Climate Change Adaptation and International Development : Making Development Cooperation More Effective 9781849775267, 9781849711524

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Climate Change Adaptation and International Development

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Climate Change Adaptation and International Development Making Development Cooperation More Effective

Edited by Ryo Fujikura and Masato Kawanishi

First published by Earthscan in the UK and USA in 2011 For a full list of publications please contact: Earthscan 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN 711 Third Avenue, New York, NY 10017 Earthscan is an imprint of the Taylor & Francis Group, an informa business

Copyright © Japan International Cooperation Agency Research Institute 2011 Published by Taylor & Francis.

The views expressed in this book are those of the authors and do not necessarily represent the official positions of either the JICA Research Institute or JICA. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Notices Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.

ISBN 978-1-84971-152-4 hardback 978-1-84971-153-1 paperback Typeset by MapSet Ltd, Gateshead, UK Cover design by Susanne Harris A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Climate change adaptation and international development : making development cooperation more effective / edited by Ryo Fujikura and Masato Kawanishi. p. cm. Includes bibliographical references and index. ISBN 978-1-84971-152-4 (hardback) — ISBN 978-1-84971-153-1 (pbk.) 1. Climatic changes—Political aspects—United States. 2. Climatic changes—Government policy— International cooperation. 3. Global warming—Government policy—International cooperation. 4. Sustainable development—Government policy—International cooperation. I. Fujikura, Ryo, 1955- II. Kawanishi, Masato. QC903.C448 2010 363.738’740526—dc22 2010027723

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Contents

Preface by Keiichi Tsunekawa List of Figures, Tables, Boxes and Plates List of Contributors List of Acronyms and Abbreviations

ix xi xvii xix

1 — INTRODUCTION 1.1 Background and Objective of the Book Ryo Fujikura and Masato Kawanishi

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2 — CASES OF CLIMATE CHANGE ADAPTATION IN ASIA 2.1 Climate Change Projections in Some Asian Countries Akio Kitoh, Shoji Kusunoki, Yasuo Sato, Nazlee Ferdousi, Mizanur Rahman, Erwin Eka Syahputra Makmur, Ana Liza Solmoro Solis, Winai Chaowiwat and Tran Dinh Trong

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Commentary on Chapter 2.1 Andy J. Challinor

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2.2 Impacts of Climate Change upon Asian Coastal Areas: The Case of Metro Manila Megumi Muto Commentary on Chapter 2.2 Daniel P. Schramm 2.3 Linking Policy Processes and Stakeholder Agencies to Coastal Change: A Case Study from Nakhon Si Thammarat, Thailand Maria Osbeck, Somsak Boromthanarat and Neil Powell

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3 — CASES OF CLIMATE CHANGE ADAPTATION IN AFRICA 3.1 The Use of Climate Science in Agricultural Adaptation in Africa Gina Ziervogel and Anton Cartwright 3.2 Integrating Climate Change Information within Development and Disaster Management Planning: Lessons from Malawi, Mozambique and Zambia Gina Ziervogel and Anna Taylor 3.3 Understanding the Dynamic Nature of Vulnerability to Support Climate Adaptation and Development: The Case of the Lesotho Highlands Sukaina Bharwani and Anna Taylor

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3.4 Complex Multiple Stressors and Adaptation to Climate Change in the Case of a Rural South African Community Takeshi Takama, Gina Ziervogel and Anna Taylor

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3.5 Communities and Climate: Building Local Capacity for Adaptation Lawrence Flint

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3.6 Community-Based Solutions to the Climate Crisis in Ethiopia Marjorie Victor Brans, Million Tadesse and Takeshi Takama

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3.7 Adaptation to Climate Change: Lessons from North African Cases Benjamin Garnaud and Raphaël Billé

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3.8 Climate Change Adaptation and Water in Kenya: Governing for Resilience Jessica J. Troell and Collins Odote Oloo

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Commentary on Part 3 S. V. R. K. Prabhakar

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CONTENTS

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4 — INTERNATIONAL COOPERATION AND EMERGING ISSUES 4.1 The International Architecture for Climate Change Adaptation Assistance Jordan Diamond and Carl Bruch

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4.2 Assessment of Bilateral Projects from the Viewpoint of Adaptation Mariko Fujimori

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4.3 Emerging Issues: Forced Migration by Climate Change Mohamed Hamza, Lezlie Morinière, Richard Taylor, Nilufar Matin and Basra Ali

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Index

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Preface

Climate change, or global warming, is unquestionably one of the most urgent issues facing humanity today. It is essential that we make every possible effort to reduce the emission of greenhouse gases into the atmosphere. An equally important task is to devise and implement measures for coping with the actual and anticipated impact of the phenomenon. The adverse effects on least developed countries, which are highly vulnerable to climate change, in general, and to climate-related natural disasters, in particular, is especially worrisome; but even developing countries whose economies are growing rapidly may lose some of their development achievement through the effects of climate change. Climate-related disasters, such as drought and flooding, cause human misery and aggravate conflict over scarce resources. Whether the primary cause of such disasters is global climate change is still being determined; however, the seriousness of the problem requires that we act now on the assumption that the risks posed by worsening climate-related disasters will increase. The Japan International Cooperation Agency (JICA), the official development assistance (ODA)-implementing agency of Japan, has declared international cooperation to tackle climate change through mitigation and adaptation to be one of its central missions. JICA’s mitigation measures encompass a wide range of activities including, among many others, reforestation, installation of desulphurization equipment and solar panels, and infrastructure improvements to alleviate traffic congestion or reduce electricity loss. Adaptation measures cover an even wider spectrum, from reforestation (again), water management and agriculture to public health, urban planning and infrastructure, and tourism. Climate change adaptation is still a relatively new theme which requires stakeholders to accumulate and share their experiences and insights for the purposes of devising better policy recommendations. This volume intends to be a part of that effort. When the JICA Research Institute (JICA-RI) was founded in October 2008 as the research arm of JICA, it adopted climate change as one of its five priority issues (the others are fragile states; African development and the Asian experience; Association of Southeast Asian Nations (ASEAN) integration; and aid effectiveness). It has since conducted three research projects on the subject.

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This book, covering adaptation measures in Asia and Africa, is the result of one of these projects. The aim of the JICA Research Institute is to conduct policy-oriented research substantiated by academically solid methodologies and analyses. We believe that this book satisfies these objectives and will serve both researchers and practitioners who are striving to better forecast and project climate change and to determine appropriate measures for adaptation to actual and anticipated change in the global climate. Keiichi Tsunekawa Director JICA Research Institute

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List of Figures, Tables, Boxes and Plates

Figures 2.1.1

Schematic diagram of the estimation method for future sea surface temperatures 22 2.1.2 Observational sites of the Bangladesh Meteorological Department with elevation 27 2.1.3 Seasonal and monthly variation of rainfall in Bangladesh 28 2.1.4 Atmospheric general circulation model (AGCM)-generated annual profile of mean rainfall during 1979–2003 and 2075–2099 along with its change 29 2.1.5 Inter-annual variability of rainfall using AGCM-generated model data during the present (1979–2003) and future (2075–2099) 29 2.1.6 Rainfall observation sites in Indonesia used for model verification 31 2.1.7 Examples for monsoonal-type rainfall verification over Indonesia: Palembang, Madiun, Waingapu, and Manado, averaged over 21-year periods, respectively 33 2.1.8 Examples for equatorial rainfall-type verification: Padang and Pontianak, for 21-year periods, respectively 34 2.1.9 An example of local rainfall-type verification for Ambon, for a 20-year period 34 2.1.10 Seasonal mean (Northeast Monsoon) precipitation for 1979–2003: (a) Observation by APHRODITE, October–December; (b) 20km model simulation, October–December; (c) observation by APHRODITE, January–March; (d) 20km model simulation, January–March 39 2.1.11 Seasonal mean (Southwest Monsoon) precipitation for 1979–2003: (a) Observation by APHRODITE, April–June; (b) 20km model simulation, April–June; (c) observation by APHRODITE, July–September; (d) 20km model simulation, July–September 40

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2.1.12 Climatological annual rainfall cycle in the Philippines (115°–122°E; 5°–22°N) from Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) synoptic rain gauges and the Meteorological Research Institute of Japan (MRI) 20km simulation 2.1.13 Inter-annual rainfall variability of the observed and the 20km MRI model and corresponding correlation coefficients 2.1.14 Seasonal mean precipitation for 1979–2003: (a) observation by APHRODITE, May–July; (b) 20km model simulation, May–July; (c) observation by APHRODITE, August–October; (d) 20km model simulation, August–October 2.1.15 Climatological annual rainfall cycle for Thailand (97°–106°E, 5°–21°N) from the 20km model and observed data (APHRODITE) 2.1.16 Comparison of seasonal rainfall between the model and observed data in the central region for: (a) February–April; (b) May–July; (c) August–October; and (d) November–January 2.1.17 Climatological annual cycles of rainfall and surface temperature over Thailand from the present-day simulation and the end of the 21st-century projection using the 20km model 2.1.18 Simulated seasonal mean precipitation by 20km model for 1979–2003: (a) June–August; (b) September–November 2.1.19 Observed seasonal mean precipitation by the Global Precipitation Climatology Project (GPCP) 1DD for 1997–2003: (a) June–August; (b) September–November 2.1.20 Yearly rainfall rates of observed (black lines) and simulated (blue and red lines) at: (a) North (Caobang) Vietnam; (b) Middle (Tamky) Vietnam; (c) South (Vungtau) Vietnam; and (d) Nhatrang, Vietnam 2.2.1 Flood-prone areas in Metro Manila 2.2.2 KAMANAVA Area Flood Control and Drainage System Improvement Project 2.2.3 Pasig-Marikina and West Mangahan areas 2.2.4 Overall downscaling procedure 2.2.5 Health impact analysis 2.3.1 Location of the Pak Phanang Bay in the Gulf of Thailand: (1) Kong Khong village; (2) Pak Nam Pak Phaya village; (3) Talad Has village 3.2.1 Map of southern Africa showing the three countries where in-depth field studies were undertaken 3.2.2 Transporting charcoal 3.2.3 A girl eating fruit from the baobab tree in Malawi 3.3.1 Map of Lesotho 3.3.2 Mohale Dam 3.3.3 Women’s group in Ha Tsiu drawing a community resource map

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3.3.4 3.4.1 3.4.2 3.5.1

3.5.2 3.5.3

3.6.1 3.6.2 3.7.1 4.2.1 4.2.2

Woman carrying water from a communal standpipe Map of Greater Sekhukhune district and Ga-Selala village Probability of three strategies used when experiencing a drought Map and summary vignette of Upper Zambezi pilot action undertaken by local community-based organization supported by ENDA Tiers Monde Migration of power and influence, including management and administration of key ecological goods and services Diagrammatic representation of possible sets of inputs and outputs to a community-based organization (CBO) seeking self-sustainability HARITA conceptual framework Selected HARITA statistics Map of North Africa Simplified methodology to evaluate the adaptation function and co-benefits Evaluation example: Project for water supply in the Afar region, Ethiopia

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Tables 2.1.1 2.1.2

2.1.3

2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.2.6 3.2.1 3.2.2 3.4.1 3.5.1

Extreme indices of precipitation Summary of correlation coefficients (R) and root mean square errors (RMSEs) of rainfall data between the 20km model and station data for the regions of Thailand Summary of correlation coefficients and root mean square errors (RMSEs) of temperature data between the 20km model and station data for regions of Thailand Global climate scenario setting and conditions of the inundation simulations for Metro Manila Climatic–hydrologic infrastructure scenarios: Summary Damage assessment (2008 Philippine pesos): 100-year return period Damage assessment (2008 Philippine pesos): 30-year return period Damage assessment (2008 Philippine pesos): Ten-year return period Economic internal rate of return (EIRR) and net present value (NPV) results Trends related to climate change in Africa Background information for Malawi, Mozambique and Zambia Binary logit model for the strategy of choice with drought Tabia administrative structure showing elected and non-elected paid officials – Kihen tabia, Mekele district, Tigray, northern Ethiopia

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48 73 74 76 77 78 81 131 133 184

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3.6.1 3.7.1 3.7.2 4.1.1 4.1.2 4.2.1 4.2.2 4.2.3 4.2.4 4.2.5

Multilayered insurance structure for low-frequency and high-impact weather-related disasters 221 Vulnerabilities of North African countries identified in national communications and initiatives, and in the scientific literature 244 Examples of North African adaptation projects and some key characteristics 246 Pledged, received and allocated Adaptation Funding (US$ million) 298 Estimated cost of adaptation through 2030 (US$ billion per year) 302 Current conditions and possible impacts of future climate changes at the project site 320 Details of the measures carried out in the projects 321 Functions and co-benefits of official development assistance (ODA) projects in terms of adaptation effect 324 Criteria to assess the adaptation function and co-benefits of the assistance projects 326 Evaluation of the adaptation function of ODA projects in the water resource sector 328

Boxes 2.3.1 3.3.1 4.3.1 4.3.2 4.3.3 4.3.4

Mangrove rehabilitation beyond trees Key attributes of social vulnerability Case study 1: Cyclone Aila, Bangladesh Case study 2: The Sundarbans, Bangladesh Case study 1: Water shortage in Turkana, Kenya Case study 2: Beekeeping in Turkana, Kenya

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Plates 1

2

3

4

Geographical distributions of June–August (JJA) mean precipitation climatology (mm/day): (a) CPC Merged Analysis of Precipitation (CMAP); (b) Global Precipitation Climatology Project (GPCP); (c) Climate Research Unit of University of East Anglia (CRU); (d) Tropical Rainfall Measuring Mission (TRMM 3A25); (e) 180km model; (f) 120km model; (g) 60km model; and (h) 20km model Geographical distributions of December–February (DJF) mean precipitation climatology (mm/day): (a) CMAP; (b) GPCP; (c) CRU; (d) TRMM 3A25; (e) 180km model; (f) 120km model; (g) 60km model; and (h) 20km model Time–latitude cross-sections of 100–120°E averaged monthly mean precipitation climatology (mm/day): (a) CMAP; (b) GPCP; (c) CRU; (d) TRMM 3A25; (e) 180km model; (f) 120km model; (g) 60km model; and (h) 20km model December–February (DJF) mean precipitation changes (mm/day) between the present and the end of the 21st century for the (a) 60km model and (b) 20km model

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March–May (MAM) mean precipitation changes (mm/day) between the present and the end of the 21st century for the (a) 60km model and (b) 20km model June–August (JJA) mean precipitation changes (mm/day) between the present and the end of the 21st century for the (a) 60km model and (b) 20km model September–November (SON) mean precipitation changes (mm/day) between the present and the end of the 21st century for the (a) 60km model and (b) 20km model Changes in simple daily precipitation index (mm/day) between the present and the near future for the: (a) 60km model and (b) 20km model, and those between the present and the end of the 21st century for the (c) 60km model and (d) 20km model, respectively Changes in the maximum five-day precipitation (R5d) total (mm) for the: (a) 60km model and (b) 20km model, and those between the present and the end of the 21st century for the (c) 60km model and (d) 20km model, respectively Changes in the number of consecutive dry days (CDD) (day) for the: (a) 60km model and (b) 20km model, and those between the present and the end of the 21st century for the (c) 60km model and (d) 20km model, respectively Spatial distribution of monsoon for June to September rainfall (mm/day) over Bangladesh during 1979–2003 obtained from (a) observed data and (b) atmospheric general circulation model (AGCM) data (a) Projection of monsoon mean rainfall (mm/day) (F, 2075–2099); (b) rainfall change (F⫺P) between the present (1979–2003) and future (2075–2099); and (c) change ratio (F⫺P)/P (%) (a) Change in the maximum number of consecutive dry days (day) between the present (1979–2003) and future (2075–2099); (b) change in the annual maximum of the consecutive five-day total rainfall (mm) between the present (1979–2003) and future (2075–2099) Gridded total annual precipitation averaged over 1997–2004 for the Global Precipitation Climatology Project (GPCP) 1° (left) and the Meteorological Research Institute of Japan (MRI) 20km model (right) Seasonal precipitation for December to February: (a) present simulation for 1979–2003; (b) future simulation for 2075–2099; (c) change = future minus present; (d) change/present Seasonal precipitation for June to August: (a) present simulation for 1979–2003; (b) future simulation for 2075–2099; (c) change = future minus present; (d) change/present Annual precipitation: (a) present simulation for 1979–2003; (b) future simulation for 2075–2099; (c) change = future minus present; (d) change/present (a) Average of the onset of the rainy season, estimated within ten-day periods of each month, and the length of the rainy season over Indonesia;

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(b) average of the onset of the dry season, estimated within ten-day periods of each month, and the length of the dry season over Indonesia Change in the onset of the rainy season – zonal component of surface wind velocity: (a, c, e) present simulations for 1979–2003; (b, d, f) future simulations for 2075–2099; (a, b) October; (c, d) November; (e, f) December Change in the onset of the dry season – zonal component of surface wind velocity: (a, c, e) present simulations for 1979–2003; (b, d, f) future simulations for 2075–2099; (a, b) April; (c, d) May; (e, f) June Maps of the Philippines for (a) topography and (b) climate type based on rainfall pattern overlaying the 53 synoptic stations (black dots) used in the study Actual tracks of tropical cyclones from 1948–2008 in the Philippine Area of Responsibility (PAR) from best track data of the Weather Division, PAGASA-Tropical Cyclone Guidance System Change in seasonal mean precipitation from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) January–March; (b) April–June; (c) July–September; (d) October–December Change in seasonal mean precipitation from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) May–July; (b) August–October Change in precipitation extreme events from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) consecutive dry days (CDD); (b) maximum five-day precipitation total (R5d) Change in precipitation (May–November) from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage Change in precipitation from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) June; (b) August Change in precipitation from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) February; (b) March Annual average temperature distribution for: (a) the present; (b) the future; and (c) the change projected by the 20km atmospheric general circulation model (AGCM) simulation Change in precipitation extreme events from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) consecutive dry days (CDD); (b) simple daily intensity index (SDII); (c) maximum five-day precipitation total (R5d)

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List of Contributors

Editors and Chapter 1 Ryo Fujikura, Hosei University, Tokyo, Japan, and Japan International Cooperation Agency (JICA) Research Institute, Tokyo, Japan Masato Kawanishi, JICA, Tokyo, Japan

Chapter 2 Somsak Boromthanarat, CORIN-Asia Foundation, Bangkok, Thailand Andy J. Challinor, University of Leeds, Leeds, UK Winai Chaowiwat, Chulalongkorn University, Bangkok, Thailand Nazlee Ferdousi, South Asian Association for Regional Cooperation Meteorological Research Centre, Dhaka, Bangladesh Akio Kitoh, Meteorological Research Institute, Tsukuba, Japan Shoji Kusunoki, Meteorological Research Institute, Tsukuba, Japan Erwin Eka Syahputra Makmur, Indonesia Meteorological Climatological and Geophysical Agency, Jakarta, Indonesia Megumi Muto, JICA, Tokyo, Japan Maria Osbeck, Stockholm Environment Institute, Stockholm, Sweden Neil Powell, Stockholm Environment Institute, Stockholm, Sweden Md. Mizanur Rahman, South Asian Association for Regional Cooperation Meteorological Research Centre, Dhaka, Bangladesh Yasuo Sato, Meteorological Research Institute, Tsukuba, Japan Daniel P. Schramm, Environmental Law Institute, Washington, DC, US Ana Liza Solmoro Solis, Philippine Atmospheric, Geophysical and Astronomical Services Administration, Quezon City, the Philippines Tran Dinh Trong, Vietnam Institute of Meteorology, Hydrology and Environment, Hanoi, Vietnam

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Chapter 3 Sukaina Bharwani, Stockholm Environment Institute, Oxford, UK Raphaël Billé, Institute for Sustainable Development and International Relations, Paris, France Marjorie Victor Brans, Oxfam America, Boston, Massachusetts, US Anton Cartwright, African Centre for Cities, University of Cape Town, South Africa, and Stockholm Environment Institute, Oxford, UK Lawrence Flint, Environment and Development Action in the Third World (ENDA) Tiers Monde, Dakar, Senegal Benjamin Garnaud, Institute for Sustainable Development and International Relations, Paris, France Collins Odote Oloo, Institute for Law and Environmental Governance, Nairobi, Kenya S. V. R. K. Prabhakar, Institute for Global Environmental Strategies, Kanagawa, Japan Million Tadesse, Norwegian University of Life Sciences, Ås, Norway, and Southern Agricultural Research Institute, Awassa, Ethiopia Takeshi Takama, Stockholm Environment Institute, Oxford, UK Anna Taylor, Stockholm Environment Institute, Oxford, UK Jessica J. Troell, Environmental Law Institute, Washington, DC, US Gina Ziervogel, University of Cape Town, Cape Town, South Africa

Chapter 4 Basra Ali, Stockholm Environment Institute, Stockholm, Sweden Carl Bruch, Environmental Law Institute, Washington, DC, US Jordan Diamond, Environmental Law Institute, Washington, DC, US Mariko Fujimori, Pacific Consultants Co. Ltd, Tokyo, Japan Mohamed Hamza, Stockholm Environment Institute, Stockholm, Sweden, and Lund University, Lund, Sweden Nilufar Matin, Stockholm Environment Institute, Stockholm, Sweden Lezlie Morinière, University of Arizona, Tucson, Arizona, US, and Stockholm Environment Institute, Stockholm, Sweden Richard Taylor, Stockholm Environment Institute, Stockholm, Sweden

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List of Acronyms and Abbreviations

ACCMA ACMAD

Adaptation to Climate Change in Morocco project African Centre of Meteorological Applications for Development ADB Asian Development Bank AESTO Advanced Earth Science and Technology Organization AF Adaptation Fund AFB Adaptation Fund Board AGCM atmospheric general circulation model AGRHYMET Centre Régional de Formation et d’Application en Agrométéorologie et Hydrologie Opérationelle AIDS acquired immune deficiency syndrome AMIP Atmospheric Model Intercomparison Project AMJ April, May, June AOGCM atmosphere–ocean general circulation model APHRODITE Asian Precipitation – Highly Resolved Observational Data Integration towards Evaluation of Water Resources AR4 IPCC’s Fourth Assessment Report ASAL arid or semi-arid land ASEAN Association of Southeast Asian Nations ASO August, September, October BMD Bangladesh Meteorological Department BMKG Indonesia Agency for Meteorology, Climatology and Geophysics BRE Barotse Royal Establishment CAAC Catchment Area Advisory Committee CBA community-based adaptation CBE Commercial Bank of Ethiopia CBO community-based organization CCAA Climate Change Adaptation in Africa CCF Climate Change Fund CDD consecutive dry days

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CDM CEI CEO CER CIF CITES CMAP CMIP3 CMP CMS CNRS CO2 COP CORIN-Asia CRMA CRU CSAG CSIRO DCA DDC DECSI DFID DJF DMCR DMP DNP DPWH EDP EIA EIRR EMCA EMDAT ENDA ENSO EPIC EPLF ERTB EU F⫺P FAO FMA GCCA

Clean Development Mechanism Climate Extreme Index chief executive officer Certified Emission Reduction Climate Investment Funds Convention on International Trade in Endangered Species of Wild Fauna and Flora CPC Merged Analysis of Precipitation Coupled Model Intercomparison Project COP Meeting of the Parties Catchment Management Strategy Centre National de la Recherche Scientifique carbon dioxide Conference of the Parties Asian Coastal Resources Institute-Foundation Climate Risk Management and Adaptation Strategy Climate Research Unit of University of East Anglia Climate Systems Analysis Group Commonwealth Scientific and Industrial Research Organization discrete choice analysis Data Distribution Centre Dedebit and Credit and Savings Institution UK Department for International Development December, January, February Department of Marine and Coastal Resources Disaster Management Policy Department of National Parks, Wildlife and Plant Conservation Department of Public Works and Highways (the Philippines) environmentally displaced person environmental impact assessment economic internal rate of return Environmental Management and Coordination Act Epidemiology of Disaster’s Emergency Database Environment and Development Action in the Third World El Niño/La Niña Southern Oscillation Erosion Productivity Impact Calculator Eritrean People’s Liberation Front environmental refugee-to-be European Union future minus present United Nations Food and Agriculture Organization February, March, April Global Climate Change Alliance

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LIST OF ACRONYMS AND ABBREVIATIONS

GCM GCOS GDP GEF GFDRR GGLM GHG GIS GLASOD GPCP GRDP GTZ HARITA HDI HIV hPa HUDCC HYDE ICDP ICPAC ICRC ICRISAT ICZM IDDRI IDP IDP IDRC IFRC IFW IGES IIED INAM INGC INGO IPCC IPCC-AR4 IPCC-TAR IRI ITCZ

general circulation model Global Climate Observing System gross domestic product Global Environment Facility Global Facility for Disaster Reduction and Recovery Group Guarantee Lending Model greenhouse gas geographic information system Global Assessment of Human Induced Soil Degradation Global Precipitation Climatology Project gross regional domestic product Deutsche Gesellschaft für Technische Zusammenarbeit Horn of Africa Risk Transfer for Adaptation project Human Development Index human immunodeficiency virus hectopascal Housing and Urban Development Coordinating Council History Database of the Global Environment Integrated Conservation Development Project Intergovernmental Authority on Development’s Climate Predictions and Applications Centre International Committee of the Red Cross International Crops Research Institute for the Semi-AridTropics integrated coastal zone management Institute for Sustainable Development and International Relations internally displaced person integrated development plan Canadian International Development Research Centre International Federation of Red Cross and Red Crescent Societies Insurance for Work model Institute for Global Environmental Strategies International Institute for Environment and Development Mozambique National Meteorology Institute Mozambique National Institute for Disaster Management international non-governmental organization Intergovernmental Panel on Climate Change Intergovernmental Panel on Climate Change’s Fourth Assessment Report Intergovernmental Panel on Climate Change’s Third Assessment Report International Research Institute for Climate and Society Intertropical Convergence Zone

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World Conservation Union (formerly International Union for the Conservation of Nature) IWRM integrated water resources management JAMSTEC Japan Agency for Marine–Earth Science and Technology JAS July, August, September JEC Japan Fund for Environmental Conservation JFM January, February, March JICA Japan International Cooperation Agency JICA-RI Japan International Cooperation Agency Research Institute JJA June, July, August JJAS June, July, August, September JMA Japan Meteorological Agency JRA-25 Japanese 25-Year Reanalysis KAKUSHIN Innovative Programme of Climate Change Projection for the 21st Century KNCF Keidanren Conservation Fund LDC least developed country LDCF Least Developed Countries Fund LED local economic development LHDA Lesotho Highlands Development Authority LHWP Lesotho Highlands Water Project LYVA Lyambai Vulnerability and Adaptation MA Millennium Ecosystem Assessment MAM March, April, May MANGROVE Reconciling Multiple Demands on Mangrove Resources programme MCGS Marikina Control Gate Structure MDG Millennium Development Goal MEMR Kenyan Ministry of Environment and Mineral Resources MFI microfinance institution MJJ May, June, July MMDA Metro Manila Development Authority MME multi-model ensemble MoNRE Ministry of Natural Resources and Environment MOWI Kenyan Ministry of Water and Irrigation MRI Meteorological Research Institute of Japan NAF National Adaptation Facility NAPA National Adaptation Programme of Action NCCRS Kenyan National Climate Change Response Strategy NDJ November, December, January NES National Environment Secretariat NESDB National Economic and Social Development Board NGO non-governmental organization NHCS Napindan Hydraulic Control Structure NOAA US National Oceanic and Atmospheric Administration

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LIST OF ACRONYMS AND ABBREVIATIONS

NPV NWRMS OCHA ODA OECD ON OND ONEP PAGASA PAR PCVA PEF PPCR PPP PRA PRECIS PSNP R R5d RDP REST RFD RMSE RVAC SADC SCCF SCF SDII SEA SECCI SEI SES SFA SGA Sida SLR SON SP SPA SRES SST START TACC

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net present value National Water Resources Management Strategy Office for the Coordination of Humanitarian Affairs official development assistance Organisation for Economic Co-operation and Development October, November October, November, December Office of Natural Resources and Environment Policy and Planning Philippine Atmospheric, Geophysical and Astronomical Services Administration Philippine Area of Responsibility participatory capacity and vulnerability assessment Poverty and Environment Fund Pilot Programme for Climate Resilience purchasing power parity participatory rural appraisal Providing Regional Climates for Impacts Studies Productive Safety Net Programme correlation coefficient maximum five-day precipitation total Reconstruction and Development Programme Relief Society of Tigray Royal Forest Department root mean square error Regional Vulnerability Assessment Committee Southern African Development Community Special Climate Change Fund Strategic Climate Fund simple daily intensity index strategic environmental assessment Sustainable Energy Climate Change Initiative Stockholm Environment Institute socio-ecological system seasonal forecast area Small Grant for Adaptation Actions Swedish International Development Cooperation Agency sea-level rise September, October, November stated preference Strategic Priority on Adaptation Special Report on Emission Scenario sea surface temperature Global Change System for Analysis, Research and Training Territorial Approach to Climate Change

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TGICA TPLF TNRS TRMM UK UN UNDP UNEP UNFCCC UNHCR UNICEF UNU-EHS US WASREB WCRP WFPF WHO WRIMS WRMA WRUA WSB WSP

Task Group on Data and Scenario Support for Impact and Climate Assessment Tigray People’s Liberation Front Tigray National Regional State Tropical Rainfall Measuring Mission United Kingdom United Nations United Nations Development Programme United Nations Environment Programme United Nations Framework Convention on Climate Change United Nations High Commissioner for Refugees United Nations Children’s Fund United Nations University Institute for Environment and Human Security United States Water Services Regulatory Board World Climate Research Programme Water Financing Partnership Facility World Health Organization Water Resources Information Management System Water Resource Management Authority Water Resource Users’ Association Water Service Board water service provider

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Part 1

Introduction

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1.1 Background and Objective of the Book

Ryo Fujikura and Masato Kawanishi

In a parallel setting at the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) in Copenhagen in December 2009, a group of states representing the major emitting countries and main negotiating groups agreed on the Copenhagen Accord, which was taken note of at the closing plenary by the COP. The accord stresses ‘the need to establish a comprehensive adaptation programme including international support’. It also recognizes that ‘enhanced action and international cooperation on adaptation is urgently required to ensure the implementation of the Convention by enabling and supporting the implementation of adaptation actions aimed at reducing vulnerability and building resilience in developing countries’ (UNFCCC, 2010). Adaptation is defined by the Intergovernmental Panel on Climate Change (IPCC) as an ‘adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities’ (Parry et al, 2007). In recent years, the scope of climate change adaptation has been broadened, with a growing focus on its linkage with development (Lasco et al, 2009). McGray et al (2007) identify adaptation as a continuum, ranging from highly specialized activities in response to specific climate change impacts to activities that address the drivers of vulnerability and build response capacity. In practice, many instances of adaptation fall between these two distinct approaches: the impact focused, on the one hand, and the vulnerability focused, on the other. The study also finds

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that adaptation activities to reduce vulnerability have a significant overlap with traditional development practices. Actions taken today to reduce vulnerability will increase resilience by providing a buffer against vulnerability to future consequences of climate change (Ribot et al, 1996). Here, the essential starting point is the present. This differs from the impact-focused approach that begins with a consideration of future climate as projected in climate models (Burton et al, 2002). The IPCC Fourth Assessment Report highlights that climate change adaptation and sustainable development share common goals and determinants (Yohe and Lasco, 2007). Similarly, Klein (2008) states that successful adaptation in developing countries depends upon a broader development process. Policies that address sustainable livelihoods and alleviate poverty can also reduce vulnerability to climate change. It is therefore possible to create synergies between official development assistance (ODA) and adaptation. One of the ways to achieve this is to integrate climate risks with development planning, which is called ‘mainstreaming’ (OECD, 2009). While developing countries call for new and additional funding for adaptation, as opposed to mainstreaming, it is difficult, in practice, to distinguish between adaptation initiatives and what can be considered good development. Persson et al (2009) argue that the issue of additionality is best addressed at the finance generation stage. They also stress that delivering on adaptation will be made more efficient and effective through strengthening of existing development processes. One of the challenges is to search for shared approaches to adaptation planning (McGray, 2009). Adaptation is highly location specific, given the wide range of potential climate change impacts, the variety of factors that shape vulnerability, and the differences in governance structures across countries. Existing strategies and capacities also vary. This suggests that adaptation solutions cannot be transferred easily from one situation to another. This does not mean, however, that each country and community must act in isolation (Tirpak and Ward, 2005). In fact, as Adger (2009) suggests, although adaptation occurs in different contexts, there are common elements in the processes, the constraints and the ways forward for adaptation action. This book is mainly intended for adaptation and development practitioners, aiming to present some important aspects of adaptation that are transferable between situations. The book contains a variety of case studies. Some are more focused on vulnerability, addressing the drivers of vulnerability in the communities or societies in question. Others are more oriented toward impacts, considering the challenges and opportunities of translating climate model outputs through impact assessment into identification of adaptation options. The cases are also examined at different levels, from community to national and international levels. The locations are varied. So are the sectors to be addressed, which include agriculture, water and disaster management. Against this diversity, this book presents emerging lessons for the practice of climate change adaptation and development.

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Outline of the book This book consists of four parts, including this introductory Part 1. Parts 2 and 3 comprise case studies of climate change adaptation in Asia and Africa, respectively, along with some commentaries. Part 4 starts with two studies that consider the relationship between adaptation and development assistance. It ends with a chapter that addresses the issue of migration forced by climate change, an emerging research topic relating to adaptation.

