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Solving the Puzzle: Written Contributions INNOVATING TO REDUCE RISK Written Contributions – A – This publication is driven by input provided by the disaster risk community. The Global Facility of Disaster Risk and Recovery facilitated the development of the report, with funding from the UK Department of International Development. The World Bank Group does not guarantee the accuracy of the data 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. Washington, D.C., June 2016 Editors: LFP Editorial Enterprises Designed by Miki Fernández Rocca ([email protected]), Washington, D.C. © 2016 by the International Bank for Reconstruction and Development/The World Bank 1818 H Street, N.W., Washington, D.C. 20433, U.S.A. All rights reserved Manufactured in the United States of America. First printing June 2016 Table of Contents Preface 5 The Current State of Risk Information: Models & Platforms Past and Future Evolution of Catastrophe Models 8 Karen Clark (Karen Clark & Company) The Current State of Open Platforms and Software for Risk Modeling: Gaps, Data Issues, and Usability 13 James E. Daniell (Karlsruhe Institute of Technology) Visions for the Future: Model Interoperability 17 Andy Hughes, John Rees (British Geological Survey) Harnessing Innovation for Open Flood Risk Models and Data 21 Rob Lamb (JBA Trust, Lancaster University ) Toward Reducing Global Risk and Improving Resilience 24 Greg Holland, Mari Tye (National Center for Atmospheric Research) Priorities for the Short and Medium Terms: Which Are Better? 29 Susan Loughlin (British Geological Survey) Open Risk Data and Modeling Platform 32 Tracy Irvine (Oasis, Imperial College London, EIT Climate-KIC) Visions for the Future: Multiple Platforms and the Need for Capacity Building 36 John Rees, Andy Hughes (British Geological Survey) The Anatomy of a Next Generation Risk Platform 40 Deepak Badoni, Sanjay Patel (Eigen Risk) Development of an Open Platform for Risk Modeling: Perspective of the GEM Foundation 44 John Schneider, Luna Guaschino, Nicole Keller, Vitor Silva, Carlos Villacis, Anselm Smolka (GEM Foundation) – 1 – Status of Risk Data/Modeling Platforms and the Gaps: Experiences from VHub and the Global Volcano Model 52 Greg Valentine (University of Buffalo) Toward an Open Platform for Improving the Understanding of Risk in Developing Countries 54 Micha Werner, Hessel Winsemius, Laurens Bouwer, Joost Beckers, Ferdinand Diermanse (Deltares & UNESCO-IHE) Oasis: The World’s Open Source Platform for Modeling Catastrophic Risk 58 Dickie Whitaker, Peter Taylor (Oasis Loss Modelling Framework Ltd) Data Open or Closed? How Can We Square Off the Commercial Imperative in a World of Open and Shared Data? 65 Justin Butler (Ambiental) Understanding Disaster Risk Through Loss Data 70 Melanie Gall, Susan L. Cutter (University of South Carolina) High-Resolution Elevation Data: A Necessary Foundation for Understanding Risk 74 Jonathan Griffin (Geoscience Australia); Hamzah Latief (Bandung Institute of Technology); Sven Harig (Alfred Wegener Institute); Widjo Kongko (Agency for Assessment and Application of Technology, Indonesia); Nick Horspool (GNS Science) Data Challenges and Solutions for Natural Hazard Risk Tools 77 Nick Horspool (GNS Science); Kate Crowley (National Institute of Water and Atmospheric Research Ltd); Alan Kwok (Massey University) The Importance of Consistent and Global Open Data 81 Charles Huyck (ImageCat Inc.) Capacity Building Australia-Indonesia Government-to-Government Risk Assessment Capacity Building 86 A. T. Jones, J. Griffin, D. Robinson, P. Cummins, C. Morgan, (Geoscience Australia); S. Hidayati (Badan Geologi); I. Meilano (Institut Teknologi Bandung); J. Murjaya (Badan Meteorologi, Klimatologi, dan Geofisika) Required Capacities to Improve the Production of, Access to, and Use of Risk Information in Disaster Risk Management 89 Sahar Safaie (UNISDR) Understanding Assumptions, Limitations, and Results of Fully Probabilistic Risk Assessment Frameworks 93 Mario A. Salgado-Gálvez (CIMNE-International Centre for Numerical Methods in Engineering) Building Capacity to Use Risk Information Routinely in Decision Making Across Scales 97 Emma Visman (King’s College London and VNG Consulting Ltd); Dominic Kniveton (University of Sussex) – 2 – Risk Communication Visualizing Risk for Commercial, Humanitarian, and Development Applications 102 Richard J. Wall (University College London (UCL) Hazard Centre) Stephen J. Edwards (UCL Hazard Centre and Andean Risk & Resilience Institute for Sustainability & the Environment), Kate Crowley (Catholic Agency for Overseas Development and NIWA), Brad Weir (Aon Benfield) and Christopher R.J. Kilburn (UCL Hazard Centre) Improving Risk Information