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RISK MODELING FOR HAZARDS AND DISASTERS This page intentionally left blank RISK MODELING FOR HAZARDS AND DISASTERS Edited by GERO MICHEL Chaucer Syndicates, Denmark Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2018 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-804071-3 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Candice Janco Acquisition Editor: Candice Janco Editorial Project Manager: Hilary Carr Production Project Manager: Paul Prasad Chandramohan Designer: Greg Harris Typeset by TNQ Books and Journals Contents List of Contributors ix Simulation of Event Intensities Introduction xi at Location 53 Modeling Damage 55 Financial Model and Validating Losses 57 I Closing Thoughts 59 Acknowledgments 60 CATASTROPHE MODELS, References 60 GENERAL CONCEPTS Appendix A 60 AND METHODS 3. Towards a More Dynamical Paradigm for 1. Quantifying Model Uncertainty Natural Catastrophe and Risk Risk Modeling NILESH SHOME, MOHSEN RAHNAMA, DAVID B. STEPHENSON, STEVE JEWSON, PAUL WILSON ALASDAIR HUNTER, BEN YOUNGMAN, IAN COOK Introduction 3 Background of Catastrophe Modeling 4 Introduction 63 Different Components of Catastrophe Models 6 Loss Simulation 63 Earthquake Hazard 7 Clustering 66 Epistemic Uncertainty in Seismic Hazard Dynamical Mixture Results 19 Models 69 Building Vulnerability 22 Concluding Remarks 74 Secondary Hazard and Subperils 28 Acknowledgments 77 Methodology for Loss Calculations 29 References 77 Loss Results and the Uncertainty 31 Overview of a Typical Hurricane Catastrophe 4. Empirical Fragility Model 32 and Vulnerability Component Level Uncertainties 34 Summary 42 Assessment: References 43 Not Just a Regression Further Reading 46 TIZIANA ROSSETTO, IOANNA IOANNOU 2. What Makes a Catastrophe Introduction 79 Challenges With Postevent Loss Model Robust and Damage Databases 81 JAYANTA GUIN Statistical Models e Which Model Fits the Data Best? 92 Introduction 47 Conclusions 101 Promise of the Stochastic References 101 Catalog 49 v vi CONTENTS II Intermediate-Term Seismicity-Based Earthquake Forecasting 184 MODEL CREATION, SPECIFIC Conclusions 187 PERILS AND DATA Acknowledgments 188 References 189 Further Reading 192 5. The Use of Historic Loss Data for Insurance and Total Loss Modeling 8. Big Data Challenges and Hazards JAMES E. DANIELL, FRIEDEMANN WENZEL, Modeling ANDREAS M. SCHAEFER KRISTY F. TIAMPO, SETH MCGINNIS, YELENA Introduction 107 KROPIVNITSKAYA, JINHUI QIN, MICHAEL A. BAUER Empirical Loss Statistics 107 Historic AAL Per Location Per Peril 114 Introduction 193 Using Empirical Statistics in Insurance Big Data 195 Modeling 118 Lessons From Climate Science 198 Case Study: Australian Earthquake Lessons From Solid Earth Science 203 Model 125 Conclusions 207 Conclusion 133 Acknowledgments 208 References 134 References 208 Further Reading 135 9. Progress Toward Hyperresolution 6. Indirect Loss Potential Index Models of Global Flood Hazard for Natural Disasters for National and PAUL D. BATES, JEFF NEAL, CHRIS SAMPSON, ANDY Subnational Analysis SMITH, MARK TRIGG JAMES E. DANIELL, BIJAN KHAZAI, FRIEDEMANN WENZEL Introduction 211 Introduction 139 Progress in Global Flood Modeling: The Drive to Background 139 Hyperresolution 213 Existing Methodologies for Quantifying Case Study: Hyperresolution Global Flood Hazard Indirect Loss Impact 141 Modeling Using SSBNflow 217 Economic Losses From Historical Events Integrating Global Exposure Data in Global Flood and Direct Study Results 144 Risk Calculations 225 An Indicator System for Vulnerability Understanding Model Deficiencies 227 to Indirect Losses 149 Conclusions 229 Results for Countries and Provinces 153 References 229 Summary 156 References 157 Further Reading 160 III Appendix A: Electronic Supplement 161 MODEL