Case studies of climate change adaptation in Asia In Chapter 2.1, Akio Kitoh, Shoji Kusunoki, Yasuo Sato and their co-authors analyse the simulation results of the global atmospheric general circulation model, developed by the Japan Meteorological Agency (JMA) and the Meteorological Research Institute of Japan (MRI), which produces a 20km resolution, the highest among the global models currently available. In response to climate change, increasing attention is paid to anticipatory adaptation, which relies on information on future climate risks (Adger et al, 2007). As adaptation is site specific, it is also often in need of localized information, resulting in growing interest in high-resolution climate models. High resolution is also important for projection of extreme events, as it can represent detailed topography, an important factor that affects extremes. Kitoh et al not only present the state of the art of one of the most advanced climate models, but also reveal uncertainties that still remain. They verify the model before projecting future climate change. The verification is done through evaluation of the results of present-day model simulations against observed weather data. The findings indicate that while the model is generally good in tracking the pattern of the observed data, it is less skilled at projecting some parameters in certain regions. It shows that in using a climate model for projection in specific areas, it is important to understand how model outputs fit with each context. Similarly, Gina Ziervogel and Anton Cartwright (Chapter 3.1) present the advantages of analysing the outputs from a set of models, referred to as an ‘ensemble’, over relying on a single model. The ensemble approach enables the user to define a range of potential climate changes and examine adaptation strategies against these plausible futures (Dessai et al, 2009). Chapter 2.1, however, points out that an ensemble does not provide high resolution. The benefit of high resolution, compared to those provided by an ensemble of models, is one of the issues that needs to be examined within the objectives and contexts of climate change projection. It should also be noted that verification of a climate model needs observed weather data. Many developing countries, however, lack historical data, a problem for which international cooperation is being sought. Chapter 2.1 is based on the outputs of the Japan International Cooperation Agency (JICA) training course Capacity Development for Adaptation to Climate Change in Asia. Started in 2008, this course aims to support five Asian

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countries – Bangladesh, Indonesia, the Philippines, Thailand and Vietnam – in developing their capacity to adapt to climate change. With the support of JMA and MRI, the course has been focusing on climate change projection for the past two years. Experience gained in the training course demonstrates that it is not mere training, but also an opportunity to obtain new findings regarding the future climate and model applicability in the relevant regions. In his commentary on Chapter 2.1, Andy Challinor argues that while climate and impact models underpin many efforts to inform adaptation, there are barriers to using models in this way. First is a mismatch that often occurs between model outputs and the needs of decision-makers. When using climate change projections for adaptation, it is important to identify decisions that will be taken based on the information and to make sure that the temporal and spatial scale of modelling is appropriate (Patt et al, 2009). Another constraint is the multiplicity of factors beyond climate that affect adaptation. Vulnerability occurs as a result of a multitude of physical, social and politicaleconomic processes and events. Hence, adaptation needs to be considered from this multi-causal perspective, with climate being placed as one causal agent among many (Ribot et al, 1996). Similar insights are offered in Chapters 3.2 to 3.6. Chapter 2.2, by Megumi Muto, is part of a joint study by JICA, the Asian Development Bank (ADB) and the World Bank on climate change impacts and adaptation in coastal Asian cities. This chapter is a case study of an attempt to simulate the impacts of future climate change and to identify adaptation options in Metro Manila. The study uses downscaling, a method of deriving local-scale information from larger scale models, to project local temperature and precipitation, thereby analysing future hydrological conditions such as river overflow and storm surge. Flood simulation maps are then produced according to a range of different scenarios of future climate conditions and infrastructure development. This is followed by economic valuation of the associated losses, which include not only direct but also indirect losses, such as incremental costs of transportation, lost wages and health hazards. In terms of health impact assessment, human health risks due to exposure to pathogens present in floodwater are characterized and quantified. For this purpose, exposure scenarios are developed that depend upon different inundation levels. Based on these analyses, adaptation options are considered. The study concludes that investment based on a previously developed master plan to fill the infrastructure gap in response to current climate variability should be the highest priority in Metro Manila. The study also examines the vulnerability of poor urban households and underlines the necessity of targeted intervention. Daniel P. Schramm, in his commentary on Chapter 2.2, argues that adaptation requires actions that qualitatively differ from existing development strategies. While recognizing the importance of engineered solutions, he stresses that they need to be placed within an integrated policy framework that includes non-technical measures, such as revising building codes and land-use planning. He also points out the importance of ‘procedural’ mechanisms,

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including scenario planning and adaptive management, to navigate uncertainty regarding future climate impacts (see Chapter 3.8). Finally, he underlines the necessity of considering trade-offs among resource-users facing scarcity (Eakin et al, 2009). While the first few chapters are oriented towards impacts, Chapter 2.3, by Maria Osbeck, Somsak Boromthanarat and Neil Powell, is more focused on vulnerability. This chapter involves a case study exploring the complex institutional relationships governing the management of a mangrove system and considering how they are related to the vulnerability of the coastal area to climate change in Thailand. The chapter places emphasis on multi-stakeholder participation and presents a way forward for sustainable mangrove management.

Case studies of climate change adaptation in Africa In Chapter 3.1, Gina Ziervogel and Anton Cartwright examine the use of climate information in agricultural development practice in Africa. They find that there is currently little evidence of climate model outputs being utilized in agricultural decision-making. They identify four challenging areas. The first is a lack of meteorological data in Africa, where weather monitoring and data collection networks have deteriorated due to civil wars and economic difficulties. The lack of historical data is one obstacle to downscaling in order to derive local information. Second, climate models do not provide information at the spatial and temporal resolution required by farmers. For instance, as climate model outputs are normally available for more than ten years into the future, farmers consider them irrelevant to their crop selection decisions and planting dates. Third, Africa lacks the human and computational capacities to run and analyse climate models. The fourth challenge is a lack of mutual understanding between climate scientists and agricultural users. In response to the challenges identified above, the chapter provides a set of recommendations. First, historical data should be ‘rescued’ and converted to digital formats. Second, seasonal weather forecasting and climate change projection should be framed coherently to provide information on specific decisions to be made. Seasonal forecasting will be able to serve the needs of small-scale farmers, for instance, who are interested in information about expected rainfall in the coming season (Adejuwon et al, 2008). Climate projection, on the other hand, supports longer-term adaptation planning. Third, climate scientists in Africa need to be trained in climate modelling techniques. Lastly, communication between climate information providers and users should be strengthened. One of the important ways of achieving this is to support ‘translators’ who understand the challenges of both groups and who can facilitate a meaningful dialogue between them. Chapters 3.2 to 3.6 are a series of case studies of community-based adaptation in Africa. Community-based adaptation is focused on those communities that are most vulnerable to climate change. The aim is to enable communities to understand and integrate the concept of climate risks within their livelihood

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activities in order to strengthen their resilience to climate variability and change. It is rooted in the local context, and seeks to work with communities to identify local problems and locally appropriate solutions (Huq and Reid, 2007). The following chapters stress the differential and dynamic nature of vulnerability, as well as a multiplicity of stressors that drive vulnerability. Some of the limits and barriers to actions that reduce vulnerability, including cognitive and social constraints, are also discussed (Adger et al, 2007). Chapter 3.2, by Gina Ziervogel and Anna Taylor, considers the issue of integration of scientific information with perceptions of climate risks. On the basis of field research in Malawi, Zambia and Mozambique, they argue that it is the social landscape that will determine the differential impacts of climate upon people’s livelihoods. Adaptation planning demands a thorough understanding of the local context of gender, culture and other socio-economic factors. The chapter emphasizes that the capacity to adapt is not equal among social groups; therefore, targeted support is necessary. The differential nature of vulnerability is further elaborated upon by Sukaina Bharwani and Anna Taylor in Chapter 3.3, based on the case study in Lesotho. The list of key attributes of vulnerability, as compiled in Downing et al (2006), is used to demonstrate that people’s vulnerability differs based on the extent to which they are exposed to stresses and their ability to respond. They argue that in order to understand how climate change will affect people differently, it is critical to view climate stress within the context of many other stressors. They also highlight the dynamic nature of vulnerability, pointing the dangers of a ‘snapshot approach’ to vulnerability assessment. This chapter suggests that existing local strategies and capacities provide an important entry point for adaptation assistance. It reminds us that human societies have always adapted to their climatic environment, and therefore adaptation policy already exists, even if it may not be recognized by that name (Burton et al, 2002). In Chapter 3.4, Takeshi Takama, Gina Ziervogel and Anna Taylor also emphasize the importance of understanding a multiplicity of stressors. They conducted interviews and compiled a survey to identify key stressors in a rural South African community. The results show that water scarcity and limited economic opportunities are two major constraints and that people relate climate stress to these two dominant stressors. The findings indicate that viewing climate stress in isolation is of limited value in terms of developing adaptive strategies in response to multiple stressors. Lawrence Flint presents a case study of community-based adaptation in western Zambia and northern Ethiopia in Chapter 3.5. He focuses on cognitive constraint barriers to adaptation. He demonstrates that interpretations of climate risk are context specific and adaptation responses to climate change can be limited by human cognition. In adaptation planning, therefore, it is critical to think about climate in a locally interpreted manner and assess vulnerability from each affected community’s purview. The communicative strategy is vital to this end. Scientific information needs to be presented in a

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way that it can be understood and deployed locally. This chapter also lays emphasis on a sense of local ownership towards adaptation activities. Increasing attention is being paid to insurance as a tool for adaptation to climate change. Chapter 3.6, by Marjorie Victor Brans, Million Tadesse and Takeshi Takama, is a case study of applying weather index-based insurance in Ethiopian communities. By presenting both opportunities and challenges, they argue that community projects should be placed within a coherent framework of national adaptation efforts so that individual households can absorb smallscale risks, while national governments may prepare for larger and more uncertain risks. In contrast to the previous chapters that focus on community-based adaptation, Chapters 3.7 and 3.8 look at adaptation initiatives from a wider scope. In Chapter 3.7, Benjamin Garnaud and Raphaël Billé examine the current landscape of adaptation in Algeria, Egypt, Morocco and Tunisia. They review these countries’ national initiatives and adaptation projects, and then compare their different levels to identify the gaps and opportunities. What they observe in North Africa is an ‘incomplete patchwork of small-scale projects’, with very little connection among the efforts. They see that projects are often detached from the larger context that drives vulnerabilities. A real need exists for small-scale projects to be integrated as a part of sequenced national adaptation implementation. They call for a much more coherent approach, where community-based adaptation efforts fit into a broader range of adaptation activities at the national level. In Chapter 3.8, Jessica J. Troell and Collins Odote Oloo explore the ways in which Kenya’s policy and legal and institutional frameworks may support effective adaptation in the water sector. They argue that the most pressing challenge to existing frameworks is their lack of capacity to cope with uncertainties posed by climate change. They underline the necessity of the adaptive nature of planning and implementation in order to take these uncertainties into account. Adaptive management is an ongoing, iterative approach that seeks to ‘learn by doing’. This includes development and adoption of a provisional policy; a legal and institutional framework; ongoing monitoring and collection of information; periodic assessment of the collected information; modification of the frameworks as appropriate; and continuing the management cycle of monitoring, assessment and revision. The evolving circumstances and understandings about climate change necessitate reviewing and revising the effectiveness of current governance structures and processes, ensuring their flexibility and sustainability (see Daniel P. Schramm’s Commentary on Chapter 2.2; Adger, 2009). They also call for clarification of institutional mandates with respect to adaptation. Part 3 concludes with a commentary by S. V. R. K. Prabhakar. One of the issues he highlights is that decision-making units at different levels produce differences in the way in which adaptation options are identified and prioritized. Integrating these differences within a coherent adaptation policy framework is a challenge for governments and donors alike.

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International cooperation and emerging issues Chapters 4.1 and 4.2 consider the link between climate change adaptation and development assistance at an international level. Jordan Diamond and Carl Bruch provide an overview of the adaptation assistance programmes in Chapter 4.1, analysing areas of overlap and concern. Fund mobilization is a significant challenge for meeting forecasted needs. With limited resources available, the efficiency and efficacy of adaptation assistance must be enhanced. This depends heavily upon coordination between adaptation assistance and development assistance. In reference to the principle of additionality, which addresses the concern of many developing countries that adaptation assistance should be provided in addition to ODA, Diamond and Bruch argue that the concept is difficult to implement, but the overlap between the two can be synergistic. They stress the need for concurrently alleviating poverty and increasing resilience to climate change. The link between adaptation and development assistance is discussed by Mariko Fujimori in Chapter 4.2 from an operational point of view. On the basis of a review of recent ODA activities (JICA, 2007), a simplified method for assessing the adaptation effects of development assistance is proposed. This chapter also discusses the issues, problems and barriers that should be addressed by a donor agency in order to integrate adaptation considerations within its development assistance activities. Mohamed Hamza, Lezlie Morinière, Richard Taylor, Nilufar Matin and Basra Ali, in Chapter 4.3, discuss the issue of migration forced by climate change, an emerging research topic relating to adaptation. They focus on how environmental change and hazards could contribute to migration by exploring mechanisms through which vulnerability and migration are linked. To this end, a drought case from Kenya and a flooding case from Bangladesh, where there are early indicators of population displacement, are examined. This chapter stresses that little is understood about the interplay between environmental change, the resulting socio-economic vulnerability, and potential outcomes in terms of population displacement or migration. So far, these relationships are poorly conceptualized and lack systematic investigation. Evidence-based research on the climate–migration nexus is required to answer the questions of how much migration may be stimulated by climate change and what will be the best policies to deal with such migration.

Lessons learned The cases presented in this book vary in terms of scope, approach, locations and sectors. Against this backdrop of diversity, there are some important lessons to be learned for the practice of climate change adaptation and development. First, many of the contributors to this book point out the criticality of understanding the multiplicity of stressors that drive vulnerability, as well as

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the differential and dynamic nature of vulnerability. Viewing climate stress within the context of other social and economic stressors will enable us to understand how climate change affects people and communities differently. It is important to recognize that the capacity to adapt is not equal among different social groups, sectors and regions. Their capacities will also vary over time, affected by multiple processes of change. A lack of understanding of the different natures of vulnerability may result in incorrect judgement on what constitutes appropriate intervention, leading to maladaptation. Another recurrent theme of this book is the opportunities and challenges of applying climate model outputs through impact assessment to help practitioners make informed adaptation decisions on the ground. High-resolution models, for example, can serve individual communities’ needs for specific local information about future climate risks. They are also useful for projection of extreme events. An ensemble approach, on the other hand, enables us to examine adaptation options that are robust over a range of plausible futures. Many barriers still remain, however, in the use of models. This is attributed to a large extent to a multiplicity of factors beyond climate that affect vulnerability. Other constraints include a lack of historical data as well as limited human and computational capacity to run and analyse climate and impact models in developing countries. This is an acute problem in Africa (see Chapter 3.1 of this volume; Boko et al, 2007) and is one of the areas for which international support is being sought. The book also reminds us that impact and vulnerability assessments are about supplying useful information to decision-makers; therefore it is critical to understand users’ needs. The temporal and spatial scales of information obtained by climate model analyses are often too long-term and too coarse for decision-makers to utilize. Modelling techniques need to be further advanced. Adaptation is a collective action, involving scientists who project climate change, analysts who assess impacts and vulnerability, and those who plan and act based on the information provided. The success of adaptation rests on mutual understanding between these different stakeholders, and donors can play an important role in facilitating communication. Finally, governance is an important facet of adaptation. Communities are concerned about coping with near-term climate impact by reducing their current vulnerabilities. Governments, on the other hand, must consider longer-term climate impact in their development policy planning. An important aspect of mainstreaming is the integration within a coherent policy framework of these differences in the temporal and spatial scales of climate concern and the different levels of responses. Policies shaped at national and international levels set objectives to be achieved at local and regional levels. In the meantime, community-based adaptation seeks to assess vulnerability from an affected community’s values and worldviews, thereby identifying locally appropriate technologies and solutions. Some of these local strategies provide good entry points for adaptation policy planning and intervention (N. Mimura, pers comm, email, 21 April 2010). Thus, adaptation is a process of interactions between

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different levels of governance from both the top down and bottom up (Adger, 2009). This book also reveals that, in response to the evolving understanding of, and uncertainty in, climate change, governance of adaptation should be adaptive, with a continuous cycle of monitoring, assessment and revision. International support needs to take these natures of adaptation governance into account. A much more integrated and coherent approach is necessary for ‘scaling up’ and ‘spreading out’ adaptation initiatives. This is one of the reasons donor coordination should be further strengthened. Their support also needs to be adaptive, with an ongoing, iterative process of learning by doing. In this way, donors will be able to play an important role in not only financing adaptation projects, but also facilitating the exchange of adaptation knowledge and practice. Furthermore, post-evaluation of relevant development projects should be emphasized in order to develop a shared approach to adaptation by integrating donors’ experiences. Fujimori (Chapter 4.2) suggests a simple evaluation methodology. This may be the initial step in evaluating projects that were not formulated explicitly for adaptation purposes. Such evaluation results should be accumulated and shared among donors.

Conclusions The overlap between climate change adaptation and development can be synergistic. Given the limited resources available, international support should exploit this synergy as much as possible. Further research, including continued stocktaking of knowledge and practices, is required to identify the factors that account for this synergy, as well as to understand how international support to promote sustainable development can also enhance adaptive capacity in developing countries and vice versa (Parry et al, 2007). The links among adaptation, mitigation and development must also be explored to identify pathways which will provide high resilience to climate change. Adaptation is not a pain-free process. Migration forced by climate change is another important frontier of research on adaptation. As Hamza et al (Chapter 4.3) discuss, little is understood about the interplay between environmental change, resulting socio-economic vulnerability, and potential outcomes of population displacement or migration. Evidence-based research is required to enhance our understanding about these relationships and the role of governments and international support in dealing with such migration. Not only in rural areas, but also in metropolitan areas, forced migration can be a crucial issue. In the absence of appropriate city planning, affected areas will face a potentially enormous social cost burden. If adequate adaptation in place is not possible, then there must ultimately be recourse to retreat. Further research is required to clarify the issue of adaptation in urban areas and to encourage city planning which, to the extent possible, incorporates the prospect of precautionary planned retreat. International support is necessary to enhance the adaptation capacity of governments and communities. At the same time, it is a challenge for donors to

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mainstream adaptation within their development assistance planning and activities. They need to further develop their own capacity and knowledge. One way to achieve this is for them to learn from each other. Moreover, they should further collaborate with research institutes on climate change in order to develop methodologies that can be shared among stakeholders for enhancing action and international cooperation on adaptation.

Acknowledgements The editors would like to thank all the dedicated contributors who submitted high-quality chapters and commentaries for this book. Professor Nobuo Mimura of Ibaraki University provided important comments on an earlier draft of this introductory chapter. Special thanks are extended to Dr Takeshi Takama, who helped us in contacting a number of the contributors. We also thank Ms Maiko Suda, Ms Tomoyo Toyota, Ms Kayoko Tamura, Ms Midori Tenmyo and Mr Yasuhiko Sato of the JICA Research Institute for their assistance. Finally, thanks are owed to Ms Alison Kuznets and Ms Anna Rice of Earthscan for their assistance with the production of this book.

References Adejuwon, J. O., T. O. Odekunle and M. O. Omotayo (2008) ‘Using seasonal weather forecasts for adapting food production to climate variability and climate change in Nigeria’, in Leary, N., J. Adejuwon, V. Barros, I. Burton, J. Kulkarni and R. Lasco (eds) Climate Change and Adaptation, Earthscan, London, UK Adger, W. N. (2009) ‘Adaptation now’, in Adger, W. N., I. Lorenzoni and K. L. O’Brien (eds) Adapting to Climate Change: Thresholds, Values, Governance, Cambridge University Press, Cambridge, UK Adger, W. N., S. Agrawala, M. M. Q. Mirza, C. Conde, K. O’Brien, J. Pulhin, R. Pulwarty, B. Smit and K. Takahashi (2007) ‘Assessment of adaptation practices, options, constraints and capacity’, in Parry, M. L., O. F. Canziani, J. P. Palutikof, P. J. van der Linden and C. E. Hanson (eds) Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK Boko, M., I. Niang, A. Nyong, C. Vogel, A. Githeko, M. Medany, B. Osman-Elasha, R. Tabo and P. Yanda (2007) ‘Africa’, in Parry, M. L., O. F. Canziani, J. P. Palutikof, P. J. van der Linden, and C. E. Hanson (eds) Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK Burton, I., S. Huq, B. Lim, O. Pilifosova and E. L. F. Schipper (2002) ‘From impacts assessment to adaptation priorities: The shaping of adaptation policy’, Climate Policy, vol 2, nos 2–3, pp145–159 Dessai S., M. Hulme, R. Lempert and R. Pielke, Jr. (2009) ‘Climate prediction: A limit to adaptation?’, in Adger, N. W., I. Lorenzoni and K. L. O’Brien (eds) Adapting to Climate Change: Thresholds, Values, Governance, Cambridge University Press, Cambridge, UK

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Downing, T. E., J. Aerts, J. Soussan, O. Barthelemy, S. Bharwani, C. Ionescu, J. Hinkel, R. J. T. Klein, L. J. Mata, N. Martin, S. Moss, D. Purkey and G.. Ziervogel (2006) Integrating Social Vulnerability into Water Management, Stockholm Environment Institute, Oxford, UK Eakin, H., E. L. Tompkin, D. R. Nelson and J. M. Anderies (2009) ‘Hidden costs and disparate uncertainties: Trade-offs in approaches to climate policy’, in Adger, N. W., I. Lorenzoni and K. L. O’Brien (eds) Adapting to Climate Change: Thresholds, Values, Governance, Cambridge University Press, Cambridge, UK Huq, S. and H. Reid (2007) Community-Based Adaptation: A Vital Approach to the Threat Climate Change Poses to the Poor, Policy Brief, International Institute for Environment and Development, London, UK JICA (Japan International Cooperation Agency) (2007) Study on JICA’s Assistance for Adaptation to Climate Change, Tokyo, Japan (in Japanese) Klein, R. J. T (2008) Financing Adaptation to Climate Change, Policy Brief, Stockholm Environment Institute, Stockholm, Sweden Lasco, R. D., F. B. Pulhin, P. A. Jaranilla-Sanchez, R. J. P. Delfino, R. Gerpacio and K. Garcia (2009) ‘Mainstreaming adaptation in developing countries: The case of the Philippines’, Climate and Development, vol 1, no 2, pp130–146 McGray, H. (2009) Adaptation Planning under a Copenhagen Agreement: Laying a Foundation of the Projects, Policies and Capacities that Countries Need, Working Paper, World Resource Institute, Washington, DC, US McGray, H., A. Hammil and R. Bradley (2007) Weathering the Storm: Options for Framing Adaptation and Development, World Resource Institute, Washington, DC, US OECD (Organisation for Economic Co-operation and Development) (2009) Integrating Climate Change Adaptation into Development Co-operation: Policy Guidance, Paris, France Parry, M. L., O. F. Canziani, J. P. Palutikof et al (2007) ‘Technical summary’, in Parry, M. L., O. F. Canziani, J. P. Palutikof, P. J. van der Linden, and C. E. Hanson (eds) Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK Patt, A. G., D. Schroter, A. C. de la Vega-Leinert and R. J. T. Richard (2009) ‘Vulnerability research assessment to support adaptation and mitigation: Common themes from diversity of approaches’, in Patt, A. G., D. Schroter, R. J. T. Richard and A. C. de la Vega-Leinert (eds) Assessing Vulnerability to Global Environmental Change: Making Research Useful for Adaptation Decision Making and Policy, Earthscan, London, UK Persson, A., R. J. T. Klein, C. K. Siebert, A. Atteridge, B. Muller, J. Hoffmaister, M. Lazarus and T. Takama (2009) Adaptation Finance under a Copenhagen Agreed Outcome, Stockholm Environment Institute, Stockholm, Sweden Ribot, J. C., A. Najam and G. Watson (1996) ‘Climate variation, vulnerability and sustainable development in the semi-arid tropics’, in Ribot, J. C., A. R. Magalhaes and S. Panagides (eds) Climate Variability, Climate Change and Social Vulnerability in the Semi-Arid Tropics, Cambridge University Press, Cambridge, UK Tirpak, D. and M. Ward (2005) The Adaptation Landscape, OECD/IEA, Paris, France UNFCCC (United Nations Framework Convention on Climate Change) (2010) Report of the Conference of the Parties on its Fifteenth Session held in Copenhagen from 7

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to 19 December, FCCC/CP/2009/11/Add.1, http://unfccc.int/resource/docs/ 2009/cop15/eng/11a01.pdf, accessed 30/03/2010 Yohe, G. W., and R. D. Lasco (2007) ‘Perspectives on climate change and sustainability’, in Parry, M. L., O. F. Canziani, J. P. Palutikof, P. J. van der Linden and C. E. Hanson (eds) Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK

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Part 2

Cases of Climate Change Adaptation in Asia

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2.1 Climate Change Projections in Some Asian Countries

Akio Kitoh, Shoji Kusunoki, Yasuo Sato, Nazlee Ferdousi, Mizanur Rahman, Erwin Eka Syahputra Makmur, Ana Liza Solmoro Solis, Winai Chaowiwat and Tran Dinh Trong

Introduction In the Intergovernmental Panel on Climate Change’s (IPCC’s) Fourth Assessment Report, a dataset of more than 20 global coupled atmosphere–ocean general circulation models (AOGCMs) is fully utilized to project future climate change under various scenarios (IPCC, 2007a). These AOGCM simulations were performed under the third phase of the Coupled Model Intercomparison Project (CMIP3) of the World Climate Research Programme (WCRP). These results mitigate the error between models by averaging the output of numerous models. However, the results (especially those related to extreme weather events) can be biased by the low resolution of the models since the models’ horizontal resolution is approximately 100km to 400km. Although these models include large-scale mountains, they cannot represent small-scale mountain ranges and detailed land–sea distributions, and there is also a limit to the representation of mountainous precipitation. As a result, a high spatial resolution model is required to study extreme weather events and to project their modification as a result of climate change for adaptation studies and measures.

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Recently, a super high-resolution atmospheric general circulation model (AGCM) with a horizontal grid size of about 20km has been developed for use in climate change studies (Mizuta et al, 2006), and has been used for climate change projections under increasing atmospheric concentrations of greenhouse gases and aerosols (e.g. Kusunoki et al, 2006; Oouchi et al, 2006). The grid size of this model is several times smaller than previously used in climate model simulations. Kitoh and Kusunoki (2008) evaluated the East Asian summer climate in a present-day climate simulation in comparison with observations, as well as lower resolution versions of the same model. Results have shown that the higher resolution model demonstrates better performance in not only orographic rainfall and weather extremes, but also in model climatology. Yun et al (2008), Rajendran and Kitoh (2008) and Kitoh et al (2008) investigated climate projections at the end of the 21st century with this 20km mesh AGCM over East Asia, India and the Middle East, respectively. Kamiguchi et al (2006) discussed changes in precipitation extremes due to global warming. Although the global 20km model is unique in terms of its horizontal resolution for global change studies, with its integration period of up to 25 years, available computer resources are still insufficient for performing ensemble simulation experiments, limiting its application to single member experiments. In order to compensate for this caveat, parallel experiments with lower resolution versions of the same model (60km, 120km and 180km mesh) are performed. In particular, ensemble simulations with the 60km resolution have been performed and compared with the 20km version. This chapter introduces the model used and some preliminary results of future climate projections for the Asian region. Case studies for five Asian countries (Bangladesh, Indonesia, the Philippines, Thailand and Vietnam) are also shown.

Model and experiment Model The AGCM used in the present study is a global hydrostatic atmospheric general circulation model developed by the Meteorological Research Institute of Japan (MRI) and the Japan Meteorological Agency (JMA). This model was developed from a JMA operational short-term numerical weather prediction model and part of a next generation climate model for long-term climate simulation at MRI. The simulations were performed at a triangular truncation of 959 with a linear Gaussian grid (TL959) in the horizontal, in which the transform grid uses 1920 ⫻ 960 grid cells, corresponding to a grid size of about 20km. The model has 60 layers in the vertical with the model top at 0.1 hectopascals (hPa) (~65km high). The Arakawa-Schubert scheme with prognostic closure is used for the cumulus parameterization. A detailed description of the model and its performance in a ten-year present-day simulation with climatological sea surface temperature (SST) is given in Mizuta et al (2006).

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Experiment design First, we performed Atmospheric Model Intercomparison Project (AMIP) experiments aimed at reproducing the present climate using four different horizontal resolutions of the model: the original 20km mesh version of the model, and the reduced resolution experiments, TL319, TL159 and TL95, which have grid sizes of 60km, 120km and 180km, respectively. All four experiments were run for 25 years during the period of 1979 to 2003 (defined as the present in this study) using the observed monthly SST and sea-ice concentration dataset (HadISST; Rayner et al, 2003). The second set of experiments is the two time-sliced 25-year simulations; these correspond to the near future (2015–2039) and the end of the 21st century (2075–2099). The boundary SST data were prepared by superposing: • • •

the trend in the multi-model ensemble (MME) of SST projected by the CMIP3 multi-model dataset; future change in MME of SST; and the detrended observed SST anomalies for the period 1979–2003.

Future change in MME of SST was evaluated by the difference between the 20th-century simulations and future simulation under the Special Report on Emission Scenario (SRES) A1B emission scenario. Linear trends for future climate simulated by the AOGCMs were also taken into account. A schematic diagram of the boundary SST set-up for the time-slice experiment is presented in Figure 2.1.1. The design retains observed year-to-year variability and El Niño and Southern Oscillation events in the future climate, but with a higher mean and clear increasing trend in SST. Future sea-ice distribution is obtained in a similar fashion. Details of the method are found in Mizuta et al (2008). Preliminary results of these experiments are reported in Kitoh et al (2009). In order to assess the uncertainty of climate change projections, we perform ensemble simulations using the 60km model. Four different SSTs are used in future climate simulations with the 60km model. One experiment uses the CMIP3 model ensemble SST and sea-ice distributions as in the 20km model experiment. The second, third and fourth experiments use the SST anomalies of the CSIRO-Mk3.0, MRI-CGCM2.3.2 and MIROC3.2(hires) models (Mizuta et al, 2008). The global annual mean 25-year averaged SST increase of each model is 0.38 (1.44)°C, 0.49 (1.72)°C and 1.25 (3.52)°C, respectively, in the near future (end of the 21st century). They are 0.73 (2.17)°C for the CMIP3 ensemble mean SST. For each of the standard set of AMIP runs and the CMIP3 SST anomaly runs, three member simulations are performed with different initial conditions.

Observed data For model verification, we used various precipitation datasets. They are the CPC Merged Analysis of Precipitation for 29 years (1979–2007) on a 2.5°

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latitude/longitude grid (CMAP: Xie and Arkin 1997); the Global Precipitation Climatology Project (GPCP) One-Degree Daily Precipitation Data Set for ten years (1998–2007) on a 1.0° latitude/longitude grid (GPCP: Huffman et al, 2001); the Tropical Rainfall Measuring Mission (TRMM) PR3A25 V6 dataset for nine years (1998–2006) on a 0.5° latitude/longitude grid (TRMM 3A25: Iguchi et al, 2000); and Climate Research Unit of University of East Anglia TS2.1 0.5 Degree Monthly Climate Time-Series for 20 years (1979–1998) on a 0.5° latitude/longitude grid (CRU: Mitchell and Jones, 2005).

Present-day simulation Climatological seasonal mean precipitation reproduced in the AMIP experiment is evaluated against observed data. The geographical distributions of June to August (JJA) averaged 25-year (1979–2003) mean precipitation for the 180km, 120km, 60km and 20km models are shown in Plate 1. Four observed climatologies (CMAP, GPCP, CRU and TRMM 3A25) are also shown. Observations show major convection centres over the Bay of Bengal, the South China Sea and the Philippines Sea, as well as over the equatorial Indian Ocean off Sumatra. Due to prevailing westerly winds in the JJA season, Bangladesh, Myanmar and the Philippines experience large precipitation, particularly over their windward sides. This feature becomes clearer as the resolution of data improves from CMAP (2.5°) through GPCP (1.0°) to TRMM (0.5°). The CRU data shows heavy precipitation along the Arakan Mountains in Myanmar, while GPCP and TRMM do not show such a feature and have peak precipitation over the ocean adjacent to the coast, which is in conjunction with the

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observed characteristics discussed by Xie et al (2006). The TRMM data shows a distinct contrast between the land and the ocean, such as Vietnam versus the South China Sea, and the Malay Peninsula versus the ocean to the east. All four resolution versions of the MRI AGCM reproduce the general features of JJA mean precipitation in this region. There are high rainfalls over the western side of mountains of the Malay Peninsula and the Philippines together with leewind side precipitation minima. Compared to the lower resolution versions, the 20km model successfully simulates narrow precipitation bands windward of the mountains and contrasting low precipitation areas in the lee-wind side. The December to February (DJF) mean precipitation climatology of the four observed data and four model realizations are shown in Plate 2. In this season, a major rain area moves southward, and large precipitation is found over the maritime continent area. It is generally dry over the Asian continent and the Indochina Peninsula, but the southern part of China is covered by a medium amount of precipitation. The model reproduces these rainfall distributions well. Due to the prevailing easterly winds, the eastern side of the landmass and islands experiences distinctly higher precipitation, such as in Vietnam and the Philippines. The time–latitude distribution of the monthly mean precipitation averaged between 100°E and 120°E for the four sets of observed data and four model realizations is shown in Plate 3. Note that the CRU data covers the land area only, and direct comparison should be made with caution. A characteristic feature of the seasonal cycle is a gradual southward shift of a large precipitation zone from around 20°N in June to 10°S in February, and a sudden northward jump during May and June. However, the latitude and amount of peak precipitation vary among the datasets. The MRI AGCM reproduces these features fairly well; but the largest precipitation is found around 10°N during July through November, and does not extend to northern latitudes. Moreover, the boreal winter maximum is underestimated.

Climate change projections This section discusses projected changes at the end of the 21st century (2075–2099) compared to the present (1979–2003). The seasonal mean precipitation changes between the present and the end of the 21st century for the 60km model ensembles and the 20km model, for DJF, March to May (MAM), JJA and September to November (SON), respectively, are shown in Plates 4 to 7. For the 60km model, results from the four different SST experiments are averaged. For each SST experiment, there are three realizations with different initial conditions, so the total number of members is 3 in the presentday simulation and 12 in the future simulation. The statistical significance of the differences is calculated as 3 (present) plus 12 (future) members, and areas with significance greater than 95 per cent are shaded in colour. Grid points where all four SST experiments (each being a three-member ensemble mean) agree in the sign of the change are marked with diagonal lines.

