1. DISASTER RISKMANAGEMENT SERIESNO. 534423NaturalDisasterHotspotsA Global RiskAnalysis THE WORLD BANK

2. Other Disaster Risk Management Series Titles1 Managing Disaster Risk in Mexico: Market Incentives for Mitigation Investment2 Managing Disaster Risk in Emerging Economies3 Building Safer Cities: The Future of Disaster Risk4 Understanding the Economic and Financial Impacts of Natural Disasters 3. Disaster Risk Management SeriesNatural Disaster HotspotsA Global Risk AnalysisbyMaxx Dilley,1 Robert S. Chen,2 Uwe Deichmann,3Arthur L. Lerner-Lam,4 and Margaret Arnold5with Jonathan Agwe,5 Piet Buys,3 Oddvar Kjekstad,6Bradfield Lyon,1 and Gregory Yetman2The World BankHazard Management Unit2005Washington, D.C.1International Research Institute for Climate Prediction (IRI), Columbia University2Center for International Earth Science Information Network (CIESIN), Columbia University3Development Economics Research Group (DECRG), The World Bank4Center for Hazards and Risk Research (CHRR) and Lamont-Doherty Earth Observatory(LDEO), Columbia University5Hazard Management Unit (HMU), The World Bank6International Centre for Geohazards (ICG), Norwegian Geotechnical Institute (NGI) 4. 2005 The International Bank for Reconstruction and Development /The World Bank and Columbia University1818 H Street, NWWashington, DC 20433Telephone 202-473-1000Internet www.worldbank.orgE-mail feedback@worldbank.orgAll rights reserved.1 2 3 4 08 07 06 05Copyright 2005, International Bank for Reconstruction and Development/The World Bank and ColumbiaUniversity. This material may be copied for research, education, or scholarly purposes. All materials are subject torevision. The views and interpretations in this document are those of the individual author(s) and should not beattributed to the World Bank or Columbia University.The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors,denominations, and other information shown on any map in this work do not imply any judgment on the part ofthe World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.Rights and PermissionsThe material in this work is copyrighted. Copying and/or transmitting portions or all of this work withoutpermission may be a violation of applicable law. The World Bank encourages dissemination of its work andwill normally grant permission promptly.For permission to photocopy or reprint any part of this work, please send a request with completeinformation to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA;telephone 978-750-8400; fax 978-750-4470; www.copyright.com.All other queries on rights and licenses, including subsidiary rights, should be addressed to the Office ofthe Publisher, World Bank, 1818 H Street NW; Washington, DC 20433, USA; fax 202-522-2422; e-mailpubrights@worldbank.org.ISBN 0-8213-5930-4 978-0-8213-5930-3e-ISBN 0-8213-5931-2Library of Congress Cataloging-in-Publication Data has been applied for. 5. ContentsPreface viiAcronyms and Abbreviationsxi1. Executive Summary 1 Project Approach 1 Key Findings of the Global Analyses 1 Key Findings of the Case Studies 12 Conclusions and the Way Forward 122. Project Objectives193. Project Approach 23 Risk Assessment Framework 23 Selection of Natural Hazards 25 Units of Analysis 26 Summary of Data Sources and Data Preparation27 Global Hotspots Classification 334. Single-Hazard Exposure Analysis 35 Cyclones 35 Drought 35 Floods 35 Earthquakes 43 Volcanoes 44 Landslides 44 Single-Hazard Analysis of Exposure 445. Multihazard Exposure Analysis 47 Simple Multihazard Index 47 Reclassification of Multihazard Areas by Population Density 526. Multihazard Risk Assessment 55 Derivation of Vulnerability Coefficients 55 Single-Hazard Risk Assessment Results607. Multihazard Risk Assessment Results818. Case Studies 93 Scale Issues 94 iii 6. iv Natural Disaster Hotspots: A Global Risk Analysis Summary of Case Study Results 94 Linkages to and Lessons for Global Analysis 1109. Conclusions and the Way Forward 113 The Costs of Disaster Risks 113 Implications for Decision Making 115 Information Development for Disaster Risk Management 117Appendix A: Technical Appendix for Global Analysis 119 A.1 Derivation of Tropical Cyclone and GDP Surfaces 119 A.2 Reclassification of Hazardous Areas Weighted by Exposure 120 A.3 World Bank Country Income Classifications 127References130BoxesBox 6.1 Risk Assessment Procedure for Both Mortality and Economic Losses, Illustratedby the Mortality Example 59TablesTable 1.1 Countries Most Exposed to Multiple Hazards 4Table 1.2 Countries at Relatively High Mortality Risk from Multiple Hazards 8Table 3.1 Ranking of Major Natural Hazards by Number of Deaths Reported in EM-DAT 26Table 3.2 Number of Input Units Used in the Gridded Population of the World (GPW) Data Sets,Versions 1-3 27Table 3.3 Summary of Data Sources for Each Hazard 29Table 3.4 Summary of Data Sources for Exposure 31Table 3.5 Summary of Exposure Data for World and Unmasked Areas 32Table 4.1 Characteristics of High-Hazard Areas by Hazard: Top Three Deciles 43Table 5.1 Summary Statistics for the Simple Multihazard Index 48Table 5.2 Hazard Profile for High-Cyclone Exposed Areas 52Table 5.3 Summary Statistics for the Population-Weighted Multihazard Index 52Table 6.1 Mortality-Related Vulnerability Coefficients 56Table 6.2 Economic Loss-Related Vulnerability Coefficients 57Table 6.3 Characteristics of High-Risk Areas by Hazard 64Table 7.1 Characteristics of High-Risk Disaster Hotspots 88Table 7.2 Countries at Relatively High Economic Risk from Multiple Hazards 89Table 8.1 Summary of Case Studies 94Table 8.2 An Expert Synthesis of Storm Surge Hotspots around the World 102Table 8.3 Potential and Actual Hotspots Vulnerable to Flooding by Storm Surge 112Table 9.1 Countries Receiving High Levels of International Disaster Assistance, 1992through 2003 114Table 9.2 Countries Receiving Emergency Loans and Reallocation of Existing Loans to Meet DisasterReconstruction Needs, 1980 through 2003 115Table 9.3 Direct and Indirect Losses for Six Major Disasters 116Table A1.1Available Tropical Cyclone Data by Region 119Table A1.2Subnational GDP Data 120Table A3.1World Bank Country Income Classifications: High Income 127Table A3.2World Bank Country Income Classifications: Low and Middle Income 128 7. ContentsvFiguresFigure 1.1 Global Distribution of Areas Highly Exposed to One or More Hazards, by Hazard Type 3Figure 1.2 Global Distribution of Highest Risk Disaster Hotspots by Hazard Type 5Figure 1.3 Proportion of National Population in Highest Risk Areas from Two or More Hazards (Mortality)10 Figure 1.4 Proportion of National Population in Highest Risk Areas from One or More Hazards (Mortality)11 Figure 1.5 Proportion of GDP in Highest Risk Areas from Two or More Hazards (Economic Losses) 13 Figure 1.6 Proportion of GDP in Highest Risk Areas from One or More Hazards (Economic Losses) 14 Figure 3.1 Mask Used to Eliminate Sparsely Populated, Nonagricultural Areas 28 Figure 4.1 Distribution of Hazardous Areas by Hazard Type 36 Figure 4.2 Exposure Measures by Hazard Decile45 Figure 5.1 Global Distribution of Areas Significantly Exposed to One or More Hazards, by Number of Hazards 49 Figure 5.2 Detailed View of Multihazard Areas50 Figure 5.3 Global Distribution of Multiple Hazards by Population Density Category 53 Figure 6.1 Global Distribution of Cyclone Risk61 Figure 6.2 Global Distribution of Drought Risk65 Figure 6.3 Global Distribution of Flood Risk 69 Figure 6.4 Global Distribution of Earthquake Risk72 Figure 6.5 Global Distribution of Volcano Risk 75 Figure 6.6 Global Distribution of Landslide Risk 78 Figure 7.1 Global Distribution of Disaster Risk Hotspots for All Hazards 82 Figure 7.2 Global Distribution of Disaster Risk Hotspots by Number of Hazards85 Figure 8.1 Frequency with Which Climatic Drought Hazard Events Were Accompanied by Drought Disasters or Not from 1979 through 2001 95 Figure 8.2 WASP Estimates of Climatic Drought and Drought Disasters for Central Southwest Asian Countries96 Figure 8.3 WASP Estimates of Climatic Drought and Drought Disasters for Lao PDR and India 97 Figure 8.4 Modeled Landslide Zonation and GEORISK Landslide Inventory in Armenia 98 Figure 8.5 Landslide Hazard Map for Central America and Andean South America 99 Figure 8.6 Landslide Mortality Risks Calibrated with Historical Landslide-Related Mortality from the EM-DAT International Disaster Database 100 Figure 8.7 Multihazard Risk Map Constructed by Weighting Each Hazard Index by Incidence Frequency Data from EM-DAT Database 104 Figure 8.8 Multihazard Risk Map Constructed by Weighting Each Hazard Index by the Relief Expenditure Data for Each Hazard between 1948 and 1992105 Figure 8.9 Multihazard Disaster Risk, Caracas 107 Figure 8.10 Location Map of Tana River and Garissa Districts with Coverage of Tana River Basin in Garissa District, Kenya 108 Figure 8.11 Livelihood Zones Overlaid on El Nio 199798 Flood Case 109 Figure A2.1 Single-Hazard Exposure Index Based on Top Three Population-Weighted Deciles 121 8. PrefaceAs this volume goes to print, millions of people in Asiawith the newly established Center for Hazards and Riskattempt to rebuild their lives and communities follow-Research (CHRR) at Columbia University to discuss theing the devastating earthquake and tsunami that occurredpossibility of a global-scale, multihazard risk analysison December 26, 2004. The earthquake occurred off the focused on identifying key hotspots where the riskscoast of Sumatra, registering 9.0 on the Richter scale, of natural disasters are particularly high. The projectand causing tsunami waves that swept through the Indian would aim to provide information and methods to informOcean at a rate of 500-700 km per hour, devastating priorities for reducing disaster risk and making deci-coastal areas of countries across South and Southeast sions on development investment. Discussions culmi-Asia and East Africa. More than 220,000 people were nated in a jointly sponsored brainstorming workshopkilled, thousands more were injured, and millions affected. held at Columbia in September 2001 at which a smallDamage to infrastructure, social systems, and the envi- group of experts examined in depth whether such anronment has been substantial. At the time of this writ- analysis was feasible and worthwhile. A summary ofing, preliminary damage and needs assessments the workshop and presentations is available on the ProVen-undertaken by the World Bank and other partners esti- tion Consortium Web site at: http://www.provention-mate the damages at nearly $6 billion for Indonesia, theconsortium.org/conferences/highriskhotspots.htm.Maldives, and Sri Lanka alone.Developed from that initial workshop, the Identifi-The tragic impacts and seeming enormity of this event cation of Global Natural Disaster Risk Hotspots (Hotspots)have thrown many around the world into a state of dis-project was implemented under the umbrella of thebelief. As shocking as the tsunami disaster is, however,ProVention Consortium by World Bank staff from theits important to remember that events of this magni- HMU and the Development Economics Research Grouptude have happened in other places around the world,(DECRG) and Columbia University staff from the CHRR,and they will happen again. In 1984, persistent droughtsthe Center for International Earth Science Informationin Ethiopia and Sudan killed 450,000. In Bangladesh inNetwork (CIESIN), the International Research Institute1991, nearly 150,000 lives were taken by a cyclone. for Climate Prediction (IRI), and the Lamont-DohertyHundreds of natural disasters, both large and small, occurEarth Observatory (LDEO). The project has also bene-each year. While the largest capture the attention of the fited greatly from close collaboration with the Norwe-global media, there are hundreds more events that wegian Geotechnical Institute (NGI), the United Nationsdont hear about. The cumulative effect of these smallerDevelopment Programme (UNDP), the United Nationsand medium-sized disasters have equally devastating Environment Programme (UNEP), the United Nationsimpacts on developing countries: loss of developmentOffice for the Coordination of Humanitarian Affairsgains, torn communities, and increased impoverishment.