Riskier Future

Transcript

1 The making of a riskier future: How our decisions are shaping future disaster risk

2

3 The making of a riskier future: How our decisions are shaping future disaster risk

4 © 2016 Global Facility for Disaster Reduction and Recovery 1818 H Street, N.W., Washington, D.C., 20433, U.S.A. The text in this publication may be reproduced in whole or in part and in any form for educational or nonprofit uses, without special permission, provided acknowledgement of the source is made. The GFDRR Secretariat would appreciate receiving a copy of any publication that uses this report as a source. Copies may be sent to the GFDRR Secretariat at the above address. No use of this publication may be made for resale or other commercial purpose without prior written consent of the GFDRR Secretariat. All images remain the sole property of the source and may not be used for any purpose without written permission from the source. Notes: Fiscal year (FY) runs from July 1 to June 30; the financial contributions and expenditures reported are reflected up to June 30, 2015; all dollar amounts are in US dollars ($) unless otherwise indicated. 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. Design: Miki Fernández/ULTRAdesigns, Inc. Cover: Kathmandu cityscape, Nepal. Photo credit: sagarmanis/Thinkstock.com; Inside cover: Bhaktapur, Nepal – May 9, 2015: Woman outside her earthquake-ruined house in Bhaktapur, Nepal, located 30 km east of Kathmandu. The town was once rich with Buddhist and Hindu temples and a popular tourist spot for those visiting Kathmandu. Photo credit: Jules2013/Thinkstock.com

5 Table of Contents Foreword vii Acknowledgments ix Abbreviations x Executive Summary/Overview xiii 1 1. INTRODUCTION 2. DISASTER RISK 5 3. DRIVERS OF EVOLVING DISASTER RISK: HAZARD 9 Hydrometeorological hazards 9 Tropical cyclone 10 12 Extratropical cyclone Flooding 13 Extreme heat 17 Drought 18 Wildfire 19 Geotechnical and geophysical hazards 20 20 Seismic and volcanic hazard Landslide 20 4. DRIVERS OF EVOLVING DISASTER RISK: EXPOSURE 23 Population growth 24 Increased socioeconomic activity 28 Land-use change 28 Data on evolving exposure 28 5. DRIVERS OF EVOLVING DISASTER RISK: VULNERABILITY 31 Structural vulnerability 31 Social vulnerability 33

6 iv 37 6. QUANTIFYING THE EVOLUTION OF DISASTER RISK Simple or complex approach 39 40 Modeling interrelated and evolving hazards Multiple influences on coastal flood risk 40 Time dependency 41 Uncertainty in risk assessment 42 Hazard uncertainty 42 42 Use of climate projections in disaster risk assessment 44 Uncertainty in exposure data and projections 46 Producing detailed risk assessments 49 Complexities in modeling evolving exposure Using socioeconomic scenarios to project population 49 Projecting urban expansion 51 Evolving vulnerability: An ongoing challenge 53 7. IDENTIFYING EFFECTIVE POLICIES FOR A RESILIENT FUTURE 59 Mitigate climate change 60 Manage urbanization 60 60 Limit harmful land-use change and resource consumption 60 Control increases in exposure Reduce vulnerability through urban design 61 Manage risk through construction 62 Building practices 62 Continuing habitability of structures 64 Consider ecosystem-based risk management 65 Improve data for risk modeling 66 Dynamic exposure and vulnerability data 66 High-resolution elevation data 66 Flood protection data 67 Implement robust, flexible adaptation 67 Enhance disaster resilience 68 Plan recovery and reconstruction before the event 69 REFERENCES 70

7 v 8. CASE STUDIES 81 81 Case Study A. World Weather Attribution Case Study B. Using Catastrophe Models to Assess Future Risk 86 Case Study C. Sinking Cities: An Integrated Approach to Solutions 90 Case Study D. The Evolving Risk of Earthquakes: Past, Present, and Future 101 Case Study E. Changing Earthquake Vulnerability Linked to Informal Building Expansion 109 Case Study F. An Interrelated Hazards Approach to Anticipating Evolving Risk 114 Case Study G. Evolution of Risk in Eastern Europe and Central Asia 122 Case Study H. Open Data and Dynamic Understandings of Risk 129 Case Study I. Science Influencing Land-Use Policy: A Story from New Zealand 135

8

9 vii Foreword Tomorrow’s risk is being built today. We must therefore move away from risk assessments that show risk at a single point in the present and move instead towards risk assessments that can guide decision makers towards a resilient future. atural disasters can have truly global impacts. this goal, we need to strengthen policies and actions that There is evidence that approximately 75,000 years enable us to support larger populations, increased asset ago, after the Toba volcano erupted in Sumatra, N wealth, and more urbanized countries without increased Indonesia, a global volcanic winter may have decimated disaster risk. the global human population to just several thousand. Tomorrow’s risk is being built today. We must therefore Since then, natural hazards have frequently affected move away from risk assessments that show risk at a communities on scales large and small, but civilization as single point in the present—which can quickly become a whole is more likely to survive a catastrophe today than outdated—and move instead towards risk assessments ever before. That is the good news. that can guide decision makers towards a resilient future. The disturbing news is that the impacts of natural Only then will they be able to visualize the potential risk that results from their decisions taken today, and see the disasters have been growing rapidly due to global benefit of enacting policies to reduce climate change, population growth, urbanization and increased halt the construction of unsafe buildings, enforce land socioeconomic activity—with a tenfold increase in losses use plans, reduce subsidence, and more. from disasters since the 1970s. Moreover, these numbers have yet to incorporate the real impact of climate We have more than 75,000 years of experience living change. By the end of the century, coastal areas will see with disasters, but today’s challenges demand that we do more frequent and intense inundation due to sea level things differently. We must continually learn, innovate, rise, and changes in rainfall patterns will trigger more and push boundaries, so that we can build a safer world frequent droughts and floods, putting many lives and for ourselves and the generations to come. livelihoods in jeopardy. In 2015, world leaders made a commitment in Sendai, Japan to reduce the number of people affected, the direct Francis Ghesquiere economic loss, and the damage to critical infrastructure Head, Global Facility for Disaster Reduction and basic services from disasters by 2030. To achieve and Recovery FACING PAGE Neena Sasaki, 5, carries some of the family belongings from her home that was destroyed after the devastating earthquake and tsunami on March 15, 2011 in Rikuzentakata, Miyagi province, Japan. Photo credit: Paula Bronstein/Thinkstock.com

10 viii / Making a riskier future: How our decisions are shaping future disaster risk

11 ix Acknowledgments his publication was prepared by a team comprising College London Hazard Centre); Gilles Erkens (Deltares Stuart Fraser, Brenden Jongman, Simone Balog, Research Institute); Alexandra Guerrero (RMS); David Alanna Simpson, Keiko Saito, and Anne Himmelfarb. T Karoly (ARC Centre of Excellence for Climate System Science, University of Melbourne); Christopher Kilburn Valuable review of the publication was provided by (University College London Hazard Centre); Andrew King Rashmin Gunasekera (World Bank), Stéphane Hallegatte (ARC Centre of Excellence for Climate System Science, (World Bank), Federica Rangieri (World Bank), and University of Melbourne); Anne Kiremidjian (Stanford Maarten van Aalst (Red Cross/Red Crescent Climate University); David Lallemant (Stanford University); John Centre; International Research Institute for Climate and Lambert (Deltares Research Institute); Catherine Linard Society). (Université Libre de Bruxelles); Hiro Miyazaki (University of Tokyo); Richard Murnane (Global Facility for Disaster Case study contributors include Axis Maps LLC; James Reduction and Recovery [GFDRR]); Geert Jan van Beban (GNS Science); Tom Bucx (Deltares Research Oldenborgh (Royal Netherlands Meteorological Institute); Institute); Zach Bullock (Stanford University); Henry Friederike Otto (Environmental Change Institute, Burton (Stanford University); Luis Ceferino (Stanford University of Oxford); Wendy Saunders (GNS Science); University); Erin Coughlan de Perez (Red Cross/Red Roop Singh (Red Cross/Red Crescent Climate Centre); Crescent Climate Centre; Institute for Environmental Dina Sperling (Climate Central); Robert Soden (GFDRR); Studies, VU University; International Research Institute Annegien Tjissen (GFDRR); Joaquin Toro (GFDRR); John for Climate and Society); Kate Crowley (National Institute Twigg (Centre for Urban Sustainability and Resilience, of Water and Atmospheric Research Ltd.); Heidi Cullen University College London); Maarten van Aalst (Red (Climate Central); Rien Dam (WaterLand Experts); Cross/Red Crescent Climate Centre; International James Daniell (Karlsruhe Institute of Technology); Research Institute for Climate and Society); Philip J. Ger de Lange (Deltares Research Institute); Alison Ward (IVM); Paul Wilson (RMS); and Hessel Winsemius Dobbin (RMS); Melanie Duncan (University College London Hazard Centre); Stephen Edwards (University (Deltares Research Institute). FACING PAGE Quickscat image showing the direction and intensity of surface winds across the Atlantic Ocean. Photo credit: Stocktrek Images

12 x Abbreviations average annual loss AAL Atlantic Multidecadal Oscillation AMO ARC African Risk Capacity AR5 Fifth Assessment Report BRT boosted regression tree CMIP5 Coupled Model Intercomparison Project Phase 5 Caribbean Catastrophe Risk Insurance Facility CCRIF DEM digital elevation model DLR German Aerospace Center DMSP Defense Meteorological Satellite Program driving forces, pressures, state, impacts, and responses DPSIR disaster risk management DRM disaster risk reduction DRR Europe and Central Asia ECA ENSO El Niño–Southern Oscillation global climate model or general circulation model GCM GDP gross domestic product Global Earthquake Model GEM Global Facility for Disaster Reduction and Recovery GFDRR Global Human Settlement Layer GHSL GIS geographical information system Global Flood Risk with IMAGE Scenarios GLOFRIS Global Positioning System GPS G-R Gutenberg-Richter Human Development Index HDI HOT Humanitarian OpenStreetMap Team IDA Incremental Dynamic Analysis IMAGE Integrated Model to Assess the Global Environment interferometric synthetic aperture radar InSAR IPCC Intergovernmental Panel on Climate Change ISI-MIP Inter-Sectoral Impact Model Intercomparison Project LIDAR Laser Imaging Detection and Ranging