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In general, the 20km model results are consistent with the 60km model results. In DJF, both models show increased precipitation over the Indonesian maritime continent, and decreased precipitation over the Indochina Peninsula and the surroundings, particularly over the South China Sea and the northern Bay of Bengal. In the maritime continent, significant precipitation increase is found over the islands, including Sumatra, Kalimantan and Papua New Guinea. In MAM, the spatial pattern of precipitation change is very similar to that in DJF, with higher areas over the maritime continent region and lower areas over the Philippines and the South China Sea. Southern China experiences a spring rain season, and will have more precipitation in the future. In JJA, southern China continues to have increased precipitation. The 20km model shows an increase in precipitation over the Indochina Peninsula; but the 60km model does not show significant change in this region. From MAM to JJA, the sign of precipitation change is opposite over the maritime continent, where a significant decrease in precipitation is projected, particularly to the south of the equator. This is the dry season over islands such as Java. Our results show more rainfall in the rainy season (DJF) and less rainfall in the dry season (JJA) in this area, thus implying an increase in the risk of both flooding and drought. In SON, the model projects a latitudinal band-like structure of change with a decrease (south of the equator), an increase (equator to 10°N) and a decrease (between 10°N and 20°N) in precipitation. It is noted that even in the latitudinal band of decreasing precipitation, precipitation is projected to increase over the land (e.g. Indochina Peninsula and the Philippines), in contrast with the adjacent ocean regions. This land–sea contrast is also seen in other seasons. Global warming will likely result not only in changes in mean precipitation, but also in the amplitude and frequency of extreme precipitation events. It is now well recognized that such changes in extremes are more important when considering our adaptation to climate changes. In Table 2.1.1, three indices for precipitation are used to illustrate changes in precipitation extremes: two for heavy precipitation and one for dryness (definitions are summarized for later sections). The changes in the simple daily intensity index (SDII) for the 60km and 20km models are shown in Plate 8. The SDII is defined as the annual total Table 2.1.1 Extreme indices of precipitation Index

Unit

Definition

CDD

days

R5d

mm

SDII

mm/day

Consecutive dry days: the annual maximum number of consecutive dry days with rainfall amount of less than 1mm/day Maximum five-day precipitation total: the annual maximum consecutive five-day precipitation total Simple daily intensity index: total annual precipitation divided by the number of days with 艌 1mm/day

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precipitation divided by the number of rainy days, where a ‘rainy day’ is defined as a day with precipitation greater than or equal to 1mm/day. We also plotted the results for the near future. In the near future, the SDII shows an increase over the maritime continent and southern China. At the end of the 21st century, the SDII becomes larger and increases more significantly over the land areas, increasing almost everywhere with the exception of some oceanic areas. The changes in maximum five-day precipitation total (R5d) for the 60km and 20km models are shown in Plate 9. In the near future, significant change is only seen over some oceanic areas. At the end of the 21st century, with increased warming, R5d shows significant increases almost everywhere except for over the South China Sea. As for the SDII, changes in R5d are also larger and more significant over land. For the R5d changes, some differences are found between the 60km and 20km model results. The 20km model projects a larger increase in R5d than the 60km model. The changes in the maximum number of consecutive dry days (CDD) for the 60km and 20km models are shown in Plate 10. Here ‘dry day’ is defined as a day with precipitation less than 1mm/day. There is little change in CDD in the near future; but an increase of CDD over Southeast Asia at the end of the 21st century is shown. The 20km model shows some hint of a decrease of CDD over China. Actually, both models show a decrease of CDD in North Asia (not shown). Overall, changes in precipitation-based extreme indices show an increase in both heavy precipitation and the length of the dry season over South Asia. This result urges relevant adaptation measures to climate change for disaster prevention as well as agriculture in affected countries. We have performed ensemble simulations with the 60km model using different future scenarios for sea surface temperature changes; but the single model was used in the present study. Multi-model or multi-physics with a single model approach should give us more information on the uncertainties associated with such climate change projections, and such an attempt should be made.

Case studies Bangladesh Due to its low-lying topography, funnel-shaped coast, vulnerable geographical location and high population density (839 persons/km2) (BBS, 2003), Bangladesh is highly vulnerable to natural disasters. Given its geography and geomorphology, Bangladesh is repeatedly affected almost every year by events such as tropical cyclones, storm surges, monsoons, heavy rain, floods, rainfall deficits, severe thunderstorms, tornadoes and weather-induced landslides. Therefore, a large number of people are affected by these meteorological or meteorology-related events, causing huge loss of life and damage to property. Significant numbers of people will be at risk from rising sea levels, storm surges

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and river flooding in the Asian mega deltas, such as the Ganges-Brahmaputra, the Mekong and the Chao Phraya, due to global warming (New Nation, 2007). Loss of life and livestock, as well as damage to agricultural crops due to these meteorological events, cannot be avoided, but can be lessened by using appropriate projections/warnings with enough lead time. Thus, well-timed and enhanced predictions of weather and climate with a higher degree of accuracy are of great social and economic importance. Climate models are the main tools available for the future development of projections concerning climate change (Islam, 2009). This case study examines the likely future change of rainfall climatology in Bangladesh resulting from a super high-resolution atmospheric general circulation model (AGCM) for the baseline period of 1979 to 2003 and a future period of 2075 to 2099.

Data Observed data This study draws on historical data from 30 observatories (both solid and open circles in Figure 2.1.2) belonging to the Bangladesh Meteorological Department (BMD) during 1973 to 2003. The numbers attached to each station show their elevation. The highest elevations of stations with open and solid circles are 63m and 36m, respectively. Thus, it is clear that Bangladesh has a very flat topography. The solid circles represent the stations used in statistical analysis. Both the solid and open circles represent stations that have been employed in the spatial analysis to understand the climatic pattern of rainfall over Bangladesh. Model data AGCM data has been used to generate rainfall scenarios for Bangladesh for two time periods: the present (1979–2003) and the future (2075–2099). A simple mathematically averaged value over Bangladesh using ten grid points of model data (i.e. the nearest points to the ten observatories of BMD shown as solid circles in Figure 2.1.2) has been used for statistical analysis.

Present-day climate simulation and verification – rainfall Since Bangladesh shows maximum rainfall during the monsoon for June to September (JJAS) season (Figure 2.1.3), the rainfall climatology of this season during 1979 to 2003 is compared with AGCM-generated data. According to Plate 11 (a) and (b), the model data show that the heavy rainfall zone is located in the north-eastern part of Bangladesh. It also shows that the western part of Bangladesh receives less rainfall than the northeast. The model, however, could not capture the rainfall pattern of the south-eastern coastal region of the country. Thus, except for the southernmost part of the country, the model simulations are quite similar to the natural climatology pattern of Bangladesh.

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Table 2.1.2 Summary of correlation coefficients (R) and root mean square errors (RMSEs) of rainfall data between the 20km model and station data for the regions of Thailand Region FMA Central Eastern Northeast North Southeast Southwest Average

0.26 0.22 –0.15 0.59 0.18 0.55 0.28

Correlation coefficient (R) MJJ ASO NDJ 0.43 0.01 0.06 0.02 0.56 –0.38 0.12

0.42 0.33 0.30 –0.16 –0.02 0.26 0.19

0.42 0.42 0.24 –0.01 0.21 0.31 0.27

Root mean square error (RMSE) FMA MJJ ASO NDJ 202.96 150.92 155.91 157.64 221.36 111.59 166.73

286.92 116.33 118.65 363.05 93.33 225.36 200.60

113.77 123.28 104.09 178.17 137.26 272.23 154.80

45.43 37.20 20.39 44.64 356.02 121.57 104.21

Note: FMA = February, March, April; MJJ = May, June, July; ASO = August, September, October; NDJ = November, December, January. Source: chapter authors

region is shown in Figure 2.1.16. The 20km resolution model captured the corresponding rainfall in the August to October (ASO) and November to January (NDJ) seasons, whereas the model has quantitative disagreement in the other seasons: February to April (FMA) and May to July (MJJ). A summary of the R values and the RMSE of seasonal average rainfall data in the regional areas of Thailand between observed station data and modelled data is shown in Table 2.1.2. For the central region, this table shows higher correlations for three seasons, except FMA, of 0.43, 0.42 and 0.42, and RMSEs of 286.92mm, 113.77mm and 45.43mm, respectively. The southwestern region shows a higher correlation of 0.55 in FMA and a RMSE of 111.59mm. Moreover, the northern region shows a high correlation, with 0.59 in FMA, and a RMSE of 157.64mm. However, the 20km model gives lower correlations in the north-eastern region in all seasons. A summary of the R values and RMSEs of seasonal average temperature data between the 20km model and observed station data is shown in Table 2.1.3 Summary of correlation coefficients and root mean square errors (RMSEs) of temperature data between the 20km model and station data for regions of Thailand Region FMA Central Eastern North-east North South-east South-west Average

0.29 0.50 0.18 –0.01 0.55 0.71 0.37

Correlation coefficient (R) MJJ ASO NDJ 0.64 0.72 0.75 0.44 0.64 0.68 0.65

0.70 0.78 0.55 0.58 0.54 0.33 0.58

0.46 0.50 0.44 0.37 0.58 0.53 0.48

Root mean square error (RMSE) FMA MJJ ASO NDJ 1.22 1.72 1.52 2.19 2.00 2.81 1.91

3.28 2.57 2.92 2.58 2.77 1.99 2.68

2.50 2.23 2.15 1.75 2.59 2.01 2.20

1.48 1.88 1.50 1.43 1.53 2.24 1.68

Note: FMA = February, March, April; MJJ = May, June, July; ASO = August, September, October; NDJ = November, December, January. Source: chapter authors

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Table 2.1.3. The 20km model has a slightly larger R value of more than 0.5, except in FMA, and the RMSE values are in the range of 1.68°C to 2.68°C, on average, for all of Thailand.

Climate change projections Climate at the end of the 21st century (2075–2099) In order to remove model biases in the future precipitation scenario, the observed data and the differences between the future and present data were used to estimate future climate scenarios in each season using the following expression: Pfuture = Pobs + (Pfuture – Ppresent)

[2.1.1]

where Pobs is the observed rainfall data, Pfuture is the 20km model future rainfall data and Ppresent is the 20km model present rainfall data. At the end of the 21st century (2075–2099), the model projects a significant increase in seasonal precipitation of 10 to 20 per cent (Plate 24) in most areas where rainfall is strong during the monsoon season. However, in the mountainous area of the northern region and parts of north-east Thailand, a future decrease in rainfall has been projected. So far, future scenarios for the Thailand summer monsoon rainfall, even using high-resolution regional climate models, have projected relatively uniform climate change. In summary, the projected changes indicate an overall intensification of the all-Thailand monsoon rainfall, with strong regional modulations in the future, in response to the anticipated increase in GHG concentration and resultant widespread warming of surface temperature. Consistent with the projected increase in seasonal mean monsoon rainfall over most parts of the country, the mean annual variations of present and future all-Thailand rainfall (Figure 2.1.17) also clearly show the overall intensification of rainfall, not only during the summer monsoon season (May through October). The annual variations in both climates are in sync with a systematic quantitative future enhancement except during the winter season. This is consistent with current understanding that the rainfall over Southeast Asia will decrease during January and February and increase during the rest of the year under global warming. This difference in the annual rainfall cycle is associated with a corresponding systematic intensification of the annual cycle of surface temperature in the future (Figure 2.1.17). Nevertheless, the pattern of precipitation change is uneven, with distinct spatial heterogeneity (Plate 24). Different factors can contribute to the change in the hydrological cycle due to climate warming. An increase in the saturationspecific humidity or the capacity to hold water vapour, with a rise in temperature, can increase the actual water vapour content in the atmosphere. Thus, if the mean residence time of water vapour in the atmosphere does not change, both precipitation and evaporation would increase with an exponen-

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360

33

300

30

240

27

l!c:

. U

E E

~-

...,.

.... ~

180

24

120

21

60

18

0

15

'; a::

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

...E

~

Dec

• Present (1979-2003) • Future (2075-2099)

Figure 2.1.17 Climatological annual cycles of rainfall and surface temperature over Thailand from the present-day simulation and the end of the 21st-century projection using the 20km model Source: chapter authors

tial dependence on temperature. However, regional factors which are resolvable at the 20km resolution, such as the availability of surface moisture, horizontal convergence/divergence of airflow in the lower atmosphere, and orography modulate the concentration of water vapour evaporated from the surface or transported into the area. Thus, the resultant spatial distribution of atmospheric moisture and precipitation changes can be uneven. The changes in seasonal mean precipitation at the end of the 21st century (2075–2099) found using the 20km mesh AGCM are shown in Plate 24. In the dry season (FMA; not shown), most of Thailand will experience decreased rainfall except the northern region; the eastern part of the northeast will experience increased rainfall. The pattern is different from that of the summer monsoon season (MJJ) when most of Thailand is projected to experience increased precipitation. There are some small belts of decreased precipitation over the mountainous area of the northern region and the lower part of the central region, which is more evident in winter (not shown). In the wet season (ASO), there is an increase in precipitation over most of Thailand, while a decrease is evident to the east, the eastern part of the central region and the western part of the northeast region. A large decrease in precipitation is found in winter (NDJ; not shown) over almost the entire north-eastern region, particularly over the western mountainous area. An area experiencing a large

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precipitation decrease of over 20 per cent has been noted to extend into the Upper Chi River Basin around the Phetchaboon and Dongprayayen mountains. The eastern coastal region, the central part of the central region and the lower part of the southern region also experience increased precipitation. Extreme weather events Global warming can affect extreme precipitation in a number of ways, including changes in frequency, intensity and timing of occurrence. Heavy precipitation episodes with subsequent surface runoff can result in catastrophic property damage and loss of human life. Thus, it is important to determine how the character of such events could change in response to GHG-induced global warming. Recent studies have shown that there are positive trends in the frequency of hot events, and heavy or, in some places, reduced precipitation events. However, it is not currently clear in what ways fine-scale feedbacks will modulate regional and local responses to such large-scale changes. Therefore, the impact of climate warming upon the regional distribution of extreme precipitation events in Thailand was investigated using the 20km model simulation. In the future projection of extreme events using climate models, intercomparison of different models is very important in order to estimate the uncertainty of results on an equal basis. Among several statistical methods used to diagnose extreme precipitation events, we focused on ‘extreme indices’ proposed by Frich et al (2002), which are widely used in recent studies and were adopted as the Intergovernmental Panel on Climate Change (IPCC) standard output data for the IPCC Fourth Assessment Report (IPCC, 2007a). Here, we mention two extreme indices: consecutive dry days (CDD) and maximum five-day precipitation total (R5d) (see Table 2.1.1). These two indices are derived for each year, and their 25-year average are compared between present and future climate simulations. Land grid results are presented here. CDD (Plate 25, left), a dry spell index, generally increases in areas where the current value is high. On the other hand, CDD decreases in areas such as the lower central and lower northeast region. The results imply that although there are some arid areas in which the length of the dry spell decreases, dryness generally become more severe in regions where these phenomena are currently severe. For severe precipitation, total maximum five-day precipitation (R5d) in the northern part of the northeast region, the southwest, north and the central regions were associated with positive changes of about 10mm to 30mm. In contrast, the western part of the northeast and the southeast regions were associated with negative changes of about 10mm to 20mm (Plate 25, right).

Summary Verification of the model based on observations of annual mean rainfall found that the rainfall simulation corresponds with observations in the mountainous area of the eastern region, the southern region and the north-eastern region of

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Thailand. On a seasonal mean rainfall basis, the 20km model yields higher correlations in the northern and southern regions in the dry season (FMA). In contrast, the 20km model gives lower correlations for rainfall in the summer monsoon season (MJJ) and the wet season (ASO), except over a specific region, so that the model bias should be corrected before future projections are made. For the temperature simulation, the 20km model provides higher correlations in all seasons over most of Thailand, except the central, north-eastern and northern regions in the dry season (FMA). For the simulated climate at the end of the 21st century (2075–2099), the model projects a significant increase in precipitation of 10 to 20 per cent in most of the areas where rainfall is already strong during the monsoon season. Furthermore, in the mountainous area of the northern region and parts of the northeast of Thailand, a future decrease in rainfall is projected. Mean temperatures are significantly higher by 2.5°C to 3.0°C over the whole of Thailand, and higher significant changes are predicted in the northern and central regions. For the simulated climate in the near future period (2015–2039), the model projects changes of one half or two-thirds of the magnitude of the changes in precipitation and temperature for end of 21st-century results. The spatial patterns of change are similar to the case at the end of the 21st century. With regard to extreme precipitation events at the end of the 21st century (2075–2099), the model results show that most regions of Thailand are associated with positive changes except the coastal areas of the eastern region. Increases in the length of the dry spell (CDD) occur in areas where the current value is high; there is also an increase in heavy precipitation, so that dryness and heavy precipitation generally become more severe in regions where these phenomena are already extreme.

Vietnam With the cooperation of Japan and other Asian countries, Vietnam was invited to take part in a three-year training programme (2008–2010) to assess the impacts of climate change in order to develop adaptation strategies (JICA, 2008). Several recent studies on climate change in Vietnam that use a combination of statistical methods, expert analysis and dynamic models have been published (MoNRE, 2003; Hoang and Tran, 2006; Le, 2006). In addition, a study using Providing Regional Climates for Impacts Studies (PRECIS), which is based on the Hadley Centre’s regional climate modelling system developed by the Hadley Centre/UK Meteorological Office, with reasonable ability to represent the spatial distribution of precipitation and temperature over Vietnam, has been implemented by the Vietnam Institute of Meteorology, Hydrology and Environment, applying the SRES A2 and B2 emission scenarios of the IPCC. The main findings of these studies on the future climate of Vietnam are as follows:

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•

53

An increase of about 0.1°C per decade in terms of annual average temperature is likely, which is also higher in some summer months (about 0.1°C to 0.3°C per decade). Precipitation rates are complex, and are region and season specific. In most territories, the rainfall amount declines in July and August and is higher in September, October and November. Drizzling rain clearly decreases in north and north-central Vietnam (MoNRE, 2003). Studies of climate change using GCMs and regional models are essential; since the results available for Vietnam are still limited, this warrants more in-depth research.

This section presents and addresses a future climate change scenario for Vietnam that was acquired after extracting and analysing the projection results of the Meteorological Research Institute of Japan (MRI) atmospheric general circulation model (AGCM) with a 20km mesh and the Special Report on Emission Scenario (SRES) A1B emission scenario during the Japan International Cooperation Agency (JICA) training programme in November and December 2008 and May and June 2009.

Observed data The Global Precipitation Climatology Project (GPCP) daily precipitation data set for seven years (1997–2003) on a 1° latitude/longitude grid was used for precipitation verification. The Japanese 25-Year Reanalysis (JRA-25) data, which is long-term global atmospheric reanalysis data covering 26 years from 1979 to 2004 (Onogi et al, 2007), was used for temperature verification. In addition, the monthly mean rainfall amount and average temperature during 1979–2007 from 45 meteorological stations distributed throughout Vietnam were also used to verify the model.

Present-day simulation Geography and climate of Vietnam A large area of Vietnam is covered by mountains and hills, with elevations mostly between 100m to 1000m. The plains are concentrated in the downstream reaches of two major rivers: the Red River and the Mekong River. The river network in Vietnam is rather dense, with 2360 rivers. The average density is 0.6km/km2. Vietnam is topographically divided into seven regions listed from north to south: North Mountain region in the north; Red River Delta region; north-central region; south-central coast region; central highland region; northeast of the south region; Mekong River Delta region. Generally speaking, Vietnam has a tropical monsoon climate, typified by heat and humidity. The country, however, straddles a wide range of latitudes and therefore experiences a range of climates even within the tropical monsoon climate. In the northern parts of Vietnam, the humidity has greater seasonal variation, whereas the southern regions of the country located close to the

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equator experience a tropical climate. The majority of rainfall in Vietnam is caused by monsoon circulation. The rainy season is complex, and spans from June to August in the north, August to October in the central regions, and June to November in the south (with an extended period one month earlier and/or later). Monthly mean rainfall in the rainy season is 200mm to 400mm. Total rainfall in the rainy season accounts for 80 to 90 per cent of annual rainfall. The long coastline of Vietnam is also vulnerable to tropical cyclones originating in the Northwest Pacific between June and November (with a frequency of four to five tropical cyclones annually), which contribute significantly to wet season rainfall totals. In several regions, the rainfall distribution is not even throughout the year, and floods and inundation occur during the rainy season, while droughts are often recorded in the dry season. The northern regions experience more distinct seasonal variations in average temperature than the south. In the northern provinces, average temperatures drop to 15°C–20°C in winter from the average summer temperatures of 22.5°C–27.5°C, while average temperature in the south drops to 26°C–27°C from 28°C–29°C. Inter-annual variations in climate are partly caused by the El Niño Southern Oscillation. El Niño episodes influence the behaviour of the monsoons in this region, and generally bring warmer and drier-than-average winter conditions across Southeast Asia, while La Niña episodes bring cooler than average summers (MoNRE, 2003). Precipitation verification The MRI AGCM realistically simulates the rainfall over Vietnam in terms of spatial and temporal distribution. The rainfall simulated by the model is shown in Figure 2.1.18, while the observed rainfall is shown in Figure 2.1.19. The rainfall season starts mainly from early June (sometimes from late May) in the north, gradually moving to the south. During the rainy season, the model captures an important aspect of the country’s rainfall distribution: the large rainfall centre located in the North Mountain region. As the wet season progresses, the rainfall centre moves southwards to the south-central coast region (Figures 2.1.18 and 2.1.19). The MRI-AGCM reproduces the important role of monsoon circulation in the seasonal distribution of precipitation. The sudden increase in monthly precipitation associated with the onset phase, persistence of intense rainfall from June to August in the north, from August to November in the central regions, and from June to November in the south are well reproduced. The reduction in precipitation after the withdrawal of monsoon circulation is also well captured. The model also produces appropriate values for the rainfall amount; however, it tends to overestimate precipitation during the rainy season in some parts of the central regions (e.g. Tamky in Figure 2.1.20 (b)). On the other hand, some features are not well captured by the model where the topography is complicated. For example, rainfall at Nhatrang station, which is near the South China Sea and is blocked from the north by a

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Precip (m m /d a y ) 1 9 7 9 — 200 3 2 0 — km model (a )

M o n th = 6 — 8

(b )

25N-

25N •

20N-

20N •

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h

1 10 ■

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•

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100E

105E

110E

100E

105E

110E

Figure 2.1.18 Simulated seasonal mean precipitation by 20km model for 1979–2003: (a) June–August; (b) September–November Source: chapter authors

high mountain pass named Deo Ca, is not well simulated by the model (Figure 2.1.20 (d)). Areas with poor simulation results should be studied in detail in order to correct the bias. This study, however, only provides initial comments on this issue and focuses more on future projection. Precip ( m m / d a y ) (a)

1 9 9 7 - 2 0 0 3 OBS GPCP 1DD

W onth=6—8

(b)

25N

25N ■

20 N

20N ■

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-- --,--

M o n th = 9 -1 1

Ai. 1 10 -

8

-

6

15N-

- 4

10N-

r

-T -~

1( 100E

105E

110E

100E

105E

110E

Figure 2.1.19 Observed seasonal mean precipitation by the Global Precipitation Climatology Project (GPCP) 1DD for 1997–2003: (a) June–August; (b) September–November Source: chapter authors

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20 18 16 14 12 10 8 6 4 2 0

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Precipitation (mm/day) Lon=108.47 Lat=15.57 TAMKY

40 36 32 28 24 20 16 12 8 4 0

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Precipitation (mm/day) Lon=107.08 Lat=10.37 VUNGTAU

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20 18 16 14 12 10 8 6 4 2 0

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6 7 Month

8

Figure 2.1.20 Yearly rainfall rates of observed (solid lines) and simulated (dashed lines) at: (a) North (Caobang) Vietnam; (b) Middle (Tamky) Vietnam; (c) South (Vungtau) Vietnam; and (d) Nhatrang, Vietnam Source: chapter authors

Climate change projections Precipitation This section analyses and discusses projected climate changes at the end of the 21st century (2075–2099) compared to the present (1979–2003) using the MRI outputs. During the rainy season, the model predicts changes in precipitation that vary from region to region (Plate 26). An increase of precipitation by 10 to 20 per cent is projected for a large area in the Red River and Mekong River deltas. The remaining areas, including the central highland and south-central regions, are projected to experience a decrease in rainfall, especially in the Phu Yen, Khanh Hoa, Ninh Thuan and Binh Thuan provinces (along the south-central coast). Increases in rainfall over the two largest river deltas where floods often occur, and decreases along the south-central coast, which is considered the driest area in Vietnam, mean that precipitation is projected to become more uneven and variable over time and space under climate change. Further analysis of the rainy season months shows an agreement between monthly and seasonal variations in each region. For example, the projected increase in precipitation in the Red River Delta and in the south will occur during the early months of the rainy season (May to July), whereas the decrease in the central highlands and the south-central region will occur throughout the rainy season (Plate 26). A general estimation for the whole country, however, reveals that monthly mean rainfall decreases in May, June

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and August, and increases in July and September to November (Plates 26 and 27). A small disagreement between this finding and that of the Ministry of Natural Resources and Environment is that the ministry found rainfall to decrease in July (MoNRE, 2003). In addition, dissimilarity exists between the MRI precipitation outputs in this study and that of the MoNRE in terms of percentage change of rainfall. The MoNRE projects a small increase of up to 8.8 per cent (MoNRE, 2003), while the model in this study projects a more significant increase of about 20 per cent by the end of 21st century. However, the MRI result is consistent with those published in the United Nations Development Programme (UNDP) report (McSweeney et al, 2008). During the dry season, there is a significant difference in the overall pattern of rainfall compared to rainfall during the rainy season. An opposite trend in precipitation changes is evidenced in the North Mountain region and downstream of the Mekong River Delta. The decline in rainfall in these regions is around 10 per cent, reaching more than 20 per cent in Tra Vinh and Ben Tre provinces (located in the Mekong River Delta). The MRI model also reveals a significant decrease in the rainy season – more than 20 per cent in Dac Nong (in the central highland region), and in Phu Yen and Binh Thuan (along the south central coast) provinces (Plate 26 and Plate 27, left). Climate change, therefore, exacerbates drought conditions in the central highland and the south-central coast regions. According to MoNRE (2003), drizzling rain, a characteristic of the northern climate during the dry season, clearly decreases in north and north-central Vietnam, resulting in a decline in precipitation. In contrast, the rainfall amount is projected by the model to increase during February and March in north and north-central Vietnam (Plate 28). Temperature Annual average temperature is projected by the MRI model to increase over the whole country. By the end of the 21st century, the annual average temperature is 2.0°C to 3.0°C higher than at present (Plate 29). This means that the annual average temperature increases by 0.2°C to 0.3°C per decade. In comparison with the spatial distribution of precipitation, temperature change occurs reasonably uniformly over the whole country, with the highest increase of 2.5°C to 3°C in the south, the central highland region and the northwest of the north, whereas the remaining two-thirds of the country experience an increase of 2.0°C to 2.5°C. In short, the MRI model projects that average temperature will increase over the entire country, with a different rate of warming during different seasons and in different regions. The rate of warming in summer is higher than that in winter, consistent with the current findings of MoNRE (2003). However, the rate of warming in the south is often 0.5°C higher than that of the north, inconsistent with the MoNRE findings in 2003. The MRI model’s results may be explained by the following factors:

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The south is at a lower latitude (closer to the equator), and thus receives more solar radiative energy than the north. The south is drier and less energy is consumed by evaporation. Consequently, the rate of warming in the south is higher than that of the north as the incoming energy in the south amplifies direct heating by decreasing latent cooling.

Projection of extreme weather events Recent studies for Vietnam (MoNRE, 2003; UNDP, 2007; McSweeney et al, 2008) have shown, in different ways, that there are positive trends in the frequency of hot events and heavy rain under climate change, or, in some places, reduced precipitation. General agreement exists between these studies as to changes in reported floods, droughts and typhoon patterns. However, some differences exist, which result for a variety of reasons (e.g. from the different emission scenarios chosen); therefore, it is necessary to study extreme weather event changes in greater detail using a variety of methods. Extreme events, such as typhoons, are poorly captured by GCMs; thus, potential changes in future frequency and intensity are very uncertain. As a result, this section focuses on ‘extreme indices’ proposed by Frich et al (2002), which are widely used in recent studies and are adopted as the IPCC standard output data for the IPCC Fourth Assessment Report (IPCC, 2007a; see Table 2.1.1). Maps of changes in extreme precipitation indices between the future climate and the present-day climate using the 20km model are shown in Plate 30. The dry spell index (CDD) increases up to 20 per cent in the south, with slight decreases in the North Mountain region (Plate 30 (a)). On the other hand, heavy rain indices (SDII, R5d) change extensively over the country. Both heavy rain indices decrease in the coastal zone from Quang Binh Province southwards to Binh Thuan Province, and increase over the remainder of the country (Plate 30 (b) and (c)).

Summary The MRI AGCM realistically simulates spatial and temporal distribution in temperature and rainfall over Vietnam. The distinct seasonal variations in average temperature are well captured and compare well with the observed data. Moreover, precipitation characteristics are well predicted by the model over most of the country. However, the model does not reproduce some features in certain areas, especially in locations with complex topography. In these areas, the bias of the model, which is not discussed in this chapter, should be studied in detail and an adjustment or correction should be applied to future projections. Under the IPCC’s A1B emission scenario, the super high-resolution future scenario for the Vietnamese climate shows a projection of spatially and temporally varying precipitation. During the rainy season, an increase in precipitation of 10 to 20 per cent is projected over a large area of the Red River

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Delta and the Mekong River Delta. The rainfall in the remaining areas, including the central highland and the south-central coast regions, is projected to decrease, with profound decreases in Phu Yen, Khanh Hoa, Ninh Thuan and Binh Thuan provinces. The increase in the two largest river deltas where floods often occur and the decrease along the south-central coast, which is already considered the driest area in Vietnam, make precipitation more variable and uneven over time and space. During the rainy season, the monthly rainfall is projected to decrease in May, June and August, and to increase in July, and from September to November. This result is consistent with the MoNRE’s result, except for the trend of rainfall in July. In terms of rainfall intensity, changes indicated by the model can reach 20 per cent with a positive or negative value depending upon the area and season. This figure is higher than the MoNRE result, but consistent with the UNDP report (McSweeney et al, 2008). During the dry season, the model outputs show a rainfall deficit in many areas except parts of north and north-central Vietnam. In part of the north and north-central areas, the model projects an increase of drizzling rain in February and March, which is not consistent with the MoNRE result. In the central highland and the south-central coast areas, rainfall amount is predicted to decline by about 10 per cent, and sometimes more than 20 per cent. Therefore, climate change leads to an exacerbation of drought problems in these areas. For the temperature simulation, annual average temperature is projected to increase over the whole country with different rates of warming in different seasons and regions of Vietnam. The rate of warming in summer is higher than that of winter, and the rate of warming in the south is often 0.5°C higher than that of the north. The model projects significant spatially heterogeneous change in both heavy precipitation and extreme hot events (not shown in the chapter) over the country at the end of the 21st century. Areas experiencing a positive trend overwhelm those with a negative trend in terms of heavy rainfall, whereas there is only a positive trend for extreme temperature. However, the overall pattern of extreme events is likely to increase over the whole country.

Acknowledgements The research for this chapter has been conducted by the Meteorological Research Institute (MRI)/Japan Meteorological Agency (JMA)/Advanced Earth Science and Technology Organization (AESTO) group as the Projection of the Change in the Future Weather Extreme Using Super-High-Resolution Atmospheric Models under the framework of the Innovative Programme of Climate Change Projection for the 21st Century (KAKUSHIN) funded by the Ministry of Education, Sports, Culture, Science and Technology of Japan (MEXT). The 20km and 60km model simulations were made on the Earth Simulator of the Japan Agency for Marine–Earth Science and Technology (JAMSTEC). Lower-resolution model simulations were made at the MRI.

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The case studies in this chapter originally appeared as working papers of the JICA training programme, Capacity Development for Adaptation to Climate Change in Asia – Climate Change Analysis, which was implemented at MRI, Tsukuba, Japan, and funded by JICA. The Bangladesh Meteorological Department (BMD), Thailand Meteorological Department and Royal Irrigation Department, Thailand, are acknowledged for providing station climate datasets. Thanks by one of our trainees (Ana Solis) are extended to colleagues from the Climate Monitoring and Prediction Section/Climatology and Agrometeorology Division of PAGASA-DOST. Further, Winai Chaowiwat was supported by the Thailand Research Fund and Faculty of Engineering, Chulalongkorn University. Finally, we also thank all workshop participants for their cooperation during the training programme.