(OCHA), the United Nations World Food ProgrammeThe poor in these countries are consistently the most (WFP), the U.S. Geological Survey (USGS), the Inter-severely affected.national Strategy for Disaster Reduction (ISDR), andThe Hotspots initiative began in 2001, when the World other individuals and groups.Banks Disaster Management Facility (DMF), now theIn November 2002, a second workshop was held atHazard Management Unit (HMU), initiated discussions Columbia University involving experts on key naturalvii 9. viiiNatural Disaster Hotspots: A Global Risk Analysishazards as well as potential case study authors. (For moreproviding complementary funding of the project andinformation on this workshop, see http://www. their support of the Caracas case study.proventionconsortium.org/conferences/high- The Hotspots project benefited enormously from inter-riskhotspots2002.htm.) This workshop reviewed the ini-actions with the project on Reducing Disaster Risk, a col-tial plans and approaches under development by thelaborative effort involving UNDP, UNEP, and others.core project staff, coordinated plans for the case stud-We especially thank Yasmin Aysan, Pascal Peduzzi, Andrewies, and obtained feedback from the World Bank andMaskrey, and Ron Witt for their willingness to exchangeothers, including the new director of the Earth Institute data, methods, and ideas. These two projects share aat Columbia University, Professor Jeffrey Sachs. This common approach with regard to analysis of disasterworkshop led to the preparation of a revised work plan, risk and vulnerability. Pablo Recalde played a key roleincluding the addition of several new case study activ- in organizing WFP participation in the project and caseities to the project. Intensive project work continued in studies. We also acknowledge the support of the U.S.2003, culminating in a working meeting in DecemberAgency for International Development (USAID) for the2003 at which key results were reviewed and plans devel-Tana River case study.oped for the final project reports and dissemination ofWe thank Kathy Boyer for her extensive help withresults. In March 2004, a review and synthesis meetingproject management and implementation, especially withwas held at the World Bank in Washington, D.C., regard to the case studies. We very much appreciate thewhere project results were presented to experts fromtireless efforts of Piet Buys of DECRG and Greg Yetmanthe ISDR Working Group III on Vulnerability, Risk and and Kobi Abayomi of CIESIN to access, transform, andImpacts; the World Bank; and other interested organi- analyze the wide range of global data used in this proj-zations.ect. We gratefully acknowledge the extensive adminis-This report contains the results of the global hotspots trative and organizational support provided by Staceyanalysis as well as summaries of the case studies, whichGander of the CHRR and Jennifer Mulvey, Ed Ortiz,are being published as a separate volume. The list of caseand Hannia Smith of CIESIN. We also thank our col-studies and contributors is provided in Table 8.1. This leagues within the Earth Institute at Columbia Univer-publication does not examine tsunami hazard risk, assity for their extensive inputs and guidance on a widecomprehensive data sets were not available during the range of issues, both organizational and technical. Thesecourse of the study. However, plans are being made to individuals include Deborah Balk, George Deodatis,include an analysis of tsunami-related risks in a subse-Klaus Jacob, Upmanu Lall, Marc Levy, Brad Lyon, Robertaquent phase of hotspots research. Balstad Miller, Chet Ropelewski, Jeffrey Sachs, AndrewThe project team wishes to thank the HMUespe-Smyth, Angeletti Taramelli, Jeff Weissel, and Lareef Zubair.cially its former manager, Alcira Kreimerfor her strongWe are grateful to Matt Barlow, Klaus Jacob, Oddvarsupport, guidance, and encouragement throughout Kjekstad, and Sylvia Mosquera for their helpful reviewsthis challenging project. We thank Maryvonne Plessis- of the final draft. Of course, the opinions, conclusions,Fraissard, Director of the Transport and Urban Devel- and recommendations provided in this report are thoseopment Department, and Eleoterio Codato, Sector of the authors and not necessarily those of the WorldManager for Urban Development, for their support of Bank, the Trustees of Columbia University in the Citythe initiative. We thank Maria Eugenia Quintero and Zoe of New York, our sponsors, partners, or colleagues.Trohanis at the HMU for their technical and organiza-Hotspots aims to provide a tool to get ahead of thetional contributions to the project. We especially thankdisaster trend by highlighting areas that are most vul-the United Kingdoms Department for International Devel-nerable to a number of hazards. We hope that develop-opment (DFID) and Norwegian Ministry of Foreign ment agencies and policymakers will use the informationAffairs for their interest and financial support. We areto plan ahead for disasters and minimize their impacts.grateful to the CHRR, the Earth Institute, and the Lamont-This implies understanding the risk facing a particularDoherty Earth Observatory of Columbia University forcommunity, city, or region, and integrating this under- 10. Prefaceixstanding into development planning decisions. Theknowledge and affordable technologies do exist toallow even low-income countries to significantlyreduce the devastating social and economic impactscaused by such hazards as droughts, floods and earth-quakes that are part of the natural cycle of so many coun-tries. The triggers may be natural, but responsibility forthe impacts of disasters belongs to all of us.Maxx Dilley, IRIRobert S. Chen, CIESINUwe Deichmann, DECRG, World BankArt Lerner-Lam, CHRR/LDEOMargaret Arnold, HMU, World Bank 11. Acronyms and AbbreviationsCAS Country Assistance StrategyCHRRCenter for Hazards and Risk ResearchCIESINCenter for International Earth Science Information NetworkCREDCentre for Research on the Epidemiology of DisastersDECRG Development Economics Research GroupDFIDUK Department for International DevelopmentDMF Disaster Management Facility (now HMU)DRI Disaster Risk IndexECLAC Economic Commission for Latin America and the CaribbeanEM-DATEmergency Events DatabaseENSOEl Nio-Southern OscillationERL Emergency Reconstruction LoanFTS Financial Tracking SystemGDP Gross domestic productGIS Geographic Information SystemGPW Gridded Population of the WorldGSHAP Global Seismic Hazard ProgramHMU Hazard Management UnitICG International Centre for GeohazardsIFPRI International Food Policy Research InstituteIFRCInternational Federation of the Red CrossIRI International Research Institute for Climate PredictionISDRInternational Strategy for Disaster ReductionLDEOLamont-Doherty Earth ObservatoryNGDCNational Geophysical Data CenterNGI Norwegian Geotechnical InstituteNIMANational Imagery and Mapping AgencyNRC National Research CouncilOCHAOffice for the Coordination of Humanitarian Affairspga Peak ground accelerationPNG Papua New GuineaPPP Purchasing power parityPreView Project of Risk Evaluation, Vulnerability, Information and Early WarningSRTMShuttle Radar Topographic MissionUNDPUnited Nations Development Programme xi 12. xiiNatural Disaster Hotspots: A Global Risk AnalysisUNEPUnited Nations Environment ProgrammeUSGSUnited States Geological SurveyVEI Volcanic Explosivity IndexVMAP(0) Vector Map Level 0WASPWeighted Anomaly of Standardized PrecipitationWFP World Food ProgrammeWRI World Resources Institute 13. Chapter 1Executive SummaryEarthquakes, floods, drought, and other natural haz- cyclones. By calculating relative risks for grid cells ratherards continue to cause tens of thousands of deaths, hun- than for countries as a whole, we are able to estimatedreds of thousands of injuries, and billions of dollarsrisk levels at subnational scales.in economic losses each year around the world. TheThe global analysis is limited by issues of scale as wellEmergency Events Database (EM-DAT), a global disas-as by the availability and quality of data. For a numberter database maintained by the Centre for Research onof hazards, we had only 15- to 25-year records of eventsthe Epidemiology of Disasters (CRED) in Brussels, recordsfor the entire globe and relatively crude spatial infor-upwards of 600 disasters globally each year (http:// mation for locating these events. Data on historical dis-www.cred.be). Disaster frequency appears to be increas-aster losses, and particularly on economic losses, areing. Disasters represent a major source of risk for thealso limited.poor and wipe out development gains and accumulated While the data are inadequate for understanding thewealth in developing countries.absolute levels of risk posed by any specific hazard orAs the recognition grows that natural disaster riskcombination of hazards, they are adequate for identify-must be addressed as a development issue rather than ing areas that are at relatively higher single- or multi-one strictly of humanitarian assistance, so must our ple-hazard risk. In other words, we do not feel that theefforts to develop the tools to effectively mainstream data are sufficiently reliable to estimate, for example,disaster risk management into development activities.the total mortality risk from flooding, earthquakes, andThis project has attempted to develop a global, synop- drought over a specified period. Nevertheless, we cantic view of the major natural hazards, assessing risks ofidentify those areas that are at higher risk of flood lossesmultiple disaster-related outcomes and focusing in par-than others and at higher risk of earthquake damage thanticular on the degree of overlap between areas exposed others, or at higher risk of both. We can also assess into multiple hazards. The overall goal is to identify geo-general terms the exposure and potential magnitude ofgraphic areas of highest disaster risk potential in orderlosses to people and their assets in these areas. Suchto better inform development efforts.information can inform a range of disaster prevention and preparedness measures, including prioritization of resources, targeting of more localized and detailed riskProject Approach assessments, implementation of risk-based disaster man- agement and emergency response strategies, and devel-In this report we assess the risks of two disaster-related opment of long-term land use plans and multihazardoutcomes: mortality and economic losses. We estimate risk management strategies.risk levels by combining hazard exposure with histor- A set of case studies explores risks from particularical vulnerability for two indicators of elements at risk hazards or for localized areas in more detail, using thegridded population and gross domestic product (GDP)same theoretical framework as the global analysis. Weper unit areafor six major natural hazards: earth-hope that in addition to providing interesting and usefulquakes, volcanoes, landslides, floods, drought, andresults, the global analysis and case studies will stimu- 1 14. 