Geographic Perspectives on Disaster Risk Management

CHRIS EWING is Head of Business Development for Impact Forecasting, the catastrophe model development centre of Aon. He has 20 years' experience across risk management, (re)insurance, engineering, and humanitarian sectors. He holds a BSc in Physical Geography and an MSc in Geographical Information Science. Chris is a Chartered Geographer (GIS) from the Royal Geographical Society and a co-founder and Chair of the Disaster Risk Management Professional Practice Group.MATTHEW FOOTE has over 30 years' experience in insurance, disaster risk financing, cartography, earth observation, and catastrophe risk analytics. He serves as UK Principal Representative to the GEO Programme Board and was the first chair of the GRMA Strategic Advisory Board. He is a fellow of the Royal Geographical Society and a co-founder of its Disaster Risk Management Professional Practice Group.WILLIAM FORDE is a specialist in geospatial analytics and catastrophe risk. Drawing on senior leadership experience at global consulting firms and a leading GIS vendor, he helps insurers and regulators translate complex data into actionable insights for hazard management and resilience. He holds an MSc in Remote Sensing and a BA in Anthropology and Geogaphy from University College London and is a Fellow of the Royal Geographical Society, where he co-founded its Disaster Risk Management Professional Practice Group.TINA THOMSON, PHD, has held senior leadership roles in analytics, research, and digital solutions spanning a career across Catastrophe Model Vendors, Property & Casualty Insurance, Reinsurance, and Reinsurance Brokers. Tina is passionate about the application of scientific concepts, emerging industry trends and new technologies. She holds a PhD in Geomatic Engineering from University College London. She is a Fellow of the Remote Sensing and Photogrammetry Society and a Chartered Geographer with the Royal Geographical Society, where she co-founded the Professional Practice Group for Disaster Risk Management.

• Editors xvList of Contributors xviiContributors and Acknowledgements xxixForeword xxxiPreface: Geography Underpins Disaster Risk Management xxxiii1 Defining Characteristics of Assets for Risk Assessment –Exposure and Vulnerability 1Anirudh Rao, Catalina Yepes- Estrada, and Vitor Silva1.1 Introduction 11.2 Overview of the Chapter’s Objectives and Scope 21.3 Exposure and Vulnerability: A DRM and DRR Perspective 31.4 Characteristics of Assets and Their Exposure and Vulnerability to Hazards 61.4.1 Different Types of Assets in the Context of Exposure 61.4.2 How Do Key Characteristics of These Assets Determine Their Vulnerability to Hazards? 71.4.3 Need for a Taxonomy System for Categorising Assets and Linking Exposure to Vulnerability 91.4.4 Geospatial and Temporal Context of Exposure and Vulnerability 91.4.4.1 Importance of the Geographic Context 91.4.4.2 The Time- Varying and Dynamic Nature of Exposure 111.4.4.3 The Role of the Historical and Geographical Contexts in Assessing Vulnerability 131.4.4.4 Impact of Different Codes/Regulations over Time 131.5 Case Study – How the Global Earthquake Model (GEM) Foundation Captures and Curates Exposure, Vulnerability and Value at Risk 141.5.1 Exposure Models Derived from Detailed Building Inventories 151.5.2 Exposure Models Derived from Building or Housing Censuses and Other Statistical Data 171.6 Case Study – The Spatial Finance Initiative GeoAsset Project 191.7 Conclusion 21References 222 Common Approaches to Exposure and Vulnerability Data Across Risk Management Sectors 25Charles Huyck, Marina Mendoza, and Melisa Huyck2.1 Introduction 252.2 Types of Exposure Data 272.3 A Taxonomy for Exposure Data Classification 292.3.1 Level 1 – Global Data 