UNDERSTANDING RISK in an EVOLVING WORLD Emerging Best Practices in Natural Disaster Risk Assessment
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UNDERSTANDING RISK IN AN EVOLVING WORLD Emerging Best Practices in Natural Disaster Risk Assessment GLOBAL FACILITY FOR DISASTER REDUCTION AND RECOVERY UNDERSTANDING RISK IN AN EVOLVING WORLD Emerging Best Practices in Natural Disaster Risk Assessment GLOBAL FACILITY FOR DISASTER REDUCTION AND RECOVERY © 2014 International Bank for Reconstruction and Development / International Development Association or The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. 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 of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2422; e-mail: [email protected]. UNDERSTANDING RISK IN AN EVOLVING WORLD TABLE OF CONTENTS 13 Foreword 14 Acknowledgments 16 Abbreviations 18 Overview 20 Risk Information as the Basis for Decision Making 22 A Framework for Quantifying and Understanding Risk 23 Advances in Disaster Risk Assessment and Key Remaining Challenges 27 Recommendations for Future Risk Assessments. 30 Recommendations toward the Next Hyogo Framework for Action 31 Endnotes 31 References Chapter 01 32 Introduction 34 About This Publication 35 A Brief History of Risk Assessment 38 The Rise of Open Models and Data: The Changing Risk Assessment Paradigm 41 Aligning and Targeting Risk Assessments 43 Endnotes 43 References Chapter 02 44 Progress, Achievements, & Remaining Challenges in Risk Assessment 45 Hazard Assessment 53 Exposure 59 Vulnerability and Loss 64 Hazard and Risk Assessment Tools 68 Creating Platforms and Partnerships to Enable the Development of Risk Assessments 71 Endnotes 71 References UNDERSTANDING RISK IN AN EVOLVING WORLD Case Study Color Key TABLE OF CONTENTS Chapter 03 74 Case Studies Highlighting Emerging Best Practices 76 Open Data for Resilience Initiative (OpenDRI) Data for Risk 80 Open Cities: Application of the Open Data for Resilience Initiative in South Asia and the Lessons Learned 86 Preliminary Survey of Government Engagement with Volunteered Geographic Information 91 Collection of Exposure Data to Underpin Natural Hazard Risk Assessments in Indonesia and the Philippines 95 International Collaboration of Space Agencies to Support Disaster Risk Management Using Satellite Earth Observation 98 Global Earthquake Model 101 Global Probabilistic Risk Assessment: A Key Input into Analysis for the 2013 and 2015 Modelling Global Assessment Reports Developments 107 Global Water-related Disaster Risk Indicators Assessing Real Phenomena of Flood Disasters: Think Locally, Act Globally 112 Government-to-Government Risk Assessment Capacity Building in Australasia Risk Assessment Case Studies 120 Informing Disaster Risk Management Plans in Aqaba, Jordan, through Urban Seismic Risk Mapping 123 Tsunami Risk Reduction: Are We Better Prepared Today Than in 2004? 127 World Bank Probabilistic Risk Assessment (CAPRA) Program for Latin America and the Caribbean: Experiences and Lessons Learned 132 Detailed Island Risk Assessment in Maldives to Inform Disaster Risk Reduction and Climate Change Adaptation 136 Malawi: How Risk Information Guides an Integrated Flood Management Action Plan 141 Reducing Seismic Risk to Public Buildings in Turkey 145 Applying Multi-Hazard Risk Assessment to the Development of a Seismic Retrofit Program for Public Schools in Metro Manila, Philippines 149 Morocco Comprehensive Risk Assessment Study 153 Risk Assessment for Financial Resilience: The Approach of the World Bank 160 The Pacific Catastrophe Risk Assessment Initiative TABLE OF CONTENTS Case Study Color Key 163 From Multi-Risk Assessment to Multi-Risk Governance: Recommendations for Future Directions Participation, Collaboration, and 168 Build Back Better: Where Knowledge Is Not Enough Communication 172 InaSAFE: Preparing Communities to Be a Step Ahead 177 Global River Flood Risk Assessments 185 Delivering Risk Information for a Future Climate in the Pacific Future of Risk 191 A Framework for Modelling Future Urban Disaster Risk 198 Endnotes 201 References Chapter 04 210 Recommendations 219 Endnotes 220 Photo Credits UNDERSTANDING RISK IN AN EVOLVING WORLD UNDERSTANDING RISK IN AN EVOLVING WORLD LIST OF FIGURES 18 Overview 21 Figure O—1 The components for assessing risk and the difference between “impact” and “risk.” 23 Figure O—2 Risk as a function of hazard, exposure, and vulnerability. Chapter 01 32 Introduction 39 Figure 01—1 What makes data “open.” Chapter 02 44 Progress, Achievements, & Remaining Challenges in Risk Assessment 47 Figure 02—1 Hypothetical drought index showing periods of extreme dryness (above the dotted red line) and periods of extreme wetness (below the dotted blue line); the historical record does not capture extreme dry and wet periods experienced prior to its start in 1900. 