2Natural Disaster Hotspots: A Global Risk Analysislate additional research, particularly at national and localThe fact that some areas of the world are subject tolevels, which will be increasingly linked to policymultiple hazards will not surprise many residents ofmaking and practice in disaster risk reduction.those areas, but what this analysis reveals is the extent Within the constraints summarized above, we devel-to which, at global and regional scales, there is sub-oped three indexes of disaster risk: stantial overlap between different types of hazards and population concentrations. The worlds geophysical1. Mortality risks, assessed for global gridded popula- hazardsearthquakes and volcanoestend to cluster tion along fault boundaries characterized by mountainous2. Risks of total economic losses, assessed for global terrain. Hazards driven mainly by hydro-meteorological gridded GDP per unit area processesfloods, cyclones, and landslidesstrongly3. Risks of economic losses expressed as a proportion affect the eastern coastal regions of the major continents of the GDP per unit area for each grid cell as well as some interior regions of North and SouthRisks of both mortality and economic losses are cal- America, Europe, and Asia. Drought is more widely dis-culated as a function of the expected hazard frequency persed across the semiarid tropics. The areas subject toand expected losses per hazard event. We obtained global both geophysically- and hydro-meteorologically-drivenhazard data on cyclones, drought, earthquakes, floods, hazards fall primarily in East and South Asia and in Cen-landslides, and volcanoes from a variety of sources. tral America and western South America. Many of theseThe global hazard data sets were improved upon or, inareas are also more densely populated and developedthe case of droughts and landslides, created specifi-than average, leading to high potential for casualties andcally for the analysis. Vulnerability was estimated by economic losses. Of particular concern in these areasobtaining hazard-specific mortality and economic lossare possible interactions between different hazards, forrates for World Bank regions and country wealth classesexample, landslides triggered by cyclones and flooding,within them based on 20 years of historical loss dataor earthquakes that damage dams and reservoirs neededfrom the EM-DAT database.for drought and flood protection.We masked out low-population and nonagriculturalThe global analysis supports the view that disasterareas where risks of losses are negligible. After calculat-risk management is a core issue of development. Com-ing the expected losses for each remaining grid cell, we paring Figures 1.1 and 1.2a illustrates the degree toranked the grid cells and classified them into deciles (10 which exposure to hazards in developed countries hasclasses composed of roughly equal numbers of cells). not led to relatively high mortality in the past two decadesCells falling into the highest three deciles for either mor- in these areas. Areas of Europe and North America thattality or economic losses are considered disaster risk hotspots. are highly exposed to natural hazards as shown in Figure 1.1, for example, have not experienced correspondingly high mortality from these hazards over the past twoKey Findings of the Global Analysisdecades. The United States is noteworthy in that more than one-third of its population lives in hazard-proneAmong the findings are that on the order of 25 million areas but only 1 percent of its land area ranks high insquare kilometers (km2) (about 19 percent of the Earths mortality risk.land area) and 3.4 billion people (more than half of theFigure 1.2 shows the types of hazards for whichworlds population) are relatively highly exposed to ateach grid cell appeared in the top three deciles of theleast one hazard. Some 3.8 million square kilometers global risk distribution for mortality (a) and economicand 790 million people are relatively highly exposed losses (b and c). Figure 1.2b shows that areas at highto at least two hazards. About 0.5 million square kilo-risk of economic losses are more widely distributed inmeters and 105 million people are relatively highlyindustrial and lower-middle-income countries than areasexposed to three or more hazards (Figure 1.1). In some of high mortality risk. In addition to portions of Cen-countries, large percentages of the population reside in tral America and East and South Asia, large areas of thehazard-prone areas (Table 1.1).eastern Mediterranean and Middle East appear at high 15. Executive SummaryFigure 1.1. Global Distribution of Areas Highly Exposed to One or More Hazards, by Hazard TypeHazard GroupsTop 3 Deciles Exposed to: Drought Only Geophysical Only Hydro Only Drought and Hydro Geophysical and Hydro Drought and Geophysical Drought, Hydro, and GeophysicalNote: Geophysical hazards include earthquakes and volcanoes; hydrological hazards include floods, cyclones, and landslides.3 16. 4Natural Disaster Hotspots: A Global Risk AnalysisTable 1.1. Countries Most Exposed to Multiple Hazardsa) Three or more hazards (top 15 based on land area)CountryPercent of Percent of Max. Number CountryPercent of Percent ofMax. Number Total Area Population of Hazards Total Area Populationof Hazards ExposedExposed ExposedExposedTaiwan, China 73.1 73.14 Vietnam8.2 5.1 3Costa Rica36.8 41.14 Solomon Islands7.0 4.9 3Vanuatu 28.8 20.53 Nepal5.3 2.6 3Philippines 22.3 36.45 El Salvador5.1 5.2 3Guatemala 21.3 40.85 Tajikistan 5.0 1.0 3Ecuador 13.9 23.95 Panama 4.4 2.9 3Chile 12.9 54.04 Nicaragua3.022.2 3Japan 10.5 15.34b) Two or more hazards (top 60 based on land area)CountryPercent of Percent of Max. Number CountryPercent of Percent ofMax. Number Total Area Population of Hazards Total Area Populationof Hazards ExposedExposed ExposedExposedSt. Kitts and Nevis 100.0 100.0 2Mexico16.5 9.6 4Macao, China100.0 100.0 2Korea, Dem. 16.413.5 3Antigua and Barbuda100.0100.0 2Peoples Rep. ofHong Kong, China 100.0100.0 2Lao Peoples15.212.6 3Taiwan, China99.198.9 4Dem. Rep. ofVanuatu80.875.6 3Turkey15.111.3 3Costa Rica 80.469.2 4Panama15.012.6 3Philippines62.273.8 5Swaziland 14.314.2 2Nepal60.551.6 3Nicaragua 12.449.8 3Guatemala56.683.4 5Afghanistan 11.129.5 3Korea, Rep. of 53.053.6 2Myanmar 10.710.4 4Ecuador47.674.6 5India 10.510.9 4Runion45.745.7 2Lesotho 10.3 3.7 2Vietnam45.138.7 3Iceland9.4 4.8 2Somalia43.153.8 2Colombia 8.9 7.5 3South Africa 43.146.9 2China8.415.7 3Japan38.148.4 4Kyrgyz Rep.8.3 5.8 2Cayman Islands 36.845.6 2Dominica 8.1 6.2 2Bangladesh 35.632.9 4Peru 7.426.3 3El Salvador32.439.7 3Iraq 7.3 9.6 3Cambodia 27.9 4.4 3Cuba 6.6 4.3 2Chile26.262.6 4Papua New Guinea 5.9 6.4 3Thailand 25.217.7 2Jamaica5.7 7.2 2Fiji 23.229.0 2Pakistan 5.618.2 2Tajikistan 23.2 9.5 3Indonesia4.514.1 3Solomon Islands22.816.6 3New Zealand4.3 1.7 3Madagascar 20.2 9.9 2United Arab Emirates 4.1 6.8 2Bhutan 20.129.2 4Armenia3.1 1.5 3Georgia17.4 5.9 3Mongolia 2.8 0.7 2Iran, Islamic Rep. of 17.1 22.2 4Nigeria2.7 6.7 2Kenya16.9 8.8 2United States2.611.2 4 17. Executive SummaryFigure 1.2. Global Distribution of Highest Risk Disaster Hotspots by Hazard Typea) Mortality RisksHigh Mortality RiskTop 3 Deciles at Risk from: Drought Only Geophysical Only Hydro Only Drought and Hydro Geophysical and Hydro Drought and Geophysical Drought, Hydro, and GeophysicalNote: Geophysical hazards include earthquakes and volcanoes; hydrological hazards include floods, cyclones, and landslides.5 18. 6Figure 1.2. Global Distribution of Highest Risk Disaster Hotspots by Hazard Typeb) Total Economic Loss Risks High Total Economic Loss Risk Top 3 Deciles at Risk from:Natural Disaster Hotspots: A Global Risk Analysis Drought Only Geophysical Only Hydro Only Drought and Hydro Geophysical and Hydro Drought and Geophysical Drought, Hydro, and GeophysicalNote: Geophysical hazards include earthquakes and volcanoes; hydrological hazards include floods, cyclones, and landslides. 19. Executive SummaryFigure 1.2. Global Distribution of Highest Risk Disaster Hotspots by Hazard Typec) Economic Loss Risks as a Proportion of GDP Per Unit AreaHigh Proportional EconomicLoss RiskTop 3 Deciles at Risk from: Drought Only Geophysical Only Hydro Only Drought and Hydro Geophysical and Hydro Drought and Geophysical Drought, Hydro, and GeophysicalNote: Geophysical hazards include earthquakes and volcanoes; hydrological hazards include floods, cyclones, and landslides.7 20. 8Natural Disaster Hotspots: A Global Risk Analysisrisk of loss from multiple hazards. These regions stillor GDP in hotspots are especially likely to incur repeatedrank high when the risk is recalculated by dividing thedisaster-related losses and costs. Comparison of theselosses per grid cell by each grid cells GDP estimatemaps with data on relief and reconstruction costs is(Figure 1.2c). In contrast, much of Europe and the instructive in this regard. Data on relief costs associ-United States no longer rank among the highest riskated with natural disasters from 1992 to 2003 areareas when grid cells are ranked according to losses asavailable from the Financial Tracking System (FTS) ofa proportion of GDP. the United Nations Office for the Coordination of Human- The statistics also suggest that future disasters willitarian Affairs (OCHA) (http://www.reliefweb.int/fts/).continue to impose high costs on human and eco-Total relief costs over this period are US$2.5 billion. Ofnomic development. In 35 countries, more than 1 in this, US$2 billion went to just 20 countries, primarily20 residents lives in an area identified as relatively highfor disasters involving the following hazards (listed inin mortality risk from three or more hazards (Table 1.2a). order of magnitude of the relief amount allocated): ChinaMore than 90 countries have more than 10 percent of(earthquakes and floods); India (earthquakes, floods,their total population in areas at relatively high mor-and storms); Bangladesh (floods); the Arab Republic oftality risk from two or more hazards (Table 1.2b and Egypt (earthquakes); Mozambique (floods); TurkeyFigure 1.3). And 160 countries have more than one- (earthquakes); Afghanistan (drought and earthquakes);fourth of their total population in areas at relatively high El Salvador (earthquakes); Kenya (drought and floods);mortality risk from one or more hazards (Figure 1.4).the Islamic Republic