322.3.2 Level 2 – Country- Level Exposure Data 332.3.3 Level 3 – Data Improvement at the Sub- national Scale 332.3.4 Level 4 – Aggregated Building- Specific Data 332.3.5 Level 5 – Site- Specific Data 352.4 Key Attributes and Considerations for Developing a Building Exposure Dataset 352.5 Use of EO Data for Exposure Development 382.6 Limitations and Challenges 402.7 Promising Trends in Exposure Development 422.7.1 Case Study – Enhancing Disaster Resilience Through Global Economic Disruption Index (GEDI) Implementation 43Acknowledgments 45References 453 Innovations in Spatial Exposure Modelling for Public Sector Disaster Risk Practitioners 49Rashmin Gunasekera, Harriette Stone, Antonios Pomonis, James Daniell, Gonzalo Pita, and Bramka Jafino3.1 Introduction 493.2 Case Studies of Exposure Modelling for the Public Sector 513.2.1 Case Study 1 – A Global Education Sector Exposure Model: Developing an Innovative Model That Is Fit for Purpose 513.2.1.1 Developing a Database of Education Infrastructure 513.2.1.2 Developing the Exposure Model 523.2.1.3 Application of the Model 523.2.1.4 Opportunities 523.2.1.5 Challenges 553.2.2 Case Study 2 – Improving Exposure Models Through Repeat Analysis of Past Disasters 563.2.2.1 Developing the Model 573.2.2.2 The Analysis and Results 603.2.2.3 Opportunities 623.2.2.4 Challenges 623.2.3 Case Study 3 – Incorporating Social Development and Gender Equity in Exposure Models 633.2.3.1 Developing the Timor- Leste Residential Exposure Model 633.2.3.2 Incorporating Socio- economic Status in the Exposure Model 643.2.3.3 Estimating Well- Being Losses Using the Residential Exposure Model 653.2.3.4 Opportunities 693.2.3.5 Challenges 703.2.4 Case Study 4 – Exposure for Public Sector Asset Management 713.2.4.1 Exploring the Requirements for an AIMS and the Challenges to Overcome in the Caribbean 713.2.4.2 Developing the Asset Information Management System in St Lucia 723.2.4.3 Opportunities 733.2.4.4 Challenges 743.2.5 Case Study 5 – Using Technology to Reduce Uncertainty in Building Data for City- Level Exposure Models 743.2.5.1 Collecting and Testing Accuracy of Building Data for Exposure 743.2.5.2 Opportunities 773.2.5.3 Challenges 773.3 Conclusion 77References 784 Geographic Nature of Hazard and Risk 81Michal Lörinc4.1 Disaster Definition and Typology 814.1.1 What Makes a Disaster? 814.1.2 Temporal Resolution of Disasters 824.1.3 General Categorisation 824.1.4 Catastrophes in Re/Insurance 834.2 Geographic Aspects of Natural Disaster Risk 844.2.1 Geographic Distribution of Natural Hazards 844.2.1.1 Earthquakes 844.2.1.2 Volcanic Eruptions 844.2.1.3 Tropical Cyclones 854.2.1.4 Extratropical Cyclones 854.2.1.5 Floods 854.2.1.6 Severe Convective Storms 864.2.1.7 Droughts 864.2.1.8 Wildfires 874.2.2 Importance of Local Environmental Conditions 874.2.2.1 Topography and Orography 874.2.2.2 Soil and Ground Conditions 874.2.2.3 Hydrological Conditions and Drainage Systems 884.2.2.4 Vegetation and Ecosystem Health 884.2.3 Physical Vulnerability as a Factor of Risk 894.2.4 Economic Vulnerability and Insurance 904.2.5 Social and Environmental Vulnerability 904.3 How the Geographic Nature of Risk Changes 914.3.1 Climate Change 914.3.1.1 Temperature Extremes 914.3.1.2 Drought 914.3.1.3 Severe Convective Storms 924.3.1.4 Tropical Cyclones 924.3.1.5 Flooding 934.3.2 Climate Variability and ENSO 934.3.3 Socio- economic and Demographic Change 944.3.4 Changing Role of Insurance 944.4 Adapting to Current and Future Risk 964.4.1 Coastal Defences and Flooding 964.4.2 Inland Flooding 984.4.3 Severe Convective Storms 994.4.4 Temperature Extremes and Droughts 994.4.5 Earthquakes 994.4.6 Wildfires 1004.5 Conclusion 100References 1005 Disaster Risk Reduction, Risk Mitigation 105Stuart Fraser and Thaisa Comelli5.1 