INSURANCE 7. Probability Gain From Seismicity-Based USE-CASES Earthquake Models 10. Insurance Pricing and KRISTY F. TIAMPO, ROBERT SHCHERBAKOV, PAUL KOVACS Portfolio Management Using Catastrophe Models Introduction 175 HELENA BICKLEY, GERO MICHEL Time-Dependent Seismicity-Based Earthquake Forecasts 177 Introduction 235 Short-Term Seismicity-Based Earthquake Catastrophe Models, the Pinnacle Forecasting 179 in the Underwriting Process 237 CONTENTS vii Exposure Data, the Foundation of 13. Creating a Probabilistic Catastrophe the Modeling Process 237 Model With the Characteristic Event Probable Maximum Losses, Methodology Capital Cost and Portfolio Constraints 241 KAREN CLARK Portfolio Optimization and Reporting 242 Introduction 271 Using a Model: Insights of Structure of a Catastrophe Model 272 a Practitioner 245 US Hurricane Example 273 Different Risk Metrics for Different Purposes 277 11. Portfolio Optimization Using Additional Benefits of the Characteristic Event Catastrophe Model Results, Approach 278 a Use Case From the Insurance Conclusions 278 Industry Further Reading 279 AUGUSTIN YIPTONG, GERO W. MICHEL 14. From Risk Management to Introduction 247 Quantitative Disaster Resilience: A New The Procedure 248 Paradigm for Catastrophe Modeling Plot Expected Profit Versus TVaR 100 252 SLOBODAN P. SIMONOVIC Computation Details 257 Discussion 257 Introduction 281 Conclusion 259 Quantitative Measure of Resilience 283 References 259 Implementation of Dynamic Resilience 286 Case StudydA Railway Under Flooding 289 Conclusions 295 Acknowledgments 296 IV References 296 MODEL RISK, 15. Beyond “Model Risk”: A Practice RESILIENCE AND NEW Perspective on Modeling in CONCEPTS Insurance ANDREAS TSANAKAS, LAURE CABANTOUS 12. Parsimonious Risk Assessment and the Role of Transparent Diverse Models Introduction 299 Cultures of Model Use 300 PATRICK MCSHARRY Beyond Model Risk: Insights From the Introduction 263 Cultures of Model Use Framework 301 Parsimonious Models 264 Evidence From the Practice of London Ensemble Modeling 264 Market Insurers 303 Big Data 266 Conclusion and Outlook 304 Index Insurance 266 Author Biographies 305 Catastrophe Models 267 References 305 Conclusion 268 References 268 Index 307 This page intentionally left blank List of Contributors Paul D. Bates School of Geographical Sciences, Seth McGinnis Institute for Mathematics University of Bristol, Bristol, UK; SSBN Flood Applied to Geosciences, National Center for Risk Solutions, Cardiff, UK Atmospheric Research, Boulder, CO, USA Michael A. Bauer Department of Earth Sciences, Patrick McSharry ICT Center of Excellence, Department of Computer Sciences, Western Carnegie Mellon University Africa, Kigali, University, London, ON, Canada Rwanda; University of Rwanda, Kigali, Helena Bickley Sompo International Rwanda; Smith School of Enterprise and the Environment, University of Oxford, Oxford, Laure Cabantous Faculty of Management, Cass UK Business School, City, University of London, London, UK Gero W. Michel Chaucer Copenhagen, Copen- hagen, Denmark Karen Clark Karen Clark & Company, Boston, MA, USA Gero Michel Western University, London, Ontario, Canada; Chaucer Underwriting A/S, Ian Cook Willis Towers Watson, Willis Research Copenhagen, Denmark Network, London, UK Jeff Neal School of Geographical Sciences, James E. Daniell Geophysical Institute & Center University of Bristol, Bristol, UK; SSBN Flood for Disaster Management and Risk Reduction Risk Solutions, Cardiff, UK Technology (CEDIM), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Jinhui Qin Department of Earth Sciences, Department of Computer Sciences, Western Jayanta Guin AIR Worldwide, Boston, MA, USA University, London, ON, Canada Alasdair Hunter Barcelona Supercomputing Mohsen Rahnama Risk Management Solutions, Centre,