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Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds), Cambridge University Press, Cambridge, UK IPCC (2007b) Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden and C. E. Hanson (eds) Cambridge University Press, Cambridge, UK Islam, M. N. (2009) ‘Rainfall and temperature scenario for Bangladesh’, The Open Atmospheric Science Journal, vol 3, pp93–103 JICA (Japan International Cooperation Agency) (2008) Capacity Development for Adaptation to Climate Change in Asia – Climate Change Analysis. JICA General Information No J08-04201, Japan Kamiguchi, K., A. Kitoh, T. Uchiyama, R. Mizuta and A. Noda (2006) ‘Changes in precipitation-based extremes indices due to global warming projected by a global 20km-mesh atmospheric model’, SOLA, vol 2, pp64–67 Kitoh, A., S. Yukimoto, A. Noda and T. Motoi (1997) ‘Simulated changes in the Asian summer monsoon at times of increased atmospheric CO2’, J. Meteorol. Soc. Japan., vol 75, pp1019–1031 Kitoh, A. and S. Kusunoki (2008) ‘East Asian summer monsoon simulation by a 20km mesh AGCM’, Clim. Dyn., vol 31, pp389–401 Kitoh, A., A. Yatagai and P. Alpert (2008) ‘First super-high-resolution model projection that the ancient “Fertile Crescent” will disappear in this century’, Hydrological Research Letters, vol 2, pp1–4 Kitoh, A., T. Ose, K. Kurihara, S. Kusunoki, M. Sugi and KAKUSHIN Team-3 Modelling Group (2009) ‘Projection of changes in future weather extremes using super-high-resolution global and regional atmospheric models in the KAKUSHIN Program: Results of preliminary experiments’, Hydrological Research Letters, vol 3, pp49–53 Kusunoki, S., J. Yoshimura, H. Yoshimura, A. Noda, K. Oouchi and R. Mizuta (2006) ‘Change of Baiu rain band in global warming projection by an atmospheric general circulation model with a 20km grid size’, J. Meteor. Soc. Japan, vol 84, pp581–611 Lal, M., T. Nozawa, S. Emori, H. Harasawa, K. Takahashi, M. Kimoto, A. Abe-Ouchi, T. Nakajima, T. Takemura and A. Numaguti (2001) ‘Future climate change: Implications for Indian summer monsoon and its variability’, Current Science, vol 81, pp1196–1207 Le, N. T. (2006) Climate Change and Activities in Vietnam, MoNRE, Hanoi, Vietnam McSweeney, C., M. New and G. Lizcano (2008) UNDP Climate Change Country Profiles: Vietnam, UNDP, New York, US Mitchell, T. D. and R. G. Jones (2005) ‘An improved method of constructing a database of monthly climate observations and associated high-resolution grids’, Int. J. Climatol., vol 25, pp693–712 Mizuta, R., K. Oouchi, H. Yoshimura, A. Noda, K. Katayama, S. Yukimoto, M. Hosaka, S. Kusunoki, H. Kawai and M. Nakagawa (2006) ‘20km-mesh global climate simulations using JMA-GSM model – Mean climate states’, J. Meteor. Soc. Japan, vol 84, pp165–185 Mizuta, R., Y. Adachi, S. Yukimoto and S. Kusunoki (2008) ‘Estimation of the future distribution of sea surface temperature and sea ice using the CMIP3 multi-model ensemble mean’, Technical Report of the Meteorological Research Institute, no 56

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MoNRE (Vietnamese Ministry of Natural Resources and Environment) (2003) Vietnam Initial National Communication under the United Nations Framework Convention on Climate Change, MoNRE, Hanoi, Vietnam New Nation (2007) ‘Global warming poses threat to Bangladesh’, New Nation, 6 April 2007 Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H. Hatsushika, T. Matsumoto, N. Yamazaki, H. Kamahori, K. Takahashi, S. Kadokura, K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji and R. Taira (2007) ‘The JRA-25 Reanalysis’, J. Meteor. Soc. Japan, vol 85, pp369–432 Oouchi, K., J. Yoshimura, H. Yoshimura, R. Mizuta, S. Kusunoki and A. Noda (2006) ‘Tropical cyclone climatology in a global-warming climate as simulated in a 20 kmmesh global atmospheric model: Frequency and wind intensity analyses’, J. Meteor. Soc. Japan, vol 84, pp259–276 Rajendran, K. and A. Kitoh (2008) ‘Indian summer monsoon in future climate projection by a super high-resolution global model’, Current Science, vol 95, pp1560–1569 Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent and A. Kaplan (2003) ‘Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century’, J. Geophys. Res., vol 108, p4407 Shepard, D. (1968) ‘A two-dimensional interpolation function for irregularly spaced data’, in Proceedings of 23rd National Conference, ACM, New York, pp517–523 UNDP (United Nations Development Programme) (2007) Human Development Report 2007/2008, UNDP, New York, US Xie, P. and P. A. Arkin (1997) ‘Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs’, Bull. Am. Meteorol. Soc., vol 78, pp2539–2558 Xie, S. P., H. Xu, N. H. Saji, Y. Wang and W. T. Liu (2006) ‘Role of narrow mountains in large-scale organization of Asian monsoon convection’, J. Climate, vol 19, pp3420–3429 Yatagai, A., O. Arakawa, K. Kamiguchi, H. Kawamoto, M. I. Nodzu and A. Hamada (2009) ‘A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges’, SOLA, vol 5, pp137–140 Yun, K.-S., S.-H. Shin, K.-J. Ha, A. Kitoh and S. Kusunoki (2008) ‘East Asian precipitation change in the global warming climate simulated by a 20km mesh AGCM’, Asia-Pacific J. Atmos. Sci., vol 44, pp233–247

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Commentary on Chapter 2.1 Andy J. Challinor

The climate change projections presented in Chapter 2.1 address an important issue. Climate modellers, and those interested in climate impacts and adaptation, are aware that despite rapid ongoing increases in computer power and improvements in techniques, computational resources are considerably less than the community would require in order to address climate change. This situation is unlikely to change in the near future. Important decisions are therefore faced when assessing climate change and its impacts. Critically, is the ensemble size adequate for the development of probabilities or will resources, instead, be focused on ensuring adequate spatial resolution to simulate key processes (e.g. orographic flow) and to provide geographically specific information for impacts modellers? Challinor et al (2009a) discuss this issue, and other related topics mentioned below. They also illustrate the accuracy and regional detail in the simulation of precipitation that can be achieved by high-resolution simulations over Africa. Chapter 2.1 takes this thinking a step further by using a parallel suite of simulations at 20km, 60km, 120km and 180km. It also assesses predictability across lead times by comparing 2015–2039 to end-of-century simulations. Interestingly, it has been suggested (e.g. Cox and Stephenson, 2007) that total uncertainty in climate prediction may be at a minimum at 30 to 50 years’ lead time, after which uncertainty in initial conditions has fallen significantly, while uncertainty in greenhouse gas emissions is not yet prohibitively large. Thus, comparisons across lead times are important, and the use of hatching to denote agreement across ensemble members – as in many of the (lower-resolution) results presented in Chapter 2.1 – is a useful visualization for assessing predictability. We now briefly examine some of the implications of the results of Chapter 2.1 by focusing principally on issues relating to crop cultivation. Through this focus on the mechanisms of climate impacts, this commentary should also serve as an introduction to the detailed analyses of other impacts presented in Chapter 2.2. Precipitation and temperature are variables which are highly important for many sectors, including agriculture. Simulation of precipitation tends to show greater uncertainty than temperature. In presenting simulations of the Philippines, the authors of Chapter 2.1 note the negative impact upon

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crops of drier areas becoming drier. It is not only seasonal totals of rainfall that are important for crops; intra-seasonal variability can also be critical (see Challinor et al, 2009a), as well as the kind of shifts in onset and monsoon duration examined in the chapter. Nonetheless, uncertainty in precipitation does not necessarily preclude some certainty in impacts responses. For example, Thornton et al (2009) suggest that maize and bean yield in Africa may sometimes be insensitive to whether projected rainfall increases or decreases. This is because increasing rainfall is coincident with increases in temperature that either increase evapotranspiration, thus reducing water use efficiency; or because the crop is being grown outside its productive temperature range. Similarly, despite a simulated intensification of the Asian monsoon, Challinor et al (2009b) show that crop yields can decrease due to an increase in crop development rate that is associated with mean temperature increases. Thus, while many of the precipitation results presented in Chapter 2.1 demonstrate the inherent uncertainties in rainfall prediction, this need not prohibit accurate impacts prediction. This is particularly salient here, since uncertainties in the simulation of precipitation are compounded when examining islands with complex coastlines (and, therefore, greater land–sea interactions), such as are found across the Asian region. High-resolution simulations permit more accurate assessment of extreme weather events and intra-seasonal variability since the spatial resolution of a climate model determines the spatial scale of the processes that can be represented. The results presented for Bangladesh demonstrate the likely increases in sea-level rise, storm surges and floods; and the authors note the consequences for crops and livestock. Interestingly, the impact of floods upon crops is not a common research topic since it is perhaps more difficult to simulate than the influence of drought. This raises the issue of model complexity, and the degree to which impacts can be simulated within a climate model. Complexity in climate models is an important determinant of skill, with recent decades seeing an increasing number of processes represented in climate models; some now, for example, include crops. Where impacts are simulated by an ‘offline’ impacts model, the issue of complexity is again important: the variety of methods used to assess impacts can lead to large variation in results. In general, the spatial scale of an environmental model is related to its complexity (Challinor et al, 2009a). Climate models and impacts models together underpin many efforts to inform adaptation. For example, what varieties of crops will be needed as climate changes? When and where should flood defence barriers be built? Nevertheless, there are many barriers to the use of models in this way, and it is worth noting some of them here. Challinor (2009) discusses some of these barriers, recognizing that in the use of climate forecasts and projections for adaptation, it is important to identify specific decisions that can be taken based on the information. The decision may be one taken by a farmer, extension worker, government or international organization. In addition, the information provided should be relevant: the variables, spatial scale and lead time of climate projections should be appropriate to the decision. A common problem with the

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use of climate model output is that the resolution is too coarse – an issue addressed by the work of Chapter 2.1. Simulations should also be reliable: uncertainty should be known to some degree of accuracy. Reliability is a formal and well-defined concept within seasonal probabilistic weather forecasting. At longer lead times, such as for climate projections, reliability cannot be quantified. At any lead time, unreliable projections are likely to result in incorrect assessment and management of risk. Another constraint on the translation of simulation model output into increases in adaptive capacity is the multiplicity of factors beyond climate that affect any decision. Thus, holistic approaches to adaptation are important since they can ensure that all the factors affecting the decision are accounted for. Many of these factors will be uncertain, and their interactions will produce further uncertainty. The condition of holism implies a need for knowledge and research from more than one discipline in order to account for multiple stresses. This highlights the importance of the later chapters within Part 2, where vulnerability and adaptation are assessed. Finally, we can note that the information provided from a modelling system should not only be relevant, reliable and in appropriate context; it also needs to be perceived as such by stakeholders. This makes the communication of methods and limitations a key part of the scientific endeavour.

References Challinor, A. J. (2009) ‘Developing adaptation options using climate and crop yield forecasting at seasonal to multi-decadal timescales’, Environmental Science and Policy, vol 12, no 4, pp453–465 Challinor, A. J., T. Osborne, A. Morse, L. Shaffrey, T. Wheeler and H. Weller (2009a) ‘Methods and resources for climate impacts research: achieving synergy’, Bulletin of the American Meteorological Society, vol 90, no 6, pp825–835 Challinor, A. J., T. R. Wheeler, D. Hemming and H. D. Upadhyaya (2009b) ‘Crop yield simulations using a perturbed crop and climate parameter ensemble: Sensitivity to temperature and potential for genotypic adaptation to climate change’, Climate Research, vol 38, pp117–127 Cox, P. and D. Stephenson (2007) ‘A changing climate for prediction’, Science, vol 317, pp207–208 Thornton, P. K., P.G. Jones, A. Alagarswamy and J. Andresen (2009) ‘Spatial variation of crop yield responses to climate change in East Africa’, Global Environmental Change, vol 19, pp54–65

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2.2 Impacts of Climate Change upon Asian Coastal Areas: The Case of Metro Manila

Megumi Muto

Introduction Climate models supporting the Intergovernmental Panel on Climate Change’s (IPCC’s) 2007 Fourth Assessment Report predict that climate change will increase local temperatures and precipitation in monsoon regions in Asia, where the number of large cities is increasing and existing urban areas are expanding particularly along the coasts. Expected to be the most prone to frequent flooding as a result of global warming, these areas will experience the most complex direct and indirect effects of climate change. In this context, Metro Manila, typical of Asian megacities, was chosen as a case study to comprehensively simulate the impacts of future climate change and to identify necessary actions. Metro Manila is the centre of the nation’s political, economic and socio-cultural activities. Its strategic location beside Manila Bay has supported the capital city’s growth and expansion into large suburbs over the last several decades. Metro Manila, whose per capita gross regional domestic product (GRDP) is by far the highest in the country, maintains its position as the premier economic centre of the nation as home to the headquarters of domestic and international business establishments. The regional economic growth of Metro Manila is expected to continue to lead the

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’MARIKINi

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M A R AB O N Q UEZO N CIT' C ALO O C A N CITY

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Figure 2.2.1 Flood-prone areas in Metro Manila Source: Muto et al (2010)

national economy. At the same time, since Metro Manila is in a low-lying area facing the sea, a large lake (Laguna de Bay) and embracing two river systems, it is prone to flooding disasters (see Figure 2.2.1). Several types of flooding affect Metro Manila: storage flooding, overbanking and interior flooding. The KAMANAVA (Kalookan–Malabon– Navotas–Valenzuela) area, for example, is vulnerable to storage-type flooding; the Pasig-Marikina River Basin is prone to overbanking; and the West Mangahan area experiences interior flooding (see Figure 2.2.2). The KAMANAVA area is low and flat with elevations ranging from around sea level to 2m to 3m above sea level. Its current population is in excess of 1 million in an area of approximately 18.5 square kilometres. Before the 1960s,

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IMPACTS OF CLIMATE CHANGE UPON ASIAN COASTAL AREAS

3B AN D 0

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the KAMANAVA area was made up of widely spread lagoons used as fishponds, but it was partially filled to its current configuration, which consists mainly of commercial districts and residential areas, along with fishponds. The Japan International Cooperation Agency (JICA)-financed KAMANAVA Area Flood Control and Drainage System Improvement Project has the design scale of a ten-year return period. The project works include

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construction of a polder dike, heightening of river walls on the Malabon and Marala Rivers, construction of a submersible radial navigation gate facility, construction of flood gates, construction of control gates, construction of pumping stations, and improvement and new construction of drainage channels. The Pasig-Marikina River System has a catchment area of 651 square kilometres, including the catchment area of the San Juan River. It is composed of the ten cities and municipalities of Mandaluyong, Manila, Marikina, Quezon, San Juan, Antipolo, Cainta, Rodriguez, San Mateo and Pasay. The downstream part of the river system belongs to Metro Manila, but the upper part is under the jurisdiction of Rizal Province. The section of the river system between the river mouth (Manila Bay) and the Napindan Channel confluence point is called the Pasig River, while the Marikina River lies upstream. The Marikina River is also connected with Laguna de Bay Lake at the Rosario Weir through the Mangahan Floodway. Excess flood runoff overflows from the Pasig and Marikina riverbanks. Similarly, storm water flowing in drainage and creek networks creates inundation. Excess runoff water from the Marikina River is diverted to the lake through the Mangahan Floodway during floods to protect Metro Manila’s city core. The flood runoff stored in Laguna de Bay is slowly released to the Pasig River through the Napindan Hydraulic Control Structure (NHCS) in Napindan Channel when the water level recedes in the Pasig River and ultimately drains into Manila Bay. Another broad flood control project, the JICA-financed Pasig-Marikina River Channel Improvement Project was formulated based on a scale of 30-year return period (see Figure 2.2.3). The total area west of the Mangahan Floodway is 39 square kilometres, covering the five cities of Makati, Pasig, Pateros, Taguig and Taytay. Here, a number of drainage channels discharge into Laguna de Bay Lake or the Napindan River, such as the Tapayan, Abasing, Taguig Pateros and Hagonoy drainage channels. The West Mangahan drainage area topography is flat and is a typical interior flood-prone area along Laguna de Bay Lake. Flooding in the area is caused by storm rainfall and high water levels in the lake. There are several drainage channels and rivers; storm water runoff is stranded due to high lake water levels. As a result of the urbanization of former paddy fields, inundation now also affects towns, communities and numerous subdivisions. Flooding in this area usually begins when the water stage of Laguna de Bay rises to approximately 11.5m; most of the area is submerged at a water stage of approximately 13.5m, although the lake is not affected by storm surges. The Mangahan Floodway was constructed in 1985 to divert floodwaters from the Marikina River into Laguna de Bay at a design discharge of 2400 cubic metres per second, with the flood flow regulated at the planned Marikina Control Gate Structure (MCGS). The north-western portion of the lake is flanked by Metro Manila, while the provinces of Rizal and Quezon bound its northeastern and south-eastern borders. Laguna, Batangas and Cavite provinces

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71

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Figure 2.2.3 Pasig-Marikina and West Mangahan areas Source: Muto et al (2010)

border the lake to the south and southwest. The construction of the JICAfinanced flood control project in the area west of the Mangahan Floodway was completed in 2007. The project work included a lakeshore dike, bridges at two sites (in Mangahan and Napindan), a parapet wall with a top elevation of 14.1m, floodgates at eight sites, four pumping stations, and regulation ponds at four sites (see Figure 2.2.3).

Downscaling and flood simulation This study is based on global climate projections provided by the IPCC’s Fourth Assessment Report (AR4), adopting the B1 and A1FI scenarios from the IPCC’s Special Reports on Emissions Scenarios (SRES), and comparing them with the status quo (SQ) scenario. B1 is the scenario projected by the IPCC to represent the least anticipated change, which makes it the most sustainable case. A1FI, on the other hand, represents a large change scenario due to high economic growth. The target year is set as 2050, the halfway mark of the IPCC SRES timeframe. The spatial spreads of flooding for the year 2050 under the SQ, B1 and A1FI scenarios are taken as the basis for impact analyses. It should be noted that the present IPCC climate models cannot be directly applied to impact studies on local climate change because of various uncertainties: emission scenarios due to economic growth rates and energy efficiency improvements; carbon cycle response to changes in climate; global climate sensitivity; discrepancies in regional climate change scenarios; and changes in

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ecosystems, etc. Simulations of local climate change are fundamentally more uncertain than global mean values. Local climate is heavily influenced by atmospheric and oceanic circulation, such as prevailing weather situations and wind directions. For example, global mean precipitation changes do not necessarily determine the changes in local precipitation, so it is impossible to conclusively determine future precipitation rate extremes. Although climate projections are based on global climate models or general circulation models (GCMs), their results contain various biases. If the raw GCM outputs were used for impact studies, the biases would surely contaminate the assessment outcome. Precipitation remains a stringent test for climate models. Many biases in precipitation statistics remain in both precipitation means and variability, especially in the tropics (Randall et al, 2007). Comparison between observations and simulations of 20th-century conditions reveals that most models do not accurately simulate precipitation extremes.1 Despite these various uncertainties, global climate scenarios can be translated to regional climate scenarios, a process called ‘downscaling’,2 which is employed for this study (see Figure 2.2.4). While there has recently been an increasing recognition of the explicit treatment of uncertainty in environmental assessments, this chapter deals with qualitative rather than quantitative uncertainties.3 Downscaling requires local-level, bias-corrected climate information. The analyses below discuss development of regional climatic changes in the period up to 2050.4 IPCC A1F[ Scenario (Specified)

Overall procedure

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0.14

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Table 2.2.1 Global climate scenario setting and conditions of the inundation simulations for Metro Manila Simulation case

1 2 3 4 5

Temperature rise (°C) (downscaled)

Status quo climate (SQ) B1 with storm level at status quo B1 with strengthened storm level A1FI with storm level at status quo A1FI with strengthened storm level

0 1.17 1.17 1.80 1.80

Sea-level rise (cm) (global) 0 19 19 29 29

Increased rate of rainfall (%) 0 9.4 9.4 14.4 14.4

Storm surge height (m) at Manila Bay 0.91 0.91 1.00 0.91 1.00

Source: Muto et al (2010)

IPCC SRES scenarios B1 and A1FI provide a basis for discussing changes in local temperature and precipitation in Metro Manila, based on which hydrological conditions, such as sea-level rise, storm surge and land subsidence, are projected. The matrix in Table 2.2.1 is a summary of climatic–hydrological conditions for the SQ, B1 and A1F1 scenarios. Return periods of 10, 30 and 100 years are considered. These conditions provide a basis for flood impact analysis for this chapter (see Table 2.2.1). In choosing an infrastructure scenario, this chapter focuses on flood control. During the past several decades, the Philippine government has been implementing a series of strategic flood control infrastructure projects protecting Metro Manila, covering the Pasig-Marikina River Basin, the KAMANAVA area, and the area west of Mangahan. Recently implemented flood control projects are included in those identified in the JICA 1990 master plan. In addition, the government has several other flood control projects planned that will complete the implementation of the priority projects identified in the 1990 master plan. In order to identify necessary adaptation measures, two flood control infrastructure scenarios were considered. The first is the existing infrastructure level, including projects completed by base year 2008. The second is the 1990 master plan scenario, which assumes continued implementation of projects identified in the 1990 master plan until the year 2050. Consequently, the two flood control infrastructure scenarios are added to the climatic–hydrological matrix in Table 2.2.2. The case code in Table 2.2.2 consists of five sets of alphanumeric symbols. The first set (100, 30, 10) indicates the assumed return period; the second set (SQ, B1, A1FI) shows the climate scenario; the third (cu, st) indicates whether storm surge was set at the current (cu) or strengthened (st) level. In the fourth set, EX or MP denotes the infrastructure scenario. Lastly, wD/nD means with or without the hypothetical Maikina Dam.

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Table 2.2.2 Climatic–hydrologic infrastructure scenarios: Summary Cases

Return period

Climate

Hydrological (storm surge)

Infrastructure: Adaptation EX : existing MP : 1990 master plan

100-SQ-cu-EX 100-SQ-cu-MP 100-B1-st-EX-wD 100-B1-st-MP-wD 100-A1FI-st-EX-wD 100-A1FI-st-MP-wD 30-SQ-cu-EX 30-SQ-cu-MP 30-B1-st-EX-wD 30-B1-st-EX-nD 30-B1-st-MP-wD 30-B1-st-MP-nD 30-A1FI-st-EX-wD 30-A1FI-st-EX-nD 30-A1FI-st-MP-wD 30-A1FI-st-MP-nD 10-SQ-cu-EX 10-SQ-cu-MP 10-B1-st-EX-nD 10-B1-st-MP-nD 10-A1FI-st-EX-nD 10-A1FI-st-MP-nD

100 years

SQ

Current Current Strengthened Strengthened Strengthened Strengthened Current Current Strengthened Strengthened Strengthened Strengthened Strengthened Strengthened Strengthened Strengthened Current Current Strengthened Strengthened Strengthened Strengthened

EX MP EX MP EX MP EX M/P EX EX MP MP EX EX MP MP EX M/P EX MP EX MP

B1 A1FI 30 years

SQ B1

A1FI

10 years

SQ B1 A1FI

– – With dam With dam With dam With dam – – With dam No dam With dam No dam With dam No dam With dam No dam – – No dam No dam No dam No dam

Notes: cu = current; EX = existing infrastructure; MP = 1990 master plan; nD = no dam; SQ = status quo; st = strengthened; wD = with dam. Source: Muto et al (2010)

Socio-economic impact This section conducts socio-economic impact analyses in order to understand the characteristics and magnitude of flood damage expected in the year 2050. For the sake of this analysis, benefits are taken to be the future aggregate-level flood damage avoided by implementing flood control infrastructure improvements. The types of benefits included in this study go beyond conventional flood impact assessments that only deal with direct losses. For example, in conventional analyses of direct losses, damage to buildings is converted into monetary terms based on simple information such as flood depth and building use. Such direct impacts are limited to damage caused by physical contact of the floodwater with humans, property and other objects. Flooding, however, interacts with the patterns of human activities in the metropolis in more complex ways. Not all tangible losses are direct losses: floods not only affect structures themselves, but also their contents and the activities undertaken within them. Disruption of traffic and business are examples of such losses. Such secondary impacts occur as a result of direct impacts and may occur outside the flood event in space or time. In addition, there are intangible impacts, such as health hazards. In this chapter, first, direct and tangible losses are assessed, as occurs in conventional flood control project analyses. Secondly, indirect and tangible

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g-«ooomIc va lue e ‘ Covers insurable and uninsurable risks --------♦Customized at community level •Robust across climate scenarios

Figure 3.6.1 HARITA conceptual framework

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With insurance as collateral, farmers and lenders can be more confident about entering into loan agreements. The HARITA model was piloted in 2009 in Adi Ha village with the Productive Safety Net Programme (PSNP) serving as the primary distribution mechanism. The PSNP is a well-established, federal social protection programme serving 8 million chronically food-insecure households. With annual funds of roughly US$500 million from large international donors, the PSNP was established in 2004 as a system of transferring cash and food to vulnerable households before they reach a crisis point. This assistance is provided in exchange for beneficiaries’ work to reduce risk (e.g. by building community assets such as water harvesting structures or reclaiming environmentally degraded areas). Early impact studies suggest that the PSNP is superior to traditional emergency food aid programmes in increasing household welfare (Sharp et al, 2006; UNDP, 2007). Through HARITA, farmers enrolled in the PSNP have the option to work extra days on risk reduction activities beyond those required for their normal payments; but instead of earning cash or grain for this additional labour, they earn an insurance certificate protecting them against deficit rainfall. We defined this as an Insurance for Work (IFW) model. Richer farmers who do not participate in the PSNP have the option to purchase insurance with their own cash; as such, they constitute a potentially important subset of clients for the Ethiopian insurance industry. By allowing very vulnerable farmers to pay their premiums by contributing labour to implement risk reduction activities, farmers benefit even when there is no payout – the risk reduction measures taken in their communities pay dividends, even during good weather years. The IFW model is innovative in allowing insurance and credit to stand as independent components. In most index insurance pilots, farmers have been required to take insurance and loans as a package. Under HARITA, however, farmers may choose to bundle the two, but they are not required to do so. The independence of credit and risk transfer means that farmers do not lose access to insurance once they have repaid their loans, and farmers who do not want a loan can still obtain insurance.

Weather index-based insurance as an adaptation tool Risk is the probability of harmful consequences resulting from the combination of vulnerability and a certain hazard. HARITA’s project partners believe that the best way to deal with climate risk and its negative impacts is to reduce and eliminate risk, using the tools that farmers and development practitioners already know and use well. But because the climate problem is so severe, it is impossible to eliminate negative risks entirely. Even though climate change will increase the intensity, frequency and potential distribution of climatic hazards, it is possible to reduce the risks by decreasing the vulnerability of people or

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spreading the impact over a wider swathe of society. In this way, insurance can support adaptation by providing a means of formal risk transfer and sharing. Because the insurance contracts are priced from year to year, the premium charged can also reflect changing risks over time, including not only climate trends but also seasonal rainfall predictions (Osgood et al, 2008). As such, the market signals to farmers what production strategies are likely to succeed given the current climate conditions. HARITA’s project partners aimed to explore how weather insurance could be incorporated within a holistic risk management approach. To date, few existing index-based insurance projects have explored ways of combining risk reduction and insurance. Moreover, few have reached the stage of actual financial transaction, and most are small-scale pilot projects or one-year test period initiatives (Hellmuth et al, 2009). While these projects prove index insurance can work at a technical level, it remains to be seen how they can be scaled up and used effectively as an ‘industrial strength’ tool for robust adaptation. The good news is that Ethiopia could be fertile ground for weather insurance to take root in a holistic approach to risk management. Despite its poverty, the country has a number of assets in its favour. First, Ethiopia is one of the few countries in the world to include microfinance in its Poverty Reduction Strategy Paper (IMF, 2007; Microned, 2008). In 1996, the Ethiopian government established a formal role for microfinance institutions (MFIs) as major economic actors (Amha, 2000). With an estimated 2 million clients as of 2009, MFIs have made impressive inroads in extending financial services to previously unbanked poor households – both rural and urban – in a short period of time (Admassie, 2004; Chamberlain and Smith, 2009). Moreover, demand for risk transfer across the country seems to be very high. In 2008, Oxfam America was commissioned by the International Labour Organization and the United Nations Capital Development Fund to investigate micro-insurance demand among four major occupational groups: with lowpaid urban workers in Addis Ababa; coffee farmers in Southern Nations, Nationalities and Peoples’ Region; pastoralists in Oromia; and agro-pastoralists, also in Oromia. Throughout the country, study participants expressed a very strong desire to access better risk management, with insurance considered highly attractive (Tadesse and Victor, 2009). It is important to acknowledge, however, that adaptation through new technology such as insurance is not simply a matter of rolling out risk transfer products. Effective adoption also requires removing important social, political and economic barriers. In Oxfam’s study on the demand for micro-insurance, we found both opportunities and challenges in this regard.

Opportunities High receptivity. In all regions studied, Oxfam found that poor people viewed the concept of insurance with enthusiasm. Participants familiar with microfinance and iddir (traditional, informal burial societies that function similarly

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to life insurance) were especially quick to recognize the inherent merits of risk transfer. Growing need for insurance as collateral in credit markets. Weather index insurance could hardly be more welcome, not only due to the increasing risks associated with agricultural lending, but also due to the ongoing phase-out of Ethiopia’s farmer loan guarantee fund. Beginning in 1994, regional governments initiated a 100 per cent credit guarantee programme to facilitate access to fertilizer, improved seeds and other inputs. Under the system, approximately 90 per cent of fertilizer and seeds were delivered to farmers on credit at belowmarket interest rates. Local governments entered into agreements with the Commercial Bank of Ethiopia (CBE), guaranteeing the farmers’ purchases. In order to finance the loans, credit was then extended to farmers by the CBE through co-operatives, local government offices, MFIs and one co-operative bank. The loan programme had reached 4 million farmers with guaranteed credit of nearly US$70 million in recent years. While the programme benefited some farmers, it also created incentives for farmers to default in large numbers. As a result, the government is ending the programme (World Bank, 2006), leaving most asset-poor farmers with no other collateral than their neighbours. Ethiopian MFIs heavily favour the Group Guarantee Lending Model (GGLM) (Borchgrevink et al, 2005), where borrowers vouch for each other and cover defaults in the group. The GGLM is widely criticized by poor clients for stimulating conflict among borrowers; yet, lenders struggle to find an acceptable alternative as the loan guarantee fund is eliminated. In this way, insurance could provide an alternative to the GGLM. In addition, while the Ethiopian government has admirably pushed MFIs to serve risky agricultural portfolios, the threat of climate change could make it much more difficult to meet this goal sustainably unless robust risk management is put into place. Micro-insurance will likely constitute a critical element in a robust agricultural credit market.

Challenges Poor access. Unfortunately, the Ethiopian insurance industry is not well positioned to enter the micro-insurance market. Almost exclusively serving large urban industry, formal Ethiopian insurers count fewer than an estimated 300,000 clients out of the country’s 79 million inhabitants (Chamberlain and Smith, 2009). While insurers recognize that their risks are overly concentrated in cities, they struggle to identify cost-effective means to reach rural, poor and financially illiterate Ethiopians. The currently embryonic micro-insurance market is limited almost exclusively to credit-life insurance programmes which cover lending risks to MFIs, but generally do not benefit surviving family or provide access to other types of cover. Fears about affordability. Most Ethiopians cannot afford more than the little informal protection they have now: their incomes are simply insufficient to cover premiums relative to their expected losses.

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Low awareness of insurance. Oxfam’s study participants who had no experiences with micro-finance and iddir struggled to grasp the concept of insurance without detailed explanation. Low levels of education and telecommunication also make awareness-raising a challenge. Less than half the Ethiopian population can read and write; as of 2001 there were only three radio broadcast stations and one television station (CSA, 2007). As such, word of mouth, direct experience and cooperation with institutions that already reach impoverished areas will be important modes of outreach (Tadesse and Victor, 2009)

HARITA pilot overview As mentioned earlier, the HARITA model was piloted in 2009 in the community of Adi Ha. With a total population of 4285, Adi Ha is the largest village in the Kola Temben district of Tigray Regional State. Teff (Eragrostis tef) is the village’s second most widely cultivated rain-fed crop, and is produced by 36 per cent of all households and 92 per cent of the poorer households. Most teff (67 per cent) is used for household consumption; the remainder (33 per cent) is sold at markets in nearby towns. Teff is exceptionally nutritious and is also prized as preferential feed for livestock. In addition, teff is used to reinforce mud and plaster the walls of homes and grain storage facilities (Ketema, 1997). Although most farmers in Adi Ha depend upon rainwater for agricultural production, some have access to irrigated land. A little more than a decade ago, Oxfam America and REST established a small-scale irrigation system in Adi Ha, the first of many in Tigray. The system has allowed farmers to begin producing high-value horticultural crops such as fruits and vegetables. Irrigated farms are much more profitable than their rain-fed counterparts. Unfortunately, given the irrigation system’s physical limitations, the majority of farmers in Adi Ha cannot access it. As such, they are particularly vulnerable to drought, evidence that hard adaptation, such as irrigation systems, is effective, but limited in its ability to reach people in broad geographic areas. In contrast, soft adaptation, such as changing crop practices and micro-insurance, can be implemented among many more people in short order with limited budgets.

A community-designed adaptation experiment Studies on community empowerment in Ethiopia have confirmed the necessity of meaningful participation in project design (Pratt and Earle, 2004); yet most weather index insurance pilots have struggled to engage farmers due to the technical nature of the index and time pressures to develop a commercial product in the first year. Involving farmers in adaptation planning is not merely an exercise in political correctness: as Walling (undated) points out: ‘adaptation is as much about changing attitudes and behaviours as finding technical solutions’. Uptake of

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weather insurance in prior pilots for subsistence farmers has not been automatic and is frequently disappointing, partly due to the relative unfamiliarity of insurance concepts in very poor communities, and partly due to underinvestment in demand-driven (as opposed to supply-driven) products (Hellmuth et al, 2009; Tadesse and Victor, 2009). While some pilots have attracted solid demand from farmers, it is unclear why take-up has not been stronger given the many theoretical benefits of risk transfer. Many industry observers have thus hastily concluded that illiterate farmers cannot understand complex financial products or afford them. HARITA’s project partners set out to challenge these assumptions, believing it necessary to develop popular educational tools around this new class of financial products. They also sought ways of increasing the attractiveness of index insurance by prioritizing the core interests of clients, in contrast to the dominant index insurance model which focuses on lenders and insurers. As such, HARITA’s project partners recognized that farmers in Adi Ha would need to play a central role in the pilot’s design. Specifically, farmers were best positioned to alert the adaptation and insurance ‘experts’ to farmers’ educational and risk management needs, as well as to show how weather index insurance could be made very attractive to the target client. Below, we highlight some of the ways in which farmers played instrumental roles in HARITA’s achievements to date.

Community-wide participants In November 2008, Oxfam America, Relief Society of Tigray (REST) and Mekele University conducted a participatory capacity and vulnerability assessment (PCVA) with roughly 200 farmers in Adi Ha to understand the villagers’ capacities for managing risk and to explore in detail the vulnerabilities and capacities of teff farmers vis-à-vis weather-related hazards. Results of the risk assessment were used to inform the identification of appropriate risk reduction activities. For instance, in response to drought risk and rising average temperatures, farmers could select more drought-tolerant crops, improve management of water resources and move planting dates. Such interventions could substantially reduce the risks posed by climate change. The PCVA uses participatory rural appraisal (PRA) tools such as risk mapping, transect walks, and historical and seasonal calendars, as well as asset inventories, livelihood surveys, focus group meetings and key informant interviews. To complement the PCVA, HARITA also conducted experimental economic risk simulation games and focus group discussions with farmers to understand their needs, desires and concerns.