13 xi MASDAP Malawi Spatial Data Portal Modified Mercalli Intensity MMI North Atlantic Oscillation NAO NGO nongovernmental organization OECD Organisation for Economic Co-operation and Development Operational Linescan System OLS OpenDRI Open Data for Resilience Initiative OpenStreetMap OSM Pacific Catastrophe Risk Assessment and Financing Initiative PCRAFI PGA peak ground acceleration RCM regional climate model RCP Representative Concentration Pathway RMA Resource Management Act 1991 SAR synthetic aperture radar Special Report on Emissions Scenarios SRES SSP Shared Socioeconomic Pathway SuDS sustainable drainage systems STRM Shuttle Radar Topography Mission TCIP Turkish Catastrophe Insurance Pool VIIRS Visible Infrared Imaging Radiometer Suite URM unreinforced masonry WWA World Weather Attribution

14 xii / Making a riskier future: How our decisions are shaping future disaster risk

15 Making a riskier future: How our decisions are shaping future disaster risk / xiii Executive Summary Key messages from this report: Most disaster risk assessment today is static, focusing only on ■ ■ understanding current risks. A paradigm shift is needed toward dynamic risk assessments, which reveal the drivers of risk and the effectiveness of policies focused on reducing risk. ■ Global disaster risk is changing extremely fast, due to ■ combined dynamics of hazard, exposure, and vulnerability. ■ ■ The drivers of disaster risk are in the control of policy makers, society, and individuals—but accurate assessment and continuous reevaluation of risk are required to enable effective risk reduction and prevent drastic increases in future losses. NEPAL Partially collapsed house after the 7.8 earthquake hit Nepal on 25 April 2015. Photo credit: © Thomas Dutour | Dreamstime.com

16 xiv / Executive Summary There is variability in annual losses and deaths from disasters, but annual total damage (averaged over a 10-year period) has increased tenfold between 1976–1985 and 2005–2014, from US$14 billion to more than US$140 billion. influence on flood hazard than natural hazard (vulnerability). Disaster risks are rapidly increasing around the world: many regions sea-level rise; the former occurs All three of these components are experiencing greater damage at a rate of up to 100 mm/year, in are dynamic, and change over and higher losses than in the past. comparison with up to 10 mm/year time under natural and human There is variability in annual losses for the latter (Erkens et al., case influences (figure ES.1). But most and deaths from disasters, but study C). risk assessments do not account annual total damage (averaged over for these changes, so they provide Exposure increases as population a 10-year period) has increased a static view of risk. As a result, grows in hazardous areas, and as tenfold between 1976–1985 and risk management policy decisions improved socioeconomic conditions 2005–2014, from US$14 billion based on such assessments do not raise the value of assets. Between to more than US$140 billion. take into account the continuous 2010 and 2050, estimated global Average population affected each and sometimes rapid changes population exposed to river and year has risen from around 60 in the drivers of risk and so may coastal flood is expected to increase million people (1976–1985) to underestimate risk. 1 from 992 million to 1.3 billion over 170 million (2005–2014). (Jongman, Ward, and Aerts 2012). Changes in hazard are driven by Disaster risk is influenced by Average annual GDP at risk of climate change, which raises sea the occurrence of potentially earthquakes in Turkey is expected to levels, changes the intensity of the dangerous naturally occurring increase by five times between 2010 strongest storms and the frequency events, such as earthquakes or tropical cyclones (hazard); the and 2080 due to socioeconomic with which they occur, increases population and economic assets growth (Murnane et al., case study extreme temperatures, and alters located in hazard-prone areas G). Urbanization—encompassing patterns of precipitation. Global (exposure); and the susceptibility both the movement of people from sea-level rise of up to 0.6 m this of the exposed elements to the rural to urban areas and population century will increase disaster growth within cities—results in risk significantly in coastal areas. 1 D. Guha-Sapir, R. Below, and larger concentrations of exposure. In addition, subsidence (sinking Ph. Hoyois, EM-DAT: International In Indonesia, river flood risk may land) will increase the likelihood Disaster Database, www.emdat.be, increase 166 percent over the next of flooding locally. In some coastal Université Catholique de Louvain, 30 years due to rapid expansion megacities subsidence has a greater Brussels, Belgium, accessed July 2015. Climate + = + + change

17 Making a riskier future: How our decisions are shaping future disaster risk / xv increase risk, we can positively (Lallemant et al., case study D). of urban areas, and coastal flood influence the risk environment of Social vulnerability also changes risk may increase 445 percent over the future. The drivers of future over time, influenced by the that same period (Muis et al. 2015). risk are within the control of occurrence of disasters, which Population is expected to increase decision makers today: there is a disrupt lives and livelihoods, and by at least 40 percent in 14 of the huge opportunity today to manage by the effects of climate change, 20 most populated cities in the the risks of tomorrow. Climate which could push over 100 million world between 2015 and 2030, with change mitigation by reduction additional people back into poverty some cities growing by 10 million of greenhouse gases remains key by 2030 (Hallegatte et al. 2015). people in that period. Many of the to preventing strong increases in largest cities are located in deltas Increased exposure and changes climate-related hazard. In addition, and are highly prone to floods and in vulnerability have already a robust hazard protection strategy, other hazards (Hallegatte et al. affected disaster risk. A large one that includes ecosystem-based 2013), and as these cities grow, an proportion of recent increases in measures, can help to limit the harm ever greater number of people and disaster losses are attributed to caused by changes in frequency more assets are at risk of disaster. development occurring in hazardous and intensity of hazard. Increases Another feature of urban expansion, areas (Bouwer et al. 2007). in exposure can be addressed the increase in impermeable Concentrations of greenhouse by implementing and enforcing surfaces, also directly affects flood gas in the atmosphere have risen effective land-use policies that hazard. in recent decades due to human prevent urban expansion in hazard- activity, and recent years have Vulnerability too changes with prone areas. Finally, increases in seen extreme temperatures, and urban and socioeconomic vulnerability can be addressed extremely damaging floods and development. Some people by strengthening construction cyclones. However, the changes become less vulnerable because practices and improving disaster observed so far are difficult to of improved construction and preparedness. All these policy separate from natural variations in a more prosperous economic measures rely on data and risk climate, and the greatest changes situation. But in many areas, modeling: enhancements in data in climate extremes are projected structural vulnerability continues collection and risk assessment to occur in the coming decades, to increase because of unregulated are therefore a crucial part of the meaning it may be several decades building practices and unplanned policy-strengthening process. before the full effects of climate development. For example, Disaster risk assessment—vital change are felt. Decisions being earthquake risk in Kathmandu for understanding risk in terms of taken today are influencing future (measured as the proportion expected population affected or disaster risk—either reducing risk of buildings that collapse in an losses incurred—underpins disaster or increasing it. By promoting earthquake) is expected to double risk management activities. In policies that reduce risk and to 50 percent by 2045 due to order to make policy and planning avoiding maladaptive actions that informal building expansion alone Hazard Exposure Vulnerability Population and assets Natural phenomena Structural and social

18 xvi / Executive Summary future climate conditions. With is possible to adjust estimates of decisions that reduce future risk, improvements in data collection, structural vulnerability to reflect present and future risk must be we can obtain higher-resolution projected changes in construction, quantified. Thus risk assessments topographic and exposure but the many interdependent factors that inform disaster risk data and can simulate trends that determine social vulnerability management must account for the in population movement and make it difficult to determine how dynamic nature of hazard, exposure, urbanization. At this stage, it is social vulnerability will evolve into and vulnerability. By quantifying important both to review the range the future. future risk with and without the of efforts to quantify future risk, effect of disaster risk management Despite the ability to quantify future and to consider how to best apply policies and comparing the results, risk (albeit with uncertainty), risk this information in managing risk management specialists can assessments typically fail to account risk. This publication provides demonstrate how policy actions for changing climate, population, an introduction to the problem taken now and in the near future urbanization, and environmental of evolving risk (chapter 1), a could affect the risk environment in conditions. They thus reduce the further background to disaster risk the medium to long term. opportunity to highlight long-term, (chapter 2), and an overview of cost-effective options for risk Evolving hazard can be captured in the factors driving the evolution reduction. This is not due to an disaster risk assessment through the of risk (chapters 3 to 5). Chapter absence of appropriate methods; implementation of climate change 6 discusses some of the issues many risk assessment tools and scenarios in global and regional that complicate efforts to quantify methods exist, with differing climate models. This approach makes evolving risk, and chapter 7 complexity, and can be used to it possible to incorporate changes in discusses a number of policy represent the evolution of risk intensity and frequency of extreme areas that can strongly affect if adequate data are available. wind, temperature, and precipitation, future disaster risk. This chapter Risk assessments most often fail along with sea-level rise, to project highlights steps that can be taken to account for evolution of risk future flood, drought, cyclone, heat, to mitigate the ongoing increase because they use information that and storm surge risk. Simulating the in risk and—like the publication as represents risk factors at a single expansion of urban areas, projecting a whole—seeks to raise awareness time point in the past, and do not future population distribution, and among decision makers of the include projections of those data implementing Shared Socioeconomic impacts planning and development into the future. Pathways (SSPs) as scenarios of decisions have on disaster risk. future socioeconomic conditions The report concludes with a Advances in the risk management can be carried out to demonstrate series of studies that highlight, sector and relevant technologies the influence of changing exposure in more depth, some of the issues mean that risk specialists are on disaster risk. Projection of future and approaches described in the now better able than in the past vulnerability has not been addressed earlier chapters. to focus on assessing risk under extensively in risk assessments. It Risk assessments need to account for... Future environmental Changing Rapid Population conditions climate urbanization increase

19 Making a riskier future: How our decisions are shaping future disaster risk / xvii Figure ES.1. The result of our choices Factors affecting the three components of disaster risk can increase future risk (top) or reduce (or mitigate increase in) future risk (bottom). A RISKIER FUTURE Warmer climate ■ ■ ■ ■ Larger population Sinking coastal land More developed ■ ■ ■ ■ hazardous areas ■ ■ Environmental More impermeable ■ ■ degradation Exposure surfaces Hazard Present Risk Future Risk Vulnerability ■ ■ ■ ■ Less social support Informal ■ More compounding ■ shocks/impacts construction AN EQUALLY/LESS RISKY FUTURE ■ ■ ■ Climate change Land-use planning ■ mitigation ■ ■ Managed urban ■ ■ Urban design expansion ■ Resource planning ■ Exposure Hazard Vulnerability Social safety nets ■ Urban planning/ ■ ■ ■ ■ Greater resilience ■ construction