59 Figure 02—2 The relationship between hazard intensity and damage to structures. 65 Figure 02—3 Sample software package review. Chapter 03 74 Case Studies Highlighting Emerging Best Practices 77 Figure 03—1 Examples of locations of GeoNodes supported by the World Bank and GFDRR. 87 Figure 03—2 Change detection using OSM. 92 Figure 03—3 LiDAR provides an opportunity to map and visualize in detail the highly urbanized environment of Manila. 93 Figure 03—4 Application of aerial imagery, LiDAR data, and land-use mapping to develop exposure database (Taguig City). 94 Figure 03—5 Growth in exposure data through crowdsourced (OSM) mapping of buildings and infrastructure in three locations in Indonesia. 99 Figure 03—6 A fuller picture of seismic history is obtained when instrumentally recorded events are combined with events from historical records (in pink). LIST OF FIGURES 103 Figure 03—7 Example of the 5km x 5km grid constituting the exposure database for GAR13. 109 Figure 03—8 Effects of water infrastructure in reducing flood inundation depths for 50- year floods. 113 Figure 03—9 Earthquake hazard map of central Sulawesi Province, developed collaboratively by Badan Geologi and AIFDR. 114 Figure 03—10 Badan Geologi and Geoscience Australia staff working collaboratively on probabilistic seismic hazard maps for Indonesia. 115 Figure 03—11 Badan Geologi and Geoscience Australia staff collect volcanic ash samples from a roadside agricultural plot of land approximately 10km from the summit of Ciremai volcano, West Java, in 2010. 116 Figure 03—12 The dispersal of volcanic ash from the last historical eruption of Guntur in 1840, as produced by the Volcanology and Geological Disaster Mitigation Centre. 117 Figure 03—13 Modelled depths for a flood equivalent to that experienced in Manila during Typhoon Ketsana in 2009. 120 Figure 03—14 Historical seismicity in Jordan. 121 Figure 03—15 Jordan’s fault system. 133 Figure 03—16 The safe island concept. 134 Figure 03—17 The islands selected for detailed multi-hazard risk assessment. 138 Figure 03—18 1-in-100-year flood extent (in pale blue) around the Elephant Marshes of the Lower Shire Valley, Malawi. 138 Figure 03—19 Flood zoning in the area of the Elephant Marshes based on different return period flood events. 142 Figure 03—20 Prioritization methodology for high seismic risk public buildings. 147 Figure 03—21 Estimated Metro Manila student fatalities per school building for a magnitude 7.2 West Valley fault scenario earthquake occurring in the daytime. 154 Figure 03—22 Flowchart for financial decision making. 169 Figure 03—23 Two common housing types in Padang: Unreinforced masonry construction using river stone and mortar with no reinforcement (left) versus confined masonry construction using steel-reinforced concrete columns in the corners and tops of walls (right). 173 Figure 03—24 InaSAFE can be used to improve understanding of the impact of disaster events, such as floods in Jakarta. UNDERSTANDING RISK IN AN EVOLVING WORLD UNDERSTANDING RISK IN AN EVOLVING WORLD LIST OF FIGURES 176 Figure 03—25 QGIS2.0 with the InaSAFE 2.0 dock showing a map and indicative results for an assessment of the impact of flooding on roads in Jakarta. 178 Figure 03—26 Observed flood extents in Bangladesh during July and August 2004: Dartmouth Flood Observatory database versus GLOFRIS model. 179 Figure 03—27 Map of modelled inundation extent and depth in Nigeria using GLOFRIS. Maps of this type can be used to assess which areas are exposed to flooding. 180 Figure 03—28 Maps of Nigeria showing the modelled results of the number of people affected per state (expressed as a percentage of the total population per state) for floods of different severities. Maps of this type can be used for identifying risk hot spots. 181 Figure 03—29 People living in flood-prone areas in different regions, 2010–2050. 182 Figure 03—30 Annual exposed GDP to flooding in 2010 and 2050, under different assumptions of flood protection standards. 184 Figure 03—31 Historical tropical cyclone tracks for the period 1981–2000 (top) and tropical-cyclone-like vortices extracted from a 20-year simulation using a general circulation model (bottom). 187 Figure 03—32 Ensemble mean proportion of cyclones for current and future climate in the Northern Hemisphere (left) and Southern Hemisphere (right). 188 Figure 03—33 Individual regional end-of-century exceedance probability curves for ensemble members (blue) compared to the current climate exceedance probability curve (green).