of Iran (earthquakes); PakistanSimilarly, many of the areas at higher risk of loss from (drought and floods); Indonesia (drought, earthquakes,multiple hazards are associated with higher-than-aver- and floods); Peru (earthquakes and floods); Democra-age densities of GDP, leading to a relatively high degreetic Republic of Congo (volcanoes); Poland (floods); Viet-of exposure of economically productive areas (Figuresnam (floods and storms); Colombia (earthquakes);1.5 and 1.6).Venezuela (floods); Tajikistan (droughts and floods); Until vulnerability, and consequently risks, areand Cambodia (floods). All of these countries exceptreduced, countries with high proportions of population Egypt have more than half of their population in areasTable 1.2. Countries at Relatively High Mortality Risk from Multiple Hazardsa) Three or more hazards (top 35 based on population)Country Percent of Percent ofCountryPercent ofPercent ofTotal Area Population inTotal AreaPopulationat RiskAreas at Riskat Risk at RiskTaiwan, China 90.2 95.1Madagascar 6.3 24.8El Salvador 51.7 77.7Trinidad and Tobago 10.0 23.5Costa Rica38.2 77.1Ecuador3.6 21.4Philippines 45.6 72.6Bhutan10.5 18.8Dominica70.8 71.1Chile1.0 18.7Antigua and Barbuda 46.2 69.5Malawi 5.5 12.9Guatemala 28.8 69.4Solomon Islands0.1 12.0Japan 23.2 69.4Mexico 4.4 10.8Dominican Rep.33.7 66.0Fiji 4.19.4Jamaica 40.5 58.8Albania4.08.6Nicaragua4.4 42.7Cuba 3.58.5Indonesia4.4 40.1Samoa0.78.3Comoros 39.6 32.0Afghanistan0.88.1Honduras18.1 31.8Pakistan 1.45.9Nepal 31.9 28.0Venezuela0.95.6Bangladesh30.0 26.2Cameroon 1.15.5Colombia 1.8 25.9Panama 2.65.1Mozambique 4.7 25.5 21. Executive Summary9Table 1.2. Countries at Relatively High Mortality Risk from Multiple Hazardsb) Two or more hazards (top 96 based on population)CountryPercent of Percent ofCountryPercent of Percent of Total Area Population inTotal Area Population at RiskAreas at Riskat Riskat RiskBangladesh97.1 97.7 Afghanistan 7.2 46.0Nepal 80.2 97.4 Georgia19.2 44.0Dominican Rep.97.3 96.8 Cameroon9.2 42.0Burundi 96.3 96.6 Fiji 20.0 42.0Haiti 93.4 96.5 St. Vincent and Grenadines 41.6 41.6Taiwan, China 92.5 95.5 Mexico 15.1 41.3Malawi70.8 95.3 Togo 61.2 39.3El Salvador 83.0 92.6 St. Kitts and Nevis31.8 39.1Honduras64.5 91.5 Zimbabwe 10.1 39.0Guatemala 54.9 89.5 Congo,Rep. Of 1.9 38.8Philippines 76.6 88.6 Benin37.2 38.6Costa Rica53.6 86.1 Belize 19.8 38.2Trinidad and Tobago 63.4 85.1 Sierra Leone 13.0 35.7Japan 34.7 84.0 United States 1.1 35.1Antigua and Barbuda 54.5 82.0 China10.6 33.4Dominica84.7 82.0 Romania14.4 33.3Nicaragua 38.1 81.9 Uzbekistan2.5 30.6South Africa12.1 78.7 Mali2.9 29.6Cuba87.0 77.5 Lebanon19.2 29.2Niger 14.0 76.4 Sudan 5.0 28.8Korea, Dem. Peoples Rep. of58.5 72.8 Tajikistan5.8 28.2Vietnam 59.3 71.4 India21.9 27.2Ethiopia29.9 69.3 United Kingdom7.9 27.0Nigeria 47.5 68.8 Liechtenstein23.1 26.6Chile5.3 68.3 Uganda 27.5 26.6Ecuador 20.3 67.2 Canada0.0425.3Korea, Rep. of25.2 66.7 Syrian Arab Rep.8.0 24.9Colombia12.8 66.3 Turkey 12.6 24.7Kenya 29.0 63.4 Bolivia 0.6 24.7Burkina Faso35.1 61.7 Lao Peoples Dem. Rep.9.1 22.4Bhutan31.2 60.8 New Zealand 0.8 22.4Venezuela6.7 60.1 Ireland 0.6 21.9Indonesia 10.6 59.3 Congo, Dem. Rep. of 2.5 21.6Mozambique16.9 58.9 Chad2.7 20.5Jamaica 40.5 58.8 Central African Rep.0.5 19.7Guam23.8 58.5 Jordan3.0 17.7Peru 5.7 57.5 Yugoslavia Fed. Rep. 17.1 17.5Albania 33.4 56.7 (Serbia/Montenegro)Madagascar15.7 56.0 Myanmar 4.5 16.8Barbados54.9 54.9 Angola0.2 14.8Comoros 59.0 54.2 Rwanda 13.3 14.2Tanzania27.7 53.7 Panama9.3 14.1Somalia 15.4 53.3 Samoa 1.4 13.9Senegal 10.1 52.9 Macedonia, FYR 22.4 13.7Grenada 52.1 52.1 Kyrgyz Rep. 2.3 13.2Lesotho 52.4 50.5 Solomon Islands 0.1 12.0Montserrat50.3 50.3 Ghana15.2 11.6Pakistan22.8 49.6 Thailand2.6 10.7Iran, Islamic Rep. of 14.3 46.6 22. 10Figure 1.3. Proportion of National Population In Highest Risk Areas from Two or More Hazards (Mortality)Population at Risk from 2+Hazards (Mortality Weighted) Natural Disaster Hotspots: A Global Risk AnalysisProportion of National Population 0 0.25 0.26 0.50 0.51 0.75 0.79 0.98 23. Executive SummaryFigure 1.4. Proportion of National Population In Highest Risk Areas from One or More Hazards (Mortality)Population at Risk from 1+Hazards (Mortality Weighted)Proportion of National Population 0.00 0.25 0.26 0.50 0.51 0.75 0.76 1.00 11 24. 12 Natural Disaster Hotspots: A Global Risk Analysisat relatively high risk from one or more hazards 1. Scale matters. Geographic areas that are identified as(Figure 1.4). The countries subject to multiple hazards hotspots at the global scale may have a highly vari-in this list also are among those countries with at least able spatial distribution of risk at finer scales.one-fourth of their populations in areas at risk from two2. Scale affects data availability and quality. Hazard, expo-or more hazards (Figure 1.3). The correspondence with sure, and vulnerability data are available at sub-economic losses is not quite as strong (Figure 1.6).national resolutions for individual countries and evenTotal World Bank emergency lending from 1980 to cities, as the analyses for Sri Lanka and Caracas show.2003 was US$14.4 billion (http://www.worldbank.org/ More comprehensive, finer resolution, and betterhazards). Of this, US$12 billion went to 20 countries,quality data permit more complete, accurate, andprimarily for the following hazards (listed in order of reliable identification of multihazard hotspots.highest loan amount): India (drought, earthquakes, and 3. Scale affects the utility of the results. Better data reso-storms); Turkey (earthquakes and floods); Bangladeshlution and a richer set of variables contribute to results(floods and storms); Mexico (earthquakes and floods); that are more relevant for risk management planningArgentina (floods); Brazil (floods); Poland (floods); at the national to local scale, as illustrated in theColombia (earthquakes and floods); the Islamic Repub- case study from Caracas. This is highly important,lic of Iran (earthquakes); Honduras (floods and storms);as decisions made at the local and national scalesChina (earthquakes and floods); Chile (earthquakes);have perhaps the greatest potential to affect risk levelsZimbabwe (drought); the Dominican Republic (storms);directly, whether positively or negatively.El Salvador (earthquakes); Algeria (earthquakes and 4. The global- and local-scale analyses are complemen-floods); Ecuador (earthquakes and floods; Mozambiquetary. In some instances, national-to-local level risk(drought and floods); the Philippines (earthquakes);assessors and planners may be able to downscaleand Vietnam (floods). All of these countries exceptglobal data for finer scale risk assessment to com-Poland have half of their population in areas at rela-pensate for a lack of local data. Ideally, however, globaltively high mortality risk from one or more hazardsanalyses would be scaled upgeneralized from more(Figure 1.4), and all of them have at least half of theirdetailed, finer scale data. In practice, many barriersGDP in areas of relatively high economic risk from onestill remain. The global infrastructure for systemat-or more hazards (Figure 1.6).ically assembling and integrating relevant data setsfor disaster risk assessment at multiple scales remainsinadequate. Nonetheless, the fact that relevant dataKey Findings of the Case Studiessets can be obtained and integrated at various scalescreates the hope that one day data can be collectedRecognizing the limitations of the global analysis, weand shared routinely to improve disaster risk assess-undertook a number of case studies designed to inves-ment both globally and locally.tigate the potential of the hotspots approach at regional,national, and subnational scales, drawing on moredetailed and reliable data sources as well as on expert Conclusions and the Way Forwardknowledge concerning specific hazards and regions.Three case studies addressed specific hazards: storm The Hotspots project has created an initial picture ofsurges, landslides, and drought. Three case studies the location and characteristics of disaster hotspots:addressed regional multihazard situations: Sri Lanka, areas at relatively high risk from one or more naturalthe Tana River basin in Kenya, and the city of Caracas, hazards. The findings of the analysis support the viewVenezuela. that disasters will continue to impose high costs on The following are the key findings from the case human and economic development, and that disasterstudies: risk should be managed as an integral part of develop- ment planning rather than thought of strictly as a human- 25. Executive SummaryFigure 1.5. Proportion of GDP In Highest Risk Areas from Two or More Hazards (Economic Losses)GDP at Risk from 2+ Hazards(Economic Loss Weighted)Proportion of National GDP 0.0 0.25 0.26 0.50 0.51 0.75 0.76 0.98 13 26. 14Figure 1.6. Proportion of GDP In Highest Risk Areas from One or More Hazards (Economic Losses)GDP at Risk from 1+ Hazards Natural Disaster Hotspots: A Global Risk Analysis(Economic Loss Weighted)Proportion of National GDP 0.00 0.25 0.26 0.50 0.51 0.75 0.76 1.00 27. Executive Summary15itarian issue. The following paragraphs detail how dis- resources from productive investments to support con-aster risk information can be useful for developmentsumption over short periods. Emergency loans havepolicy and decision makers, and how it can be further questionable value as vehicles for long-term investmentdeveloped in order to increase its usefulness.and contribute to country indebtedness without nec-essarily improving economic growth or reducing poverty.As disasters continue to occur, high-risk countriesThe Costs of Disaster Riskswill continue to need high levels of humanitarianThe combination of human and economic losses, plusrelief and recovery lending unless their vulnerabilitythe additional costs of relief, rehabilitation, and recon-is reduced.struction, make disasters an economic as well as ahumanitarian issue. Until vulnerability, and conse-Implications for Decision Makingquently risks, are reduced, countries with high pro-portions of population or GDP in hotspots are The Hotspots analysis has implications for develop-especially likely to incur repeated disaster-relatedment investment planning, disaster preparedness,losses and costs. Disaster risks, therefore, deserveand loss prevention. The highest risk areas areserious consideration as an issue for sustainable those in which disasters are expected to occur mostdevelopment in high-risk areas. frequently and losses are expected to be highest. Thisprovides a rational basis for prioritizing risk-reduc-The significance of high mortality and economic losstion efforts and highlights areas where risk man-risks for socioeconomic development extends wellagement is most needed.beyond the initial direct losses to the population andeconomy during disasters. Covariate losses accompa- International development organizations are keynying mortality, for example, include partial or total loss stakeholders with respect to the global analysis. Theof household assets, lost income, and lost productivity.analysis provides a scientific basis for understandingWidespread disaster-related mortality can affect house- where risks are