20 xviii / Making a riskier future: How our decisions are shaping future disaster risk

21 1 Introduction 1 ollowing the adoption of the Sendai Framework for Disaster Risk Reduction , the disaster risk management (DRM) sector seeks to build on 2015–2030 progress made under the Hyogo Framework for Action and to tackle the F continued increase in annual disaster losses over the last decades. The goal of the framework is to prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience (United Nations 2015, 6, emphasis added). Adaptation and risk It is well known that disaster risk is subject to change in its underlying (the potentially dangerous naturally occurring hazard components: the management policies and (the population exposure event, such as an earthquake or tropical cyclone), practices will be more and economic assets located in hazard-prone areas), and vulnerability (the susceptibility of the exposed elements to the natural hazard) (IPCC 2012). successful if they take In an environment of rapid urbanization, population growth, unplanned the dynamic nature of development, unsafe building practices, and changing climate, investment in vulnerability and exposure and design of disaster risk management activities must account for changes in the nature of hazard, exposure, and vulnerability. As the Intergovernmental into account. adaptation and risk management policies Panel on Climate Change asserts, “ and practices will be more successful if they take the dynamic nature of vulnerability and exposure into account” (IPCC 2012, 67). FACING PAGE Bukittinggi, Sumatra, second largest city in West Sumatra. It is located near the Mount Singgalangand and Mount Marapi volcanoes. Photo credit: ElenaMirage/ Thinkstock.com. 1

22 2 / 1. Introduction Risk . It first disaster risk assessments reduction Preparedness describes the nature of evolving hazard, exposure, and vulnerability, and then reviews the extent to which disaster risk assessments Risk Territorial actually incorporate evolving risk. It Risk assessment planning identification highlights methodologies that have been used to include evolving risk in assessments—and in doing so highlights how the future riskscape Financial Resilient looks for a range of perils. The reconstruction protection report also points to current gaps in assessment of evolving disaster usually act on the time scale of Information on future disaster risk risk and makes recommendations a single year; and engineered is essential for improving resilience on how to take risk evolution solutions may act over a typical to extreme weather events (Royal into account going forward. The design lifetime of around 50 Society 2014) and indeed to any second part of the publication years. These long-term structural, natural hazard . The post-2015 presents case studies that highlight infrastructural, and programmatic Sendai Framework encourages DRM particular issues for evolving risk investments are inherently likely to to take account of future risks: and showcase methodologies for be affected by changes in disaster assessing it. It is urgent and critical to risk that arise from future changes anticipate, plan for and act on in environmental, social, and risk scenarios over at least the economic conditions. next 50 years to protect more Box 1.1 It should be said explicitly that effectively human beings and Keep Abreast in order to promote the utility of their assets, and ecosystems of Evolving Risk DRM programs into the future and (United Nations 2015, 3). “Risk assessments need to to assess the benefits of current account for temporal and spatial Disaster risk assessment informs decisions on future risk, disaster changes in hazard, exposure, risk identification, risk reduction, risk assessments must be able and vulnerability, particularly in preparedness, territorial planning, rapidly urbanizing areas or where to quantify future risk both with financial protection, and resilient climate change impacts will be felt and without the effects of DRM reconstruction. Assessments the most. A risk assessment that policies. The ability to compare provides an estimation of evolving provide the basis for disaster risk the two sets of results will allow or future risk is a way to engage management and decision making risk management specialists to stakeholders in carrying out actions in multiple sectors by quantifying how policy actions demonstrate now in order to avoid or mitigate the effects of disasters in terms the risk that is accumulating in taken now and in the near future of potential casualties and asset their city or country. For example, could affect the risk environment losses. The wide selection of tools, risk analysis offers an opportunity of the mid- to long-term future . By to quantify the decrease in future policies, and programs available to promoting actions that reduce risk risk that arises from better manage disaster risk all depend on avoiding maladaptive actions and enforcement of building codes, and the accurate assessment of current we can positively that increase risk, hence to demonstrate the benefit and future risk, over a range of time of spending additional funds on influence the risk environment of scales. Risk management policies building code enforcement.” the future (see box 1.1). and actions may be required to Source: GFDRR 2014, 29. This publication focuses on the act over multi-decade time scales; incorporation of evolving risk into risk transfer products (insurance)

23 Making a riskier future: How our decisions are shaping future disaster risk / 3 Nepal, 2015 earthquake. Photo credit: © Mumbaiphoto | Dreamstime.com On November 12, 2012, the Visible Infrared Imaging Radiometer Suite Coquimbo, Chile, 2015 earthquake. Photo credit: Wikimedia Commons (VIIRS) on the Suomi NPP satellite captured city, village, and highway lights in India. Photo credit: NASA

24 4 / Making a riskier future: How our decisions are shaping future disaster risk

25 5 Disaster Risk 2 isaster risk is a function of three interlinked components: hazard, exposure, and vulnerability. Hazard refers to the likelihood and intensity of a potentially destructive natural phenomenon, such as D ground shaking induced by an earthquake or extreme winds associated with a cyclone. Exposure refers to the location, attributes, and value of people and assets (such as buildings, agricultural land, and infrastructure) that are exposed to the hazard. Vulnerability is the potential extent to which physical, social, economic, and environmental assets may become damaged or disrupted when exposed to a hazard event. Vulnerability includes physical vulnerability, which refers to the level of damage sustained by built structures due to the physical load imparted by a hazard event. It also includes social vulnerability (also termed “socioeconomic vulnerability” or “socioeconomic Disaster risk is a resilience”), which refers to damage as it relates to livelihood, social connections, gender, and other factors that influence a community’s ability function of three to respond to, cope with, and recover from a disaster. Social vulnerability interlinked components: can affect the number of casualties, the loss or disruption sustained, and a community’s subsequent recovery time. hazard, exposure, and vulnerability. Disaster risk evolves spatially and temporally in response to changes in one or more of these components, and to the inherent interactions between them— i.e., changes in one factor can influence the other factors. The influences on disaster risk include climate, development, and risk management (figure ES.1). Over time, disaster risk may increase or decrease, and it may evolve differently at the local, regional, national, and global scales. Indeed, risk rarely evolves uniformly in a community or region; it often increases FACING PAGE Landslide and flood risk in Phong Nha, Vietnam. Photo credit: Simone Balog/World Bank 5

26 6 / 2. Disaster Risk Changes in hazard may arise from natural variability or human influences. The latter are particularly important for changes in hydrometeorological hazards, which are driven in large part climate change, changing land surface types, and altered ground elevation. Reducing the hazard involves Changes in exposure, on the other most with respect to particular reducing the frequency or intensity types of assets, or for sectors hand, are driven by socioeconomic of the event. This is done by of the population with greatest development. Globally, exposure building protective systems (e.g., vulnerability. Thus poor residents to natural hazards is increasing; increasing river channel flood living on unstable hillsides or in economic progress is driving capacity so that a greater volume flood hazard zones are especially population growth and raising the of water is contained before susceptible to increases in disaster value of physical assets. Thus more spilling over onto adjacent land), risk arising from more frequent and people and economic assets are now and by avoiding environmental intense rainfall in a future climate. exposed to the potential impacts of degradation (e.g., deforestation) disasters than in the past, and this Changes in hazard may arise that can increase hazard. Reducing trend is expected to continue. from natural variability or exposure (or preventing future human influences. The latter are Vulnerability evolves as a result increases in exposure) might particularly important for changes in of decisions made during the involve changing land-use zoning hydrometeorological hazards, which development process—or in to restrict new construction in are driven in large part by climate the absence of effective policy hazardous areas or to manage the change. As global temperature change making. Like changes in exposure, retreat of existing development to influences the frequency, severity, changes in vulnerability occur safer areas. Reducing vulnerability and seasonal patterns of hand-in-hand with socioeconomic involves structurally strengthening precipitation and monsoon events, change. Appropriate investment existing buildings or complying with regional changes occur in flood, of increased wealth can reduce building codes to ensure that future drought, and heat wave hazards vulnerability, while the absence construction can better withstand (see case study A). Climate change of construction guidelines can damage from extreme winds, water is likely to affect the frequency increase it, for example by enabling ingress, or ground shaking. and severity of tropical cyclones, informal construction of buildings Disaster risk evolves in response to extratropical cyclones, river floods, that may be highly susceptible policy decisions (or their absence), and storm surges. Rising sea levels to damage from earthquakes. and some policy decisions can associated with ice-sheet melt Disasters themselves can increase inadvertently increase disaster and thermal expansion of ocean vulnerability, because they often risk by encouraging development waters will contribute to increased leave communities with reduced in hazardous areas or allowing coastal flooding and storm surge access to resources or shelter. practices that increase vulnerability. hazard. Changing land surface types Disaster risk management operates Such decisions often result from (through urban development and by reducing one or more of the neglecting to consider risks in deforestation) and ground elevation disaster risk components in order planning or decision-making (through groundwater extraction) also processes. to reduce disaster risk overall. affect hydrometeorological hazards.