Impacts via the Public Sphere and Critical “Soft” Infrastructure Investments 108 Mark Harvey (Resurgence); Lisa Robinson (BBC Media Action) Perceiving Risks: Science and Religion at the Crossroads 112 Ahmad Arif (Kompas); Irina Rafliana (LIPI, Indonesian Institute of Sciences) – 2 – – 3 – Collaborators – 4 – Preface hese written contributions are part of the Solving the Puzzle: Where to Invest to Understand Risk report. The report provides a community perspective on priorities for future collaboration and investment in the development and Tuse of disaster risk information for developing countries. The focus is on high-impact activities that will promote the creation and use of risk-related data, catastrophe risk models, and platforms, and that will improve and facilitate the understanding and communication of risk assessment results. The intended outcome of the report is twofold. First, that through the community speaking as one voice, we can encourage additional investment in the areas highlighted as priorities. Second, that the consensus embodied in the report will initiate the formation of the strong coalition of partners whose active collaboration is needed to deliver the recommendations. The written contributions are part of the input received from the disaster risk community in response to open calls to the Understanding Risk Community and direct solicitation. The papers offer analysis around challenges that exist in disaster risk models and platforms, data, capacity building and risk communication. – 4 – – 5 – The Current State of Risk Information: Models & Platforms – 7 – Past and Future Evolution of Catastrophe Models Karen Clark (Karen Clark & Company) atastrophe models were developed in the late FIGURE 1. Catastrophe Model Component 1980s to help insurers and reinsurers better Cunderstand and estimate potential losses from natural hazards, such as hurricanes and earthquakes. Over the past few decades, model usage has grown considerably throughout the global insurance industry, and the models are relied upon for many risk management decisions. In short, the models have become very important tools for risk management. Now, new open loss modeling platforms are being developed to advance the current state of practice. The first generation catastrophe models are cl`sed “black box” applications, proprietary to the model vendors. Open models make more visible the key assumptions driving insurers’ loss estimates, along with giving them control over those assumptions. Market demand is driving the development of new tools because today’s model users require transparency on the model components and more consistency in risk management information. Insurers are also expected to develop their own proprietary views of risk and not simply rely on the output from third-party models. The The event catalog defines the frequency and physical following reviews the traditional catastrophe models and severity of events by geographical region. It is typically their limitations and how advanced open risk models generated using random simulation techniques, in which are addressing these issues. It also illustrates how other the underlying parameter distributions are based on users, such as governments of developing countries, can historical data and/or expert judgment. The reliability benefit from this new technology. of the event catalog varies considerably across peril regions, depending on the quantity and quality of Overview of catastrophe models historical data. A catastrophe model is a robust and structured For example, enough historical data exist on Florida framework for assessing the risk of extreme events. hurricanes to estimate credibly the return periods For every peril region, the models have the same four of hurricanes of varying severity there. In contrast, components, as shown in figure 1. nine hurricanes have made landfall in the Northeast – 8 – Solving the Puzzle: Innovating to Reduce Risk—Written Contributions since 1900—none of them exceeding Category 3 FIGURE 2. Representative EP Curve intensity. Model estimates of the frequency of Category 4 hurricanes in this region are, therefore, based on subjective judgments that can vary significantly between models and even between model updates from the same vendor. Because scientists don’t know the “right” assumptions, they can develop very different opinions, and they can change their minds. For each event in the catalog, the models estimate the intensity at affected locations using the event parameters the catalog provides, site information, and scientific formulas developed by the wider scientific community. Over time, the fundamental structure of the models The catastrophe models
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