highest and why, as well as a method-holds and communities for years, decades, and evenological framework for regional- and local-scale analy-generations.sis. The identified risks then can be evaluated further In addition to mortality and its long-term conse-using more detailed data in the context of a regions orquences, both direct and indirect economic losses mustcountrys overall development strategy and priorities.be considered (ECLAC and the World Bank 2003).This would serve development institutions and the coun-Direct losses are losses to assets, whereas indirect losses tries in several ways to facilitate the development ofare the losses that accrue while productive assets remain better-informed investment strategies and activities.damaged or destroyed. During disasters, both directand indirect losses accumulate across the social, pro-Assistance Strategies. A development institution suchductive, and infrastructure sectors. The pattern of lossesas the World Bank may use the analysis at the globaldepends on the type of hazard and the affected sec- and/or regional level to identify countries that are attors vulnerabilities to the hazard. In large disasters,higher risk of disasters and flag them as priorities tocumulative losses across sectors can have macro-ensure that disaster risk management is addressed ineconomic impacts. the development of a Country Assistance Strategy (CAS). Disasters impose costs in addition to human andWhile in some countries there can be a seemingly longeconomic losses. Costs include expenditures for dis-list of urgent priorities to address in a CASe.g., reduc-aster relief and recovery and for rehabilitation anding extreme poverty, fighting HIV/AIDS, promoting edu-reconstruction of damaged and destroyed assets. Incation, achieving macroeconomic stabilitymanagingmajor disasters, meeting these additional costs can disaster risk should be considered an integral part ofrequire external financing or international humanitar-the development planning to protect the investmentsian assistance. Disaster relief costs drain development made rather than as a stand-alone agenda. The CAS 28. 16 Natural Disaster Hotspots: A Global Risk Analysisshould consider the consequences of unmitigated dis- resources toward investments that would restore eco-aster risk in terms of possible tradeoffs with long-term nomic activity quickly and relieve human suffering.socioeconomic goals. This reports global disaster risk analysis provides a basis for identifying situations in which future emer-Sector Investment Operations. In high-risk regions gency recovery loans are likely to be needed. This cre-and countries, it is particularly important to protect ates an opportunity for preappraising emergencyinvestments from damage or loss, either by limitingloans, that is, designing a risk management strategy tohazard exposure or by reducing vulnerability. Risks of guide the allocation of emergency reconstructiondamage and loss should also be taken into account when resources should such resources become necessary, orestimating economic returns during project prepara-to arrange for other types of contingency financing withtion. Investment project preparation, particularly in thedevelopment banks.high-risk areas identified in the global analysis, wouldbenefit from including a risk assessment as a standard Improved Information for Disaster Risk Managementpractice. This reports theory and methods can be trans-lated easily into terms of reference for such assessments. The Hotspots project provides a common frameworkSuch assessments should identify probable hazards, asfor improving risk identification and promoting riskwell as their spatial distribution and temporal charac-management through a dialogue between organiza-teristics (including return periods), and should evalu-tions and individuals operating at various geographicate vulnerabilities to the identified hazards that shouldscales. The methods and results provide useful toolsbe addressed in the project design.for integrating disaster risk management into devel- opment efforts and should be developed further.Risk Reduction Operations. In high-risk countries andAs a global analysis conducted with very limited local-areas within countries, repeated, large-scale loss eventslevel participation and based on incomplete data, thecan harm economic performance (Benson and Clay results presented here should not provide the sole2004). It may be impossible to achieve development basis for designing risk management activities. Thegoals such as poverty alleviation in these areas without analysis does, however, provide a scientific basis forconcerted efforts to reduce recurrent losses. Increas- understanding where risks are highest and why, as wellingly, risk and loss reduction are being seen as invest- as a methodological framework for regional- and local-ments in themselves, and disaster-prone countries arescale analysis. The identified risks then can be evalu-demonstrating a willingness to undertake projects in ated further using more detailed data in the context ofwhich disaster and loss reduction are the principal aims.a regions or countrys overall development strategySuch projects can include both hard and soft compo-and priorities.nents: measures to reduce the vulnerability and expo- We have designed the Hotspots approach to be open-sure of infrastructure, as well as emergency funds and ended to allow additional studies to be incorporatedinstitutional, policy and capacity-building measures on an ongoing basis. It provides a common frameworkdesigned to increase the abilities of countries to managefor improving risk identification and promoting riskdisaster risks.management through a dialogue between organizations and individuals operating at various geographic scales.Contingency Financing. Emergency recovery andThe Hotspots analysis can be improved upon as a toolreconstruction needs after a major disaster may create and developed in several directions.a high demand for emergency financing. While suchloans are usually appraised and approved relativelyImprove Underlying Databases. The first direction isquickly, at times there can be delays in disbursing theto pursue the many opportunities in both the shortfunds, which increase the social and economic impactsand long term to improve the underlying databases forof the disaster. Advance planning for recovery and assessing disaster risks and losses. A range of new global-resource allocation would allow for better targeting ofscale data sets is currently under development, includ- 29. Executive Summary17ing a new global urban-extent database being devel-Explore Long-term Trends. A third direction is tooped by CIESIN in support of the Millennium Ecosys-explore a key long-term issue: the potential effect oftem Assessment. A joint project between the Earthunderlying changes in hazard frequency (for example,Institute, the World Bank, and the Millennium Projectdue to human-induced climatic change) coupled withwill develop a much more detailed and complete data- long-term trends in human development and settlementbase on subnational poverty and hunger. Much morepatterns. To what degree could changes in tropical stormcomprehensive regional data sets will become avail-frequency, intensity, and position interact with contin-able in specific areas of interest. On a regional scale, ued coastal development (both urban and rural) tothere are also much longer records of hazard events forincrease risks of death and destruction in these regions?specific hazards that could be harnessed to improve esti-Are agricultural areas, already under pressure frommates of hazard frequency and intensity in high-risk urbanization and other land use changes, likely to becomeareas (for example, OLoughlin and Lander 2003). Sig-more or less susceptible to drought, severe weather, ornificant improvements could be made in characterizingfloods? Could other hazards such as wildfires poten-flood, drought and landslide hazards in particular. Exist- tially interact with changing patterns of drought, land-ing data on disaster-related losses is being compiled into slides, deforestation, and land use to create new typesa multi-tiered system through which regularly updatedof hotspots? Although some aspects of these questionshistorical data from multiple sources can be accessed. have been addressed in the general context of researchAdditional work to link and cross-check existing dataon climate change impacts, the interactions betweenis needed, however, as is improvement in the assess- climate change, the full range of hazards, and evolvingment and documentation of global economic losses.human hazard vulnerability have not been fully explored (for example, Brooks and Adger 2003; Chen 1994).Undertake Case Studies. A second direction is toPursuing work in these directions will necessarilyexplore more fully the applicability and utility of theinvolve a wide range of institutionsnational, regionalHotspots approach to analysis and decision making at and international, public and private sector, academicregional, national, and local scales. The initial case stud- and operational. We hope that the Hotspots projecties are promising, but are certainly not on their ownhas contributed a building block in the foundation ofsufficient to demonstrate the value of the overall approacha global effort to reduce disaster-related losses by man-or the specific data and methods under different con-aging risks rather than by managing emergencies. Weditions. More direct involvement of potential stake- look forward to continuing collaboration with part-holders would be valuable in extending the approachners at all levels to put in place a global disaster riskto finer scales of analysis and decision making. To be management support system in order to mobilize theeffective, efforts to improve risk identification in hotspot knowledge and resources necessary to achieve this goal.areas should be part of a complete package of techni-cal and financial support for the full range of measuresneeded to manage disaster risks, including risk reduc-tion and transfer. 