27 Making a riskier future: How our decisions are shaping future disaster risk / 7 KIRIBATI Building Seawalls. Photo credit: Lauren Day/World Bank

28 8 / Making a riskier future: How our decisions are shaping future disaster risk

29 Making a riskier future: How our decisions are shaping future disaster risk / 9 Drivers of Evolving 3 Disaster Risk: Hazard he evolution of hazard is felt through changes in the geographic distribution of potentially damaging events, as well as changes in the frequency and intensity of these events. The cause of these changes is T hazard-dependent. Human activity influences hydrometeorological hazards by altering conditions in the oceanic and atmospheric systems, primarily through emission of greenhouse gases. The changes in these systems manifest as changes in global temperature, rainfall patterns, and mean sea level, which influence wind, flood, drought, heat, and wildfire hazards. The evolution of hazard also involves interactions between hazards. Changing rainfall patterns, for example, influence soil stability, whicj in turn influences landslide hazard and have a further impact on flood hazard. The evolution of hazard is Hydrometeorological hazards felt through changes in the The most commonly considered example of evolving hazard is the effect of geographic distribution climate change on hydrometeorological hazards. Globally, the climate is of potentially damaging becoming warmer. Annual global temperature has shown an increasing trend over the last 130 years (figure 3.1), and all of the 10 warmest years on record events, as well as changes since 1880 have occurred since 1998 (NOAA National Climatic Data Center in the frequency and 2014). Changing climate has been linked to changes in the characteristics of disasters: “A changing climate leads to changes in the frequency, intensity, intensity of these events. spatial extent, duration and timing of extreme weather and climate events, and can result in unprecedented extremes” (IPCC 2012, 111). The various changes in risks resulting from those changes are described in box 3.1. Research into the mechanisms and risks of changing climate shows that disaster risk has been affected already. FACING PAGE Extratropical cyclone over the United Kingdom. February 16, 2014. Photo credit: NASA Earth Observatory image by Jesse Allen 9

30 10 / 3. Drivers of Evolving Disaster Risk: Hazard Temperature time series for land only, ocean only, and combined Figure 3.1. Tropical cyclone land and ocean. Temperature scale is relative to the average global occur in several Tropical cyclones temperature across the duration of the time series. regions, and are known as typhoons Annual Global Temperature (Land, Ocen, and Combined) in the western Pacific Ocean, hurricanes in the eastern Pacific and North Atlantic Oceans, and cyclones in the Indian and South Pacific Oceans (Figure 3.2). While there is very high confidence in short-term trends in tropical cyclone activity in some regions, long-term trends are more uncertain. Nonetheless, degrees Celsius projected warming in the 21st century is expected to result in continued increase in frequency of the most intense storms (Stocker et al. 2013). Tropical cyclones are known to occur in clusters of activity, characterized by the sea surface Source: NOAA National Climatic Data Center 2014. and wind conditions in their region of formation, trajectory of or “oscillations.” These circulation to form and sustain their energy. movement, and landfall intensity. patterns vary naturally as well as Because of each cluster’s varying As well as being spatially clustered, in response to changes in climate characteristics and locations, cyclones show strong seasonality conditions, meaning that they affect cyclone activity in each cluster is and occur in temporal clusters when cyclone activity in each cluster in a related to different atmospheric different way. Year-to-year cyclone and oceanic circulation patterns, conditions are suitable for them Box 3.1 Risks of Climate Change The list below indicates how some of the risks associated with extreme weather and climate-related hazards will evolve as a result of climate change. The Intergovernmental Panel on Climate Change (IPCC) has “high confidence” in each of these risks, which arise due to warming, extreme temperatures, drying trends, and extreme precipitation. ■ ■ Negative impacts on average crop yields and increases in yield variability, leading to volatility in food security ■ ■ Urban risks associated with water supply systems, energy, and housing Displacement of people with increased climate extremes ■ ■ ■ Declining work productivity, increasing morbidity (e.g., dehydration, heat stroke, and heat exhaustion), and ■ mortality from exposure to heat waves Reduced access to water for rural and urban poor people due to water scarcity and increasing competition for water ■ ■ Source: Field et al. 2014, table TS.4.

31 Making a riskier future: How our decisions are shaping future disaster risk / 11 Figure 3.2 Regional distribution of tropical cyclone occurrence and intensity. Regional terms are denoted as abbreviations: CY = cyclone; TY = typhoon; and HU = hurricane. HU TY HU CY CY CY Based on earthobservatory.nasa.gov. Source: estimated that an 18 percent activity in the Pacific is strongly and Bruyère 2014). The increase increase in intensity would cause in the proportion of high-intensity affected by fluctuations in sea a 64 percent increase in damage. cyclones is expected to impact surface temperature due to the El And using an existing catastrophe losses significantly. Several studies Niño Southern Oscillation (ENSO); model framework, the Association suggest that, based on empirical and in the North Atlantic it is of British Insurers (2005) estimated relationships between wind speed affected by the Atlantic Multidecadal that average annual loss (AAL) and loss, future increase in losses Oscillation (AMO). Abrupt changes might increase by 45–118 percent will occur at a proportionally in such circulation patterns can in the United States and 40–100 greater rate than changes in storm cause rapid increase or decrease in percent in Japan in response to just activity, independent of exposure hazard from year to year or across a a 4–9 percent increase in hurricane change. Murnane and Elsner (2012) period of several years. There is low wind speeds. demonstrated an exponential confidence in projected changes to relationship between cyclone wind ENSO in the 21st century because Evolution in cyclone hazard is not speed at landfall and normalized the range in projection across limited to an increase in intensity in economic loss, in which loss climate models is wide (Stocker et areas already affected by cyclones. increases by 5 percent for every al. 2013). Changes in climate have caused 1 m/s increase in wind speed. spatial shifts in cyclone tracks, The intensity and frequency of the Based on the rate of increasing which effectively move the hazard most extreme tropical cyclones storm strength (0.1 m/s/y) (Elsner, into new areas. For example, have increased in the North Atlantic Kossin, and Jagger 2008), this such spatial shifts have resulted since 1980 (Kossin et al. 2007), relationship points to a 5 percent in increased landfall intensity of and some data show the same increase in cyclone loss over 10 cyclones in East Asia (Park, Ho, and trend for all basins globally—that years, independent of exposure Kim 2014). is, an increase in the proportion change. Based on a relationship of Category 4 and 5 cyclones and Cyclone-associated storm surge between maximum landfall wind a decrease in the proportion of hazard is directly influenced by speed and normalized loss from change in cyclone activity, but U.S. hurricanes, Pielke (2007) Category 1 and 2 cyclones (Holland

32 12 / 3. Drivers of Evolving Disaster Risk: Hazard Regional distribution of extratropical cyclone occurrence. Figure 3.3. Source: Based on www.giss.nasa.gov. in hurricane frequency account for a also by sea-level rise. Mousavi et evolution in hazard. Extratropical cyclones are most frequent and greater proportion of loss (see case al. (2011) demonstrated that peak intense over northern Europe when study B). hurricane storm surge heights would there is a positive NAO, that is, a rise by 0.3 m by the 2030s and by stronger than average pressure Extratropical cyclone 0.8 m by the 2080s for a portion difference. of the coastline of Texas; this Extratropical cyclones are a type analysis was based on sea-level rise, Clustering of European extratropical of storm system formed in regions increased sea surface temperatures, cyclones occurs due to a prevalence of large horizontal temperature and hurricane intensity (landfall of suitable atmospheric conditions, variations in middle or high latitudes. pressure), all derived from climate some of them relatively poorly They stand in contrast to the more 1 modeling of three SRES scenarios, understood, in which multiple storm violent tropical cyclones, which as well as on local subsidence. systems form and are directed into form in regions of relatively uniform An analysis of hurricane loss by the same area by strong winds temperatures. Short-term evolution Rhodium Group LLC (2014) used such as the jet stream. Short-term of extratropical cyclone risk occurs projected change in hurricane evolution in hazard is brought because extratropical cyclones are frequency and intensity plus the about by these varying conditions. strongly seasonal; there is temporal impact of sea-level rise to show that The clustered windstorms can clustering of multiple storms when annual losses in the United States result in repeated damage in some large-scale atmospheric conditions (East Coast and Gulf of Mexico only) areas, with the potential for very are most suitable for storm formation could rise by as much as US$62 high losses during a single cyclone and propagation. The North Atlantic billion to US$91 billion by the end season. Oscillation (NAO)—the difference of the century compared to present in sea level pressure between The expected impact of climate day. This study demonstrated that northern and southern regions in change on extratropical cyclones as we look into the future, changes the North Atlantic Ocean—has a appears to vary. There has been no strong influence on extratropical clear upward trend in extratropical 1 The SRES scenarios are those from cyclone frequency, intensity, and cyclone activity in the North Atlantic Special Report on Emissions the IPCC’s Scenarios (Naki ́cenovi ́c et al. 2000). track position, causing short-term basin (Leckebusch et al. 2007), but

33 Making a riskier future: How our decisions are shaping future disaster risk / 13 there have been increases in the melting of ice sheets and glaciers, South Atlantic–Indian Ocean basin thermal expansion of seawater, and Sea-level rise is a major and decreases in the South Pacific change in liquid water storage on source of evolving hazard, (Wang et al. 2013). To illustrate land. Very few coastlines around the resulting in more frequent the impact that potential increases world will avoid the effects of sea- in extratropical cyclone intensity level rise; sea levels are expected and severe coastal could have on insured losses in to rise in more than 95 percent of flooding. Europe, the Association of British the ocean area (although there will Insurers (2005) determined that a be regional and local variation in 20 percent increase in wind speed magnitude). The global increase in regional trends in timing, severity, for the top 5 percent of European flood hazard, along with the coastal and geographical distribution of extratropical cyclones could lead location of significant populations extreme flood events (table 3.1). to a 35 percent increase in AAL. and assets, makes this evolving The steeply rising trend in global In a future climate, the tracks of hazard an especially important flood losses over the past decades, Southern Hemisphere and North one for disaster management and however, has primarily been driven Pacific extratropical cyclones are climate adaptation to address. by increasing exposure. Various expected to shift toward the poles, In combination with increased analyses of historical loss databases but such a shift is less likely in the tropical cyclone hazard, sea-level have not yet been able to derive a North Atlantic (Stocker et al. 2013). rise contributes to an increase in clear signal of climate change in The large natural variability in NAO frequency and intensity of storm these increasing losses (Kundzewicz means that any changes detected surge. Moreover, subsidence due to et al. 2014; Visser, Petersen, and in the strength of the NAO have not groundwater extraction and coastal Ligtvoet 2014). There is a strong been attributed to climate change, erosion has a profound effect on relationship between river flooding and there are no robust conclusions the relative elevation of land and and interannual climatic variability, on how this circulation pattern (and sea, and thus alters coastal flood such as that associated with El resulting impact on extratropical hazard. In some locations, the rate Niño and La Niña, which influences cyclone) is likely to change in future of decrease in land elevation from flooding in river basins covering due to climate change. subsidence is greater than the rate almost half of the earth’s surface of increase in water levels from sea- (Ward et al. 2014). Flooding level rise (Erkens et al., case study C). Increased sea levels may also Individual studies do suggest Both coastal and river flood hazard contaminate agricultural land and meaningful changes in flood hazard, are dynamic and evolve over time. water supplies with saline water, although the results from any one Sea-level rise is a major source as seawater infiltrates into coastal climate model may predict an of evolving hazard, resulting in aquifers. increase or decrease (Hirabayashi coastal more frequent and severe et al. 2013). They suggest that flood is influenced by River flooding . Between 1901 and 2010, flooding frequency is likely to increase in changes in rainfall patterns, which the global average sea-level rise as much of South America, central may be affected by natural cycles recorded using tidal gauges totaled Africa, and East and Southeast Asia such as El Niño as well as long-term an estimated 19 cm (Church et al. in the period 2071–2100 compared climate change. There is significant 2013). Global mean sea-level rise to 1971–2000. Meanwhile, southern at 2100 is likely to be 0.28–0.61 m natural variability in patterns of South America, southern and above mean sea level in the period river flooding, and low confidence in Eastern Europe, and Central Asia are 1986–2005, even if climate policies any global trend in flood magnitude likely to experience decreased flood are effective in reducing greenhouse and frequency in the historical frequency. Based on a fixed (2005) gas emissions from 2020 (Church record (Stocker et al. 2013). There et al. 2013). Sea levels rise due to are varying degrees of confidence in population distribution, an increase