Why Take a Geographic Perspective to DRR 1055.2 History, Good Practices and Knowledge Frontiers 1065.3 DRR and Adaptation Across Sectors 1085.4 Unpacking Geographic Perspectives 1095.5 Application of Geographic Perspectives Approaches in DRR and Adaptation 1105.5.1 Methodological Approaches 1115.5.2 Tools and Data to Build Geographic Perspectives 1155.5.2.1 Data Portals and Decision Support Systems 1155.5.2.2 Deeper Spatial Analysis and Risk Models 1165.5.2.3 Spatial Planning and Policymaking 1185.6 The Future of Geographic Perspectives for DRR and Adaptation: Three Key Points 1195.6.1 Relying on Interdisciplinary Geographic Approaches for Improved Decision- Making at the Local Level 1205.6.2 Closing Gaps in Geographic Risk Understanding and Analytics Globally 1205.6.3 Grasping Different Future Options Through Inclusive and Equity- Focused Anticipatory Approaches 1205.7 Conclusion 121References 1226 Insurance and Risk Transfer Mechanisms 127Chris Ewing and Alec Wild6.1 Introduction – Geospatial Data in Risk Transfer and Insurance 1276.1.1 Assessing Risk Using Maps 1276.1.2 Location Accuracy and Precision 1326.1.3 Risk Transfer Value Chain and Geospatial Data Quality 1336.1.4 Exposure Enrichment and Augmentation 1346.2 Premium Pricing Considerations 1346.2.1 Motor 1366.2.2 Marine 1366.2.3 Cyber 1376.2.4 Health 1416.2.5 Aviation 1416.3 When Losses Occur 1426.3.1 Claims/Loss Data and Analysis 1426.3.2 Case Study – Real- Time Loss Forecasting for (re)Insurers 1476.3.3 Business Interruption 1476.3.4 Demand Surge 1486.4 Catastrophe Model Components and Geospatial Data 1486.4.1 Exposure – What Is at Risk 1486.4.2 Hazard – Where, What, and How Frequent Is the Impact 1516.4.2.1 Event Footprint Data for Earthquakes 1516.4.2.2 Event Frequency/Severity Data for Atmospheric Perils 1516.4.3 Vulnerability – Damageability of a Structure, Preparedness of a Population 1526.5 Accumulation Management 1536.5.1 Accumulation in Insurance 1536.5.2 Accumulation in Reinsurance 1546.6 Parametric Insurance and Insurance- Linked Securities 1566.7 Catastrophe Pools 1566.8 Conclusion 156References 1577 Disaster Preparedness in Fragile, Conflict- and Violence-Affected Humanitarian Settings 161Madeline Ewbank, Laura E.R. Peters, Juliane Schillinger, Liesa Sauerhammer, Cornelia Scholz, Catalina Jaime, Tesse de Boer, and Simphiwe Laura Stewart7.1 Introduction 1617.2 The Complexities of Fragility, Conflict and Violence in Disaster Preparedness 1637.2.1 Fragility 1637.2.2 Conflict 1647.2.3 Violence 1657.2.4 Humanitarian Programming and Disaster Preparedness 1667.3 Geospatial Tools to Detect FCV Spatial Patterns 1677.3.1 Ethical Considerations 1677.3.2 Perspectives and Tools 1687.4 Multi- hazard/Multi- risk Analysis in FCV Settings 1717.5 Geospatial Tools in Risk Analysis for FCV Settings 1737.6 Conclusion 176References 1788 Event Response: Mobilisation and Logistics 185Claire Byrne, Samir Gandhi, Mark Gillick, Edith Lendak, Naomi Morris, Claudia Offner, Matt Pennells, and Matthew Sims8.1 Introduction 1858.2 Mobilisation and Logistics 1868.2.1 Case Study – Madagascar Cyclone Response February 2022 1878.2.1.1 Background 1878.2.1.2 Response Actions 1878.2.1.3 Technologies Used 1878.2.1.4 Outcomes and Lessons Learned 1898.3 Logistical Frameworks 1898.3.1 Local and National Frameworks 1918.3.2 Regional Frameworks 1918.3.3 International Frameworks 1918.3.4 Case Study – Haiti Earthquake Response August 2021 1928.3.4.1 Background 1928.3.4.2 Response Actions 1958.3.4.3 Technologies Used 1958.3.4.4 Outcomes and Lessons Learned 1958.4 Historical Context and Technological Advances 1998.4.1 Technological Integration 2008.5 Timeline of Events 2018.5.1 Case Study – Belize Wildfires May and June 2024 2038.5.1.1 Background 2038.5.1.2 Response Actions 2038.5.1.3 