30. Chapter 2Project ObjectivesHundreds of disasters occur worldwide each year in For the most part, both scientists and decision makerslocations without sufficient local capacity or resourcestend to deal with different hazards separately. For exam-to prevent death and destruction and to support rapid ple, seismologists, structural engineers, and urban plan-recovery. Continuing rapid urbanization and coastal ners typically focus on mitigating earthquake risksdevelopment in hazard-prone regions and the poten-through such efforts as strengthening building codestial for long-term changes in the intensity and frequency and structures, whereas climatologists, agronomists,of some hazards pose a serious challenge to sustainable and water resource managers address flood and droughtdevelopment in both the developing and industrial risks through the development and maintenance ofworlds. Decision makers at all levels of governance, from dams, reservoirs, and other water resource systems orthe international to community levels, will face difficultthrough demand management. Although this approachchoices about priorities for mitigating the risks of, for is appropriate to some degree, given the differences inexample, frequent, smaller hazards such as floods and hazards and vulnerabilities, it is also important to con-landslides versus the risks of less frequent, moresider and manage the combined risks of all hazards anduncertain, but potentially much more deadly hazards vulnerabilities.such as earthquakes and tsunamis.Disaster response is often handled by a variety of Natural disasters occur when large numbers of people organizations at different levels of government and soci-or economic assets are damaged or destroyed during aety, ranging from local volunteer groups to national civil-natural hazard event. Disasters have two sets of causes.ian and military agencies to international relief agenciesThe first set is the natural hazards themselves, includ-and nongovernmental organizationseach with its owning floods, drought, tropical storms, earthquakes, vol- areas of expertise with regard to particular disaster typescanoes, and landslides. The second set comprises theand its own limitations in terms of jurisdiction and modevulnerabilities of elements at riskpopulations, infra- of operation. A more complete picture of multihazardstructure, and economic activitiesthat make them risks can assist in developing coordinated strategies formore or less susceptible to being harmed or damaged total risk management.by a hazard event. The Hotspots project seeks to contribute to existing Disaster-prone countries can be identified readily knowledge on global natural-disaster risks in the fol-from existing databases of past disasters. Countries them-lowing ways:selves may be aware of disaster-prone areas, either1. Development of a spatially uniform, first-order, globalthrough local knowledge and experience or through disaster risk assessment through the use of globalformal risk assessments and historical data. The role of data sets in which the spatial distributions of haz-vulnerability as a causal factor in disaster losses tends ards, elements at risk, and vulnerability factors, ratherto be less well understood, however. The idea that dis- than national-level statistics, are the primary inde-asters can be managed by identifying and managing spe- pendent variablescific risk factors is only recently becoming widelyrecognized. 19 31. 20Natural Disaster Hotspots: A Global Risk Analysis2. Rigorous and precise definition of specific socialnerability, and risk? An international relief organization and economic disaster-related outcomes, the risks concerned with prepositioning disaster relief supplies of which can be quantitatively assessed globallymight ask, What hazards are likely to be of concern in areas3. Identification of the hazard- and vulnerability-related inhabited by vulnerable populations? How can limited sup- causal components of risk on a hazard-by-hazard plies be positioned optimally to address a range of possible basis, taking into account the damaging character-hazard scenarios? istics of each hazard and the contingent vulnerabil- In the long run, we also expect the Hotspots approach ity characteristics of potentially affected exposed to be useful at the national and subnational levels. A elementsnational government might ask, In areas that face risks from multiple hazards, which pose the most significant risks?4. Assessment of overall, multihazard, global natural What measures would be most effective in reducing vulner- disaster risks, stated in terms of specific disaster ability to all hazards? How much will achieving an accept- outcomes (mortality and economic losses) for pop- able level of risk cost, and how should resources be allocated? ulations, infrastructure, and economic activities at A local government or community organization might risk ask, Should certain risk management measures be avoided5. Verification of the global risk assessment through abecause they increase risks from other hazards? Can simple limited number of case studies of limited geo-changes to development and mitigation plans result in graphic scope that allow risk factors to be charac- long-term risk reduction? Is it possible to combine mitiga- terized in greater detail through the use of larger scale tion measures for single hazards cost-effectively? data and involvement of national- to local-level stake-Both international institutions and the regions and holders countries they serve may seek a deeper understanding6. Documentation of the hazard, vulnerability, and of potential barriers to disaster mitigationnot only risk assessment methods used or generated in thetechnical and economic, but also cultural and politi- analysis to extend the projects scope by enlisting cal. They may wish to understand the long-term con- others who wish to contribute to an ongoing, long-sequences of unmitigated disaster risk in terms of possible term, scientific effort to assess global risk tradeoffs with long-term socioeconomic goals. What areDisaster relief and recovery not only consume the lions the opportunity costs and benefits of addressing disaster risk?share of resources available for disaster management,How would overall wealth and the distribution of wealth bebut also drain resources away from other social andaffected in the longer term? Could persistent impacts of dis-economic development priorities. Risk management asters alter a countrys global position in terms of future lend-investments in high-risk areas can be cost-effective ining opportunities, trade, public health, or military security?preventing disaster losses and increasing disasterThere is growing recognition of the need for betterpreparation, leading to quicker, better planned recov- data and information on hazards and disasters at bothery. Currently, high-risk areas typically are identified onnational and international levels. Within the Unitedthe basis of national-level data of historical disasters States, several recent reports by the U.S. National Researchand unevenly applied local knowledge. This project seeks Council (NRC) and the U.S. government have high-to assess the geographic distribution of risks acrosslighted the importance of both historical and currentnational boundaries. Uniform data and methods pro- data on hazard events and their associated impacts (NRCvide comparability from one area to another. 1999a, 1999b; Subcommittee on Disaster Reduction Key stakeholders for the global analysis are interna- 2003). At the international level, there is strong inter-tional organizations that promote disaster risk man- est in improving disaster information systems and asso-agement. For example, a global or regional lending ciated decision support tools (for example, ISDR 2003).organization might ask, Where could a new lending pro-A welcome shift in emphasis appears to be under waygram have the greatest risk reduction impact over the next from managing disasters by managing emergencies to10 years? To what extent can existing data provide an ade- managing disaster risks. This shift is evident in recentquate assessment of the degrees of hazard, exposure, vul-publications such as the 2002 World Disasters Report: 32. Project Objectives 21Focus on Reducing Risk (International Federation of Red Near-term applications of the analysis are expectedCross and Red Crescent Societies 2002), Living with Risk to include the following:(ISDR 2004), and Reducing Disaster Risk: A Challenge for 1. A basis for further focus on high-risk areas by inter-Development (UNDP 2004). Risk assessment, reduction,national institutions concerned with disaster riskand transfer are the major elements of risk managementmanagement(Kreimer and others 1999), offering a desirable alter-native to managing disasters through emergency response. 2. Promotion of global/local partnerships for additionalRisk reduction requires risk assessment in order to deter-risk assessment and collaborative development andmine which areas are at highest risk of disaster and why, implementation of risk reduction plans in high-riskso that appropriate and cost-effective mitigation meas- areasures can be identified, adapted, and implemented.3. Stimulation of further research on hazard and vul- As a global analysis conducted with very limited local-nerability risk factors in high-risk areas and on appro-level participation and based on incomplete data, the priate and cost-effective risk reduction and transferresults presented here should not provide the solemeasuresbasis for designing risk management activities. The4. A model mode of analysis based on consistent dis-analysis does, however, provide a scientific basis foraster risk theory, assessment methods, and data thatunderstanding where risks are highest and why, as wellcan be improved upon and applied globally and inas a methodological framework for regional- and local-particular locationsscale analysis. The identified risks then can be evalu-ated further using more detailed data in the context of5. A platform of static risks over which dynamic risksa regions or countrys overall development strategycan be overlaid at varying time scales, capturingand priorities. seasonal-to-interannual fluctuations in hazard prob- We have designed the Hotspots approach to be open- abilities such as those associated with El Nio-ended to allow additional studies to be incorporatedSouthern Oscillation (ENSO) events or long-term cli-on an ongoing basis. It provides a common framework matic trends, as well as socioeconomic risk factorsfor improving risk identification and promoting riskand trends fluctuating on both short and long timemanagement through a dialogue between organizations scalesand individuals operating at various geographic scales. 