Community pilot leaders In May 2008, a team of five community members representing key groups (identified by farmers as village administrators/elders, rain-fed and irrigated farmers, landless youth and women) were elected to become pilot leaders. The leaders’ duties included serving on the pilot’s broader Design Team, consisting

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also of representatives from local stakeholder organizations and Oxfam. The team was charged with planning and implementing the HARITA project under the advice of international experts. Throughout the year, the pilot leaders provided critical inside information about common attitudes and concerns in the village. They also helped to brainstorm product options and identify possible pitfalls (for more detail, see below).

Community pilot participants In July 2008, the pilot leaders recruited 21 farmers (including themselves and 9 women) to participate in ongoing focus group discussions. The group’s composition was balanced to include experience and interest in farming teff; geographic distribution; gender; access to irrigated land; wealth; and size of landholding. Participants attended a number of test workshops on climate change, weather data collection, financial literacy and insurance.

What community participation produced Farmer participation enhanced the HARITA model and lead to numerous positive results, including the following:

Identification of vulnerabilities Through many interactions, the community helped the project partner organizations to identify farmers’ vulnerabilities to specific hazards, as well as their capacity to adapt. Farmers shared the following information: • •

• • •

The biggest threats in Adi Ha are agricultural, particularly drought, pests, disease, water logging, hail, soil infertility, salination and weeds. The majority of farmers surveyed expressed interest in micro-insurance: agriculture and livestock insurance ranked as the highest priority, while health cover came in second. Affordability of insurance premiums is a big concern. The most vulnerable members of the community are female-headed households. Maize, teff and finger millet are the crops that have failed the most. Maize is most vulnerable to drought, but teff is more economically valuable.

Identification of informal risk-coping measures The community also identified the strengths and weaknesses of their informal modes of self-insurance and community risk, and resource-sharing measures for a wide variety of risks. Manifesting in many different forms, self-insurance entails the retention of risk, meaning that any loss is absorbed and ‘compensated’ by one’s own assets (e.g. savings and current or future income). Savings are the most common form of self-insurance. While more and more people in Adi Ha are beginning to save

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through formal financial institutions, most still stash their cash in a box at home. The greatest benefit of self-insurance methods are their risk coverage and timeliness, allowing a quick response to any type of loss. Unfortunately, savings on hand are seldom sufficient to compensate for loss. Common community-based risk-sharing arrangements in Adi Ha include sharecropping and livestock sharing. While useful mechanisms, they only cover a fraction of the risks that households face and are unable to handle mass risks (e.g. drought). Also, as a reflection of their social construction, poorer marginalized groups are often excluded from community risk-sharing as they are deemed unable to reciprocate assistance from their richer neighbours.

The Insurance for Work (IFW) model To cope with their poverty, many farmers in Adi Ha turn to external assistance, especially from REST, DECSI and the local government. Many rain-fed farmers also depend upon the PSNP. When a few farmers in Adi Ha suggested that they pay for insurance in other forms than cash, it became obvious that HARITA should be based on an innovative ‘insurance for work’ through the PSNP. We stress that without the urging of farmers to make insurance affordable in creative ways, this major project innovation would never have been realized.

Insights and preferences relevant to major pilot activities Risk reduction Farmers identified a number of ways to enhance the production and yield of teff (the crop favoured for the pilot) as a strategy for reducing risk to drought. Key questions that farmers raised were how to: • • • •

boost drought tolerance; manage poor soil fertility given high fertilizer prices; reduce the threat of pests, water logging and flooding; and decrease the need for frequent ploughing and weeding.

Given the fact that teff is grown almost exclusively in Ethiopia, it has garnered relatively little attention from international crop scientists. As such, HARITA’s project partners concluded that researching teff and sustainable agricultural practices in Adi Ha would be worthwhile. In the end, the study on teff headed by Mekele University, along with the results of the community risk assessment, informed the identification of the risk reduction activities that were actually undertaken by farmers in Adi Ha during the following agricultural season. More specifically, the team at Mekele University trained farmers in making compost, which is critical for rebuilding soil nutrients and improving soil moisture retention. In addition, they constructed small-scale water harvesting structures on farm land, and planted nitrogen-fixing trees and grasses to promote soil regeneration and water conservation, and to reduce the risk of flooding. Finally, farmers cleaned teff seeds as a way of boosting productivity

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and controlling weeds. These risk reducing interventions are key to reducing exposure to natural hazards, boosting income over time and promoting quicker recovery from disaster.

Index insurance Farmers also helped to design a weather index insurance product to complement the risk reduction activities taking place in the village. Design areas that garnered valuable farmer input included the following: Filling in data gaps. Designing drought insurance normally requires at least 30 years of reliable daily precipitation data. In most developing countries, rain gauges are sparsely distributed and limited in quality and durability. Not surprisingly, most weather insurance pilots have been located in villages with good rainfall records. These data supply-driven pilots have demonstrated that weather insurance products are viable at the pilot level; but they have not shown how to overcome the data barrier in poor communities who have poor weather records. For this reason, HARITA explored new techniques to enhance sparse local datasets through a combination of farmer-collected data, official meteorological data, satellite imagery, rainfall simulators and statistical tools. While continued research will be necessary, the International Research Institute for Climate and Society (IRI) developed a viable index and an opensource methodology for handling data gaps in Adi Ha’s mere seven years of (unreliable) precipitation data. Plastic rain gauges were installed in each of the 21 pilot participants’ fields, evenly distributed geographically around the village. Farmers collected – and are still collecting – rainfall data to help the technical teams better understand the microclimates across the village. Farmers’ readings proved reliable; average readings were only 2.2 per cent different from the automated rain gauge placed in the village’s tree nursery. This example demonstrates that poor communities can be meaningfully involved in the technical aspects of an adaptation project. Moreover, by definition, data obtained from multiple sources should be more scientifically reliable than from a single source. Indicating contract preferences. Farmers participated in numerous consultations regarding various insurance product options (i.e. types of cover, crop selection, coverage levels and frequency of payouts). In particular, the community pilot leaders forecasted farmer reactions to product features and roadblocks to insurance adoption. For instance, they indicated that the local farmers’ co-operative must be included in the product’s distribution strategy in order to win support from village leaders. Furthermore, HARITA’s technical advisers assumed that farmers would not trust what they cannot see. However, farmers in focus group discussions explained that their neighbours would probably not resist a satellite-based insurance product.

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Informing and finally selecting the product distribution model. The HARITA partner organizations generated four viable options for the product’s distribution model. Ultimately, farmers on the Design Team recommended the product provider model that was implemented and supported in community-wide consultations. Protecting clients. Given the relatively sophisticated nature of weather index insurance and the illiteracy of most farmers in Adi Ha, the Design Team established a grievance process to handle farmers’ complaints or questions. The process was documented in a pictorial diagram so that farmers who could not read could refer to it later when needed. Farmers also elected two of their relatively educated and respected peers to serve as ombudsmen. These two men were trained in the grievance process so that they could shepherd complaints through the system, although none have been forwarded to date.

Benefits of participation Participatory methods are resource intensive – the question, then, is: are they worthwhile? Uptake of the insurance package offered by Nyala Insurance Company in late May 2009 constituted a major test of HARITA’s popular education and outreach efforts, as well as the attractiveness of the IFW model. There is reason to believe that the participatory method produced tangible and substantial results, as evidenced in the strong uptake of the product. Over the course of two days, approximately 600 farmers attended the enrolment activities, and 200 households signed up for the package, representing roughly 20 per cent of the village, but 34 per cent of the households who knew about and understood the product. An impressive 38 per cent of enrollees were female-headed households (recognized as the poorest of the productive poor) and 65 per cent of enrollees were chronically food-insecure participants of the PSNP. By definition, these two groups constitute the most vulnerable farmers in Adi Ha (see Figure 3.6.2). Labour-paying farmers earned their premium by implementing the risk reduction projects identified through the PCVA process on their farms. At the outset of this project, the received wisdom was that for foodinsecure farmers, agricultural risks were nearly uninsurable. However, the IFW model is a direct challenge to this notion. Over time, as livelihoods improve and farmers graduate from the PSNP, they become candidates for the commercial insurance market where they can pay for risk transfer in cash. Lead by Nicole Peterson, a team at Columbia University and individual researchers in Ethiopia conducted a follow-up study of enrolment with 200 farmers (both buyers and non-buyers, 31.5 per cent of whom were women). Among the report’s preliminary findings were: •

Insurance clients were more likely to be female, younger and PSNP participants, compared with non-buyers and the general population. They were

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180 160 I 140 CQ cro 120 g- 100

I cq uj

80

60 40 20

0 Farmers Paying in Cash

Farmers Paying in Labour

Total

Farmers Willing to Purchase Insurance by Gender and Method of Payment 35.0% 30.0% 25.0% 20.0%

■ Farmers Paying in Cash

15.0%

■ Farmers Paying in Labour

10.0% 5.0% 0.0% Female-Headed households

Male-Headed households

Figure 3.6.2 Selected HARITA statistics

•

•

•

•

also more likely to have less land and grow less teff. These results demonstrate that the HARITA approach successfully targeted poorer farmers. Community trainings on insurance, especially the one conducted through popular theatre, were key to farmers being able to answer questions about insurance correctly. Farmers said that the most common reason for non-purchase was being unaware of the opportunity (40.7 per cent), while 12.8 per cent said that they did not understand the product. Just over 30 per cent said they had no reason not to buy. These findings suggest areas for improvement in outreach and education. Survey participants were satisfied with the product’s current price (93 per cent), period of coverage (95.6 per cent), crop used (82.5 per cent), satellite data use (89.5 per cent) and complaint process (92.1 per cent). Farmers stated that they are more likely to buy insurance if it is connected to loans and improved loan terms. In the pilot’s first year, the insurance

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product was not tied to a custom loan package, although this is envisioned in future years. The study revealed that farmers who plan to take loans next year expect a higher average loan amount than this year (US$122 versus US$115). Plans for next year also show a greater interest in loans for agricultural inputs (20 per cent higher) and investing in other businesses (68 per cent higher, albeit from a low initial basis) (Peterson, 2009).

HARITA in the broader context In this section we discuss a few ways in which HARITA provides lessons on a broader scale beyond Adi Ha. As a micro-insurance and disaster risk management programme, HARITA focuses on helping farmers to deal with the small-scale unreported disasters described in the background section. Again, these disasters do not attract much attention; but over enough years, they erode coping capacity. Arguably, the infamous 1984 famine grew out of a series of small droughts that eroded coping capacity amongst rural communities (Fraser, 2007). While it is important to explore how reaching scale can help countries such as Ethiopia to cope with large catastrophic disasters, helping farming villages to deal with small shocks along the way is just as important as helping them to deal with catastrophic regional and national emergencies (which are often a reflection of weakness in the mechanisms to deal with minor shocks).

Linking community-based adaptation to regional- and national-level interventions Community-based adaptation projects such as HARITA can be valuable sources of information about climate impacts at the local level. When replicated and scaled up, community-sourced data can be gathered systematically to ensure that adaptation across the country is coherent and not a mere patchwork quilt of interventions that, at best, fail to work synergistically and, at worst, stand at cross purposes. In the end, however, limited human resources and time probably means that only a handful of communities will participate fully in designing their own adaptation projects. While it is important to acknowledge the idiosyncratic nature of individual village community-risk assessments and solutions, van Aalst et al (2008) suggest that policy-makers can assess ‘archetypal livelihoods’, which are closely linked with ‘economic activity combinations’ in the target region by community risk assessments. In this way, PCVAs can serve as a sampling mechanism of climate impacts that enables decision-makers to analyse the potential disruptions of those archetypal livelihoods or activities in communities who are unable to participate directly in formal adaptation planning.

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Replicability and scalability at the national/global level The HARITA team has been exploring options for gradual expansion of the pilot to test its replicability in other parts of Ethiopia. During 2009 to 2010, HARITA’s partners pursued a modest replication of the model in four additional villages in Tigray, with expansion to new regions in the country. This will eventually diversify the risk pool and strengthen the risk management programme. HARITA’s approach is highly replicable because it can accommodate most or all types of community-level variability in Ethiopia. However, creating access to insurance on a sustainable basis – at scale – is an enormous challenge. HARITA’s project partners envision leveraging the PSNP to reduce the costs of product distribution, management and oversight, and to ease the logistical hurdle of outreach to remote villages. The PSNP has already established a financial system to distribute cash at the household level, so micro-insurance payments can travel along the same lines. While the PSNP is the most obvious platform for scale, it would also be possible to replicate through the country’s existing network of microfinance institutions (with approximately 2 million clients) or co-operatives. In terms of scaling outside of Ethiopia, although the PSNP is unique, many countries have food-for-work programmes as well as ‘conditional cash transfer’ programmes that reach hundreds of millions of poor people. It is important to clarify that HARITA is designed to help PSNP participants graduate out of annual assistance and into self-sufficiency, and to afford insurance in the commercial market. Yet, so long as there are farmers who need help to purchase insurance, HARITA requires a perennial source of financing. Fortunately, many international actors would like to facilitate insurance access in developing countries. Oxfam is actively researching opportunities among three major sources of premium financing – private donors, the public sector and the commercial carbon markets. True long-term sustainability will, however, require more than financing. It will also require good programme governance, transparency, accountability, a supporting regulatory framework, knowledge networks and steady information flows from the local to global level. Fortunately, HARITA enjoys support from numerous influential early adopters (farmers and organizations locally and abroad). The government of Ethiopia has named weather index insurance as one of its top priorities in its National Adaptation Programme of Action (Federal Democratic Republic of Ethiopia, 2007). And finally, the PSNP, while not perfect, is still widely viewed as a relatively well-governed programme. A survey of independent assessments from 2006 to 2009 revealed no systemic corruption and a high degree of satisfaction among participants (Sharp et al, 2006; Devereux et al, 2008; Food Security Programme Review, 2009). HARITA seeks to sustain and build upon these good practices.

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Conclusions HARITA has found fertile terrain in promoting the resiliency of smallholder farmers to climate stress and disaster risks through community-based efforts guided by regional, national and global adaptation strategies that are informed by global science. While the HARITA model must still be evaluated to determine its effectiveness and impact upon poor people, the pilot’s results, to date, indicate that farmers can create more stable, resilient communities if they have access to the right tools. It also suggests that poor people can play extremely valuable and unique roles in identifying successful and robust climate change adaptation strategies.

References Admassie, A. (2004) ‘A review of the performance of agricultural finance in Ethiopia: Pre and post reform periods’, Paper presented at the International Conference on Macroeconomic, Agrarian Change and Poverty Reduction, Addis Ababa, Ethiopia Amha, W. (2000) Review of Microfinance Industry in Ethiopia: Regulatory Framework and Performance, Occasional Paper No 2, Association of Ethiopian Microfinance Institutions, Ethiopia Ayers, J. and T. Forsyth (2009) ‘Community-based adaptation to climate change: Strengthening resilience through development’, www.allbusiness.com/sciencetechnology/earth-atmospheric-science-climatology/12579172-1.html, accessed 1 September 2009 Barnett, B. J., C. B. Barrett and J. R. Skees (2008) ‘Poverty traps and index-based risk transfer products’, World Development, vol 36, no 10, pp1766–1785 Borchgrevink, A., T. Woldehanna, G. Ageba and W. Teshome (2005) Marginalized Groups, Credit and Empowerment: The Case of Dedebit Credit and Savings Institution (DECSI) of Tigray, Ethiopia, Occasional Paper No 14, Association of Ethiopian Microfinance Institutions, Addis Ababa Botzen, W. J. W. and J. C. J. M. van den Bergh (2008) ‘Insurance against climate change and flooding in the Netherlands: Present, future, and comparison with other countries’, Risk Analysis, vol 28, no 2, pp413–426 Chamberlain, D. and A. Smith (2009) Opportunities and Challenges for Microinsurance in Ethiopia: An Analysis of the Supply-Side and Regulatory Environments, Center for Financial Regulation and Inclusion (CENFRI), Johannesburg CSA (Ethiopian Central Statistical Agency) (2007) Atlas of Selected Welfare Indicators, Statistical Survey 2007, Federal Democratic Republic of Ethiopia, Addis Ababa Dessai, S., M. Hulme, R. Lempert and R. Pielke (2009) ‘Climate prediction: A limit to adaptation?’, Chapter 5 in W. N. Adger, I. Lorenzoni and K. O’Brien, (eds) Adapting to Climate Change: Thresholds, Values, Governance, Cambridge University Press, Cambridge, UK Devereux, S., R. Sabates-Wheeler, R. Slater, M., Tefera, A. Teshome and T. Brown (2008) Ethiopia’s Productive Safety Net Programme: 2008 PSNP Assessment Summary Report, commissioned by the PSNP Donor Coordination Group, Institute of Development Studies, Brighton, UK

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DFID (UK Department for International Development) (2004) Key Sheet 06. Adaptation to Climate Change: Making Development Disaster-Proof, DFID, London Environment Canada (2007) Downscaling, Canadian Climate Change Scenarios Network, www.cccsn.ca/Help_and_Contact/Downscaling-e.html, accessed 1 September 2009 Federal Democratic Republic of Ethiopia (2007) Climate Change Adaptation Programme of Action (NAPA) of Ethiopia, Abebe Tadege (ed), Addis Ababa, Ethiopia Food Security Programme Review (2009) Detailed Report for the Productive Safety Net Programme, Zero Draft Fraser, E. (2007) ‘Travelling in antique lands: Using past famines to develop an adaptability/resilience framework to identify food systems vulnerable to climate change’, Climate Change, vol 83, pp495–514 Girma, T. (2001) ‘Land degradation: a challenge to Ethiopia’, Environmental Management, vol 27, no 6, pp815–824 Hellmuth, M. E., D. E. Osgood, U. Hess, A. Moorhead and A. Bhojwani (eds) (2009) Index Insurance and Climate Risk: Prospects for Development and Disaster Management, Climate and Society No 2, International Research Institute for Climate and Society (IRI), Columbia University, New York, NY Hoeppe, P. and E. N. Gurenko (2006) ‘Scientific and economic rationales for innovative climate insurance solutions’, Climate Policy, vol 6, no 6, pp607–620 IGAD and ICPAC (2008) Climate Change and Human Development in Africa: Assessing the Risks and Vulnerabilities of Climate Change in Kenya, Malawi and Ethiopia’, Human Development Report 2007–08: Fighting Climate Change: Human Solidarity in a Divided World, United Nations Development Programme, Intergovernmental Authority on Development and the Climate Prediction and Applications Centre, Djibouti, Republic of Djibouti ILO (International Labour Organization) (2009) The Social Economy: Africa’s Response to the Global Crisis, ILO, Geneva, Switzerland IMF (International Monetary Fund) (2007) Country Report for Ethiopia, IMF, Washington, DC IPCC (Intergovernmental Panel on Climate Change) (2007) Working Group II, Fourth Assessment Report, www.ipcc-wg2.org, accessed 15 January 2008 Ketema, S. (1997) Eragrostis tef (Zucc.) Trotter, International Plant Genetics Research Institute, Addis Ababa, Ethiopia Marengo, J. A., B. Hewitson, M. Tadross and M. F. Zermoglio (2008) ‘Climate modelling and downscaling under the Nairobi Work Programme’, UNFCCC In-Session Workshop on Climate Modelling, Scenarios and Downscaling under the NWP on Impacts, Bonn, Germany, http://unfccc.int/files/adaptation/application/pdf/ipcc.pdf, accessed 1 September 2009 Microned (2008) Ethiopia: Microfinance Country Scan, Oxfam Novib, The Hague, The Netherlands Möhner, A. and R. J. T. Klein, (2007) The Global Environment Facility: Funding for Adaptation or Adapting to Funds?, Climate and Energy Programme Working Paper, Stockholm Environment Institute, Stockholm, Sweden Osgood, D. E., P. Suarez, B. Hansen, M. Carriquiry and A. Mishra (2008) Integrating Seasonal Forecasts and Insurance for Adaptation among Subsistence Farmers: The

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Case of Malawi, Policy Research Working Paper 4651, World Bank, Washington, DC Peterson, N. (2009) Livelihoods, Coping, and Microinsurance in Adi Ha, Tigray State, Ethiopia: A Report on Surveys and Interviews Conducted in June and August 2009, Unpublished report, Columbia University, New York, NY Pratt, B. and L. Earle (2004) Study on Effective Empowerment of Citizens in Ethiopia, International NGO Training and Research Centre, Oxford, UK Rosenhead, J., M. Elton and S. K. Gupta (1972) ‘Robustness and optimality as criteria for strategic decisions’, Operational Research Quarterly (1970–1977), vol 23, no 4, pp413–431 Sharp, K., T. Brown and A. Teshome (2006) Targeting Ethiopia’s Productive Safety Net Programme (PSNP), IDL Group and Overseas Development Institute, www.odi.org.uk/resources/download/3035.pdf, accessed 15 December 2008 Tadesse, M. and M. Victor (2009) Estimating the Demand for Micro-Insurance in Ethiopia, Report by Oxfam America for the ILO/UNCDF, Geneva, Switzerland UNDP (United Nations Development Programme) (2007) Fighting Climate Change: Human Solidarity in a Divided World, Human Development Report 2007–08, Palgrave McMillan, New York, NY van Aalst, M., T. Cannon and I. Burton (2008) ‘Community level adaptation to climate change: The potential role of participatory community risk assessment’, Global Environmental Change, vol 18, no 1, pp165–179 Walling, L. J. (undated) ‘Continuing the legacy of participatory planning in climate change adaptation planning initiatives in the Caribbean’, www.sidsnet.org/docshare/LWallingPresentation.pdf, accessed 1 September 2009 World Bank (2006) Project Appraisal Document: Financial Sector Capacity Building Programme, Report No 36272 – ET, World Bank, Washington, DC

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3.7 Adaptation to Climate Change: Lessons from North African Cases

Benjamin Garnaud and Raphaël Billé

Introduction During recent years, adaptation to climate change has gained serious momentum in development practices, with many development agencies and non-governmental organizations (NGOs) launching adaptation projects and programmes. While most of these organizations decided to start ‘doing’ adaptation three to four years ago, the available knowledge on what to do and how to do it was not particularly satisfactory. With a ‘trial-and-error’ mindset, it was thus argued that starting at that time would allow such organizations to ‘learn by doing’, which seems fairly sensible in a context of great pressure from civil society and poor countries themselves. The current situation is that the United Nations Development Programme (UNDP), the UK Department for International Development (DFID), the German Gesellschaft für Technische Zusammenarbeit (GTZ), the Canadian International Development Research Centre (IDRC), ENDA, Oxfam and many others have managed, or are currently managing, adaptation projects in most of the developing world. We are now entering an uncertain phase where, on the one hand, there is a need for critical review of what has been done so far in terms of adaptation projects and programmes, and, on the other hand, a very strong push is being felt to rapidly scale up the current initiatives in response to both much more pressure and much more financing. This push is related to the international

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context of climate change, and most obviously to the talks under the United Nations Framework Convention on Climate Change (UNFCCC): the momentum around adaptation has been growing since the 11th Conference of the Parties (COP 11) in Montreal in 2005 and has sped up since COP 13 in Bali in 2007 and COP 14 in Poznan in 2008 (Garnaud, 2009).1 Yet, the two terms of the adaptation equation – first, a need to pause for reflection and, second, the push to go faster and to scale up – are more contradictory than compatible. In practice, it seems that the need to review what has been done so far in order to draw lessons from this ‘learning by doing’ phase is something that often falls off the agenda. It is the precise aim of this chapter to contribute towards this requirement for re-evaluation. We focus here on four North African countries – namely, Algeria, Egypt, Morocco and Tunisia (see Figure 3.7.1). These countries have reasonably similar current socio-economic and climate contexts, and will face the same type of climate change impacts that will exacerbate comparable current stresses (Benoit and Comeau, 2005). They are also in a region – the Mediterranean Basin – which is regarded as particularly worrying in terms of climate change, both because it is already prone to unsustainable development and because it is considered to be an area that will experience relatively intense climatic changes. These countries are home to several adaptation projects and programmes and offer a good opportunity to study the realizations in terms of implementing adaptation. In this chapter, we follow a three-phased approach. We first review the scientific facts in terms of impacts and vulnerabilities in these four countries. We then examine the macro-level (i.e. national initiatives such as national communications to the UNFCCC) and the micro-level (i.e. small-scale adaptation projects). Finally, we compare these different levels and the scientific research to identify gaps, potential opportunities and threats to implementation, drawing lessons and recommendations for development cooperation practitioners. The work presented here is part of a larger research programme that includes fieldwork, interviews and literature review.

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What does science tell us in terms of impacts and vulnerabilities? Although the reality of human-induced climate change has reached an overwhelming consensus among scientists, the projections of its impacts are subject to many uncertainties. The state of knowledge about the physical mechanisms of the climate system, the necessary relative simplicity of the models, and the uncertainty regarding future emissions of greenhouse gases are examples of factors that contribute to these uncertainties. It is still impossible to have a precise image of future impacts at workable levels, and it is unsure whether we will be able to build such an image in the near future. Yet, we can already be reasonably confident about trends at regional or even national levels: in the Mediterranean Basin, in general, and its southern shore, in particular, the picture is actually unequivocal. It is reckoned that the Mediterranean will be one of the regions of the world that will experience the most climate changes and impacts, an acknowledgement that has earned its designation as a climate change ‘hot-spot’ (Giorgi, 2006). There is a strong agreement among scientists on the main symptoms of climate change in North Africa. Air temperatures will rise (somewhere between ⫹2.2°C and ⫹5.1°C by 2080 to 2099 compared to 1980 to 1999 levels, according to the Intergovernmental Panel on Climate Change)2 more than the global average, and the number of hot and very hot days will increase at the same pace as the number of cold and very cold days will decrease. There will be a general and significant decrease in mean precipitation (between ⫺4 and ⫺27 per cent by the period between 2080 and 2099, compared to 1980 to 1999)3 and drought periods will increase (Christensen et al, 2007). It is important to remember that these numbers will be subject to consequent local variations due to small-scale factors that models do not currently take into account. Other extreme events such as floods and heatwaves are also likely to become more frequent and stronger. Regarding sea-level rise, studies and data are too scarce at the moment for reliable projections for the Mediterranean Basin, and there is a high probability of significant disparities at sub-regional levels within this region. These symptoms will themselves bring numerous changes to the natural environment. While it is not the aim of this chapter to compile an exhaustive list of such physical impacts, some examples are given here that we regard as important in the North African context: the water cycle will be modified because of more evaporation and less precipitation; there will be an increase in the speed of desertification and in the porosity of the soils, contributing to the increase in floods; some species will migrate northwards and to higher altitudes; the risk of forest fires and tree parasites will increase; human health will be affected by repeated heatwaves and new diseases (Boko et al, 2007; Van Grunderbeeck and Tourre, 2008). It is important to note here that these impacts will not bring new threats, but will, rather, exacerbate existing stresses mainly caused by human pressure on the environment (Magnan et al, 2009).

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Climate and environmental conditions of the Mediterranean Basin have changed considerably throughout history. The temperature fluctuated between ⫺8°C (20,000 years ago compared to current levels) and ⫹1°C to ⫹3°C (6000 years ago). Sea level has also varied by several tens of metres. The main difference, though, between current/future climate change and these historical changes is that the pace of change over the coming centuries will be much faster (Van Grunderbeeck and Tourre, 2008). Mediterranean societies have been subject to, and have adapted to, structural water stress, flash floods and forest fires for several millennia. Until relatively recently these societies remained well adapted to this variable environment, which makes them interesting case studies. But the expected climate change raises a key question in terms of adaptation: will North African populations be able to cope and adapt to the expected climate change with the same success as in the past? There are two challenging issues involved: first, the changes may occur too rapidly for ecosystems, populations and economies to adapt autonomously; second, it is widely recognized that North Africa – and the rest of the Mediterranean Basin – has developed in such a way that threatens the ability of these ecosystems, populations and economies to adapt successfully. The region presents much vulnerability to climate change impacts, and autonomous adaptation seems both unlikely and insufficient. Yet, elements of culture and history that have shaped past adaptations to what has already proved to be a demanding environment should be considered for their future potential.

Identified vulnerabilities Placing future climate changes into a socio-economic context is critical to understanding the risks (and opportunities) that climate change will bring. This is true in many parts of the world; but it is seldom as relevant as in North Africa, where years of unsustainable development have not enabled societies to adapt even to the current climate, most critically with respect to water resources and agricultural activities (Benoit and Comeau, 2005; Dougherty et al, 2007). Algeria, Egypt, Morocco and Tunisia are all considered as ‘medium human development’ countries (UNDP, 2009) with Human Development Indexes ranking from 98th for Tunisia to 130th for Morocco (out of 182 countries). Economic growth and development have been relatively slow in the past decades, leading to (and partly caused by) exportation of labour and capital resources to developed nations. In parallel, the demographic transition leads to rapid demographic growth and pushes millions of the active workforce onto the labour market, while slow growth and an inadequate institutional environment lead to high unemployment rates, mainly among the young, urban and even educated population (Benoit and Comeau, 2005; Ould Aoudia, 2008). High inequality, poorly diversified economies and uncontrolled urbanization combine to make North African societies ill equipped to deal with current climatic and environmental stresses, not to mention the additional burden imposed by climate change (Agoumi, 2003). Furthermore, occasional

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political instability and insecurity act as disincentives for central governments and agencies to adopt long-term perspectives. Not totally disconnected to this complex picture for adaptation is the fact that North African countries currently face a series of environmental stresses that pose a great threat to their future, caused, of course, by the natural conditions experienced, but also by the development path that has been taken (Benoit and Comeau, 2005). North Africa is a land of transition between the Sahara and Southern Europe, and although it has relative agricultural potential, it is nonetheless mainly arid or semi-arid. Water resources are naturally scarce, ecosystems (such as oases, deltas or coastal zones) are fragile and severe droughts and floods are frequent. Years of globally acknowledged unsustainable development have led to, inter alia, uncontrolled and destructive urbanisation (most critically on coastal zones), destruction of ecosystems and arable land, impermeabilization of soils, pollution, overexploitation of natural resources (including water), and have aggravated the natural stresses of the region. The current scarcity of water resources is a national concern in all four countries, and it has little to do with climate change. Agriculture, still an important economic component in these countries,4 while suffering from the effects of water scarcity, also contributes to the problem through current practices (e.g. through the production of waterintensive export crops or the use of highly inefficient irrigation systems). The Nile Delta is severely threatened by sea-level rise, mainly due to the construction of the Aswan Dam in the 1970s, which has trapped the sediments and hampered the necessary recharge of the delta, inducing massive erosion and shoreline retreat (Frihy, 2003). Several hundreds of thousands, if not millions, of Egyptians are threatened by rising water, leading to loss of habitats, homes and – above all – a great proportion of Egypt’s productive land. Again, this has little to do with climate change, at least for the moment. Climate change thus does not really constitute a new threat; rather, it exacerbates existing threats – for instance, by reducing the amount of available water, increasing the number and the severity of droughts or contributing to sea-level rise. The fact that North African countries are not adapted to these existing threats is of great importance in the design of adaptation strategies: the causes of this current maladaptation will be barriers to adaptation to climate change. Adaptation in Algeria, Egypt, Morocco and Tunisia thus requires the placement of future climatic stresses into the context of current ones and their underlying causes. In North African countries, national or regional vulnerabilities are largely identified in the scientific literature and in national initiatives (such as national communications to the UNFCCC or in national plans) (see, for example, Egyptian Environmental Affairs Agency, 1999; Dougherty et al, 2007). Unsurprisingly, they differ little and relate mainly to water, agriculture, coastal zones and fragile ecosystems (e.g. oases, mountain ecosystems and the Nile Delta). Health, livestock, fisheries and aquaculture, tourism and urban centres have also been repeatedly identified as important vulnerabilities to address.5 Identified vulnerabilities are detailed in Table 3.7.1.

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Table 3.7.1 Vulnerabilities of North African countries identified in national communications and initiatives, and in the scientific literature V ulnerabilities Co

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potential impacts of climate change and the damage cost will be in the range of 17 to 34 per cent of total project cost (e.g. US$15 to 29 billion annual loss, without any adaptation measures) (Takemoto and Mimura, 2007). Therefore, if the project design includes adaptation functions, damage to the project itself will be reduced, the outcome of the project will be secured, and total costbenefit balance of the project will be improved. This means that integration of adaptation within assistance projects is important not only for the project goal, but also from a cost-benefit viewpoint.

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Simplified method to assess the adaptation function and co-benefits of ODA projects A simplified assessment method will be useful to support participants of an ODA project for their assessment and understanding of the adaptation function and co-benefits of the project. For this reason we developed the simplified assessment method shown in Figure 4.2.1. The method uses a chart for semi-quantitative visualization. It covers six points of adaptation and two co-benefits – namely, adaptation effects under present and future condition; adverse impacts caused by the adaptation; sustainability of effectiveness and operation of the project; vulnerability of the project site; and co-benefits in the present and future. Scores on the axis correspond to the ranks in Table 4.2.4, from A (definitely applicable) to NA (not available). For the evaluation, data availability with regards to the project should be taken into account. Assessment steps are listed in Figure 4.2.1 in accordance with the data availability. Co-benefits in the present and future are expressed by layered disks outside of the chart. The number of disks gives the evaluation score. The method was applied to the Ethiopian project case study to examine its advantages and disadvantages. The result is shown in Figure 4.2.2. The adaptation function under present conditions received a high score, including some co-benefits. However, the contribution under future conditions was scored lower: sustainability of effect/operation of the project and co-benefits were scored at the ‘possible’ rank only, and the potential for future adverse impacts due to the adaptation measures was unclear. The distortion or imbalance of the chart is caused by limited information to assess future climate change conditions. If minimum basic data were collected during the project, such as the past precipitation trend and/or groundwater capacity, it would be helpful to assume possible climate change impacts, as well as the future adaptation function of the project.