34 14 / 3. Drivers of Evolving Disaster Risk: Hazard Table 3.1. IPCC Summary of Observed Regional Changes in Flood Extremes Region Description (degree of confidence, contribution from climate change) Africa Reduced discharge in West African rivers (low confidence, major contribution from climate change) Europe Changed occurrence of extreme river discharges and floods (very low confidence, minor contribution from climate change) Asia Increased flow in several rivers due to shrinking glaciers (high confidence, major contribution from climate change) Earlier timing of maximum spring flood in Russian rivers (medium confidence, major contribution from climate change) Reduced inflow in river systems in southwestern Australia (since the mid-1970s) (high confidence, major Australasia contribution from climate change) Shift to earlier peak flow in snow-dominated rivers in western North America (high confidence, major North America contribution from climate change) Increased runoff in the western and northeastern United States (medium confidence, minor contribution from climate change) Changes in extreme flows in Amazon River (medium confidence, major contribution from climate change) Central/South America Changing discharge patterns in rivers in the western Andes (medium confidence, major contribution from climate change) Increased streamflow in subbasins of the La Plata River, beyond increase due to land-use change (high confidence, major contribution from climate change) Source: Field et al. 2014, table TS. 1. of between four and 14 times faster than in a natural catchment, Sinking ground/subsidence current flood-exposed population is where the water infiltrates and Another important factor in evolving projected. flows through the ground to reach flood hazard is the reduction the river channel. The large amount Land-use change affecting in ground elevation caused by of flow reaching the channel at hazard subsidence. Subsidence may occur once makes it more likely that naturally, due to earthquakes or River and flash flood hazard in the channel will be overwhelmed the settlement of sediment under urban and rural environments is its own weight, or as a result and that flash flooding will occur. affected by environmental change of anthropogenic effects such Deforestation also contributes to resulting from socioeconomic as groundwater extraction for increased surface runoff (figure 3.4) development. The expansion of water supply. Co-seismic uplift, by reducing the amount of moisture impermeable surfaces—which or subsidence due to earthquake trees absorb from the soil, but also occurs as concrete or paved motion, modifies ground elevation surfaces replace natural ground by removing the tree canopy, which rapidly and can result in temporary cover—decreases infiltration and intercepts precipitation; without the or permanent change in flood hazard increases runoff during precipitation canopy, more rainwater reaches the (see box 3.2). events (Roesner 2014; see figure ground, and reaches it more quickly As a natural process, subsidence 3.4). In addition, the presence (Savenije 2004). Deforestation also may occur within a balanced of urban drainage systems can destabilizes the soil, contributing ecosystem but to a limited extent. reduce the time for precipitation to increased sedimentation of river The Mississippi delta in the United to reach river channels: in urban channels and drainage systems, States had achieved a natural areas, surface flow is directed which reduces their capacity and balance in which sediment carried into drainage systems that route by the river from its upper reaches increases the likelihood of overflow. the water to river channels much

35 Making a riskier future: How our decisions are shaping future disaster risk / 15 Figure 3.4. compensated for natural settlement, Relationship between ground cover and surface runoff. and the ground elevation of 38% evapotranspiration 40% evapotranspiration the delta remained constant or subsided slowly while the delta expanded. Disruption of sediment supply by the construction of flood 20% 10% runoff runoff levees and removal of sediment- stabilizing vegetation resulted in net subsidence and shrinking of the 25% shallow 21% shallow delta (Propublica 2014). The delta is infiltration infiltration 21% deep 25% deep expected to largely disappear in the infiltration infiltration next 50 years, as a combination of 10-20% Imprevious Surface Natural Ground Cover sea-level rise and subsidence causes 30% evapotranspiration 35% evapotranspiration accelerated land loss. A major cause of subsidence is the extraction of groundwater from 30% 55% underground aquifers, for irrigation runoff runoff or for water supply to urban areas such as Jakarta (see box 3.3 and case study C). Groundwater 20% shallow 10% shallow infiltration infiltration extraction is closely linked to urban 15% deep 5% deep infiltration infiltration expansion; as urban populations 75-100% Imprevious Surface 35-50% Imprevious Surface grow and urban areas expand, the rate and spatial extent of extraction Source: Adapted from Roesner 2014. increases. Where aquifers are Box 3.2 Effects of Co-seismic Subsidence in Recent Earthquake Events In Christchurch, New Zealand, faulting and liquefaction from the 2011 earthquake caused subsidence of up to 1 m. Built on a floodplain, the city was at risk of flooding from tidal events and heavy precipitation even before the earthquake, and the Christchurch City Council had sought to account for projected sea-level rise by requiring new houses to be built with floor levels 3 m above sea level. As a result of the earthquake, however, flood risk from the Avon River has significantly increased, specifically because of subsidence, lateral spreading and heaving of the riverbed (which reduced river channel volume), and settling of riverbanks and levees. To mitigate the new level of risk, the city has had to dredge channels, construct emergency levees, and build a new storm water network (Giovinazzi et al. 2011), and there have been additional efforts to mitigate flooding of individual homes (Christchurch City Council 2014). While reconstruction of properties focuses on repairing earthquake damage, homes in the floodplain must be reconstructed with consideration for increased flood risk—that is, must be rebuilt with higher floor levels. In subduction zone earthquakes, the area of co-seismic subsidence can be large, and primarily affects the near-shore or onshore side of the fault because of the fault structure and rupture mechanism. The Research Center for Prediction of Earthquakes and Volcanic Eruptions, at Tohoku University, found that subsidence due to the Great East Japan Earthquake lowered the ground level at the Oshiki Peninsula, close to the cities of Onagawa and Ishinomaki, by up to 5.3 m. As a result, the harbor areas of these cities now flood daily at high tide.

36 16 / 3. Drivers of Evolving Disaster Risk: Hazard a sediment transport deficit that and subsidence into account, annual replenished (by rainfall) at slower damage in 2030 is expected to enhances erosion. For example, the rates than water is extracted, increase by 263 percent. Subsidence the water table is lowered and development of a coastal highway alone contributes an increase of 173 extraction must be conducted at in Alexandria, Egypt, has reduced percent, while the contribution from sites further afield. This increases the amount of sediment reaching increased precipitation intensity is the area affected by extraction- coastal areas, contributing to highly uncertain (median 4 percent induced subsidence. “chronic long-term coastal erosion” decrease in annual damage; -38 to of about 20 cm per year (World The rate of subsidence can exceed +197 percent range in 5th to 95th Bank 2011a, 37). This is a global that of sea-level rise, meaning percentiles). issue, occurring from the coasts of that subsidence may be a greater Yorkshire, England (Winn, Young, Coastal erosion causes the coastal influence on the increased coastal and Edwards 2003), to Small flood hazard to evolve by effectively flood hazard than climate change. In Island Developing States such as moving the coastline inland, either Manila Bay, Philippines, extraction Maldives (Yan and Kishore 2014). gradually over time or in single continues to lower the land, in The degradation of coastal habitats periods of intense erosion during some years by more than 10 cm (such as mangroves or coral reefs) extreme storm events. Erosion (Rodolfo and Siringan 2006). The through human activity can also reduces any buffer distance that subsidence rate in Bangkok reached increase risk, since these degraded exists between the shoreline and over 12 cm a year in the 1980s habitats are less effective in coastal populations or assets, (Phien-wej, Giao, and Nutalaya protecting the coastline from storm allowing comparatively minor 2006). And some parts of Jakarta, waves, storm surge, and tsunami. inundations (from storm surge or Indonesia, subside by as much as tsunami events) to affect coastal 20 cm per year due to groundwater Sea-level rise exposure. Erosion that occurs extraction. Budiyono et al. (2015) Sea-level rise is an extremely naturally because of long-term analyzed future flood hazard in important influence on evolving physical trends (e.g., cliff erosion or Jakarta with explicit consideration hazard, contributing as much as or longshore drift) can be exacerbated of future climate conditions and more than other associated factors by sea-level rise or more extreme declining ground elevations due to to increased risk. For example, coastal flooding. Additionally, the subsidence. The results demonstrate sea-level rise contributes more construction of coastal works, such the importance of incorporating to increased extreme storm tide as dams on rivers that discharge subsidence in analysis of affected sediment at the coast, can disrupt heights in Victoria, Australia, than areas such as Jakarta. Taking change the natural sediment refill and cause in precipitation, sea level, land use, higher wind speeds (McInnes et Box 3.3 Effects of Subsidence in Jakarta, Indonesia In rapidly urbanizing Jakarta, Indonesia, groundwater extraction has led to an estimated 2 m of subsidence between 1999 and 2013, with an additional 1.8 m expected between 2013 and 2025 (Deltares 2014). The greatest subsidence is occurring in north Jakarta, where the rivers and canals that flow through the city discharge into Jakarta Bay. In conjunction with rising sea level and the occurrence of extreme weather events, subsidence is contributing to the increasing urban and tidal flood hazard. At current rates of subsidence and sea-level rise, and without coastal protection, residential and industrial areas of north Jakarta, major transport links (including the international airport), and ports could be submerged within 100 years (World Bank 2011b). Coastal protection in the form of the Jakarta Coastal Defence Strategy (a dike and polder system), along with land reclamation and improved pumping capacity, are proposed to tackle the problem. But the long-term solution lies in replacing groundwater extraction with piped water supply, thus reducing the rate of subsidence.

