 137-139, 141, 143-154, 159, 163, 173, 193, 194, 196, 232, 251, 256, 278-281, 308, 312, 313 Non-structural 193, 250, 251, 253, 267, 271, 293, 294, 303, 304, 311

O Occurrence 10, 18, 82, 145, 171, 172, 175, 196, 202, 205, 222, 256, 257, 264, 278, 279, 288, 306, 313

P Pakistan 2, 4, 5, 9, 10, 12, 16-19, 35, 46-48, 56, 73, 80, 104, 147, 157, 159, 160, 163, 197-202, 204-208, 210, 211, 213-218, 222, 224-230, 242, 253, 256, 279-281, 291, 300 Peak discharge -mean 23, 37, 39, 64, 107, 108, 110, 111, 114, 115, 119-124, 133, 291 Population 5-8, 12, 14, 15, 18, 21, 68, 69, 75, 93, 95, 133, 150, 155, 156, 160, 163, 175-179, 182, 187-191, 194, 197-200, 202, 207, 208, 211, 213, 216, 221, 222, 225, 227-230, 237, 239, 241, 244, 245, 252, 255, 260, 268, 272, 280, 286-291, 302, 304, 306, 310, 311 Precipitation 1, 8-10, 15, 17, 20, 23-27, 32, 36-39, 47-49, 51, 57, 59, 64, 67, 74, 78-81, 84-87, 103, 104, 106-120, 122, 134, 139, 150, 153, 155, 158, 164-168, 171, 173, 180, 182, 184, 187, 190, 194, 201, 205, 206, 216, 218, 222, 230, 236, 237, 246, 247, 257, 259-263, 279, 297, 304, 305, 312, 313

320

INDEX

Probability 10, 65, 81-83, 91, 92, 132, 168, 191, 246, 263, 278, 305-308, 312 Productivity 6, 8, 28, 54, 127, 132, 189, 213, 215, 216, 225, 226, 228, 229, 264, 299

Q Quality -water 16, 21, 46, 108, 155, 180, 182, 183, 185, 189-191, 201, 202, 205, 211, 213, 217, 222, 228, 266 Quantity -water 21, 158, 188-190, 227

R Rabi 105, 127, 131, 203, 218, 219, 221, 231, 232, 234, 237-239, 241, 245-247, 265, 275, 298 Rainfall 1, 3, 6, 8, 10-13, 16, 19, 20, 24, 29, 30, 32, 35, 39-41, 43, 44, 46, 47, 49-54, 73, 80, 82, 83, 105, 106, 123, 128, 129, 131, 135, 155, 156, 158, 161-163, 165, 166, 168-173, 180, 184, 186, 187, 189, 195, 200, 205, 206, 218, 221, 255-257, 260-264, 268, 287, 288, 291-293, 295-299, 303-307, 312 RCM-Regional Climate Model 46, 260 Recharge 8, 25, 27, 29, 107, 158, 175, 187, 189, 193, 201, 202, 206, 209, 241, 246, 257, 271, 299, 303 Regional Cooperation 75, 271, 276 Reservoir 11, 16-19, 46-48, 157-159, 180, 184, 191, 193, 201, 203, 207, 208, 218, 251, 302, 304, 307-309 Return period 82, 83, 127, 132, 233, 246, 248, 249, 257, 291, 292, 303, 305 Rice 6, 20, 75, 105, 131-133, 135, 189, 198, 199, 214, 215, 217, 226, 232, 234, 239, 240, 250, 257, 259, 266, 287 Risk 15, 68, 69, 74, 81-83, 89, 93, 95, 96, 99, 101, 132, 134, 137, 143, 144, 147, 151, 152, 175, 190, 191, 195, 222, 233, 234, 239, 246, 248, 252, 256, 257, 259, 265-267, 269, 271, 272, 275, 279, 282-285, 290, 305, 306, 308, 309, 311-313 Runoff 6, 8, 10, 13, 18, 23-30, 32, 33, 35-38, 41, 46-54, 57, 74, 80, 82-85, 87, 100, 101, 103, 105, 107-109, 113, 117, 135, 171, 180, 184, 187, 207, 216, 218, 225, 237, 246, 257, 263, 264, 288, 289, 291, 297, 311

S Salinity 6, 17, 180, 185, 194, 202, 209, 211, 212, 215, 223, 225, 226, 238, 250, 256, 259, 263, 266, 268, 269, 272, 275, 277, 278, 300 Sanitation 200, 246, 267, 289 SAR-Sodium Absorption Ratio 182, 183 Scenarios 8, 14, 15, 21, 23, 32, 41, 46, 48, 50, 78-80, 86, 87-89, 90, 91, 96, 97, 99, 100, 107, 108, 113, 115, 117-119, 121-123, 125, 126, 128, 130-132, 134, 165, 166, 168-170, 195, 196, 218, 220-222, 230, 246, 247, 260, 265, 276, 306, 308, 310 Sea level rise 10, 17, 21, 166, 222, 223, 231, 236, 244, 245, 247, 248, 250, 254, 262, 272, 277 Sedimentation 7, 17, 18, 48, 59, 73, 104, 138, 139, 154, 184, 207, 225, 232, 246, 248, 263, 264, 308, 309 Semi-arid 9, 23, 48, 155, 168, 218, 230, 293, 296, 313 Seawater 17, 166, 185, 222, 224, 236, 248, 250, 298