Results and future issues3 Based on the analysis in this study, many ODA projects were anticipated to benefit from adaptation, although no consideration for adapting to future climate change at the planning stage was included. Because of the current limited availability of reliable country-specific future climate change projections, it may be difficult to assess the future adaptation effects of these projects at the implementation stage. In this study, considering limited information on likely future climate change scenarios in developing countries, a simplified method was proposed to assess the presence or absence of adaptation functions and the co-benefits of ODA projects. The proposed method can contribute to assessing ex-ante adaptation function and co-benefits of ODA projects. Moreover, the method can be applied to the activities of integrating the adaptation function and co-benefits

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within the process of developing ODA projects. Considering such incorporation and integration, the following issues are outlined:

Review and understand past climate-related phenomena and their risks at a local scale It is most important to review past climate-related phenomena such as flood and drought, storm surges and tsunamis, and to understand the risks introduced by them at a local level. Reviewing past climate-related phenomena is essential not only for understanding basic information of possible future climate change impacts upon the region, but also for considering whether the ODA project can cope with the risks at a local level.

Clarify the ‘safety factor’ of conventional ODA projects Most conventional ODA projects have considered a certain amount of ‘safety factor’ or ‘allowance height’ to cope with fluctuating natural conditions such as interannual variation of rainfall. It will be important to clarify whether these factors are sufficient for adaptation to climate change. In the case that this is insufficient, such safety factors should be updated considering future climate change scenarios.

Implement awareness-raising and capacity development of project participants The most serious barrier to understanding the impacts of climate change by ODA project participants may be the difficulty in distinguishing between impacts of ‘present climate variability’ and that of ‘future climate change’. This difficulty is attributable to the sequence of present climate variability and future climate change. ODA project implementers sometimes mention the difficulty of identifying what should be done for adaptation in addition to conventional projects. This question may be derived from the uncertainty of climate change impacts. In order to develop an ODA project that is adaptive to future climate change, it is necessary for project implementers to understand the latest climate change predictions, possible impacts and adaptation options. Therefore, it is important to implement awareness-raising and capacity development of project implementers concerning climate-related scientific output. In addition, establishment of a climate-related cross-sectoral data collection system and effective dissemination of such data are important in promoting awareness-raising and capacity development for the project implementers. The proposed simplified method should be further elaborated upon in the future in order to produce a feasible and user-friendly assessment method to develop better ODA projects in terms of adaptation functions for developing countries.

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Notes 1 2 3

This section is based on JICA (2006a). This section was adapted from Fujimori et al (2008). This section is based on JICA (2006b).

References Fujimori, M., M. Kawanishi and N. Mimura (2008) ‘Proposal of adaptation function assessment to integrate adaptation to climate change into ODA projects’, Environmental Systems (in Japanese), vol 36, pp27–35 IPCC (Intergovernmental Panel on Climate Change) (2001) Climate Change 2001: Impacts, Adaptation and Vulnerability, IPCC Third Assessment Report: Working Group II, Cambridge University Press, Cambridge, UK IPCC (2007) Climate Change 2007: Impacts, Adaptation and Vulnerability, IPCC Fourth Assessment Report: Working Group II, Cambridge University, Cambridge, UK JICA (Japan International Cooperation Agency) (2004) Integrated Water Use Project (Phase 2) in the Republic of the Gambia (in Japanese), JICA, Tokyo, Japan JICA (2006a) Basic Design Study Report on the Project for Water Supply Development in the Afar National Regional State in the Federal Democratic Republic of Ethiopia (in Japanese), JICA, Tokyo, Japan JICA (2006b) Japanese Technical Cooperation for the Project on Rural Water Supply Technology in the Central Dry Zone (in Japanese), JICA, Tokyo, Japan JICA (2007) JICA’s Assistance for Adaptation to Climate Change (in Japanese), JICA, Tokyo, Japan Klein, R (2001) Adaptation to Climate Change in German Official Development Assistance: An Inventory of Activities and Opportunities, with a Special Focus on Africa, GTZ Climate Protection Programme, Eschborn, Germany MOEJ (Ministry of the Environment, Japan) (2008) Wise Adaptation to Climate Change – Conference Minutes of the Second Meeting of Committee on Climate Change Impacts and Adaptation Research (in Japanese), MOEJ, Tokyo, Japan OECD (Organisation for Economic Co-operation and Development) (2005) Bridge Over Troubled Waters: Linking Climate Change and Development, OECD, Paris, France Stern, N. (2006) The Economics of Climate Change: The Stern Review, Cambridge University Press, Cambridge and New York Takemoto, A. and N. Mimura (2007) ‘Study on international framework on adaptation to climate change in developing countries’, Environmental Systems (in Japanese), vol 35, pp1–11 UNFCCC (United Nations Framework Convention on Climate Change) (2006) Background Paper for the African Workshop on Adaptation Implementation of Decision 1/CP.10 of the UNFCCC Convention, UNFCCC UNFCCC (2007a) Decision -/CP.13, Bali Action Plan, UNFCCC UNFCCC (2007b) Background Paper: Impacts, Vulnerability and Adaptation to Climate Change in Asia, UNFCCC World Bank (2006) An Investment Framework for Clean Energy and Development, Development Committee, 5 April 2006, World Bank

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4.3 Emerging Issues: Forced Migration by Climate Change

Mohamed Hamza, Lezlie Morinière, Richard Taylor, Nilufar Matin and Basra Ali

There is now sufficient scientific data to conclude, with a high degree of certainty, that the likely speed and magnitude of climate change in the 21st century will be unprecedented in human experience, posing daunting challenges of adaptation and mitigation for all life forms on the planet. (Dupont and Pearman, 2006) There are well-founded fears that the number of people fleeing untenable environmental conditions may grow exponentially as the world experiences the effects of climate change. This new category of ‘refugee’ needs to find a place in international agreements. We need to better anticipate support requirements, similar to those of people fleeing other unviable situations. (Janos Bogardi, United Nations University Institute for Environment and Human Security (UNU-EHS))1 This is a highly complex issue, with global organizations already overwhelmed by the demands of conventionally-recognized refugees, as originally defined in 1951. We should prepare now, however, to define, accept and accommodate this new breed of ‘refugee’ within international frameworks. (Hans van Ginkel, United Nations University)2

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Introduction Climate change will have a progressively increasing impact upon environmental degradation and environmentally dependent socio-economic systems, with potential to cause substantial population displacement. There is increasing evidence that serious and relatively rapid alterations to ecosystems induced by climatic and anthropogenic factors will have direct and indirect impacts upon societies who, when other coping mechanisms are overcome, will have no other option but to migrate as a permanent or temporary coping strategy. The key concerns in least developed countries (LDCs) will include serious threats to food security and health, considerable economic decline, inundation of coastal areas, and degradation of land and freshwater resources (Reuveny, 2007). These environmental events and processes have also been predicted to lead to large-scale displacement of people, both internally and internationally, with estimates of some 200 million to 1 billion climate change-induced migrants by 2050. Many recent reports on global environmental trends have highlighted the degradation of the environment and the capacity of our ecosystems to provide or maintain services. The Millennium Ecosystem Assessment (MA, 2005) concluded that 15 of 24 ecosystem services reviewed were being degraded or used unsustainably, affecting, in particular, poor resource-dependent communities. Particularly highlighted by the MA (2005) is the fact that 2 billion people living in arid, semi-arid and sub-humid regions are extremely vulnerable to the loss of ecosystem services, including water supply. In particular, it notes that: • • •

•

10 to 20 per cent of dry lands are already degraded (there is, however, uncertainty in the measurement of the extent of desertification). Pressure is increasing on dry land ecosystems for providing services such as food and water for humans, livestock, irrigation and sanitation. Climate change is likely to increase water scarcity in regions that are already under water stress as they accommodate close to one third of world population but harbour only 8 per cent of global renewable freshwater resources. Droughts are becoming more frequent and their continuous reoccurrence can overcome the coping mechanisms of communities.

The fourth Global Environment Outlook of the United Nations Environment Programme (UNEP, 2007) has similar general conclusions as the Millennium Ecosystem Assessment reports in that it highlights the fact that environmental degradation observed worldwide (air pollution, land and water resources degradation, loss of biodiversity) undermines development, human well-being and the achievement of some of the Millennium Development Goals (MDGs). The report notes that one of the many consequences of environmental degradation is human migration even though establishing direct links is difficult because of the numerous push factors at play.

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When people are faced with severe environmental degradation they usually have one of three options or go through three stages: 1 2 3

stay and adapt to mitigate the effects; stay, do nothing and accept a lower quality of life; or leave the affected area.

The process of movement and migration is usually subject to a complex set of push-and-pull forces, where push forces relate to the source area, while pull factors relate to the destination. These forces are in constant flux, as is environmental change, and interact with socio-economic and political conditions, including state or government decision-making powers which can tip the balance at any point by either denying movement or the right to settle elsewhere. The relationship between environmental change and potential humanitarian crises has been captured by McGregor (1993); Myers (2002); Kibreab (1994, 1997); Myers and Kent (1997); Black (2001); Lee (2001); Castles (2002); Christian Aid (2007); and Massey et al (2007). However, we know little about the interplay between environmental change and stresses on ecological systems, resulting socio-economic vulnerability, and potential outcomes in terms of population displacement or induced migration. So far these relationships are poorly conceptualized, lack systematic investigation and are reduced to simplistic causal explanations. This leads to misleading conclusions that deny the complex multivariate processes – environmental, political, social and economic. Given the complex nature of migration, the inherent uncertainty of climate change predictions and the lack of evidence-based research on the climate–migration nexus, the questions of how much migration might be stimulated by climate change previously, now and in the future, let alone what might be the best policies adopted to deal with such migration, remain largely unanswered. This chapter will focus on how environmental change and environmental hazards could contribute to migration by exploring mechanisms through which vulnerability and migration are linked – via multi-drivers such as livelihoods, relocation policies and other factors. The chapter will begin by exploring the complex nature of environment–migration interaction. It will delve into the origins of the term ‘environmental refugees’ and how it has been constructed, highlighting the problems and consequences of such terminology. It will then proceed to assess the accuracy of current estimates of environmentally induced migration or displacement. Exploring both, problems with definitions/typologies/categorization and estimates, will lead to exploring the multi-causality and complexity of the phenomena itself and the inadequacy of current methodologies. The chapter will then present the beginnings of current work under way to develop new approaches to understanding the potential scale of displacement: estimating the global footprint for migration as an alter-

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native approach to the simplistic counting. Evidence is presented from countries and regions whose populations are most affected by climate change and proactive approaches of resilience, sustainability and adaptation are advocated to mitigate environmentally induced displacement – a drought case from Kenya and a flooding case from Bangladesh where there are early indications of population displacement. The chapter will conclude with policy recommendations.

Exploring the environment–migration debate Migration – whether permanent or temporary; internal, regional or international – has always been a part of history and a possible coping strategy for people facing environmental changes such as sudden disasters, creeping processes or cyclical climate conditions. Pre-history and history are marked by (episodic and localized) human movement from one climate zone to another, as people have sought out environments that would support both survival and aspirations to a more stable existence. Migration in the past may have been accompanied by some sense of despair that familiar landscapes no longer provided safe or supporting habitats for people. Today, environmental change, including climate change, presents a new threat to human security and a new face of migration. Three factors distinguish the present era and foreseeable future from the past and add to the complexity of investigating population movement due to environmental factors. First, the global scale of environmental change and potential impacts are new and unprecedented phenomena. Second, impacts will no longer be episodic or localized. And, finally, human agency is at the centre of environmental change and the potential to respond to it. Due to such complexity and the fact that lines are blurred between what is environmental and what is economic when populations’ livelihoods are largely environmentally based, it has always been difficult to differentiate between ‘environmental refugees’ and ‘economic migrants’. A decision to move may often be a function of a push to leave one disaster-affected location and the economic pull of another more promising location. There are cases in modern history that demonstrate just that. Three million people fled the Dust Bowl of the 1930s, while 700,000 mostly poor black people departed to northern states following the Mississippi Delta flood of 1927 (Boano et al, 2007). Their decisions in many instances reflected a combination of pressures and aspirations. There are three main dimensions to the debate surrounding the notion of environmental migrants/refugees (e.g. Castles, 2002): 1

Definitions: refugee versus migrant? There is the definitional debate over the terminology ‘environmental refugee’ and who can be classified under such a definition.

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Is it a measurable phenomenon? There is a debate over whether environmentally induced migrants even exist (i.e. can environmental factors be identified as a root cause of migration or displacement?). Who provides policy direction, protection/assistance? There is the debate over who will provide protection to such a category of people should they be identifiable as a category. The debate is important because it shapes the way in which policy-makers analyse and address environmental change and human movement.

This section explores the extreme complexity of labelling or estimating population displaced due to environmental factors, broadening its scope as it progresses to highlight the multiple drives/factors interacting with complex feedback loops to influence migration decisions.

Definition, typology and categorization problems The label ‘environmental refugees’ is highly contested not least because it grossly oversimplifies the multi-causality of social, economic and political factors which underpin environmentally forced migration. Debates around linkages between environmental degradation and forced migration have led to the emergence of a range of highly contested terms – environmental refugee, environmental migrant, forced environmental migrant, environmentally motivated migrant, climate refugee, climate change refugee, environmentally displaced person (EDP), disaster refugee, environmental displacee, eco-refugee, ecological displaced person and environmental refugeeto-be (ERTB). It is widely agreed that these terms have no accepted place in international refugee law and environmental conditions do not constitute a basis for international protection. Boano et al (2007) also indicate that these terms are descriptive, not a status that confers obligations on states. Debate about their validity is often shaped by simplistic judgements and preconceived definitional labels. It is still important to explore how terms and definitions originated and the complexity of agreeing on a suitable definition. Black (2001) noted that Lester Brown of the Worldwatch Institute introduced the concept of environmental refugees during the 1970s. It was subsequently addressed in a November 1984 briefing document of the Londonbased International Institute for Environment and Development (IIED) (Kibreab, 1997; Black, 1998) and entered into common usage after a 1985 United Nations Environment Programme (UNEP) policy paper written by E. El-Hinnawi entitled ‘Environmental Refugees’ (El-Hinnawi, 1985). In the aftermath of the displacements caused by the gas leak in Bhopal in India and the nuclear catastrophe in Chernobyl, El-Hinnawi (1985, p4) defined environmental refugees as: … those people who have been forced to leave their traditional habitat, temporarily or permanently, because of a marked

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environmental disruption (natural and/or triggered by people) that jeopardized their existence and/or seriously affected the quality of their life. Norman Myers, who has written extensively on environmental change and population displacement for several decades, has defined environmental refugees as: … people who can no longer gain a secure livelihood in their homelands because of drought, soil erosion, desertification, deforestation and other environmental problems, together with associated problems of population pressures and profound poverty. In their desperation, these people feel they have no alternative but to seek sanctuary elsewhere, however hazardous the attempt. Not all of them have fled their countries, many being internally displaced. But all have abandoned their homelands on a semi-permanent if not permanent basis, with little hope of a foreseeable return. (Myers, 2005, pp6–7) A recent paper from the United Nations University’s Institute for Environment and Human Security (UNU-EHS) defined a ‘forced environmental migrant’ as somebody ‘who “has” to leave his/her place of normal residence because of an environmental stressor … as opposed to an environmentally motivated migrant who is a person who “may” decide to move because of an environmental stressor’ (Renaud et al, 2007). The term ‘environmental refugees’ has been increasingly used despite having no agreed definition in international law, never having been formally endorsed by the United Nations, and the failure of experts to reach any kind of consensus. Furthermore, the term does not readily fit within the globally recognized labels used to define forced displacement: refugees (who have crossed internationally recognized borders) and internally displaced persons (IDPs). Even though the term ‘environmental refugee’ is used, the authors encapsulate population movements that are not of the refugee type, at least not as per the definition of the 1951 Refugee Convention. In addition, of the four aspects of the 1951 Refugee Convention,3 the one most difficult to define in the context of ‘environmental refugees’ is the fear of persecution. Unless it is assumed that ‘nature’ or the ‘environment’ can be the persecutor, the term refugee does not appear suitable for describing those displaced by environmental factors. Castles (2002) argued that the environmental refugee terminology and conceptualization is inadequate, but nevertheless did not dismiss the possibility that environmental factors can be very important for triggering migration in certain circumstances. Anthony Oliver-Smith has argued the term ‘environmental refugee’ can be misleading as it ‘tends to suggest that nature is at fault, when in fact humans are deeply implicated in the environmental changes that

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make life impossible in certain circumstances’ (discussion with chapter author, July 2008). However, there have been several attempts to promote the idea that a new category of refugees (the extreme case of population movement) is needed in order to protect people who have to move because of environmental factors (e.g. Conisbee and Simms, 2003). The lack of precise definition of the terms routinely deployed, fears around the emotionally charged issue of migration, vastly divergent estimates of the likely scale of climate-induced displacement, and lack of dialogue between ecologists and social scientists render the links between environmental change and forced migration complex and debatable. In this chapter the term ‘environmentally induced migrant’ is used to characterize cases when people must move – either swiftly because of an environmental stressor or in response to gradual negative environmental change – regardless of whether or not they cross an international border.

The politics of numbers Studies of climate-induced migration in the past have commonly calculated the numbers of environmental migrants by projecting physical climate changes, such as sea-level rise, rainfall decline and drought, on exposed populations. The fact of multi-causality of environment-induced migration and how extraordinarily difficult it is to develop and defend methodologies for calculating such numbers has not stopped researchers and policy-makers from trying. Some of the more prominent and often quoted estimates are as follows: •

•

•

• •

• • •

The International Federation of Red Cross and Red Crescent Societies (IFRC) estimated in 2001 that for the first time the number of environmental refugees exceeded those displaced by war. The United Nations High Commissioner for Refugees (UNHCR, 2002) estimated there were then approximately 24 million people around the world who have fled because of floods, famine and other environmental factors. El-Hinnawi (1985) estimates there are already some 30 million refugees, and 50 million environmental refugees by 2050 – equivalent to 1.5 per cent of 2050’s predicted global population of 10 billion.4 The Almeria Statement (1994) observed that 135 million people could be at risk of being displaced as a consequence of severe desertification. Myers (2005), who made a 1994 prediction of 150 million environmental refugees, now believes the impact of global warming could potentially displace 200 million people. The Stern Review, commissioned by the UK Treasury, agrees that it is likely there could be 200 million displaced by 2050 (Stern, 2006). Nicholls (2004) suggested that between 50 and 200 million people could be displaced by climate change by 2080. Friends of the Earth (2007) predict climate refugees at 200 million worldwide – and 1 million from small island states – by 2050.

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UNEP argues that by 2060 there could be 50 million environment refugees in Africa alone. Christian Aid (2007) has postulated that 1 billion people could be permanently displaced by 2050 – 250 million by climate change-related phenomena such as droughts, floods and hurricanes and 645 million by dams and other development projects (Christian Aid, 2007).

There is almost a consensus now that these studies make simplistic assumptions conflating exposure with vulnerability, thereby ignoring people’s ability to cope with variations in climate. Migration is assumed to be a failure to cope. In reality, migration is the response of much more complex behavioural decisions and risk trading. In some cases, it could represent planned adaptation, while in others it could be the last choice when all else fails. Those unable to migrate are often considered the most vulnerable of all.

Multi-causality and complexity Migration is a multifaceted phenomenon influenced by a gamut of factors and complex interactions of push-and-pull forces. These influences include economic causes (actual, expected and perceived differences in wages, material standards of living, etc.), socio-cultural aspects (language, ethnicity, religion, values, and proximity of family) and geopolitical considerations (conflict, political instability). Developed countries tend to regard migration as a threatening factor that affects their sovereignty and national identities, while developing countries regard it as a possible escape from their political, economic, social and overpopulation problems (Kapstein, 2006). A central question to all cases facing environmentally induced migration is the degree to which environmental factors contribute to displacement or migration. Another pressing question involves differential vulnerability to environmentally forced migration. Not everyone is vulnerable in the same degree to environmental change. In some cases, social class or occupation may cushion against the impacts of environmental change. In other cases, the unequal or distorted application of governmental assistance programmes may protect some groups and expose others to pressures to migrate (Warner et al, 2009). Current migration patterns manifest ‘spikes’ of migration when the threshold of socio-ecological tipping points has been reached or crossed. However, tipping points are very complex to measure, let alone predict. We use the term and concept of tipping points here only to demonstrate the complexly interacting multi-stressors involved in generating migration and reaching a potential point of no return in terms of the onset of a humanitarian crisis. And as much as there is debate over the definition of who is an environmental migrant, there is no consensus on the factors and drivers of the process. Black (2001) is an opponent of the concept of environmental refugees. He argues that:

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… although environmental degradation and catastrophe may be important factors in the decision to migrate, and issues of concern in their own right, their conceptualization as a primary cause of forced displacement is unhelpful and unsound intellectually, and unnecessary in practical terms … the linkages between environmental change, conflict and refugees remain to be proven … rather, migration is … perhaps better seen as a customary coping strategy. Castles (2002) takes a more nuanced view, noting that migration involves ‘complex patterns of multiple causality, in which natural and environmental factors are closely linked to economic, social, and political ones’. Environmental change does not undermine human security in isolation from a broader range of factors – poverty, the degree of state support to community, access to economic opportunities, the effectiveness of decision-making processes, and the extent of social cohesion within and surrounding vulnerable groups. Boano et al (2007) concur that the importance of multi-causality in any explanation of environmentally induced migration and, thus, policy responses, is confirmed in the cases of El Salvador, Haiti, the Sahel and Bangladesh, scrutinized by Lonergan (1998). A plethora of processes has been responsible for displacement in a complex mixture of social, economic and institutional factors. The same argument has been strongly developed in the cases of Bangladesh, North Korea and Sudan, where people fled their homes because of multiple causalities, which included environmental factors but also involved human-induced disasters, international and governmental triggers (Lee, 2001). Between supporters and opponents, it is important first to look into the complexity of migration as a process – especially in terms of it being an adaptive and coping mechanism. Who migrates and how are key questions. The interactive dynamics between migration, attempting to reduce vulnerability and increasing resilience need to be explored first before any attempt to look into any linkages between environmental factors and migration. Investigating any relationships between environmental factors and displaced population needs to first address a number of questions grouped under three main categories: 1

2

Typology of environmental stressors: • direct or indirect displacement? • slow, rapid onset or acute disasters (Renaud et al, 2007)? • temporal, permanent or progressively displaced (El-Hinnawi, 1985)? • disaster, livelihood displaced or habitat changes (Jacobson, 1988)? • environmentally motivated, induced or environmental refugee (Renaud et al, 2007)? Attribution: • Is there a continuum from voluntary to forced migration where adaptations to periodic stress become unsustainable?

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When does environmental degradation affect the social, economic and institutional fabric to the extent of producing significant population displacement? • Have regions at risk reached tipping points in the past (i.e. a point that characterizes two distinguishable states of a system)? • What are the indicators of an impending tipping point or humanitarian crises? Policy agenda: • Are the negative consequences of environmental change inevitable? • Who has adapted? Why and how? • Who has been displaced? Why? • What is the role of social capital, governance structures and political economy in mediating environmental change and potential displacement?

In an era of globalization with burgeoning access to knowledge, information and technology, migration has already emerged as a difficult political issue in its own right. When a layer of serious environmental stresses expected to unfold with climatic change is added, it bears the potential to become exacerbated to intractable levels unless mitigated by anticipatory planned adaptation.

In search of credible methodologies The lack of agreement on typologies and definitions, the vast range of estimated figures (20 million to 1 billion) and the underlying assumption of such estimations is the subject of fierce criticism and debate. At present, our knowledge about the relationship between environmental change/degradation and migration is relatively scant and such wild estimates instil a fear of waves of migrants and humanitarian crises. We have only just begun to answer the basic questions about who has been displaced by global environmental change and why. Uncertain global estimates compromise the possibility of producing reliable, usable and comparable data – without which action is not possible. Boano et al (2007) make a strong and valid point that by homogenizing the concept of environmentally induced displacement, they deny the need to design a complex variety of policy interventions adjusted to many different situations of such displacement. Continuing to search for one all-encompassing definition or one typology of environmental migrants and including them in one figure is not only impossible, it wastes valuable time and detracts from the more important task of understanding the complexity of the process and its multiple drivers. Rather than have recourse to global estimates of future migration, a more valuable route to understanding the potential scale of displacement, and, thus, the scope for policy intervention, is to develop precise typologies and the geography of the phenomenon. Precise typologies require a more nuanced understanding of different forms of environmental displacement. Renaud et al

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(2007) and Dun (2007) offer the most useful typology, amongst a number currently available, by distinguishing three different categories: 1 2 3

environmental emergency migrants/displacees who flee the worst of an environmental impact upon a permanent or temporary basis; environmentally forced migrants who ‘have to leave’ in order to avoid the worst of environmental deterioration; environmentally motivated migrants who ‘may leave’ a steadily deteriorating environment in order to pre-empt the worst.

And instead of playing the numbers prediction game, a much more empirically grounded approach to the issues of environmentally induced population displacement is needed. This effort would identify and map potential environmental ‘hotspots’ and problem locations (both rural and urban, and both long-term processes and specific episodic events) and monitor changing conditions; examine ‘tipping points’ that trigger displacement rather than adaptation in these localities/regions; track migration trends (in relation to environmental depletion, competition for resources and potential or actual sources of conflict); and tailor development policies of resilience and sustainable development to evolving local/regional needs. A step up from the ‘hotspots’ approach is the ‘hot systems’ approach – in other words, monitoring and mapping of not just climate change impact upon highly vulnerable locations, but of multiple systems within these locations, including issues of governance, poverty reduction, distribution of, and access to, resources, coping mechanisms and capacities, etc. The rest of this chapter will focus on understanding the complex interactive processes between migration’s multiple drivers where environmental change is but one of many. Global data currently available are of sufficient quantity and quality to understand a lot more about environmentally induced migration than we do today. Data exist to allow a more systematic assessment of both the geo-temporal distribution of such migrants and the sets of drivers that describe historical documented trends. The chapter will then present two case studies to further illustrate the multiple drivers in environmental-induced migration, where empirical research work was carried out. The first case is from the Turkana region in Kenya, subject mainly to drought stress, and the second is from the Bangladesh Delta, where flooding and sea-level rise are affecting the population’s livelihood system and communities’ overall socio-economic base.

Global footprint A methodology currently being applied to produce a systematic estimate of the weight of the environment in influencing human migration is entitled the global footprint. The global footprint is a very literal quest to discover when and where migrants have been forced or compelled to leave their homelands

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from past to present time, and what the strongest influences on such mobility were. The global footprint compiles the best available evidence through space (multiple scales: gridded, national or regional) and time (up to hundreds of years, time series). All possible existing global datasets will be tapped to explore two discrete elements: 1

2

Potential drivers or triggers of migration. We use the term ‘driver’ widely to include not only direct causes, but also triggers or strong influences of human mobility. Human mobility. The term ‘mobility’ is chosen here to encompass voluntary migration, forced displacement and all profiles in between these two extremes.

This section will describe the contents of the footprint database, the methodologies that will be applied to align the data through space and time, and the desired impacts of the resulting geography of environmentally induced migration.

Footprint contents The global footprint will compile three sets of data: datasets A and B fall under the category of potential drivers, and dataset C, human mobility. For each, an explanation of the best supported link in scholarly literature to mobility and an example of existing candidate datasets are described below.

Potential drivers or triggers of migration The first category of datasets, potential drivers, explores all possible push factors of mobility, whether forced, motivated or voluntary migration. Rather than the host of factors and influences that may pull migrants in one direction or another, only push factors are explored here. Not only will environmental events or processes be included (dataset A), but also datasets on the less debated non-environmental drivers (dataset B), such as economic influences and rapid population growth. Given the multiple-driver consensus, inclusion of a wide set of drivers will enable a systematic assessment of the weight of the environment against that of other push factors. Only when we align all possible influences in a systematic way are we able to explore which driver (or, more likely, which combination of drivers) weighs most heavily on a trend or a specific mobility event (time and place).

Dataset A: Environmental drivers Drivers originating in the natural environment (but not necessarily devoid of human influence) that have been documented to impact upon mobility are included as follows: •

Natural hazards: floods, drought and storms (quakes and volcanoes have been removed because they are not directly influenced by climate). Often

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referred to as sudden-onset disasters, these events have a proven link to homelessness – either permanent or temporary destruction of one’s home. Results of a recent Office for the Coordination of Humanitarian Affairs (OCHA) study show that in 2008 at least 36 million people were displaced by sudden-onset disasters, of which over 20 million were climate-related disasters (OCHA, 2009). Candidate dataset: University of Louvain, Research Centre for the Epidemiology of Disaster’s Emergency Database (EMDAT), 1900 to present, event-based, sub-national spatial resolution. Climate extremes: the impact of extreme temperatures and precipitation in various combinations has been a growing focus of research. Less likely to be direct drivers of migration, these influences are most often filtered through flooding or resource scarcity. Candidate dataset: Climate Research Unit (CRU)/Tyndall Centre’s CRU TS 2.1, nine climate variables, 1901 to 2002, gridded spatial resolution, will be used to construct a Climate Extreme Index (CEI). Slow onset environmental processes: the slow onset faces of degradation (land erosion, deforestation, desertification, glacier melt, sea-level rise, air and water pollution, coral reef destruction) are triggers of human mobility. Often difficult to measure, they are generally considered indirect drivers of migration (Lonergan, 1998). Candidate datasets: Commonwealth Scientific and Industrial Research Organization (CSIRO) reconstructed sea-surface heights for 1950 to 2001, near global, monthly and Global Assessment of Human Induced Soil Degradation (GLASOD), one point in time: 1990, gridded spatial resolution. Need to identify or construct a time series. Resource scarcity: diminished eco-system services (e.g. forests and biodiversity) and reduced access to land or water will all result in endangered livelihoods. Mobility linked to these elements is less well documented and most visible at the local level. Candidate dataset: water scarcity from the United Nations Children’s Fund (UNICEF), one point in time: 2002, national-level spatial resolution. Need to identify or construct a time series. Development projects: development efforts such as dam construction, irrigation or forest protection have been, and continue to be, implemented across the globe, casting people out of their native homelands. It has been estimated that over 20 million people have been cast from their homes due to development efforts over the past 30 years in India alone (Fornos, 1992). Candidate dataset: none identified thus far.

Dataset B: Non-environmental drivers Drivers that originate in society, even if also influenced by the environment, have long been undisputed pushes for human migration.

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Economics: it is reasonable to assume that as a nation’s human development strengthens or as general poverty levels across a country are reduced, levels of mobility or urbanization may lessen. The hypothesis is that when the quality of life is adequate, people generally prefer to stay in their homelands. Candidate dataset: the United Nations Development Programme’s (UNDP’s) Human Development Index (HDI) details life expectancy, education and gross domestic product (GDP) per capita, 1975 to 2005 annual, national-level spatial resolution. Conflict and persecution: it is reasonable to assume that as conflict heightens in a region, inhabitants are likely to try to put distance between themselves and the violence, thus setting into motion certain levels of human mobility. Candidate dataset: International Peace Research Institute, Oslo (PRIO), 1946 to 2008, event-based, national-level spatial resolution. Population growth: it is reasonable to assume that the faster that populations grow, the more pressure is put on ecosystems and natural resources. When in short supply, scarcity may trigger outmigration. Candidate dataset: History Database of the Global Environment (HYDE), 1700 to 2000, gridded spatial resolution, and calculate inter-period growth.

Human mobility The second category will populate dataset C to reflect or capture human mobility. Migration is the demographic variable that has been most strongly regulated across the globe, and yet one with no globally accepted definition or systematic and international registry mechanism. No single database described below will portray the whole picture of global human migration. The best way to capture the many faces of mobility is to include every available proxy for human movement.

Dataset C: Mobility •

• •

Migrant stocks: the Population Division of the Department for Economic and Social Affairs of the United Nations Secretariat maintains a data bank on international migration statistics covering most countries of the world. The data bank includes information on the number of international migrants enumerated by population censuses or population registers. Candidate dataset: United Nations Population Division, trends in migrant stocks, ten-year intervals, 1960 to 2005, national-level spatial resolution. Urbanization: it is reasonable to assume that as internal displacement is widely recognized to exceed that of international mobility, a good proportion of any type of movement will manifest itself in urbanization. The World Bank claims that most scarcity-induced migration will result in burgeoning cities (World Bank, 2009).

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Candidate datasets: United Nations Population Division, urbanization by five-year increments, 1950 to 2050, national-level spatial resolution; and night-time lights from Earth Observation Group NOAA-NESDIS National Geophysical Data Centre E/GC2, 1993 to present, annual, gridded spatial resolution. Remittances: it is reasonable to assume that a good proportion of funding coming into a country is linked to those that have migrated away from that country, for whatever reason. Candidate dataset: World Bank, 1970 to present, annual, national-level spatial resolution. IDP and refugees stocks: it is safe to assume that the countries of origin of IDPs and refugees house some element (societal or environmental) that have catalysed out-migration. Candidate dataset: United Nations Human Development Reports, 1998 to present, annual, national-level spatial resolution. Residual migration: given the dearth of systematic migration statistics, a common measure of mobility is residual migration. This entails taking the difference between population estimates at two time periods, subtracting the natural growth rate and assuming that the remainder, or residual, represents migration. There are high errors associated with residual migration estimates; but the global 200-year database cited below makes it a valuable proxy. Candidate dataset: History Database of the Global Environment (HYDE), 1700 to 2000, annual, gridded spatial resolution.

Footprint methodology The above datasets will be aligned through space and time using Arc GIS and statistical software (e.g. Matlab). Given the varying spatial and temporal resolutions of the datasets, the data will not conform across the globe. For example, if remittance data is only available at the national level from 1980 to 2005 and climate data is available at pixel level from 1905 to present, the remittance data will be repeated for every pixel falling within a country’s boundaries and certain time-series and time-lag analyses will be restricted to the periods in common. Once a full global footprint data set is compiled, statistical analysis will be conducted using correlation (parametric, non parametric), regression and time series. Surfacing trends associating drivers and mobility will be visualized using geographic information systems. The goal is to learn as much as is possible from the best global data available to date, identifying what combinations of drivers are, and have been, the most influential. The global footprint will subsequently guide a GIS-based micro-simulation exercise (in a gridded environment) to systematically explore the weight of various drivers acting on independent agents within specific yet dynamic contexts of policy (set up as random variables using UNEPs four scenarios), individual perception (random human impressions) and capacity.