37 Making a riskier future: How our decisions are shaping future disaster risk / 17 Denmark. A 0.5 m sea-level rise is expected to result in a 60 percent increase in losses for 50-year and 100-year return periods, compared to losses due to surge at current mean sea level (even without the uncertain effect of change in storm frequency and change in exposure). A rise in sea level of 1 m, however, results in a 140 percent increase over present losses, because losses rapidly increase once a storm surge exceeds the current defense protection level. Extreme heat Rising temperatures have resulted in more severe, frequent, and events, widespread extreme heat which are already considered a significant issue for public health (Luber and McGeehin 2008). Example of chronic long-term coastal erosion in Alexandria, Egypt. Photo credit: krechet/Thinkstock. com Increases are expected in both “highly unusual” events, such as those in Russia and Central Asia al. 2013). This suggests that storm surges with a current return period in 2010, the United States in surge risk is likely to increase of around a century become decadal 2012, and Australia in 2015, and under climate change, despite events by 2050. “unprecedented” events, which the remaining uncertainty around Losses due to coastal flood are do not occur under present-day regional changes in cyclone expected to occur at an increasingly climate conditions (World Bank frequency and intensity. To cite rapid rate as sea levels rise. The 2014). Recent research suggests another example of the influence relationship between sea-level rise for example that the probability of sea-level, analysis shows that and increase in loss (i.e., whether of extreme heat waves in eastern future peak hurricane storm surge there is a proportional or nonlinear China has increased sixtyfold since heights in Texas, United States, are threshold response) is determined the 1950s due to anthropogenic driven almost equally by sea-level by local topography (McInnes et influences (Sun et al. 2014). rise and hurricane intensification al. 2013). For example, given the Similarly, an analysis of the 2014– (Mousavi et al. 2011), demonstrating same rise in sea level, the newly 2015 heat wave in Europe shows the importance of including both flooded area of a wide low-lying that many of the extremes recorded factors in an analysis of evolving coastal plain will be proportionally during this event are at least twice coastal flood hazard. Using greater than in a narrow steep- as likely to happen today than they projections of sea-level rise and sided bay. Hallegatte et al. (2011) would have been in a world without global temperature change, Tebaldi, demonstrated an additional climate change (case study A). Strauss, and Zervas (2012) found a threshold effect in storm surge The expected increase in number significant increase in frequency of losses due to sea-level rise related of hot days over a larger area of storm surges on the U.S. coastline: to coastal protection in Copenhagen, North America (Rhodium Group

38 18 / 3. Drivers of Evolving Disaster Risk: Hazard Extreme heat events are very important from a humanitarian point of view, since they are a prime driver of mortality and since long-duration temperature extremes lead to drought, which may trigger climate-related human migration. LLC 2014) means an increase in the various forms, is a complex hazard, It is thus important to be able to spatial extent of regions affected driven by the interaction of climatic quantify the risk to agricultural by heat-related mortality, wildfire production in a changing climate. and socioeconomic factors over risk, and drought. One effect of the Deryng et al. (2014) showed a global different time periods. To simulate average decrease in maize yield to global trend of increasing urban these factors, modeling of drought 2080, and found that extreme heat population is a greater exposure risk under future climatic and stress occurring around the time of to heat extremes; urban centers socioeconomic conditions requires crop reproduction contributed to are susceptible to amplified heat the use of climate models. almost half of all maize yield loss extremes both because of waste Some studies have found signals and to a 50 percent decrease in yield heat emission from buildings and of increasing trends in drought gains for spring wheat. Soy, which transport and the thermal properties occurrence under climate change has a higher critical temperature of urban construction materials (Briffa, van der Schrier, and threshold, is less adversely affected (McCarthy, Best, and Betts 2010; Jones 2009; Dai, Trenberth, and by extreme heat and shows a 25 McCarthy et al. 2012). McCarthy, Qian 2004). Such trends are not percent decrease in yield gains. Best, and Betts (2010) showed considered significant on a global that in a future with a doubling of scale, however (Sheffield, Wood, and Drought , daily minimum and maximum CO 2 Roderick 2012), and given the lack temperatures would be expected to of direct observations there is a low hazard encompasses Drought increase by at least 3°C in all world degree of confidence concerning drought (a deficit meteorological regions, and there would be a 30 global drought trends (Stocker et al. agricultural or of precipitation), percent increase in nocturnal heat 2013). There are distinct regional soil moisture drought (a deficit in urban areas of South America and variations in the projected direction of soil moisture in the root zone), Southeast Asia. of change and the magnitude of and hydrological drought (negative Extreme heat events are very factors contributing to drought anomalies in groundwater, important from a humanitarian (such as precipitation, runoff, soil streamflow, or lake levels) (IPCC point of view, since they are a prime moisture, and evapotranspiration). 2012). These natural drought driver of mortality and since long- phenomena are different from A reduction in precipitation is likely duration temperature extremes but linked to water scarcity , or in the Mediterranean, southwest lead to drought, which may trigger socioeconomic drought, which United States, and southern Africa; climate-related human migration. may be partially or fully caused by decreases in runoff and soil moisture Agricultural crop yield can be human activities such as intensive are likely in southern Europe and the adversely affected by extreme heat, agriculture or groundwater Middle East (Stocker et al. 2013); extraction (Dai, Trenberth, and particularly if the heat stress occurs and wetter conditions are expected Qian 2004). Drought hazard, in its in key stages of the growing season. in the Horn of Africa (World Bank

39 Making a riskier future: How our decisions are shaping future disaster risk / 19 2014). There is high confidence that heat and drought stress will reduce crop productivity, increase pest and disease damage, disrupt food system infrastructure through flooding, and generally be harmful to livelihoods and food security. Analyses of the evolution of drought risk in the past and the future have been conducted at varying scales. On a global scale, the estimated share of the world population facing water scarcity increased from 20 percent in 1960 to 50 percent in 2000 (Veldkamp et al. 2015). In the short term (6 to 10 years), hydroclimatic variability is responsible for almost 80 percent of the yearly change in water scarcity, whereas socioeconomic development is the driving force Due to its tropical region, different physical effects of climate change—increased temperature and precipitation, increased salinity and extreme weather events such as floods, cyclones and drought—are felt in Sundarban, India. behind long-term changes. The IPCC Photo credit: samrat35/Dreamstime.com has high confidence that in drought- prone regions of Africa, drought stress will be exacerbated by current world. Both observed wildfire risk 500 wildfires around Moscow, overexploitation and degradation and the expected future evolution Russia, during the hottest summer and by future increases in demand of wildfire risk are linked to long- for 400 years, resulting in crop for water resources. Global change term temperature and precipitation, failure of about 25 percent, 55,000 in precipitation, evapotranspiration, among multiple other factors (Liu, deaths, and economic losses of and mean surface temperature to Stanturf, and Goodrick 2010). US$15 billion (2010); and 3 million 2100 is expected to significantly Wildfire causes loss of lives and acres of burnt land in four southern increase the number of annual homes, damages ecosystem U.S. states during a record heat 2 over North and South drought days wave and drought, resulting in America, central and southern services, is harmful to human US$6–8 billion in economic loss Africa, the Middle East, southern health, and entails substantial (2011). The February 2009 fires Asia, and central and western costs for fire suppression. Several in Victoria, Australia, demonstrate Australia (Hirabayashi et al. 2008). high-profile wildfire events have how phenomena related to weather occurred in the last several years and climate—specifically a decade- Wildfire (World Bank 2012), including long drought, record extreme are The impacts of wildfire devastating wildfires in southern heat, and record low humidity of substantial in many regions of the Europe during a summer of record 5 percent (Karoly 2010; Trewin temperatures (2007); the worst and Vermont 2010)—interact 2 “Drought days” are days when daily Australian bushfires on record in the with rapidly increasing exposure discharge is lower than the 10th state of Victoria during a heat wave to drive the evolution of risk percentile of all river discharge data from the 20th-century simulation. of record temperatures (2009); (IPCC 2012). Together the climate

40 20 / 3. Drivers of Evolving Disaster Risk: Hazard volcanic activity As far as is known, phenomena created the conditions of large-magnitude earthquakes. is unaffected by human activity, for major uncontrollable wildfires Thus exposure and vulnerability and there is no evidence to suggest (2009 Victorian Bushfires Royal are the main anthropogenic that trends in activity are affected Commission 2010). drivers of evolving earthquake by changing climate. As with risk. Regional earthquake hazard Frequency and severity of large earthquakes, the driving influences does evolve through time due to wildfires (in terms of area burned) of evolving volcanic risk are natural variation. An earthquake is are expected to increase in a changing exposure and vulnerability the rupture at a fault when stress, warmer climate (Flannigan et al. in areas affected by volcanoes. That caused by the movement of rock 2009), in which hotter and drier is not to say that volcanic hazard is around the fault, builds to such a conditions, more fuel, and more static. Levels of volcanic activity are level that it exceeds the strength frequent lightning will lead to longer time-varying in the short term and of that rock. The movement due fire seasons. Climate change is long term. An “active” volcano (one to an earthquake increases stress expected to have a minor impact that has erupted in the last 10,000 in some parts of the surrounding on wildfire risk in North America years) will exhibit varying levels rock and decreases stress in other and South America, but a major of volcanic activity (and therefore parts. An increase in stress can impact in southern Europe and East hazard) as it transitions between increase the probability of (or Africa (Field et al. 2014). Under 4°C non-eruptive and eruptive states, decrease the time before) another warming, some models project large perhaps over many years, decades, earthquake in that area, because increases in fire risk in southern or centuries. Several volcanoes are the fault is brought closer to its Europe, Russia, and North America. known to erupt very frequently or maximum capacity, i.e., closer to A common trigger of natural almost constantly (e.g., Stromboli, rupture. Likewise, a decrease in wildfires, lightning, may increase Italy), but volcanoes can also stress can lengthen the time before in a warmer climate: annual mean exhibit different styles of eruption the next rupture occurs. As a result lightning strike frequency has been or different levels of activity that of this chain reaction effect, the shown to increase across the United present a changing hazard level. occurrence of one large earthquake States by 12 percent per 1°C of Some eruptions can persist for can increase regional earthquake warming (Romps et al. 2014). months or years (e.g., Soufriere hazard for months, several years, or Hills, Monserrat); and within such even decades. a long-duration eruption, hazard Geotechnical and can vary from day to day depending Although earthquakes themselves geophysical hazards on short-term changes in eruptive may not be influenced by climate activity or wind direction (affecting change, the chance that earthquakes Seismic and volcanic hazard ash fall hazard). will trigger landslides in steep terrain can be increased as a result Seismic hazard can be affected by Landslide of changes in precipitation patterns, human activity. Mining, geothermal which can increase the amount energy production, and the The IPCC (2012) expresses of moisture in soil and decrease construction of reservoirs may high confidence that climate stability of slopes. In such cases, induce seismicity—that is, locally change–driven increases in heavy landslides can be triggered by a increase the frequency of small- precipitation will cause changes lower level of earthquake shaking magnitude earthquakes (Simpson, in slope instability and hence in than would otherwise have been Leith, and Scholz 1988; Majer et al. hazard. Landslides are landslide required, or an earthquake may 2007). But there is no compelling a product of geological and, often, trigger larger landslides than it evidence to suggest that human meteorological factors. Heavy would otherwise have done (also activity or changing climate rainfall is a significant contributor see the section on landslide below). affects the frequency or severity to slope instability because it