INDEX

321

SHE model 30, 31, 35, 36, 50, 53 Sikkim 2, 7, 149, 188 Siltation 17, 103, 172, 180, 207, 213, 248, 264, 289, 290 Simulation 8, 20, 23, 24, 28, 37, 39, 40-43, 45-47, 50-54, 106, 115, 116, 123, 127, 168, 194, 195, 260, 262, 277, 293, 306 Socio-Economic 69, 93, 105, 131, 143, 177, 193, 218, 230, 237, 246, 260, 276, 283-286, 293, 298, 309, 310 Soil moisture 8, 10, 11, 13, 16, 25, 27-29, 32, 33, 47, 49, 52, 80, 84, 85, 155, 187, 303, 304 Soil quality 16, 223, 306 South Asia 1-3, 5-10, 13, 16-18, 20, 21, 23, 27, 55, 60, 74, 75, 77, 106, 134, 153, 155, 186, 196, 218, 229, 230, 254, 276-280, 282, 283, 286, 287, 298, 303, 305, 308, 310-313 Southern Oscillation 13, 103, 155, 172, 194 SRES 8, 14, 15, 166, 169, 171, 260 Sri Lanka 1, 2, 4, 5, 10, 157, 163, 229 Statistical -analysis 65 -significance 67, 87, 99, 110, 261 Sugarcane 198, 199, 209, 214, 215 Summer monsoon 1, 4, 9, 20, 74, 103, 107, 161, 162, 166, 168, 170, 172, 173, 193, 195, 196, 260 Sundarbans 156, 180, 236, 248, 263, 272, 273, 278, 309 Supply -water 4, 15, 18, 19, 54, 155, 182, 184, 186, 188, 189, 192, 194, 200, 207, 217, 218, 221, 225, 228, 234, 241, 246, 269, 270, 272, 289, 299 Surface water 11, 13, 21, 51, 53, 103, 108, 115, 156, 158, 177, 182, 185-187, 200-202, 206, 207, 214, 221, 222, 230, 234, 244, 246, 250, 257-259, 266, 268, 271, 303 Sustainable 19, 74, 160, 184, 186, 187, 193, 194, 196, 197, 225, 230, 236, 241, 242, 244, 253-255, 274-276, 278, 282, 299, 313 SWAT model 45, 46

T Temperature 4, 8-10, 13, 15, 16, 20, 23-27, 32, 36-38, 46-49, 76, 78, 81, 85-87, 99, 107-109, 113, 115, 117-133, 138, 139, 149, 150, 153, 160, 161, 163-169, 173-175, 187, 194, 205, 206, 218, 222, 231, 232, 236, 237, 245, 247, 257, 259-261, 263-265, 272, 278, 279, 298, 306, 307 Temporal 31, 40, 78, 89, 101, 113, 115, 135, 151, 155, 156, 168, 170, 171, 187, 218, 254, 255, 257, 259, 263, 284 Third Assessment Report (TAR) 14, 236 Threshold 18, 46, 77, 85, 86, 89, 94, 174, 191, 259, 305 Tributary 48, 65, 139

U UBC model 46, 49 Uncertainty 18, 29, 59, 64, 75, 77-81, 89, 91, 99, 107, 115, 166, 191, 192, 218 UNDP 89, 93, 101, 282-284, 307, 310, 313 Upstream 15, 17-19, 25, 48, 55, 59, 65, 67, 69, 103, 105, 107, 114, 158, 203, 205, 207, 223, 235, 245, 248, 250, 271, 302, 309, 310

322

INDEX

Urbanization 5, 184, 188, 270, 289, 291, 293, 311 Uttar Pradesh 18, 57, 60, 175-177, 179, 182, 183, 185, 186, 295, 304

V Vulnerability 20, 69, 79, 89, 93, 95, 97, 99, 101, 134, 135, 137, 144, 218, 227, 246, 248, 252, 254, 257, 261, 262, 264, 266-270, 272, 275-278, 282-285, 287, 289, 293, 296, 305, 309-313

W Water-balance 23-25, 27, 33-36, 48, 49, 51-54, 84, 100, 101, 108 Water levels 39, 40, 60, 64, 105, 139, 147, 158, 180, 185, 237, 239, 241, 246, 248, 264, 266, 267, 291, 292, 298 Water logging 172, 209, 211, 215, 225, 246, 248, 290, 300 Water management 95, 96, 186, 192, 213, 225, 228-230, 235-238, 241, 243, 244, 252-254, 269, 272, 274, 277, 299, 301, 302, 313 Water quality 16, 21, 46, 108, 155, 180, 182, 183, 185, 189-191, 201, 202, 205, 211, 213, 217, 222, 228, 266 Water resources 1, 8, 9, 19-21, 23, 26, 27, 32, 33, 46, 50-54, 61, 63, 67, 74, 77, 101, 103, 106, 108, 134, 135, 153, 155, 156, 158-160, 163, 171, 177, 180-182, 184-197, 200-202, 207, 208, 215, 218, 224-232, 234, 237, 242-244, 252-255, 257, 259, 260, 262, 263, 265, 266, 268, 269, 272-278, 297, 298, 300, 301, 312, 313 Water sharing 18, 19, 134, 268 Wheat 132, 127, 146, 198, 199, 214-216, 226, 257, 265, 268 World Bank 17, 105, 131, 133, 135, 186, 190, 194, 233, 243, 247, 248, 250, 254, 259, 269, 271, 274, 278, 311, 312 Water demand -agriculture 5, 6, 13, 144, 187, 188, 207, 299 -industrial 5-8, 144, 187, 188, 190, 207, 263, 264

Y Yamuna 2, 12, 35, 56, 80, 104, 159, 180, 256