33. Chapter 3Project ApproachA wide range of natural hazards cause death, damage, 3. The vulnerability of the elements exposed to specificand other types of losses in both industrial and devel- hazards.oping countries. Small-scale hazard events such as a Disaster losses are caused by interactions between hazardsmall flood, tornado, landslide, lightning strike, or earth events and the characteristics of exposed elements thattremor may cause very localized damage, injuring or make them susceptible to damage. A hazards destruc-killing a few individuals and destroying or damaging a tive potential is a function of the magnitude, duration,limited number of structures. In contrast, large-scale location, and timing of the event (Burton and othersevents such as hurricanes and tropical cyclones, strong 1993). To be damaged, however, elements exposed toearthquakes, large volcanic eruptions, tsunamis, major a given type of hazard must also be vulnerable to thatfloods, and drought can kill tens of thousands of people hazard; that is, the elements must have intrinsic char-and injure many more; they can also cause significant acteristics that allow them to be damaged or destroyedeconomic and social disruption as a result of both direct (UNDRO 1979). Valuable but vulnerable elementsdamage and indirect economic losses. Often large- include people, infrastructure, and economically or envi-scale events such as storms, earthquakes, and droughts ronmentally important land uses.spawn ancillary hazards such as floods, landslides, andThe destructive power of natural hazards combinedwildfires that may add to casualties and economic losses. with vulnerabilities across a spectrum of exposed ele-The severity of such secondary events may depend in ments can lead to large-scale covariate losses duringpart on environmental conditions such as soil moisture, hazard events in areas where population and economicland cover, and topography as well as on the presence investment are concentrated. Aggregate losses start withand condition of protective works such as dams, dikes, losses to individual elements, reaching a point in dis-and drainage systems. aster situations where economic and social systems break down partly or completely, leading to higher net socioe- conomic impacts.Risk Assessment FrameworkRisks of individual element losses or of aggregate covariate losses can be stated as the probability of loss,General Framework or as the proportion of elements that will be damagedIn this project, we use the commonly accepted risk or lost, over time (Coburn and others 1994). Disasterassessment framework for natural hazards (for exam-risk assessment examines the factors that cause lossesple, Coburn and others 1994; Mileti 1999). In essence, in order to estimate loss probabilities. Risk factors includewe distinguish among three components that contributethe probability of destructive hazard events as well asto the overall risk of natural hazards:the contingent vulnerabilities of the exposed elements at risk.1. The probability of occurrence of different kinds andThe hazards research community has evolved a intensities of hazards dynamic paradigm for hazards analysis that includes a2. The elements exposed to these hazards four-stage process of hazard preparedness, response,23 34. 24Natural Disaster Hotspots: A Global Risk Analysisrecovery, and mitigation (Mileti 1999). Within this 2. Multihazard hotspots. Some areas may be subject to aparadigm, assessment of vulnerability and risk is most variety of natural hazards and associated moderateuseful at the stage of assessing hazard preparedness to high levels of risk of loss. In some cases, the haz-and designing hazard mitigation strategies. Indeed, Mileti ards themselves may be largely independent of eachand colleagues have recommended adoption of a globalother; that is, the occurrence of one hazard does notsystems perspective that recognizes the complex inter- significantly affect the probability that other hazardsactions between earth and social systems, within and will occur. However, even if this is the case, the occur-across the global-to-local levels of human aggregationrence of one hazard might significantly affect the over-(Mileti 1999: 27). The Hotspots approach is consistent all impacts of other hazards. For example, after awith this perspective. major tsunami hit Papua New Guinea (PNG) in July 1998, the PNG embassy issued an appeal in which it noted, The tsunami is the latest of a series of nat-Terminology ural disasters striking Papua New Guinea in the lastIn its simplest terms, we define a natural disaster hotspotthree and a half years. The volcano eruption in Rabaul,as a specific area or region that may be at relatively highcyclone Justins destruction in the Milne Bay area,risk of adverse impacts from one or more natural hazardand the El Nio-induced drought in most parts ofevents. Use of the term adverse implies a normativethe country, have caused a horrendous burden onjudgment that at least some of the major consequencesthe Government and the people of Papua New Guineaof a hazardous event are considered undesirable by those (International Disaster Situation Reports, 23 July 1998;affected: for example, the death or injury of people,see http://www.cidi.org/disaster/98b/0021.html).damage to, or loss of, economically valuable assets, or For both types of hotspot, exposure and vulnerabil-lost income and employment. Impacts on natural eco-ity must be high before risks are considered signifi-systems may also be of concern but are not explicitlycant. Such exposure and vulnerability could be in theaddressed in this project. However, it is important toform of important economic assets, such as agricul-recognize that, for example, tropical storms may havetural areas that are vulnerable to drought or flood haz-adverse impacts on coastal populations in their imme-ards. In areas of relatively low population density,diate path but beneficial effects on agriculture and watersome hazards could still pose high mortality (and mor-resources over much larger areas. The focus of disasterbidity) risks if vulnerability is high because of fragilemanagement is to reduce or ameliorate the adverseinfrastructure or other factors. In very high-density areas,impacts, generally in the context of other societal effortseven low vulnerability (low casualty rates) could resultto take advantage of beneficial effects.in substantial losses in absolute terms (many deaths), Given the variety of natural hazards that continue toespecially among those who may have higher-than-aver-cause significant adverse impacts in both industrial andage vulnerability (for example, slum dwellers living ondeveloping countries, we categorize hotspots into twosteep slopes).major types: Throughout this report, we use the term hazard1. Single-hazard hotspots. Some areas or regions may be to represent a specific family of natural phenomena at relatively high risk of adverse impacts associatedand degree of hazard to signify a particular hazard- with one major natural hazard. For example, seis-dependent measure of severity. Exposure represents the mologists have predicted that there is a 4777 per-overlap of time and spatial distribution of human cent probability that the city of Istanbul, Turkeywithassets and the time and spatial distribution of hazard a population estimated at 8.7 million in 2000 (U.N.events. We use the term vulnerability to represent the Population Division 2004)will experience strong apparent weaknesses of physical and social systems to shaking during the first 30 years of this century, particular hazards. Physical system vulnerability is usu- with great potential for death, injury, damage, andally defined (especially in the engineering community) economic disruption (Hubert-Ferrari and others 2000; in terms of fragility curves, in which the weaknesses of Parsons and others 2000).physical systems (buildings and infrastructure, for exam- 35. Project Approach25ple) are quantified as a function of hazard severity. fragility, and loss, especially in the time frames requiredSimilar fragility curves for social systemsthat is, afor policy decisions and mitigation investments. Instead,quantification of social vulnerabilityare complex func-we propose that this analysis be a basis for developingtions of social, economic, political, and cultural vari-scenarios and counter-factual analyses of mitigationables and are addressed in this report through the usealternatives to give policymakers a framework for theirof proxies. In general terms, risk is a multiplicative func-investment decisions. The role of uncertainties can betion of hazard severity, exposure, and fragility. included in such scenarios, as they relate to decisionsupport, but the actual degrees of uncertainty are unlikelyLimitations and Uncertainty to be useful in the near future. However, this lack ofcertainty should not be taken as an excuse for inaction.In designing the methodology for this report, we havebeen forced to accommodate the inherent hetero-geneity that characterizes risk assessments across mul- Selection of Natural Hazardstiple natural hazards. Although some attempts have beenmade (most notably by the insurance industry) to developData on natural hazards have been collected by differ-common risk metrics (such as average annualized loss: ent groups for different purposes in different ways.see Risk Management Solutions 2004), such methods The most comprehensive, publicly available global data-are themselves based on highly variable data quality, base on natural hazards and their impacts is the EM-incomplete fragility analysis, and insufficient historicalDAT data set maintained by the Centre for Research onrecords. Where such data and analysis exist, as they do the Epidemiology of Disasters (CRED) in Brussels (Sapirfor some regions, more comprehensive risk assessmentand Misson 1992; see www.cred.be). This database con-is possible (as we point out in the case studies).tains more than 12,000 records of disasters from 1900 Our goal in this report is to estimate the relative multi- to the present, compiled from multiple sources. It includeshazard risk countries face using defensible measures of estimates of numbers of people killed and affected asdegree of hazard and defensible proxies for physical andwell as estimates of economic losses, derived fromsocial vulnerabilities. Our metrics for degree of hazarddocumented sources. In many cases, these loss estimatesor hazard severity vary according to the hazard. In our include direct losses not only from the primary eventview, the science of hazard occurrence and magnitude(for example, a cyclone or earthquake) but also fromhas not developed enough to permit a globally consis- subsequent related events such as landslides andtent single metric for multihazard severity. Such met-floods. The database generally does not include geo-rics are currently the subject of basic research programs.physical or hydro-meteorological events that were notLacking widely applicable measures of physical fragilityreported as causing heavy losses, either because the eventsand social vulnerability, and lacking even uniformoccurred in areas that were thinly populated at thestandards for collecting the loss data needed to calibratetime, or because the losses were not reported in English-fragility, we have chosen to use broadly accepted and or French-language periodicals.relatively uniform proxies for vulnerability in the formIn addition to the EM-DAT database, this projectof masked population density, GDP, and transporta-has taken advantage of data sets developed by differenttion network density, as normalized by total losses ingroups around the world focused on specific hazardthe Emergency Events Database (EM-DAT). As we explain probabilities, occurrences, or extents. This approachin the next section, we use a geographic mask designedhas permitted us to identify areas that will be at rela-to identify agricultural land use and high population tively high risk of particular types of hazard events indensity as first-order selection criteria to quantify the the future, regardless of their past levels of exposure orgeographic distribution of exposure.actual losses. Our approach assumes that existing An analysis of this sort is not amenable to a quanti-databases are more likely to underreport smallertative estimation of either aleatoric or epistemic uncer- events than large events. Areas at higher risk from largetainties. A meaningful error analysis may not be possible events therefore probably will be more accuratelygiven the state of knowledge about hazard occurrence, identified than areas that suffer from smaller, more fre- 36. 