41 Making a riskier future: How our decisions are shaping future disaster risk / 21 share of landslide fatalities is susceptibility to other triggers, can increase soil water pressure, reported in China and South Asia such as earthquakes. Landslide while flooding or coastal erosion during the Northern Hemisphere hazard may also evolve through can increase the landslide hazard summer (Petley, Dunning, and destabilization of slopes by by undercutting the supporting Rosser 2005). deforestation or urban development toe of slopes or cliffs. These of hillsides. The majority of factors may not trigger a landslide damaging landslides occur in independently in all cases, but remote areas in less developed they may provide the antecedent countries. In most years, the major conditions that enhance slope Before-and-after photographs of Nepal’s Langtang Valley, following a massive landslide caused by the 2015 Gorkha earthquake. More than 350 people are estimated to have died as a result of the earthquake-induced landslide. Photos from 2012 (pre-quake) and 2015 (post-quake). Photo credit: David Breashears/GlacierWorks

42

43 Making a riskier future: How our decisions are shaping future disaster risk / 23 Drivers of Evolving 4 Disaster Risk: Exposure he rise in disaster losses over the past decades is due mainly to changes in socioeconomic factors, specifically population and wealth (for regional and global trends see figure 4.1 and figure 4.2). There is T evidence of this for several hazards and regions, including hurricanes in the United States, and river floods and extratropical cyclones in Europe (e.g., Barredo 2009, 2010; Bouwer et al. 2007; Mohleji and Pielke 2014; Visser, Petersen, and Ligtvoet 2014). The effect of exposure on increasing disaster losses has been established with much more confidence than the effect of hazard and vulnerability, in part because of the relatively short time series of losses and the lack of well-developed methodologies for quantifying hazard and vulnerability (Visser, Petersen, and Ligtvoet 2014). The IPCC (2012, 9) has high confidence that “increasing exposure of people and economic assets Increasing exposure of has been the major cause of long-term increases in economic losses from weather- and climate-related disasters.” According to Freire and Aubrecht people and economic (2012), moreover, “for many hazard occurrences, especially those above a assets has been the certain magnitude or intensity, population exposure is arguably the greatest determinant of vulnerability and resulting losses and impacts.” While the major cause of long-term general trend is one of increasing exposure, of course decline in population increases in economic and gross domestic product (GDP) can lead to a reduction in risk, as shown for losses from weather- and earthquake risk in case study D. climate-related disasters. FACING PAGE Taipei commuters. Photo credit: fazon1/Thinkstock.com 23

44 24 / 4. Drivers of Evolving Disaster Risk: Exposure Cities are dense, highly 520 million in 1970 to almost 1 Population growth concentrated locations of exposure, billion in 2010 (Jongman, Ward, and Increased global exposure to natural so when they are affected by a Aerts 2012). Population growth is hazards has largely been driven by disaster, losses can be significant. expected to continue this trend into population growth and the trend Rapid and unplanned expansion the future. There is a 95 percent of an increased proportion of that of urban populations increases probability that world population population living in cities rather exposure either through increased will increase from 7.2 billion people than rural areas (urbanization). All density, as cities build upward, in 2014 to between 9.0 and 13.2 regions of the world experienced or by outward expansion, as the billion people by 2100 (Gerland et a vast increase in total population increasing population spreads over al. 2014). Regional contributions between 1960 and 2013 as well a wider area and causes changes to growth are variable, with South as an increase in the proportion of in land use. The urbanization Asia, East Asia, and Africa showing urban population (figure 4.1 and of unstable slopes or reclaimed the largest regional population figure 4.3). The global population land (which is often susceptible increases (Gerland et al. 2014) and exposed to river and coastal to flooding and liquefaction) contributing the majority of the flooding, to choose one hazard, leads to a disproportionate annual growth in individual cities increase in exposure to hazards (table 4.1). doubled—increasing from around Figure 4.1. Total population in World Bank income groups, 1960–2014, shown alongside total affected population. 700.00 8 7 600.00 6 500.00 5 400.00 4 300.00 3 Total population (billions) Total affected population (millions) 200.00 2 100.00 1 – – 1985 1960 1970 2000 1995 1975 2005 1980 2010 1965 1990 Year High income World Middle income Linear (Total affected (millions)) Low income Total affected population (millions) http://data.worldbank.org/data-catalog/world-development-indicators World Development Indicators Database, World Bank, Washington, DC, Sources: , Université Catholique de Louvain, www.emdat.be (for total population); D. Guha-Sapir, R. Below, and Ph. Hoyois, EM-DAT: International Disaster Database, Brussels, Belgium (for total affected population).

45 Making a riskier future: How our decisions are shaping future disaster risk / 25 GDP per capita (constant 2005 US$) in World Bank income groups, 1960–2014, shown alongside total Figure 4.2. damage (2014 US$). 35 400.00 350.00 30 300.00 25 250.00 20 200.00 15 150.00 Total damage (billions) 10 100.00 GDP per capita (constant 2005 US$ thousands) 5 50.00 – – 1995 1970 1965 1960 1980 2000 2005 1975 1990 2010 1985 Year High income World Linear (Total damage (millions)) Middle income Low income Total damage (millions) http://data.worldbank.org/data-catalog/world-development-indicators World Bank, World Development Indicators Database, (for GDP per capita); Sources: D. Guha-Sapir, R. Below, and Ph. Hoyois, EM-DAT: International Disaster Database, , Université Catholique de Louvain, Brussels, Belgium www.emdat.be (for total affected population). in all regions except Sub-Saharan storm surge. These cities are also and socioeconomic vulnerability. some of the most rapidly growing in Urbanization can change disaster Africa. Urban and rural GDP exposed terms of population (see table 4.1). risk significantly. Evolution of flood to 1-in-10-year floods was found Coupled with the effects of evolving risk varies regionally, but also to increase significantly between coastal hazards, this swift increase differs in urban and rural contexts. 2010 and 2050 in all regions, with in exposure makes cities such as The global flood model GLOFRIS smaller increases found for urban Mumbai, Karachi, Jakarta, and Lagos (Global Flood Risk with IMAGE and rural GDP exposed to 1-in-100- among the key areas in which to Scenarios) was used to estimate year floods across the same time address evolving disaster risk. It regional urban and rural population scales. is important to note that increased at risk of flooding for 2010, 2030, Increased exposure in coastal exposure to hazards does not occur and 2050 (Ligtvoet et al. 2014). The cities is an important driver of risk. only in expanding urban areas. One study found a significant increase in These cities are already among example of increased exposure in urban population at risk of flooding the most populous in the world low-density areas—those that could for the whole world, developing (Hallegatte et al. 2013) and have still be considered rural—is the countries, and each World Bank a huge amount of infrastructure observed movement of population region. However, rural population at to locations at risk of wildfire, such risk of flooding was found to decline exposed to coastal flooding and

46 26 / 4. Drivers of Evolving Disaster Risk: Exposure Figure 4.3. Growth in population between 1960 (above) and 2013 (below). Size of pie chart shows total population, while segments indicate what proportion is urban and rural. Sources: World Development Indicators Database, World Bank, 2015, http://data.worldbank.org/data-catalog/world-development-indicators .

47 Making a riskier future: How our decisions are shaping future disaster risk / 27 Top 20 Cities by Population in 2015 and 2030, with Change in Rank and Percentage Change in Population in Table 4.1. the Intervening Years 2030 Percentage 2015 Urban 2015 population 2030 population Rank change in agglomeration change (1,000s) rank population Country or area (1,000s) rank 25,703 1 36,060 India = 40 Delhi 1 Mumbai 2 27,797 2 = 32 India 21,043 20,384 3 27,706 3 = 36 China Beijing Dhaka Bangladesh 4 27,374 4 = 56 17,598 Pakistan 16,618 5 24,838 5 = 49 Karachi Lagos 6 24,239 6 = 85 Nigeria 13,123 Guangzhou 12,458 7 17,574 8 – 41 China Congo, Dem. Rep. Kinshasa 8 19,996 7 + 73 11,587 Indonesia 10,323 9 13,812 11 – 34 Jakarta India Bangalore 10,087 10 14,762 9 + 46 India Chennai 13,921 10 + 41 9,890 11 8,944 + 12,774 13 Hyderabad 43 India 12 Lahore 13 13,033 12 – 49 Pakistan 8,741 Chengdu 7,556 14 10,104 18 – 34 China Nanjing 15 9,754 19 – 32 7,369 China Ahmadabad 7,343 16 10,527 15 + 43 India 10,200 Ho Chi Minh City 7,298 Vietnam 17 17 = 40 6,837 Kuala Lumpur Malaysia 9,423 18 21 - 38 Baghdad 6,643 Iraq 9,710 20 - 46 19 China Hangzhou 6,391 8,822 20 22 - 38 Tanzania 5,116 10,760 Dar es Salaam 26 14 + 10 Angola Luanda 5,506 10,429 23 16 + 89 Source: United Nations, Department of Economic and Social Affairs 2014.