Technologies Used 2048.5.1.4 Outcomes and Lessons Learned 2078.6 Key Stakeholders 2078.6.1 Case Study – Ukraine Complex Emergency 2108.6.1.1 Background 2108.6.1.2 Response Actions 2108.6.1.3 Technologies Used 2128.6.1.4 Outcomes and Lessons Learned 2128.7 Communication and Knowledge Sharing Networks 2138.8 Challenges and Solutions 2138.9 Future Trends and Recommendations 2168.9.1 Emerging Technologies 2168.9.2 Policy and Framework Recommendations 2188.9.3 Capacity Building 2198.10 Conclusion 220References 2219 Real-Time Monitoring and Communication 225Richard Teeuw9.1 Introduction 2259.2 The Disaster Risk Management Context 2259.2.1 Comprehensive Preparedness vs. Rapid Targeted Responses 2259.3 Real- Time Earth Observation (EO) Monitoring 2279.3.1 Eyes in the Skies 2289.3.2 Case Study – Using Real- Time Satellite Imagery in Response to a Pacific Cyclone Disaster 2289.3.3 Early Warning Systems (EWS) 2329.3.3.1 Hydrometeorological EWS 2339.3.3.2 Geospatial Data Integration Systems 2349.3.3.3 The United Nations (OCHA) Global Disaster Alerts and Coordination System (GDACS) 2349.3.4 Case Study – The Previsico Flood Intel Platform 2359.4 Real- Time Communications 2389.4.1 Satellite Communication (Satcom) Systems 2399.4.2 Local WiFi Hubs: Balloons and Drones 2409.4.3 Case Study: World Food Programme Emergency Telecom Systems 2419.4.4 Professional and/or Amateur Radio 2439.5 Reducing Disaster Risk by Closing the Digital Data Divide 2449.6 Conclusion 244References 24510 Damage Assessment 251David Heathcote10.1 Introduction 25110.2 Data Acquisition 25110.3 Windstorm Damage Assessment (Hurricanes and Storms) 25310.4 Flood Damage Assessment 25610.5 Wildfire Damage Assessment 25810.6 Conflict and War Damage Assessment 26010.7 Case Study: MIS Assessment Process for Hurricane Helene 26210.7.1 Exposure Layer 26210.7.2 Claims Layer 26210.7.3 Building- Level Layer 26410.7.4 Data Anomalies 26410.7.5 Future of Assessments 26610.8 Conclusion 268References 26911 Long-Term Resilience: Recovery Finance and Implementations of Lessons Learned 271Alastair Norris and Claire Souch11.1 Introduction: Defining Disaster Response vs. Long- Term Resilience 27111.1.1 Case Study: Reduction in Tropical Cyclone- Related Deaths in Bangladesh from 1970 to 2024 27311.2 Response and Recovery Finance: Foundations for Effective Recovery 27511.3 Transitioning from Response to Recovery: Timeline and Key Needs 27811.4 Short- to Medium- Term Recovery (Weeks to Months) 28111.4.1 Case Study: Recovery and Reconstruction Following the Great East Japan Earthquake of March 2011 28211.5 Long- Term Recovery and ‘Building Back Better’ (Months to Years) 28411.6 Building Long- Term Resilience 28611.7 Conclusion 290References 29112 Data Accuracy and Requirements: ‘What Data Are Appropriate?’ 297Alec Wild, Heather Craig, James Knight, Charles Lan, Ryan Paulik, Liam Wotherspoon, and Conrad Zorn12.1 Introduction 29712.2 Navigating the Geospatial Terrain: Challenges and Considerations 29712.3 The Unique Challenge Spectrum of Disaster Risk Management 29812.4 Key Dimensions of Data Requirements 29812.5 Case Study – Hunga Tonga– Hunga Ha’apai Eruption Response 29912.6 Case Study – Assessing Indirect Impacts of Extreme Sea Level Flooding on Critical Infrastructure in South Dunedin 30412.6.1 Hazard Data 30512.6.2 Asset and Network Data 30512.6.3 Results 30912.7 Case Study – Development of Fragility Functions for the Agriculture Sector from Volcanic Tephra Fall 31112.8 Case Study – Population Exposure and Evacuation Clearance Times in the Auckland Volcanic Field, New Zealand 31612.9 Discussion 32012.10 Conclusion 323References 32313 Communicating Uncertainty 329Giacomo Favaron13.1 Introduction 32913.2 The Use of Decision Frameworks to Understand Uncertainty 33013.3 Communicating