26Natural Disaster Hotspots: A Global Risk Analysisquent events. However, the short record periods for 1975), these efforts were severely constrained by thesome large but infrequent hazard events (for example, lack of detailed data, especially at the global level, asvolcanic eruptions) suggest that efforts to assess absolute well as by limitations in computational capabilitieslevels of risk or to compare risk levels across hazards and data integration methods.would be premature.In 199495, the first global-scale gridded popula- Table 3.1 lists the major natural hazards reported intion data set, known as the Gridded Population of theEM-DAT, ranked by the total number of deaths reported.World (GPW), version 1 data set, was developed withFor this analysis, we selected six major disaster types primary support from CIESIN (Tobler and others 1995).for analysis: drought, tropical storms, floods, earth-This data set transformed population census data, whichquakes, volcanoes, and landslides. We did not attempt most countries collected for subnational administrativeto assess extreme temperature events (heat and cold units, into a regular grid of spherical quadrilateralswaves), wildfires, and wave/surge events such as tsunamis,with the dimensions of 5 minutes (5) of latitude and 5owing to data and resource limitations. Nor did we assess minutes (5) of longitude and an average area of aboutsome hazards that are primarily of regional or economic 55 square kilometers each (85 square kilometers at theconcern, such as tornadoes, hail, and lightning. How- equator). Each cell contained an estimate of total pop-ever, in principle these hazards could be included in ulation and population density (on land) for 1994, basedfuture efforts to improve and expand the hotspots on the overlap between the irregular boundaries of theapproach. administrative units and the regular boundaries of thegrid. Version 2 of GPW was developed by CIESIN incollaboration with the International Food Policy ResearchUnits of Analysis Institute (IFPRI) and the World Resources Institute(WRI). Its cells have a nominal resolution of 2.5 lati-Most efforts to assess the impacts of natural hazards havetude by 2.5 longitude and contain population estimatesused either events or countries as the basic unit offor 1990 and 1995 (CIESIN and others 2000). A betaanalysis. That is, they have examined known occurrences test version of Version 3 is currently available withof hazards and associated impacts either on an event- population estimates for 1990, 1995, and 2000 withby-event basis or as aggregated to the national level.the same nominal resolution as GPW Version 2 (CIESIN This project takes advantage of new methods andand others 2004). With each new version, the numberdata that make possible a more detailed geospatial analy- of subnational administrative units used to createsis across multiple hazards. Although hazard mappingthese gridded population estimates has increased,efforts began in the 1970s (for example, White and Haas from about 19,000 units in Version 1 to 127,000 in Ver-sion 2 to about 375,000 in Version 3. The underlyingTable 3.1. Ranking of Major Natural Hazards bydetail of the spatial distributions has therefore increasedNumber of Deaths Reported in EM-DAT dramatically. The improvement in resolution is sum-RankDisaster Type All DeathsDeathsmarized in Table 3.2. 19802000*19922001** Using the 2.5 x 2.5 grid as a base, it is possible to1 Drought563,701 277,574make a variety of estimates of hazard probability, occur-2 Storms 251,38460,447rence, and extent on a common geospatial frame of ref-3 Floods 170,01096,507erence. It is also possible to add supplementary measures4 Earthquakes158,55177,7565 Volcanoes 25,050 259of exposure such as the density of roads and railroads,6 Extreme temperature 19,24910,130the amount of agricultural land, and the economic value-7 Landslides18,200 9,461added to the same framework. The result is a grid of8 Wave/surge 3,068 2,708approximately 8.7 million cells covering most of the9 Wildfires1,046 574Total1,211,159 535,416occupied land area of the Earth within latitudes 85N* Compiled by O. Kjekstad, personal communication to 58S. Each grid cell contains estimates of land area,** 2002 IFRC World Disaster Report (http://www.cred.be/emdat/intro. population, population density, various hazard proba-htm) 37. Project Approach 27Table 3.2. Number of Input Units Used in the Gridded Summary of Data Sources and Data PreparationPopulation of the World (GPW) Data Sets, Versions 13Version Year ReleasedEstimates for Input Units Hazard DataGPW v11995199419,000GPW v220001990, 1995 127,000 The first step in the hotspots analysis was to examineGPW v32003/04 1990, 1995, 2000 ~ 375,000 each hazard individually in terms of available spatial data on probability, occurrence, or extent. The most desirable input data would be complete probability den-bilities, and associated exposure and vulnerability char-sity functions for each hazard, that is, the probabilitiesacteristics. These grid cells may be aggregated, eitherof occurrence of a specific hazard for a range of sever-to a larger grid (for example, a 1 x 1 latitude/longi- ities or intensities in a specific future time period. Unfor-tude grid) or to national boundaries (making simpletunately, detailed probabilistic data of this type do notassumptions about grid cells along borders). exist for any hazards at the global level. A more limited Since the objective of this analysis is to identify hotspotsprobabilistic estimate is available for earthquakes: thewhere natural hazard impacts may be large, it need not Global Seismic Hazard Program (GSHAP) has used bothinclude the large proportion of the Earths surface that ishistoric data and expert judgment to derive a globalsparsely populated and not intensively used. We have map of the peak ground acceleration (pga) for whichtherefore chosen to mask out grid cells with populationthere is a 10 percent chance of exceedance in the nextdensities less than five persons per square kilometer (cells 50 years.with less than about 105 residents) and without signifi- Even without detailed probabilistic data, however,cant agriculture. Even if all residents of such cells were it is still possible to distinguish between areas of higherexposed and highly vulnerable to a hazard, total casual- and lower risk using occurrence data, that is, data onties would still be relatively small in absolute terms, andspecific events that took place during a given histori-the potential agricultural impact would be limited.1 cal period. The area affected by the events must be deter- Masking these cells reduces data processing require-mined by analysis or modeling of available data.ments and ensures that the large number of very lowThe data identified and used for each hazard are sum-risk cells do not dominate the results. In addition, hazardmarized in Table 3.3. More detailed descriptions of thereporting and frequency data are likely to be poorest in individual data sets acquired and the transformationsrural, sparsely populated areas, so masking could reduce applied are given in Appendix A.1. A brief summaryanomalies caused by poor data. A total of approximatelyfor each hazard follows:4.1 million grid cells remain after applying the mask(Figure 3.1). These cells (colored orange, blue, or1. Cyclones. For cyclones, we used storm track datagreen in the figure) include slightly more than half of collected from multiple sources and assembled intothe worlds estimated land area (about 72 million squaregeographic information system (GIS) coverages bykilometers, or about 55 percent of the total), but most the UNEP/GRID (Global and Regional Integratedof the worlds population (6 billion people, or about Data)-Geneva Project of Risk Evaluation, Vulnera-99.2 percent of the population estimate in GPW forbility, Information and Early Warning (PreView). Thisthe year 2000). data set includes more than 1,600 storm tracks forthe period 1 January 1980 through 31 December2000 for the Atlantic, Pacific, and Indian Oceans.2As described in detail in Appendix A.1, we modeled1 To determine agricultural land use, we used the U.S. Geologi-the wind speeds around the storm tracks in order tocal Survey (USGS) Global Land Cover Classification database atassess the grid cells likely to have been exposed to30" resolution and dropped from the mask any cells with any high wind levels.one of three land covers typically associated with agriculture(Sebastian, personal communication, 2003). If any of the 252The record for the 1980s for some parts of the Indian and Pacific30" cells in a 2.5 cell included an agricultural land cover, we Oceans are incomplete in some cases. See: http://www.grid.dropped the entire 2.5 cell from the mask.unep.ch/data/grid/gnv199.php. 38. 28Figure 3.1. Mask Used to Eliminate Sparsely Populated, Nonagricultural Areas Natural Disaster Hotspots: A Global Risk Analysis Analysis MaskAgriculture OnlyInhabited OnlyAgriculture and InhabitedNote: Colored cells are those retained. 39. Project Approach 292. Drought. For drought, we used the Weighted Anom- through 2002 (Advanced National Seismic System aly of Standardized Precipitation (WASP) devel-1997). The GSHAP data were sampled at 1 inter- oped by IRI, computed on a 2.5 x 2.5 grid from vals, with a minimum peak ground acceleration of monthly average precipitation data for 1980 through2 meters per second per second (m/s2), or approxi- 2000. The WASP assesses the precipitation deficitmately one-fifth of surface gravitational acceleration. or surplus over a specified number of months,5. Volcanoes. For volcanoes, we used a spatial coverage weighted by the magnitude of the seasonal cyclic of volcanic activity (79 A.D. through 2000 A.D.) variation in precipitation. A three-month running developed by UNEP-GRID Geneva based on the average was applied to the precipitation data and Worldwide Volcano Database and available at the the median rainfall for the 21-year period calculated National Geophysical Data Center (http://www. for each grid point. A mask was applied to eliminate ngdc.noaa.gov/seg/hazard/vol_srch.shtml). This data- grid points where the three-month running average base includes nearly 4,000 events categorized as mod- precipitation was less than 1 millimeter per day. erate or above (values 28) according to the Volcano This excluded both desert regions and dry seasons Explosivity Index (VEI) developed by Simkin and from the analysis. For the remaining points, a drought Seibert (1994). Some volcanoes are located to the event was identified when the magnitude of a monthly nearest thousandth of a degree, but most have been precipitation deficit was less than or equal to 50 georeferenced to the nearest tenth or hundredth of a percent of its long-term median value for three or degree. more consecutive months.6. Landslides. The NGI, working with UNEP GRID-3. Floods. The Dartmouth Flood Observatory has com- Geneva and this project, has developed a global land- piled a global listing of extreme flood events from slide and snow avalanche hazard map that has been diverse sources and georeferenced to the nearest used for global a