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Expected results and impact The global footprint exploratory effort will reveal important historical patterns that will help to identify both potential out-migration ‘hotspots’ and ‘hot systems’. This new understanding will facilitate disaster risk management and may contribute to the early warning of eventual migration. A systematic estimate of the volume of the phenomenon will inform policy aiming to support a yet unrecognized vulnerable and growing group. Creating an identity for this ‘new’ category of migrants will get us closer to protecting their homelands – giving them the choice to remain there securely.

Case studies The case study approach is perhaps one of the best ways to improve knowledge on transformation pathways in vulnerable areas (by investigating adaptation in real-life context) and to put forward policy recommendations concerning the issue of migration. The case studies were selected to exemplify places where a changing climate has been identified as one of many factors inducing environmental change with potentially serious human consequences – in the absence of appropriate adaptation processes. The main limitations of case studies are their lack of generalizability and the difficulty with projecting trends where future vulnerability is uncertain and may have very different forms than today. Yet, just as it is possible to describe biophysical ‘tipping elements’ in environmental change (Lenton et al, 2008), it is also possible to identify social ‘tipping points’ leading to perhaps irreversible change, including behavioural change. We know that climate is one of many factors that may together be re-enforcing, and may also have ripple effects beyond the immediate local scale. Narrative storylines from case study fieldwork have shown this to be important (i.e. climate is rarely the only or even the most important driver). Given the multi-causality in complex systems, it is appropriate to select analytical tools that allow us to study (to measure and reason about) complexity. To support case study investigation, a mixed methods approach was taken, combining fieldwork – interviews and stakeholder meetings, ethnographic knowledge elicitation – with the analysis of this information to produce narrative stories, models of actors’ decision-making, and computer simulation of these processes using agent-based models. Moving to a higher level of abstraction, ‘archetypes’ are an important conceptual tool that could allow researchers to build universal, albeit simplified, stories linking stresses, processes and outcomes. Conceptual relationships can be compared with correlations found in analyses of the global footprint (see previous section). Testing methodologies and establishing good practice in generating policy advice is a careful and incremental process. A survey of promising methods can be found in Piguet (2009), selected by the research community for exploring the climate–migration nexus. The remainder of this

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section will describe adaptation and migration processes in the two selected case studies.

Bangladesh: Background Bangladesh is one of the most climate vulnerable countries in the world (MoEF, 2008). The coastal area of Bangladesh is about 710km long and is home to more than 8 million people, with a high population density of 930 persons per square kilometre. Most of the coastline and small islands are protected by embankments and polders that are built to save the land from tidal flooding and salinity, and to protect crops. Despite this, these areas remain vulnerable to frequent cyclone, storm surge and tidal intrusions. It is widely recognized that climate change-induced rise in sea surface temperature, change in the frequency and intensity of cyclones, and sea-level rise may aggravate this situation. An analysis of tidal data collected from four coastal points in Bangladesh during 1975 to 2005 reveals that the mean sea level is rising and observed range of sea-level variance can be taken as 5.05mm to 7.4mm per year (CEGIS, 2009). A major climate change impact will be changes in the frequency and magnitude of tropical and extra-tropical storms, with potentially serious implications. The cyclone risk areas will move further inland, affecting 14.6 million people in the 2020s and 20.3 million in the 2050s (Tanner, 2007). The National Adaptation Programme of Action (NAPA) reports that flooding due to storm surges is also expected to increase in future. The rainfall rates in cyclones are also expected to rise. The combined effect of sea-level rise, increased storm surge height and added rainfall rate will aggravate the situation. Sea-level rise will also result in drainage congestion, requiring improvement and raising of embankments, incurring considerable costs to the people and the economy (IWM and CEGIS, 2007; GOB, 2008). A large proportion of the local population who depend upon natural resources will be affected by the projected sea-level rise, expected to result in the inundation of cultivable land, saline water intrusion and loss of terrestrial and marine biodiversity. With projected sea-level rise (SLR) of 32cm and 88cm, the coastal cultivable land will be reduced from 45 per cent at the current level to 40 and 15 per cent, respectively. Due to the rise in salinity, the major paddy crop will be reduced from the current 88 per cent to 60 per cent and 12 per cent, with 32cm and 88cm SLR, respectively. Once the world’s largest stretch of mangrove ecosystem, the Sundarbans, a World Heritage Site, is located in the southwest coastal area. This is particularly vulnerable to SLR as 20 per cent of the Sundri (Heritiera fomes, a major species in the Sundarbans) dominant area will be reduced to 10 per cent with 32cm SLR, and to 2 per cent with 88cm SLR (GOB and UNDP, 2006). The climate change scenario is further complicated by a high level of coastal environmental degradation. The coastal ecosystems (mangrove, marine environments and forests) provide habitat for a large number of plant species as well as

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BOX 4.3.1 CASE STUDY 1: CYCLONE AILA, BANGLADESH On 25 May 2009, Cyclone Aila hit southwest Bangladesh, causing massive damage, homelessness and raising worldwide humanitarian concerns. The coastal embankment, built in the 1960s, had burst in several places, and villages were under 3m of water, with everything they had swept away. Crop and shrimp farms were also washed away. The Sundarbans was inundated by 6m of water, with enormous damage to animals and plants. People were forced to take shelter on what was left of the embankment, and locals described the damage as the worst of its kind in living memory. Aila followed Cyclone Sidr, which hit the coast in 2007. Although the number of deaths directly resulting from Sidr was higher than that of Aila, the recovery period has been much longer. Six months after the cyclone, the area was still under saline water and open to the tidal flow of the Bay of Bengal. This has exposed the population for longer, increasing the risk of other stresses compounding the disaster, further delaying rebuilding and recovery. People in these areas have adapted to environmental changes in various ways – migration being one in order to secure a living during difficult times. Although people prefer to live in their forefathers’ homes, more and more people are now migrating in search of jobs. The process often starts with temporary migration to nearby areas that might offer employment opportunities. The period of such migration varies from one week to six months, or even more. In these cases, a single or a couple of members from a family leave, while others stay at home. If the situation does not improve, or if a catastrophic event such as a cyclone hits the area, seasonal migration may lead to permanent migration, where entire families move out. Although it is difficult to establish a causal link between climate change and these migration episodes, climate change-induced vulnerabilities are playing an increasingly important role in triggering permanent moves – when no other alternatives remain.

fish and wildlife. Extensive resource extraction from coastal ecosystems and unsustainable land-use practices (such as intensive shrimp cultivation) have created a situation where employment opportunities are being reduced steadily. In addition, food security and access to safe drinking water are being threatened in many coastal areas. The situation is often aggravated by cyclones that result in human deaths, loss of valuable resources and damage to ecosystems.

Kenya: Background Kenya is a country of great disparities in natural phenomena and human landscape. Of its 580,000 square kilometres, only 17 per cent is arable and 2 per cent under forest. About 75 per cent of its population (the current total is around 38.5 million) derive direct and indirect livelihoods from agriculture, which depends upon rainfall. The country’s mean annual rainfall is distributed over two major rainfall seasons during March to May and October to November. It is, therefore, the onset, amount, intensity and spread of rainfall that threaten livelihoods and settlement patterns in the country. Settlement in the country is still largely rural in character as 80 per cent of the total population reside in rural areas, with a high growth rate of 2.9 per cent annually; average population densities range between 27 persons per square kilometre in the Rift Valley and 280 persons per square kilometre in

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BOX 4.3.2 CASE STUDY 2: THE SUNDARBANS, BANGLADESH Kabir started life as a fisherman, working in the numerous rivers and creeks in the Sundarbans. He would also collect forest products: forming a group of four to six people with approval from the Forest Department. They would go for about two to four weeks and were allowed to bring certain non-timber forest products with them (e.g., fish, crab, reeds (Nipah palm), honey, etc.). However, the Sundarbans’ decreasing resources and ever increasing number of people meant that it was often difficult to get enough from the ‘approved’ areas, and people tended to venture deeper into the forests, knowing well that this was risky. The risks came not only from the forest guards, but also from the Royal Bengal tigers (an endangered species found in the Sundarbans) that often killed people if they treaded too close to their habitat. On 15 April 2009, Kabir went to the Sundarbans with his group. After a day’s work, he anchored in one of the small creeks for the night. Suddenly, a tiger jumped into Kabir’s boat and grabbed one of the fishermen, Kabir’s uncle, named Gazi. Kabir and others joined a life and death struggle to free him from the tiger. Finally, the tiger left; but on the way back to the village, Gazi died of the injuries. This was a life changing shock for Kabir. He decided not to risk his life in this way. However, changing jobs was not easy. Employment opportunities have lessened in the area due to large-scale conversion of paddy fields to shrimp culture, which employs far fewer people. Kabir also observed that the climate is drastically changing. The monsoon is often late and when it comes, it is short and intense, resulting in waterlogged fields and crop losses. Tide levels are increasing, as is the salinity of water and land. The devastating ecological consequences of shrimp culture, together with these environmental changes, have created a situation where Kabir found that he could hardly make a living in his village. The final blow came from Cyclone Aila, which struck the area in May 2009. Kabir lost all hopes of making a living in his area. He decided to leave the village and went to the city in search of employment. There he started life again, from scratch, working as a day labourer, sometimes pulling rickshaws, at others carrying loads in the factories, or whatever is on offer.

Nyanza Province. The total urban population is now estimated at 27 per cent, with an annual growth rate of 2.1 per cent. Nairobi City has the highest settlement density of 1911 persons per square kilometre. Climate change poses a serious challenge to Kenya’s social and economic development. This change has led to major economic, social and environmental adjustments. Kenya is particularly vulnerable to climate change since the key drivers of the economy are climate sensitive. Key sectors affected include, but are not limited to, agriculture, livestock, tourism and forestry, among primary livelihood systems such as pastoralism. Drought is probably the most prevalent climatic hazard, affecting about 70 per cent of the country categorized as arid or semi-arid lands (ASALs), mainly in the east, northeast, parts of the Rift Valley and coastal provinces. Kenya experiences drought in a cyclic pattern. The major droughts come every ten years, while the minor ones occur every three to four years. The recorded shortfalls in precipitation were in 1928, 1933–1934, 1937, 1939, 1942–1944, 1947, 1951, 1952–1955, 1957–1958, 1974, 1983–1984, 1985 and 1999–2000. The severest droughts resulting in great loss of human life and livestock were in 1983–1984 and 1999–2000.

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ASAL areas, which are predominantly occupied by pastoral communities, show the lowest development indicators and development potentials. They are viewed as wastelands by both the communities themselves and other Kenyans. About 60 per cent of the total population in ASAL areas live under abject poverty, with minimal service provision and an approximate expenditure of US$1 per day. Migration and, specifically, the movement of pastoralist communities has persisted for many years as a form of coping strategy; but the lack of rainfall in places which people previously used as escape zones is clear evidence of new uncertainties in climate and of the impacts for marginal livelihoods. This has led to complete movement and migration to other urban centres and other countries without returning back to original homes. There are a limited number of scientific studies addressing climate changes and their associated social, economic and cultural impacts. It is difficult to ascertain the increased incidence of disease and the frequency of loss of livelihood for many communities in pastoral dryland areas and in coastal regions. Moreover, quantifying the extent to which such changes can be attributed to climate variation and change – in isolation from other drivers – is not possible without significant guesswork. Pastoralist systems and their livelihood issues have proved rather complex to understand, and planning conducted in conventional style has led to massive exclusion of the communities and their traditional approaches for decades. The impacts of climate variability bring a dynamic perspective to the whole issue;

BOX 4.3.3 CASE STUDY 1: WATER SHORTAGE IN TURKANA, KENYA Water shortage is an everyday reality in Kenya. Some households in Nairobi remain up to four days without water, while others get water after two or three days. It all depends upon where one lives. It has been known in some middle-income areas and slums where there are few facilities for households to remain for up to five months without running water in their taps. The situation is even worse in dryland areas of Kenya. In Turkana there is a clear linkage between dry spells and water shortages. In a ‘normal’ year, during the dry season most pastoral communities shifted temporarily to areas with better water access for grazing. The situation according to recent field research visits (in August 2009) in the Kalokol and Kakuma division of Turkana district is very different; the rural centres are also now experiencing massive water shortages due to population movement from nearby areas. The images of young boys and girls in school uniforms searching for water remain quite disturbing. Restaurants and public hospitals and schools buy water from water vendors, increasing school costs and diverting attention from other key school needs. Kalokol, which is a centre for Lake Turkana, has common images of young boys and girls fetching water from the lake, despite the fact that the water is quite salty. Interview sessions with the International Committee of the Red Cross (ICRC) and World Vision partners working in the area indicate that the water table is deep, making it difficult to access groundwater. The Ministry of Water has also pointed out that some of the water drilled had levels of chlorine above World Health Organization (WHO) minimal standards.

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BOX 4.3.4 CASE STUDY 2: BEEKEEPING IN TURKANA, KENYA Beekeeping has been one of the top success stories of Turkwel and Lorgumu division of Turkana. The United Nations Development Programme (UNDP) Biodiversity Project, the Forest Department, and the Practical Action programme supported this initiative as a livelihood alternative for pastoral communities. Since the early 1990s many families have received additional income from beekeeping, selling honey and natural gum produced in most rural towns, bottled locally and transported to supermarkets in Nairobi where there is high demand. With high population pressure on the forest, and drought and deforestation in the area, beekeeping and harvesting have been drastically affected. What is shocking is the fact that some of the beekeeping farmers, now having no alternative livelihoods, resort to boiling and cooking sugar from local shops and packaging this as honey products. Most buyers only find this out after having bought what they thought was the original honey products that were once available. Most farmers attributed the low honey harvest to environmental changes and drying of forest soils where beehives were placed (bees do not settle in trees without flowers and water nearby). The already vulnerable communities in pastoral areas have seen their alternatives seriously threatened. Farmers have pointed out that with both livestock farming and beekeeping under threat, there has been a great deal of migration to urban centres for casual employment.

migration variables need to be carefully selected to shed light upon the specific impacts of climate change for pastoralists. The key variables relating to migration may be formulated from population size, the availability of rainfall in specific areas, pasture availability, and composition as well as inter- and intra-border movements, not forgetting rural–urban migration and its impacts upon infrastructure such as health services, housing and shelter, education, police protection, water and sanitation, as well as communication linkages. Migration along highways linking Kenya and southern Sudan and settlement around the areas were also studied.

Conclusions and policy recommendations The natural environment and the climate, both varying and changing, clearly influence human migration. The exact extent of this influence and the drivers that align to directly trigger human mobility are little understood today. Climate change expresses itself in two distinct but related ways – slow onset environmental degradation (i.e. slow shifts in average environmental conditions over relatively long periods) and rapid onset or acute extreme events (i.e. extreme weather events due to increased energy within the climate system). Slow onset will affect entire cities. Sana’a, the capital of Yemen, and Quetta, the capital of Pakistan’s Baluchistan Province, are cities said to be at particular risk of having to be abandoned within the foreseeable future (Boano et al, 2007). A shift to a permanent El Niño would increase water resource stress across large parts of Asia and South and East Africa, reducing crop productivity, affecting fishing stocks and increasing risk of hunger and malnutrition (Arnell, 2006).

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Paradoxically, water shortage and depletion are complemented by increased propensity for flooding. Rising sea levels caused by climatic change may take away the living space and source of living for millions of people in the future. Seventeen million Bangladeshis live less than 1m above sea level. Seven per cent of Bangladesh could be permanently lost to sea-level rise, land subsidence, melting Himalayan glaciers and increased monsoon rains. By 2050, sea-level rise may displace more than 14 million Egyptians: intrusion of saltwater up the foreshortened Nile would further reduce the irrigated lands supporting virtually the whole of Egypt’s agriculture. The effect will be similar on other deltas at risk in Indonesia, Thailand, Pakistan, Mozambique, Gambia, Senegal, Surinam and elsewhere. Slow onset migration is frequently caused by depletion of resources (land and water), deforestation, desertification and pollution. But it is one of the most difficult to predict because of the types of migration (seasonal, return, repeat, permanent and temporary), the multi-causality of intervening variables (socio-economic status and migrant selectivity) and the complexity of environmental outcomes (deforestation and fisheries depletion) (Curran, 2002). Data on the impact of rapid onset events upon population displacement are disputed; but the trend is unmistakable, with the greatest impact felt in the global South. One estimate contends that, from 1980 to 2000, 141 million people lost their homes in 3559 natural hazard events, of whom over 97 per cent lived in developing countries (Gilbert, 2001).5 The International Federation of Red Cross and Red Crescent Societies (IFRC, 2006) notes that during the past decade, weather-related natural hazards have been the cause of 90 per cent of natural disasters and 60 per cent of related deaths, and have been responsible for 98 per cent of the impacts upon disaster-affected populations.6 Adger (2006) indicated that the impacts of these physical events upon factors such as migration are mediated by the erosion of socio-ecological resilience. The impacts and recovery from extreme events, or the ability of small islands to cope with weather-related extremes, demonstrate how discrete events in nature expose and even exacerbate underlying vulnerability and push systems into new domains where resilience may be reduced (Adger et al, 2005). Although disasters are self-evidently a more obvious cause of forced migration than slow onset environmental change, it is important not to neglect the fact that the impacts and the responses only reflect a multiplicity of social, economic and political variables. This all points to the conclusion that it is extremely difficult to justify predictions of future patterns of climate-induced migration. Tacoli (2009) argues that most estimates of future environmentally induced migrants are, in fact, estimates of the numbers of people at risk, rather than the numbers of people who are likely to move. The principal difficulty springs from the fact that, methodologically, it is very difficult, if not impossible, to unpack the different environmental drivers and triggers of migration. People move for complex sets of reasons, of which a changing environment is only one. As such,

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it is important to try to understand the impacts of a variety of factors, including social dynamics, institutional capacities, demographic growth, inter-community tensions, social cohesion, natural resource management, poverty, politics and power. Given such levels of uncertainty, it is equally difficult to make any clear policy recommendations. Much of the literature stresses the importance of focusing on sustainable ecosystems, although it is recognized that some places may be well beyond this point already (e.g. Darfur). Adaptation and disaster risk reduction in developing countries are also seen as key to minimizing the push factor of climate change given that individual migration is most often the expression of the incapacity of communities to cope with change and uncertainty. But adaptation in development- and donor-led projects needs to be extremely sensitive to people’s own methods, as shown from the Kenyan case study. Given the multi-causality of environmentally induced displacement, in which development programmes and projects themselves may accentuate the destructive impacts of climate change, there is an urgent need for donors and development agencies to ‘environment-proof’ their projects and programmes, and for national governments to ensure that issues of environmental migration are embraced by Poverty Reduction Strategy Papers and conflict reduction strategies (Boano et al, 2007). Drawing on the German Advisory Council on Global Change’s 2008 Climate Change as a Security Risk, three main general concluding statements can be made: 1

2

3

Individual attributes. Environmentally induced migration is usually chosen by an individual as a coping strategy. The decision to migrate is therefore significantly determined by individual attributes such as age, level of education, the personal impact of a disaster and the subjective perception of general structural conditions. In addition, there is also the importance of a history of migration within the individual’s community and the presence of social networks outside the community. This is where the view of migration as a failed adaptation is challenged by some commentators who argue that mobility can be an important (positive) strategy to reduce vulnerability to both environmental and non-environmental risks. Vulnerability. The extent of environmentally induced migration is also influenced by the extent to which the effects of environmental changes and a range of political and socio-economic factors have an impact upon them. These factors may overlay or reinforce each other. For example, families’ economic vulnerability may be increased by unfavourable aspects of the regional economic structure or the level of regional economic activity, such as a low or markedly fluctuating per capita income, unequal rights of ownership, restrictions on access to markets (labour, credit or sales markets), or the absence of social security arrangements. Functioning institutions and governance structures. The extent of migration is significantly determined by the functionality of the relevant local

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and national institutions. In countries that lack early warning systems or evacuation plans, extreme weather events cause relatively greater damage and compel more people to flee than is the case in countries that are institutionally well prepared for emergencies. The same applies to the problem of gradual environmental degradation. For example, continuing soil degradation can be avoided through efficient land-use technologies and land-use systems. Until such time as there is enough empirical evidence or clear global estimates of past and future environmentally induced migration, there are policy and practice areas that could be acted upon today to reduce vulnerability and risk of humanitarian crises, whether they lead to displacement or not. There is a strong need to strengthen the knowledge base and understanding of such complex processes through sustained and rigorous scientific research. While there is still a lot of work needed in terms of unpacking drivers of migration, including environmental factors, as well as a much stronger engagement of migration theorists in this area, in the meantime locations and hotspots that are heading towards an irreversible tipping point of socio-environmental collapse could benefit from the following: •

•

•

• • • •

• •

strengthening the adaptive capacity of affected populations, including agricultural diversification and investment in disaster risk reduction and early-warning systems, including speedy and efficient humanitarian responses; the removal of barriers to internal mobility which could play a role in facilitating the diversification of rural livelihoods (given that the poorest and most vulnerable are least likely to move, continued attention to pro-poor policies is needed in rural source areas); attention to urban planning, service provision and human security in areas where people are already migrating – especially in slum areas of major coastal cities where population growth is likely to accelerate; enhancing the capacity of urban labour markets to absorb large and youthful migrant populations if secondary migration is to be avoided; supporting initiatives to defuse tensions and encourage peaceful cohabitation of both internal and intra-regional migrants and local populations; further discussion on the responsibility to protect those who may be forced to leave their homes, and especially their countries, due to climate shocks; developing principles and practices for ‘environment-proofing’ development policies, projects and programmes, and requiring donors and development agencies to urgently adopt them; the development of more sophisticated typologies of environmentally induced migration; the identification and mapping of historical migration trends linked to the environment, leading to potential environmental ‘hotspots’, monitoring the potential ‘tipping points’ in these localities/regions and migration trends in

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•

357

relation to environmental depletion, competition for resources and potential sources of migration; further research evidence on the role of migration in adapting to climate change in particular national and regional contexts, and on the impact of climate change upon push–pull factors of migration in destination areas.

Finally, a continuing high-level dialogue in order to develop, strengthen and harmonize international understanding of concepts, knowledge base, vocabulary and experience related to the multiple cause–effect links and feedback loops between environmental degradation, socio-economic impacts and environmentally-induced migration is not only needed, but has to be sustained with commitment to research, policy and action.

Notes 1 2 3

4 5

6

See www.physorg.com/news8213.html. See www.ehs.unu.edu/file.php?id=58. The definition for the term refugee is provided under Article 1A of the 1951 Convention. This definition relates to the status of refugees amended by the 1967 Protocol and the Status of Refugees. There are four key parts to this definition: i) The person must be outside their country of nationality or former habitual residence. ii) The person must fear persecution. iii) The fear of persecution must be for reasons of one of the five convention grounds (race, nationality, religion, membership of a particular social group, or political opinion). iv) The fear must be well founded. See www.alternet.org/environment/19179. Hurricane Mitch, 1998 (300,000 homeless); Gujarat earthquake, 2001 (1 million families homeless); Mozambique floods (2000 to 550,000 people in need of relocation); the tsunami of 2004 (231,000 people dead or missing, and more than 1 million displaced across 12 affected countries); Hurricane Katrina, 2005 (1.5 million displaced temporarily, of whom 300,000 will never return). See www.kpbooks.com/details.asp?title=World+Disasters+Report+2005.

References Adger, W. N., T. P. Hughes, C. Folke, S. R. Carpenter and J. Rockström (2005) ‘Socialecological resilience to coastal disasters’, Science, vol 309, no 5737, pp1036–1039 Adger, W. N. (2006) ‘Vulnerability’, Global Environmental Change, vol 16, no 3, pp268–281 Almeria Statement (1994) The Almeria Statement on Desertification and Migration, Statement following the International Symposium on Desertification and Migrations, Almeria, 8–11 February, www.unccd.int/regional/northmed/meetings/ others/1994AlmeriaSpain.pdf; www.osce.org/documents/eea/2005/05/14488_en.pdf Arnell, N. W. (2006) Global Impacts of Abrupt Climate Change: An Initial Assessment, Working Paper 99, Tyndall Centre for Climate Change Research,

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University of Southampton, www.tyndall.ac.uk/publications/working_papers/ twp99.pdf Black, R. (1998) Refugees, Environment and Development, Addison Wesley Longman Limited, New York, NY Black R. (2001) ‘Environmental refugees: Myth or reality?’, in New Issues in Refugee Research, Working Paper No 34, United Nations High Commissioner for Refugees, Geneva Boano, C., T. Morris and R. Zetter (2007) Environmentally Displaced People: Understanding the Linkages between Environment Change, Livelihoods and Forced Migration, Policy Briefing by the Refugee Studies Centre for the Conflict, Humanitarian and Security Department, Department for International Development, UK Castles, S. (2002) Environmental Change and Induced Migration: Making Sense of the Debate, Working Paper No 70, United Nations High Commissioner for Refugees (UNHCR), Geneva. CEGIS (2009) Factoring Climate Change Considerations in the Design of Padma Multipurpose Bridge, Bangladesh Bridge Authority, Dhaka Christian Aid (2007) Human Tide: the Real Migration Crisis. London, Christian Aid, www.christianaid.org.uk/Images/human_tide3__tcm15-23335.pdf Conisbee, M. and A. Simms (2003) Environmental Refugees: The Case for Recognition, New Economics Foundation, London Curran, S. (2002) ‘Migration, social capital and the environment: Considering migrant selectivity and networks in relation to coastal ecosystems’, Population and Development Review, Supplement: Population and Environment: Methods of Analysis, vol 28, pp89–125 Dun, O., F. Gemenne and R. Stojanov (2007) ‘Environmentally displaced persons: Working Definitions for the EACH-FOR project’, paper presented at the International Conference on Migration and Development in Ostrava, Czech Republic, 5 September Dupont, A. and G. Pearman (2006) Heating up the Planet: Climate Change and Security, Paper 12, Lowy Institute, Australia El-Hinnawi, E. (1985) Environmental Refugees, United Nations Environment Programme, Nairobi Fornos, W. (1992) Desperate Departures: The Flight of Environmental Refugees, Population Institute, Washington, DC Friends of the Earth (2007) A Citizen’s Guide to Climate Refugees, Friends of the Earth, Australia, www.liser.org/Citizen’s%20Guide_2007_small.pdf Gilbert, R. (2001) Doing More for Those Made Homeless by Natural Disasters, Disaster Risk Management Series No 1, World Bank, Washington, DC, www.proventionconsortium.org/themes/default/pdfs/housing.pdf GOB (Government of Bangladesh) (2008) Economic Modeling of Climate Change Adaptation Needs for Physical Infrastructure in Bangladesh, Report prepared for Comprehensive Disaster Management Programme by Centre for Environment and Geographic Information Services (CEGIS), Dhaka GOB and UNDP (Government of Bangladesh and United Nations Development Programme) (2006) Impact of Sea Level Rise on Land Use Suitability and Adaptation Options, Report prepared for GOB and UNDP by Centre for Environment and Geographic Information Services (CEGIS), Dhaka.

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IFRC (International Federation of Red Cross and Red Crescent Societies) (2006) World Disasters Report. Focus on Neglected Crises, IFRC, London IWM and CEGIS ((Institute of Water Modelling and Centre for Environmental and Geographical Information Services) (2007) Investigating the Impact of Relative SeaLevel Rise on Coastal Communities and their Livelihoods in Bangladesh, IWM and CEGIS, Dhaka Jacobson, J. L. (1988) Environmental Refugees: A Yardstick of Habitability, Worldwatch Paper 86, Worldwatch Institute, Washington, DC Kapstein, E. (2006) Does Migration Hurt Development Countries?, Globalist Press Online, www.theglobalist.com/storyid.aspx?StoryId=5372 Kibreab, G. (1994) ‘Migration, environment and refugeehood’, in B. Zaba and J. Clarke (eds) Environment and Population Change, International Union for the Scientific Study of Population, Derouaux Ordina Editions, Liège, Belgium Kibreab, G. (1997) ‘Environmental causes and impact of refugee movements: A critique of the current debate’, Disasters, vol 21, no 1, pp20–38 Lee, S. (2001) Environmental Matters: Conflict, Refugees and International Relations, World Human Development Institute Press, Seoul and Tokyo Lenton, T. M., H. Held, E. Kriegler, J. Hall, W. Lucht, S. Rahmstorf, H. J. Schellnhuber (2008) ‘Tipping elements in the Earth’s climate system’, PNAS, vol 105, no 6, pp1786–1793 Lonergan, S. (1998) ‘The role of environmental degradation in population displacement’, Environmental change and Security Program Report, Issue no 4, Woodrow Wilson International Centre for Scholars, Washington, DC, pp5–15 MA (Millennium Ecosystem Assessment) (2005) Ecosystems and Human Well-Being: Synthesis, Island Press, Washington, DC Massey, D., W. Axinn and D. Ghimire (2007) Environmental Change and OutMigration: Evidence from Nepal, Report 07-715, Population Study Center, University of Michigan, Institute for Social Research, www.psc.isr.umich.edu/pubs/ pdf/rr07-615.pdf McGregor, J. A. (1993) ‘Refugees and the environment’, in R. Black and V. Robinson (eds) Geography and Refugees: Patterns and Processes of Change, Belhaven Press, London, pp159–170 MoEF (Ministry of Environment and Forests) (2008) Bangladesh Climate Change Strategy and Action Plan 2008. Ministry of Environment and Forests, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh Myers, N. (2002) ‘Environmental refugees: A growing phenomenon of the 21st century’, Philosophical Transactions of The Royal Society B., vol 357, pp609–613 Myers, N. (2005) ‘Environmental refugees: An emergent security issue’, contribution to the 13th Economic Forum, Prague 23–27 May, Organization for Security and Cooperation in Europe, www.osce.org/documents/eea/2005/05/14488_en.pdf 22/10/07 Myers, N. and J. Kent (1997) Environmental Exodus: An Emergent Crisis in the Global Arena, Climate Institute, Washington, DC Nicholls, R. J. (2004) ‘Coastal flooding and wetland loss in the 21st century: Changes under the SRES climate and socioeconomic scenarios’, Global Environmental Change, vol 14, no 1, pp69–86 OCHA (Office for the Coordination of Humanitarian Affairs) (2009) Monitoring Disaster Displacement in the Context of Climate Change, findings of a study by OCHA, the Internal Displacement Monitoring Centre (IDMC) and the Norwegian Refugee Council (NRC), Geneva

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Piguet, E. (2009) ‘Environment and migration: A methodological challenge’, Paper presented at the Workshop on Environmental Change and Migration: Assessing the Evidence and Developing Norms for Response, 8–9 January 2009, University of Oxford, Oxford, UK Renaud, F., J. J. Bogardi, O. Dun and K. Warner (2007) Control, Adapt or Flee: How to Face Environmental Migration?, InterSections, Interdisciplinary Security Connections Publication Series of UNU-EHS No 5/2007, www.ehs.unu, du/file.php?id=259 Reuveny, R. (2007) ‘Climate change-induced migration and violent conflict’, Political Geography, vol 26, pp656–673 Stern, N. (2006) Stern Review on the Economics of Climate Change, UK Treasury, www.hm-treasury.gov.uk/independent_reviews/stern_review_economics_climate_ change/stern_review_report.cfm Tacoli, C. (2009) Crisis or Adaptation? Migration and Climate Change in a Context of High Mobility, Expert Group Meeting: Population Dynamics and Climate Change, London, 24–25 June, www.unfpa.org/webdav/site/global/users/schensul/public/ CCPD/papers/Tacoli%20Paper.pdf Tanner, T., A. Hassan, K. Islam, D. Conway, R. Mechler, A. Ahmed and M. Alam (2007) ORCHID: Piloting Climate Risk Screening in DFID Bangladesh, Detailed Research Report, Institute of Development Studies, University of Sussex, Sussex, UK UNEP (United Nations Environment Programme) (2007) Global Environment Outlook GEO4, Environment for Development, UNEP, Nairobi UNHCR (United Nations High Commissioner for Refugees) (2002) ‘A critical time for the environment’, Refugees, no127, UNHCR, Geneva Warner, K., M. Hamza, A. Oliver-Smith, F. Renaud and A. Julca (2009) ‘Climate change, environmental degradation and migration’, Natural Hazards, www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s11069-0099419-7 World Bank (2009) World Development Report: Reshaping Economic Geography, World Bank, Washington, DC

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Index

adaptation definition 3, 262 development 3, 4, 12, 88, 304–306, 317 governance 11–12 holistic approaches 65 implementation 244–246, 252–253 mainstreaming 4, 11, 279, 304, 306 measures 88, 89 mitigation 12, 130, 193, 291–292 models 114 need for 129–130 official development assistance 4 options 80–82 planning 4 principles 113 strategies 143–144, 181–188 see also financial adaptation assistance; official development assistance Adaptation to Climate Change in Two Rural Communities on the Plains and in the Mountains of Morocco 247 Adaptation Fund (AF) 221, 294, 297–298, 307 Adaptation Fund Board (AFB) 293, 294, 297 Adaptation to the Impacts of Sea-Level Rise in the Nile Delta Coastal Zone 248 adaptive capacity differential 11, 130, 146 Lesotho highlands 165–168 southern Africa 139–146, 148 see also capacity-building

adaptive water management 264–265 additionality 305, 306 Adi Ha, Ethiopia 218, 223–231 AF (Adaptation Fund) 221, 294, 297–298, 307 AFB (Adaptation Fund Board) 293, 294, 297 Africa Central Africa 112 climate change projections 111–113, 131 East Africa 260 vulnerability 109, 110 water 286 see also agricultural adaptation (Africa); agriculture; North Africa; Southern Africa; specific countries African Development Bank 298 AGCMs (atmospheric general circulation models) 20–25, 26–31 agricultural adaptation (Africa) 110–125 barriers to model use 114, 115–121 challenges 121–123 climate change 110–111 climate impacts 111–113 recommendations 124–125 agriculture Africa 110–111, 286 Ethiopia 210–211, 217 Lesotho highlands 162–163, 167 North Africa 243, 251 South Africa 175, 186–187, 189 Zambia 208–209 see also agricultural adaptation (Africa)