Uncertainty While Navigating the DIKW Framework: Challenges and Implications for Disaster Risk Management 33113.3.1 Why Effectively Communicating Uncertainty Matters in Disaster Risk Management 33213.4 The Importance of Cartographic Design in Representing Uncertainty Effectively 33213.4.1 Cartographic Techniques for Representing Uncertainty 33213.4.2 Proposed Cartographic Techniques to Represent Uncertainty 33313.4.3 Understanding Data Characteristics and Choosing Colour Palettes 33713.4.4 Data Categorisation 33713.4.5 Representation of Temporal Uncertainty 34013.4.6 Considering Accessibility 34013.5 Case Study – Analysing Uncertainty in Tropical Cyclone Forecasts 34113.5.1 Putting Tropical Cyclones in Context 34113.5.2 Sources of Uncertainty in Tropical Cyclone Forecasts and Forecasting with Ensembles 34213.5.3 The Cone of Uncertainty: What It Is and How It Can Be Misinterpreted 34313.5.4 Common Misconceptions About the Cone of Uncertainty 34413.5.5 Beyond the Cone: Exploring Alternative Visualisations for Tropical Cyclone Uncertainty 34413.5.6 From Tropical Cyclone Foresting to Impact Base Forecasting: Developing Tools to Support Pre- event Mitigation Actions Using Uncertainty Information 34513.6 Case Study – The 2009 L’Aquila Earthquake and The Consequences of Miscommunication 34813.6.1 Introduction 34813.6.2 Is It Possible to Predict an Earthquake? 35013.6.3 Understanding Risk Maps 35013.6.4 Challenges of Historical Mapping Materials 35113.6.5 The Importance of Probabilistic Seismic Hazard Analysis and Hazard Maps in Risk Assessment, Risk Management and Risk Mitigation 35113.6.6 The Challenges of Communicating Risk Uncertainty for High Impact Low Probability Risks 35413.6.7 Learning from the Past 35513.7 Conclusion 357References 35714 Reporting and Decision-Making 363Kelvin Wong14.1 Introduction 36314.2 The Importance of Audience 36414.2.1 Types of Decision- Makers and Their Decisions 36514.2.1.1 Designing Effective and Efficient Reporting for Decision- Making 36514.2.1.2 Clarity and Succinctness 36514.2.1.3 Scale and Frequency 36614.2.1.4 Timeliness 36714.2.2 Choosing the Right Format 36714.2.3 Accuracy and Building Trust 36814.2.4 Collaboration and Coordination 36814.2.5 Understanding Socio- cultural Nuances 36914.2.6 Ethical Considerations 36914.2.7 Role of Technology and GIS 37014.2.8 Barriers to Successful Communication 37014.2.8.1 Physical Barriers 37114.2.8.2 Psychological Barriers 37114.2.8.3 Semantic Barriers 37114.2.8.4 Cultural Barriers 37214.2.8.5 Technological Barriers 37214.3 Case Studies in Reporting and Decision- Making 37314.3.1 Case Study 1 – Humanitarian 37314.3.2 Case Study 2 – Oil and Gas 37614.3.3 Case Study 3 – Risk Communication Consulting 37814.4 Discussion 38014.4.1 Types of Audience and Decision- Makers 38114.4.2 Location and Geography 38114.4.3 Format and Delivery Mechanisms 38114.4.4 Priorities for Communication 38214.4.5 Directionality of Communication 38214.5 Conclusion 382References 38315 Wrapping Up and Looking Ahead 385Chris Ewing, Matthew Foote, William Forde, and Tina Thomson15.1 Representing the Elements at Risk – Exposure and Vulnerability 38515.2 Disaster Preparedness and Risk Reduction 38615.3 Event Mobilisation, Monitoring, Damage Assessment and Resilience Building 38715.4 Communicating and Understanding Risk 38815.5 A Call to Action to Advance Best Practice 39015.5.1 Enhance Data Collection and Sharing 39015.5.2 Foster Interdisciplinary Collaboration 39015.5.3 Implement Real- Time Monitoring and Forecasting 39115.5.4 Promote Community Engagement and Education 39115.5.5 Develop Adaptive Policies and Frameworks 39215.5.6 Invest in Resilience and Sustainability 39315.5.7 Advocate for Global Cooperation and Knowledge Exchange 394References 395Glossary 397Index 409