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AIDS see HIV/AIDS Aila (Cyclone) 350 Algeria 242, 250 Amihan ( Northeast Monsoon) 39–40 AMIP (Atmospheric Model Intercomparison Project) 21 anthropogenic climate change 305 anticipatory adaptation 109, 123–124, 129–130 AOGCMs (atmosphere-ocean general circulation models) 19 aquaculture 98–99 arid or semi-arid lands (ASALs) 277–278 art competitions 142 Asia see specific countries Asian Development Bank 299, 300 Aswan Dam, Egypt 243 atmosphere-ocean general circulation models (AOGCMs) 19 atmospheric general circulation models (AGCMs) 20–25, 26–31 Atmospheric Model Intercomparison Project (AMIP) 21 Australia 89 autonomous adaptation 113 awareness-raising 331 Bangladesh adaptation measures 88 climate change projections 28–31 migration 349–350, 351 models 25–27 sea levels 354 Barotseland.com 202, 207–209 Barotse Royal Establishment (BRE) 203 baseline understanding, lack of 115–116 basic services 188 basins see catchments bilateral projects see official development assistance Black, R. 340–341 boundary partners 196 BRE (Barotse Royal Establishment) 203 Bulozi floodplain, Zambia 201, 202–205 business-as-usual policies 255

CAACs (Catchment Area Advisory Committees) 268, 270, 274 capacity-building adaptation 324, 325, 327, 331 Africa 287 city planning 83–84 climate management 283 disaster preparedness 84 model use 121 South Africa 189 water sector 275, 283 capacity constraints 11, 118–120 Castles, S. 341 Catchment Area Advisory Committees (CAACs) 268, 270, 274 Catchment Management Strategy (CMS) 268 catchments, water management 263, 267, 274 cattle 163, 166 CBA see community-based adaptation CCAA (Climate Change Adaptation in Africa) programme 247 CCF (Climate Change Fund) 299, 300–301 CDD (consecutive dry days) 25, 30, 51 Central Africa 112 charcoal production 138, 139 Chilwa, Lake, Malawi 144 CIF (Climate Investment Funds) 294, 296 CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora) 301 cities 254 city planning 83–84 Clean Technology Fund 296 climate Lesotho highlands 158 Mediterranean 242 Philippines 37, 38 South Africa 174 Vietnam 53–54 Zambia 201 climate change African agriculture 110–111 anthropogenic 305 Bangladesh 349 climate variability 331

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communication 143, 147 development 254 Lesotho highlands 158–159 South Africa 181 southern Africa 135–136, 142 understanding 142 see also climate change projections; climate impacts Climate Change Adaptation in Africa (CCAA) programme 247 climate change fatigue 142 Climate Change Fund (CCF) 299, 300–301 climate change gatherings 142 climate change projections Africa 111–113, 131 Bangladesh 28–31 biases 72 Ethiopia 217 experiment design 21 Indonesia 33–36 Mediterranean 240, 241 models 20 North Africa 241 Philippines 43–44 South Asia 23–25 southern Africa 134 Thailand 49–52 Vietnam 56–59 climate impacts agricultural development 111–113 Bangladesh 349 developing countries 292, 305 North Africa 241 Philippines 37 southern Africa 133, 135–139 Climate Investment Funds (CIF) 294, 296 climate models see models climate prediction 42, 63, 65, 71, 110–111 climate projections see climate change projections Climate Risk Management and Adaptation Strategy (CRMA) 298 climate scenarios 72, 73–74 climate science 124, 133–135, 142–143 Climate Systems Analysis Group (CSAG) 117, 131

363

climate variability 116, 133, 135–136, 159, 331 climatic-hydrological conditions, scenarios 73–74 CMS (Catchment Management Strategy) 268 coastal areas 37, 92–93 coastal resource management 95–97 coastal zone management 248, 250 co-benefits, development projects 323, 324, 327–330 collaborative action 123, 125 communal gardens 179–180 communication 11, 124, 197, 203–204 communicative dynamics 194 communicative strategy 194 communities 195, 197–200 community-based adaptation (CBA) 11, 195–197 conditions for success 206–207, 214 Morocco 247 promoting 219 southern Africa 141 Zambia 202–205 see also HARITA community-based organizations 206–213, 214 community-based risk-sharing strategies 144–145 community ownership 199 community participation 103, 227–231 community projects 182–183, 188 community risk-sharing 229 Conference of the Parties (COP; Copenhagen 2009) 3, 293 conferences 142 consecutive dry days (CDD) 25, 30, 51 Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) 301 coordinated response 140–141 coordinating mechanisms, adaptation assistance 292, 307 Copenhagen Accord 3, 293, 307 Copenhagen Green Climate Fund 307 costs 302–303, 306, 307 Coupled Model Intercomparison Project (CMIP3) 19 crime 145, 159, 209

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CRMA (Climate Risk Management and Adaptation Strategy) 298 crops cultivation 63–64 Ethiopia 210–211, 226, 229 production 112 Zambia 208–209 CSAG (Climate Systems Analysis Group) 117, 131 Cyclone Aila 350 cyclones 38, 42–43, 46–47, 135, 145, 350 dams Egypt 243 Lesotho highlands 154, 155, 162 South Africa 174, 177, 178, 189 datasets, precipitation 21–22, 230 decentralization 96–97 decision-making 87–88, 111, 219, 287 Dedebit and Credit Savings Institution (DECSI) 218 deforestation 136, 139 De Hoop Dam, South Africa 178 demand-side water management 271–273 Department for International Development (DFID) 131, 212 Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) 318 developed countries 293, 302, 305, 340 developing countries 292, 302, 305, 340 development adaptation 3, 4, 12, 88, 304–306, 317 climate change 254 co-benefits 323, 324, 327–330 definition 129 good 148 people-centred 196 planning 11 production 97–99 sustainable 12, 97–99, 194, 255, 263–264 Thailand 96 unsustainable 243 see also official development assistance development programming 88 DFID (Department for International Development) 131, 212 disaster management plans 178

disaster preparedness 84 disaster risk reduction 141, 147–148, 222, 299–300, 303 donors 12–13 see also official development assistance; stakeholders downscaling 72, 110 Africa 117, 124, 287 community-based adaptation 219 southern Africa 134 Draft Disaster Management Policy (2009) 279–280 dramas 142 drivers, migration 344–346 drought Africa 113 Kenya 259–260, 277, 351 North Africa 249, 251 risk reduction 278 southern Africa 133, 138, 139 see also water scarcity drought/flood cycles 259–260, 277 East Africa 260 economic evaluation 80–81 ecosystem goods 158 Egypt 242, 243, 248, 250, 354 EIAs (environmental impact assessments) 88, 267, 272, 275–277 El-Hinnawi, E. 337 El Niño/La Niña Southern Oscillation (ENSO) 54, 134 EMCA (Environmental Management and Coordination Act) 275 employment 164, 176, 179, 180, 186, 189 ENDA Energy Programme 202 engineered adaptation solutions 88 ensemble simulations 11, 20, 111 ENSO (El Niño/La Niña Southern Oscillation) 54, 134 envelopes (multiple model outputs) 111 environmental degradation 334–335 environmental drivers, migration 344–345 environmental impact assessments (EIAs) 88, 267, 272, 275–277 environmentally induced migrants 339, 355

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Environmental Management and Coordination Act (EMCA) 275 environmental migrants 336 environmental refugees 336, 337, 338 epistemologies 197 Ethiopia 210–213, 215, 319–322 see also HARITA European Commission 299 existing organizations 125 experiential evidence 199 extreme indices, precipitation 24, 25, 30, 51, 58 extreme precipitation events 24, 51 extreme weather events 247 extreme weather events projections Bangladesh 30–31 Kenya 279 North Africa 241 southern Africa 134, 136, 141 Thailand 51, 93 Vietnam 58 farming see agriculture financial adaptation assistance 292, 293–300, 302–303, 304, 307 financing mechanisms 221 firms 80 Flag Boshielo Dam, South Africa 177 flood control 73, 81–83 floods 68, 70, 93, 133, 259–260 food 179, 187 food insecurity 133, 139, 145, 188 food security 110, 133, 179 food societies 179 forced environmental migrants 338 forced migration see migration forecasts see weather forecasts foreign funding 119 Fourth Assessment Report (IPCC) 3, 19, 111, 219 fragmentation, adaptation implementation 252–253 fuelwood 159–160, 163–164 funding 119, 245, 286 see also financial adaptation assistance; financing mechanisms Gambia 323 gardens 179–180, 187

365

Ga-Selala, South Africa 173, 174–175, 179, 180, 181, 182 GCCA (Global Climate Change Alliance) 299 GCOS (Global Climate Observing System) 121 GEF (Global Environment Facility) 293–294 gender issues 145–146 see also women gender mainstreaming 275 GFDRR (Global Facility for Disaster Reduction and Recovery) 299–300 GGLM (Group Guarantee Lending Model) 225 Global Climate Change Alliance (GCCA) 299 Global Climate Observing System (GCOS) 121 Global Environment Facility (GEF) 293–294 Global Facility for Disaster Reduction and Recovery (GFDRR) 299–300 global footprint 343–348 global warming 24, 51 good adaptation 125 good development 148 governance 11–12 government 11, 96–97, 100–101, 140, 189, 190 government grants 180–181 greenhouse gas emissions 291 groundwater 174, 177 Group Guarantee Lending Model (GGLM) 225 hailstorms 159 HARITA (Horn of Africa Risk Transfer for Adaptation) 218, 222–235 broader context 233–235 community participation 227–231 conceptual framework 222–223 pilot overview 226–228 Ha Tsiu, Lesotho 155, 157–158, 160, 162, 165–166 health 161, 181 healthcare 167 health impacts 74–75 historical meteorological data 11, 121, 124

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HIV/AIDS 132, 146, 161, 167, 177, 181 home gardens 180, 187 Horn of Africa Risk Transfer for Adaptation see HARITA hotspots, migration 343, 356 hot systems, migration 343 household strategies 181–182 hydrological system 265 see also water IFW (Insurance for Work) model 223, 229, 231 illegal abstraction, water 272 impact assessments 189 impacts prediction 64 impacts simulation 64 incremental costs 306 index insurance see weather index insurance indigenous knowledge 165, 168 Indonesia 31–36 information 203–204, 265 information-sharing 123, 125 infrastructure development, water sector 272 innovation 194 institutional challenges 140–141 institutional frameworks, water sector 265, 266–269, 274–275 insurance 219–221, 223–226, 230–233, 300 Insurance for Work (IFW) model 223, 229, 231 integrated water resources management (IWRM) 262–263, 267 Inter-American Development Bank 298, 300 Intergovernmental Panel on Climate Change (IPCC) 131 Fourth Assessment Report 3, 19, 111, 219 internally displaced persons (IDPs) 338 international cooperation 12, 245, 255, 256, 291–308 coverage and needs 301–304 financial adaptation assistance 292, 293–300 technical assistance 292, 293 water sector 266

International Research Institute for Climate and Society (IRI) 218 investment 264 invisible adaptation 245 IPCC see Intergovernmental Panel on Climate Change irrigation 226 islands 64 IWRM (integrated water resources management) 262–263, 267 Japan International Cooperation Agency (JICA) 318–319, 323 Japan Meteorological Agency (JMA) 20 Java, Indonesia 34 jobs see employment KAMANAVA (Kalookan–Malabon–Navotas–Vale nzuela) area, Philippines 68–70, 82 Kenya baseline understanding 115 climate impacts 113 drought 259–260, 277, 351 floods 259–260 Kenyan National Climate Change Response Strategy 281–282 migration 350–353 see also water (Kenya) Kenyan National Climate Change Response Strategy (NCCRS) 281–282 Kitoh, A. 20, 21 Kusunoki, S. 20 land availability 159 land management 163 land-use planning 279 La Niña episodes 54, 134 leadership 195 learn by doing 239–240, 255, 264–265 Least Developed Countries Fund (LDCF) 295 legal frameworks, water sector 265, 266–269 Lesotho highlands 153–169 adaptive capacity 165–168 dynamic vulnerability 161–164 Ha Tsiu 155, 157–158, 160, 162, 165–166

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vulnerability 154–161 Lesotho Highlands Water Project (LHWP) 155 lessons learned 292 livelihoods 92, 103, 136–139, 157–158, 164, 167 livestock 112–113, 139, 163, 166, 203 loans 225, 233 local climate change simulation 72 local government 96–97, 100–101 local knowledge 263 local level, water management 263 local ownership 199 long-range weather forecasting 42 see also climate prediction Lozi 203, 204–205 Lyambai Vulnerability and Adaptation (LYVA) project 202, 204 Maboella land management 163 mainstreaming adaptation 4, 11, 279, 304, 306 gender 275 Makololo 205 maladaptation 148, 161, 198, 243 Malawi adaptation strategies 144 case study context 132–133 climate impacts 139 climate variability 135, 136 disaster risk reduction 141 institutions 140, 141 stakeholders 131 management 195, 213–214 MANGROVE (Reconciling Multiple Demands on Mangrove Resources) 92 mangrove rehabilitation 92, 99–102 mangrove species 94, 101 mangrove systems 92–93, 349 Manila see Metro Manila Marikina, Philippines 82–83 maximum five-day precipitation total (R5d) 25, 30, 51 medicine, traditional 161 Mediterranean 240, 241, 242 meso-scale projects 249–250 meteorological data Africa 116–117, 121

367

Bangladesh 26 historical 11, 121, 124 Indonesia 32 Philippines 38 precipitation datasets 21–22, 230 Thailand 45 Vietnam 53 Meteorological Research Institute of Japan (MRI) 20 meteorological services 134–135, 142–143 Metro Manila (Philippines) 67–84 adaptation options 80–82 damage assessments 76–78 flood control 73, 81–83 flooding 68, 70 flood simulation 71–74 health impacts 45 policy discussions 82–84 socio-economic impacts 74–75 vulnerabilities 79–80 microfinance institutions (MFIs) 224 micro-insurance 218, 219–221, 225 migrants see migration migration 12, 333–357 case studies 348–353 complexity 340–342 definition problems 337–339 drivers of 344–346 global footprint 343–348 Lesotho 164 Malawi 144 methodologies 342–343 mobility 344, 346–347 numerical estimates 339–340 overview 334–337 South Africa 180, 182 Millennium Ecosystem Assessment 334 mining 175, 176, 177, 180, 186, 189 Ministry of Water and Irrigation (MOWI) 282 mitigation adaptation 12, 130, 193, 291–292 Metro Manila 84 UNFCCC 291, 301 Mizuta, R. 20, 21 mobility 344, 346–347 models adaptation 114

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advances in 117 ambiguous results 87 atmospheric general circulation model (AGCM) 20–25 barriers to use of 11, 64–65, 114, 115–121 climate change projection development 26 communication issues 11, 124 interpretation 119, 120 scales 117–118 translators 120, 122, 124 uncertainty 111 users of 11, 122, 124 see also climate change projections; extreme weather events projections; present-day climate simulations; rainfall projections; simulations; temperature projections; verification (models) Mohale Dam, Lesotho 155 money, lack of 179 monsoons 37, 39–41, 47–48, 49, 53, 54 moral obligation 305 Morocco 242, 244, 247–248, 249, 250, 251, 252 MOWI (Ministry of Water and Irrigation) 282 Mozambique adaptation strategies 145 case study context 132–133 climate variability 136 disaster risk reduction 141 meteorological services 134–135 resettlement approach 137 stakeholders 131 MRI (Meteorological Research Institute of Japan) 20 multiple model outputs (envelopes) 111 multi-stakeholder participation 103 Murray-Darling Basin, Australia 89 Myanmar 322 Myers, N. 338 Nakhon Si Thammarat, Thailand 92, 93, 93–94 National Adaptation Programmes of Action (NAPAs) 140, 294 National Economic and Social

Development Board (NESDB) 96, 100 National Land Commission 279 National Land Policy 278–279 National Land Policy for the Sustainable Development of Arid and Semi-Arid Lands of Kenya 277–278 National Policy on Water Resource Management and Development 266–267 national projects 248–249, 252, 255 national water policy 266–267 National Water Resource Management Strategy (NWRMS) 269 natural disasters 25 natural resource management 165–166 natural resource use 163–164, 197–198 NCCRS (Kenyan National Climate Change Response Strategy) 281–282 NESDB (National Economic and Social Development Board) 96, 100 networks 125 NeWater project 155 newspapers 142, 204 NGO sector 140, 147 Nile Delta, Egypt 243, 248 ‘no regrets’ measures 318 North Africa 112, 240–256 adaptation implementation 244–246, 252–253 climate change projections 241 pro-poor adaptation 251, 253–255 review of projects 247–250 vulnerabilities 242–244 Northeast Monsoon (Amihan) 39–40 NWRMS (National Water Resource Management Strategy) 269 Nyala Insurance 218, 231 observatories 207–208 observed data see meteorological data official development assistance (ODA) 292, 304, 305, 306 and adaptation 4 adaptation effects 318–331 Organisation for Economic Co-operation and Development (OECD) 306 outside agencies 200

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ownership 197–199, 214, 287 oxen bank 208, 209 Oxfam America 218, 224, 234 Oxfam GB 130–131 Oxfam International 305–306 Pak Phanang Bay, Thailand 93–94 participative action research see participatory rural appraisal participatory capacity and vulnerability assessment (PCVA) 227, 233 participatory rural appraisal (PRA) 156, 196–197, 202, 227 Pasig-Marikina River System 70, 81, 82–83 PEF (Poverty and Environment Fund) 300 people-centred development 196 permitting system, water sector 270–271 Philippines 37–44 see also Metro Manila pigs 203 Pilot Programme for Climate Resilience (PPCR) 296–297 pilot projects 247 Pitsuwanthat, S. 93 planned adaptation 114 policy adaptation 92 policy implementation 95–97 poor 79–80, 84 adaptation for 251, 253–255 see also poverty population displacement see migration post-colonialism 198 poverty 132, 174, 175, 188 see also poor Poverty and Environment Fund (PEF) 300 PPCR (Pilot Programme for Climate Resilience) 296–297 PRA (participatory rural appraisal) 156, 227 precautionary adaptation 324, 325 precautionary planned retreat 12 precipitation datasets 21–22, 230 extreme events 24, 51 extreme indices 24, 25, 30, 51, 58 hailstorms 159

369

islands 64 model verification 21–22, 72 South Asia 22–23 see also rainfall; rainfall projections precipitation-based extreme indices 24, 25, 30, 51, 58 precipitation datasets 21–22, 230 present-day climate simulations Bangladesh 26–27 Indonesia 32–33 South Asia 22–23 Thailand 46–49 Vietnam 53–55 private sector 220–221 procedural mechanisms, uncertainty 87 production, and development 97–99 Productive Safety Net Programme (PSNP) 212, 223, 234 projections see climate change projections; rainfall projections; temperature projections projects community 182–183, 188 durability 254 meso-scale 249–250 national 248–249, 252, 255 pilot 247 preference for assessable 303 small-scale 247–248, 252 see also community-based adaptation; official development assistance proofs of concept 122 pro-poor adaptation 251, 253–255 prostitution 145, 146 see also sex for food transactions PSNP (Productive Safety Net Programme) 212, 223, 234 public awareness 142 public participation 89 quantitative analysis, adaptation strategies 183–185 R5d (maximum five-day precipitation total) 25, 30, 51 radio 135, 142, 204 rainfall Bangladesh 26–27 capacity constraints 119

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Ethiopia 210, 230, 319, 320 Kenya 350 Lesotho highlands 159 monitoring 117 Myanmar 322 Philippines 39–43 South Africa 174, 179 southern Africa 134, 135–136 Vietnam 54–57 Zambia 201 see also precipitation rainfall projections Bangladesh 28–29, 30 Indonesia 33–35 Philippines 43 Thailand 49–50 Vietnam 56–59 RANET project 135 rangelands 159 Reconciling Multiple Demands on Mangrove Resources (MANGROVE) 92 recycling water 272 refugees 336, 338 regional climate science centres, Africa 122 regional financial adaptation assistance 298–299 regional planning for climate change, challenges 87 Regional Vulnerability Assessment Committee (RVAC) 143 reinsurance 221 reliability, climate prediction 65 Relief Society of Tigray (REST) 212, 218 religious belief 203 resettlement approach 137 resilience 4, 114 resolution, models 65 responsibility 305 REST (Relief Society of Tigray) 212, 218 retreat, precautionary planned 12 rice 93, 208–209 risk 194, 223 risk communication 197 risk-coping measures 228–229 risk management 224

risk reduction 194, 227, 229–230, 278 see also disaster risk reduction robust adaptation 219 robust decision-making 87–88, 219 RVAC (Regional Vulnerability Assessment Committee) 143 Safety Net (Productive Safety Net Programme; PSNP) 212, 223, 234 Sahara, Africa 112 scaling adaptation efforts 265–266, 287, 303–304 scarcity, resources 89 SCCF (Special Climate Change Fund) 294 scenarios, climate 72, 73–74 SCF (Strategic Climate Fund) 296 SDII (simple daily intensity index) 24–25 sea levels Bangladesh 88, 349, 354 North Africa 241, 242, 243, 248, 250, 251 Thailand 91 SEAs (strategic environmental assessments) 276 sea surface temperatures 134 SECCI (Sustainable Energy Climate Change Initiative) 298 secretariats, multilateral environmental agreements 301, 308 sector focus, financial assistance 303 Sekhukhune, South Africa 174–175, 179–180, 185, 186–187 self-insurance 228–229 self-sustaining community-based organizations 206–213, 214 SES (socio-ecological system) 198, 214 sex for food transactions 139, 146 see also prostitution SGAs (Small Grants for Adaptation Actions) 299 share-cropping 166 sharing lessons learned 292 shocks, vulnerability 162 short-term projects 254 shrimp farming 94–95, 98–99 Sida (Swedish International Development Cooperation Agency) 173

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simple daily intensity index (SDII) 24–25 simulations ensemble 11, 20, 111 flood 71–74 impacts 64 local climate change 72 see also present-day climate simulations Small Grants for Adaptation Actions (SGAs) 299 small-scale projects 247–248, 252 social networks 179, 180, 182 social vulnerability 154 socio-ecological system (SES) 198, 214 socio-ecological values 197 socio-economic impacts 74–75 socio-political challenges 144–146 soft systems analysis 95 soil moisture 119 South Africa 173–190 adaptation strategies 181–188 climate impacts 113 Ga-Selala 173, 174–175, 179, 180, 181, 182 local authorities perceptions of stressors 176–179 Sekhukhune 174–175, 179–180, 185, 186–187 villagers perceptions of stressors 179–181 South Asia 22–25 Southern Africa 112, 132–148 capacity 139–146, 148 case study context 132–133 climate impacts 133, 135–139 climate science 133–135 see also Lesotho Highlands; South Africa Southwest Monsoon 40–41 SPA (Strategic Priority on Adaptation) 295–296 Special Climate Change Fund (SCCF) 294 stakeholders cognitive discourse 194 communication 11 diversity of 95 livelihoods 92

371

mangrove rehabilitation 101, 102 negotiation 89 ownership 199 participation 263, 274, 275 START African Climate Change Fellowship Programme 121–122 story-telling 142 Strategic Climate Fund (SCF) 296 strategic environmental assessments (SEAs) 276 Strategic Priority on Adaptation (SPA) 295–296 sub-Saharan Africa 260 subsidiarity principle 263 Sudan 295 sustainable development 12, 97–99, 194, 255, 263–264 Sustainable Energy Climate Change Initiative (SECCI) 298 Swedish International Development Cooperation Agency (Sida) 173 Swiss Re 218 tabia administration 210, 211–212 TACC (Territorial Approach to Climate Change) 250, 253 Taguig, Philippines 83 technical adaptation 261 technical adaptation assistance 292, 293, 300–301, 307–308 technical challenges 142–144 teff 226, 229 temperature projections 30, 52, 57–58, 59, 241, 260 temperatures Ethiopia 319 Kenya 260 Lesotho highlands 158–159 sea surface 134 southern Africa 134, 136 Zambia 201 temporal resolution, models 117–118 tenure system 278 Territorial Approach to Climate Change (TACC) 250, 253 Thailand climate change projections 49–52 development 96 mangrove rehabilitation 92, 99–102

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models 44–49 monsoon 47–48 Nakhon Si Thammarat 92, 93, 93–94 policy implementation 95–97 sustainable development 97–99 water resources 44 theft see crime Tigray, Ethiopia 210–213, 215, 218, 226 tipping points 340, 348 trade-offs 89 traditional knowledge 165, 168 traditional medicine 161 traffic 84 training, farming 167 training centres 212–213 translators (of models) 120, 122, 124 transparency 89 trends, vulnerability 162 tropical cyclones 38, 42–43, 46–47, 135, 145, 350 Tunisia 242, 249, 251 typhoons see tropical cyclones uncertainty climate prediction 63, 65, 71, 110–111 impacts prediction 64 robust decision-making 87–88, 219 water sector 264, 265, 269 unemployment 159, 175, 177, 179 UNFCCC see United Nations Framework Convention on Climate Change United Nations Development Programme–Global Environment Facility (UNDP–GEF) 247 United Nations Framework Convention on Climate Change (UNFCCC) adaptation 88, 308 additionality 306 Conference of the Parties (COP; Copenhagen 2009) 3, 293 mitigation 291, 301 unsustainable development 243 Upper Zambezi River 201 urban areas 254 urban poor 79–80, 84 users of models 11, 122, 124

verification (models) 32 Bangladesh 26–27, 30 Indonesia 32–33 Philippines 39–42 precipitation 21–22, 72 Thailand 46–49 Vietnam 54–55 Vietnam 52–59 Vision 2030: 280 volunteer stations 121 vulnerability Africa 109, 110 Ethiopia 228 factors exacerbating 193 Kenya 269 Lesotho highlands 157–164 Metro Manila 79–80 migration 335, 355 nature of 10–11 North Africa 242–244 shocks 162 social 154 southern Africa 143, 145, 146 trends 162 understanding 154–155 vulnerability-oriented adaptation 263 WASREB (Water Services Regulatory Board) 271 wastewater 272 water Africa 286 allocation/reallocation 269–271 catchments 263, 267, 274 data 267, 273 Ethiopia 210, 212, 319–322 financial assistance 303 governance frameworks 261, 264 laws 261 Lesotho highlands 154, 160 Myanmar 322 North Africa 243, 251 pricing 271–272 as primary climate change influence 260 South Africa 174, 177–178, 185–187, 188 Thailand 44 water-based lifestyles 84

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see also water (Kenya); water management; water scarcity water (Kenya) 259–262, 266–280, 282 legal and institutional frameworks 266–269, 274–275 policy instruments, related sectors 275–280 shortages 352 water quality regulation 273–274 water quantity regulation 269–273 Water Act (2002) 267–268 Water Financing Partnership Facility (WFPF) 300 water management 261 adaptive water management 264–265 demand-side 271–273 integrated water resources management (IWRM) 262–263, 267 Water Resource Management Authority (WRMRA) 268 Water Resources Information Management System (WRIMS) 273 Water Resource Users’ Associations (WRUAs) 268, 274 water scarcity Africa 113 Australia 89 Lesotho highlands 160 North Africa 243, 249, 252, 253 South Africa 176, 185–187, 189 see also drought; Kenya Water Sector Working Group 274 Water Service Boards (WSBs) 272 Water Services Regulatory Board (WASREB) 271 watersheds see catchments WCRP (World Climate Research Programme) 19 weAdapt 113, 123 weather forecasts 42, 135 weather index insurance 219–220, 223–226, 230–233

373

weather monitoring see meteorological data wells 319, 322 Western Africa 112 West Mangahan area, Philippines 83 wetlands 158, 166 WFPF (Water Financing Partnership Facility) 300 wind projections 35 women female-headed households 231 HIV/AIDS 132 models use 120 prostitution 145, 146 sex for food transactions 139, 146 southern Africa 138, 139 water sector 274, 322 see also gender issues wood 159–160, 163–164 workshops 142 World Bank 293, 294, 300 World Climate Research Programme (WCRP) 19 WRIMS (Water Resources Information Management System) 273 WRMRA (Water Resource Management Authority) 268 WRUAs (Water Resource Users’ Associations) 268, 274 WSBs (Water Service Boards) 272 Zambezi River 201 Zambia case studies 132–133, 201–205 climate variability 135–136 community-based organizations 206–210 meteorological services 135 rainfall 134 self-sufficiency 215 stakeholders 131

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Plate 1 Geographical distributions of June–August (JJA) mean precipitation climatology (mm/day): (a) CPC Merged Analysis of Precipitation (CMAP); (b) Global Precipitation Climatology Project (GPCP); (c) Climate Research Unit of University of East Anglia (CRU); (d) Tropical Rainfall Measuring Mission (TRMM 3A25); (e) 180km model; (f) 120km model; (g) 60km model; and (h) 20km model

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Plate 2 Geographical distributions of December–February (DJF) mean precipitation climatology (mm/day): (a) CMAP; (b) GPCP; (c) CRU; (d) TRMM 3A25; (e) 180km model; (f) 120km model; (g) 60km model; and (h) 20km model

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Plate 3 Time–latitude cross-sections of 100°E–120°E averaged monthly mean precipitation climatology (mm/day): (a) CMAP; (b) GPCP; (c) CRU; (d) TRMM 3A25; (e) 180km model; (f) 120km model; (g) 60km model; and (h) 20km model

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Plate 4 December–February (DJF) mean precipitation changes (mm/day) between the present and the end of the 21st century for the (a) 60km model and (b) 20km model Note: For the 60km model, areas statistically significant at the 95 per cent level are in colour, and areas where all four different sea surface temperature (SST) experiments show consistent changes in sign are hatched. For the 20km model, areas statistically significant at 90 per cent level are in colour. The contour interval is 1mm/day.

Plate 5 March–May (MAM) mean precipitation changes (mm/day) between the present and the end of the 21st century for the (a) 60km model and (b) 20km model

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Plate 6 June–August (JJA) mean precipitation changes (mm/day) between the present and the end of the 21st century for the (a) 60km model and (b) 20km model

Plate 7 September–November (SON) mean precipitation changes (mm/day) between the present and the end of the 21st century for the (a) 60km model and (b) 20km model

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Plate 8 Changes in simple daily precipitation index (mm/day) between the present and the near future for the: (a) 60km model and (b) 20km model, and those between the present and the end of the 21st century for the (c) 60km model and (d) 20km model, respectively Note: For the 60km model, areas where all four different SST experiments show consistent changes in sign are hatched. For the 20km model, statistical significance is not shown.

Plate 9 Changes in the maximum five-day precipitation (R5d) total (mm) for the: (a) 60km model and (b) 20km model, and those between the present and the end of the 21st century for the (c) 60km model and (d) 20km model, respectively

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Plate 10 Changes in the number of consecutive dry days (CDD) (day) for the: (a) 60km model and (b) 20km model, and those between the present and the end of the 21st century for the (c) 60km model and (d) 20km model, respectively

Plate 11 Spatial distribution of monsoon for June to September rainfall (mm/day) over Bangladesh during 1979–2003 obtained from (a) observed data and (b) atmospheric general circulation model (AGCM) data

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Plate 12 (a) Projection of monsoon mean rainfall (mm/day) (F, 2075–2099); (b) rainfall change (F–P) between the present (1979–2003) and future (2075–2099); and (c) change ratio (F–P)/P (%)

Plate 13 (a) Change in the maximum number of consecutive dry days (day) between the present (1979–2003) and future (2075–2099); (b) change in the annual maximum of the consecutive five-day total rainfall (mm) between the present (1979–2003) and future (2075–2099)

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Plate 14 Gridded total annual precipitation averaged over 1997–2004 for the Global Precipitation Climatology Project (GPCP) 1° (left) and the Meteorological Research Institute of Japan (MRI) 20km model (right)

Plate 15 Seasonal precipitation for December to February: (a) present simulation for 1979–2003; (b) future simulation for 2075–2099; (c) change = future minus present; (d) change/present

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Plate 16 Seasonal precipitation for June to August: (a) present simulation for 1979–2003; (b) future simulation for 2075–2099; (c) change = future minus present; (d) change/present

Plate 17 Annual precipitation: (a) present simulation for 1979–2003; (b) future simulation for 2075–2099; (c) change = future minus present; (d) change/present

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Plate 18 (a) Average of the onset of the rainy season, estimated within ten-day periods of each month, and the length of the rainy season over Indonesia; (b) average of the onset of the dry season, estimated within ten-day periods of each month, and the length of the dry season over Indonesia Source: BMKG

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Plate 19 Change in the onset of the rainy season – zonal component of surface wind velocity: (a, c, e) present simulations for 1979–2003; (b, d, f) future simulations for 2075–2099; (a, b) October; (c, d) November; (e, f) December

Plate 20 Change in the onset of the dry season – zonal component of surface wind velocity: (a, c, e) present simulations for 1979–2003; (b, d, f) future simulations for 2075–2099; (a, b) April; (c, d) May; (e, f) June

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(b)

Plate 21 Maps of the Philippines for (a) topography and (b) climate type based on rainfall pattern overlaying the 53 synoptic stations (black dots) used in the study Source: PAGASA

Plate 22 Actual tracks of tropical cyclones from 1948–2008 in the Philippine Area of Responsibility (PAR) from best track data of the Weather Division, PAGASA-Tropical Cyclone Guidance System

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Plate 23 Change in seasonal mean precipitation from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) January–March; (b) April–June; (c) July–September; (d) October–December

Plate 24 Change in seasonal mean precipitation from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) May–July; (b) August–October

Plate 25 Change in precipitation extreme events from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) consecutive dry days (CDD); (b) maximum five-day precipitation total (R5d)

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Plate 26 Change in precipitation (May–November) from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage

Plate 27 Change in precipitation from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) June; (b) August

Plate 28 Change in precipitation from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) February; (b) March

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Plate 29 Annual average temperature distribution for: (a) the present; (b) the future; and (c) the change projected by the 20km atmospheric general circulation model (AGCM) simulation

Plate 30 Change in precipitation extreme events from present simulation for 1979–2003 to future simulation for 2075–2099: Change ratios (future minus present)/present are shown in percentage (a) consecutive dry days (CDD); (b) simple daily intensity index (SDII); (c) maximum five-day precipitation total (R5d)