48 28 / 4. Drivers of Evolving Disaster Risk: Exposure the proportion of urban population as areas close to national parks in 2010, and exposed GDP could be the United States (Hammer, Stewart, is expected to rise to 54 percent 3.2–4.2 that exposed in 2010. A and Radeloff 2009). by 2030 (CLUVA 2015). Africa’s study of drought by Veldkamp et al. urban population is expanding into (2015) also considered exposure as existing and new urban areas at well as hazard. It assessed changes Increased socioeconomic the fastest rate in the world—3.5 in water scarcity between 1960 activity percent per year—and driving a and 2000, accounting for changes A major component of increased significant amount of land-use in socioeconomic conditions as socioeconomic activity is the change. In developed countries, a well as hydroclimatic variability. development of concentrations trend of large cities becoming less While hydroclimatic variability of industrial, service, and dense reflects the expansion of was found to be responsible for trade activity. Wherever these urban development into rural areas the largest share (79 percent) concentrations develop, they drive previously dominated by natural of year-to-year changes in water large increases in high-value assets; surfaces. scarcity, socioeconomic changes in hazardous areas, these assets (population growth and increasing can be significantly affected by a water demand per capita) were Data on evolving single event. These concentrations the main drivers behind long- exposure also drive increases in residential term increases in water scarcity. exposure in the form of the The study emphasized that Data showing changes in global population that works in and is socioeconomic factors interact with exposure have been collected supported by the activities. Given and can strengthen or attenuate via remote sensing technologies, the national and global connectivity each other, which suggests an primarily low-light imagery, of so many trade and industry integrative modeling approach is from the U.S. Air Force Defense networks, impacts at one location needed to account for such changes Meteorological Satellite Program can propagate disruption and loss effectively. (DMSP) Operational Linescan to other parts of the network. The System (OLS) since the 1970s, 2011 Thailand floods, for example, and from the NASA/NOAA Visible Land-use change inundated 7,500 industrial facilities Infrared Imaging Radiometer Suite in 40 provinces, disrupting (VIIRS) instrument since 2011 In addition to increasing exposure production (and global supply) of (Elvidge et al. 2013). The night-time to hazards, population growth and automobiles and electronics. light data have been used to show increased socioeconomic activity economic activity and population drive land-use change, which alters The effect of socioeconomic (Elvidge et al. 1997) and trends ground surface conditions and can activity on flood losses has in urbanization (Zhang and Seto increase hazard (see section on been demonstrated by several 2011), and have been used as a flooding in chapter 3). Between studies, but few present the proxy for poverty (Noor et al. 2008; 1970 and 2010, the total urban relative contributions of evolving Wang, Cheng, and Zhang 2012). surface area exposed to flooding and hazard exposure. In a study Time series of regional night-time more than doubled, from 18,000 presenting a new framework for the 2 2 light data between 1992 and 2012 km to 44,000 km (Jongman, Ward, global flood risk model GLOFRIS, for West Africa (figure 4.4) and and Aerts 2012). The increase Winsemius et al. (2013) showed Southeast Asia (figure 4.5) show in urban land use is expected to that socioeconomic change has the patterns of steadily increasing continue, and to do so particularly a greater influence than climate concentrations of people and rapidly in developing countries. change on future flood risk. economic activity in cities and Approximately 38 percent of Africa’s Asset values exposed to flood coastal areas and along transport population (297 million people) in Bangladesh in 2050 could be networks. currently lives in urban areas, but 2.7–3.7 times those exposed in

49 Making a riskier future: How our decisions are shaping future disaster risk / 29 Night-time light coverage in 1992 (red) and 2010 (orange), showing expansion of multiple urban areas, e.g., Figure 4.4. Accra, Ghana, and Lagos and Abuja, Nigeria. Small pockets of light in 2010 show increased economic activity and the presence of night-time light in rural areas since 1992. The large area of intense light around Port Harcourt indicates high levels of industrial activity in that area in 1992 and 2010. Source: World Bank based on data from NOAA National Centers for Environmental Information 2015. Night-time light coverage in 1992 (red) and 2010 (orange), showing expansion of Bangkok and Ho Chi Minh Figure 4.5. City, and increased economic activity along transport routes and coastal areas in Thailand, Cambodia, and Vietnam. Source: World Bank based on data from NOAA National Centers for Environmental Information 2015.

50 30 / Making a riskier future: How our decisions are shaping future disaster risk

51 Drivers of Evolving 5 Disaster Risk: Vulnerability ulnerability refers to the susceptibility of exposed people, assets, and livelihoods to the harmful effects of natural hazards. Physical, or structural, vulnerability refers to the damage associated with buildings V and infrastructure, which determines asset losses. These losses are typically the concern of the (re)insurance and engineering industries, which focus on estimating loss to insured assets and mitigating structural damage, respectively. Social vulnerability refers to people’s ability to cope with the impacts of asset losses on their livelihoods and security. These impacts, along with losses to public assets, are a focus for governments. Structural vulnerability It is vital to improve the The physical vulnerability of a structure or piece of infrastructure determines the level of damage the asset sustains in response to a given level of way the evolution of hazard intensity. Physical vulnerability is usually presented in the form of vulnerability in time and a vulnerability curve (or fragility curve; see figure 5.1), which shows the space is incorporated into probability of a damage state being exceeded for a given hazard intensity. The primary factors determining a structure’s vulnerability to damage are disaster risk assessment. construction type (e.g., timber, unreinforced masonry, reinforced concrete, or steel), number of stories, and (for wind hazards) roof construction. For example, a tsunami occurring with flow depth of 2 m may cause collapse (100 percent damage) of a timber house, but cause only minor damage to a less vulnerable reinforced concrete building (Suppasri et al. 2013). Multiple other factors contribute to vulnerability, including the quality of construction (e.g., the type of connection between structural components, which is an important FACING PAGE Thousands displaced due to flooding in Cap-Haïtien, Haiti, after days of continuous rains. The region suffered serious flooding, leaving more than a dozen dead and thousands homeless. Photo credit: UN Photo/Logan Abassi 31

52 / 5. Drivers of Evolving Disaster Risk: Vulnerability 32 Sample fragility curves. Each curve shows the probability of a physical vulnerability are often seen Figure 5.1 particular level of damage occurring for the hazard intensity experienced. in structures that are intended to be in use for at least several decades 1.0 (the design life) and that remain in 0.9 use much longer than that. A house built in 1960, for example, may have 0.8 a floor level that is above the 1-in- 100-year flood level; but as a result 0.7 of increased frequency and severity 0.6 of flooding over time, by 2100 the floor level exceeds only that of a 0.5 1-in-50-year flood. That building 0.4 has become more susceptible Collapse to flooding and may require Very high damage Probability of damage 0.3 improvements (i.e., installation of High damage flood defenses) to maintain low 0.2 Moderate damage vulnerability. Low damage 0.1 Very low damage Vulnerability also evolves as a 0.0 result of modifications made to 6 16 8 2 18 0 20 12 14 4 10 structures. Informal construction Hazard intensity is common in many parts of the world, given inadequate building factor in the extent of earthquake redundancy in case of shocks. Poor standards and informal planning damage) and quality of construction maintenance of drainage systems and construction practices in many material. The configuration (shape) and blockage by solid waste, rapidly developing urban areas of a structure also influences the for example, have been shown (Lallemant, Wong, and Kiremidjian seismic damage level. Daniell to increase flood vulnerability 2014). Where individuals undertake (2014) shows that as building in Jakarta, Indonesia (Marfai, expansion of their own buildings stock becomes newer, earthquake Sekaranom, and Ward 2014). Poorly without planning restrictions vulnerability declines (see case designed or unfinished drainage or engineering guidance—and study D). systems contributed significantly when these buildings were likely to flooding in Jeddah, Saudi nonengineered to begin with— Physical vulnerability can Arabia, in 2009 (Verner 2012). the construction of additional increase over time if a structure As infrastructure becomes more stories and changes to buildings’ or infrastructure is inadequately susceptible to damage in disasters, configuration can increase maintained such that connections the populations it supports become vulnerability (case study E). and material deteriorate. more susceptible to disruption and Vulnerability of supporting systems A community’s vulnerability may loss. is intrinsically linked to evolution evolve due to widespread changes of exposure. As population grows, Even a structure maintained in the building stock, such as occurs the demand for functioning to avoid deterioration—that is, when building practices adopted infrastructure grows. Without proper kept in its original condition but from other regions replace traditional development and maintenance, without improvement—can become local practices that developed in the interrelated infrastructure systems relatively more vulnerable if the context of local risks. The adoption may suffer from insufficient capacity, hazard it is designed to protect of or improved adherence to building deterioration, and ultimately less against intensifies. Increases in design standards (i.e., structural

53 Making a riskier future: How our decisions are shaping future disaster risk / 33 recover. Socioeconomic or social vulnerability may evolve over time positively or negatively in response to many influences, including education, age, wealth, degree of access to resources, and political power (see for example Cutter, Boruff, and Shirley 2003; Cutter et al. 2013; Fekete 2009; Koks et al. 2015). Vulnerability is found to be higher in low-income countries than in high-income countries, and global vulnerability is gradually declining (Mechler and Bouwer 2015; Jongman et al. 2015). This is reflected in decreasing life loss in developed countries (UNISDR 2011; World Bank and United Nations 2010); the fact that fatalities are rising slower than exposed population in lower-middle- income countries; and the absence of a clear trend in low-income countries in the face of rising exposure Destroyed house after an earthquake near Mount Kinabalu, Malaysia, July 11, 2015. Photo credit: © Muslianshah Masrie (Jongman et al. 2015). According to Wisner et al. (2004), codes) can reduce vulnerability; the alone the interrelated nature of those development processes produce or hazards, is often overlooked. As one decrease in masonry construction influence the vulnerability of certain study says, in New Zealand since the 1930s, social and economic sectors; this for example, has led to a decrease Risk reduction strategies for view suggests that vulnerability in vulnerability (see box 7.2). Of one hazard should take into is an ever-evolving component of course, it sometimes happens that account coincidental and chains disaster risk. Social vulnerability is construction practices intended to of hazards both in the short influenced by multiple interacting reduce vulnerability to one hazard and long term, to ensure that social, cultural, and economic inadvertently increase vulnerability decisions made to mitigate factors, including the following: to another. This can occur when hazards today do not increase Population size and ■ ■ focus on the more obvious or well- vulnerability to future events demographics (age, gender, known hazard in an area results in (Duncan 2014; see also case disabilities) neglect of other hazards present. study F). ■ Household structures, gender ■ Specifically, it can occur when design roles or construction takes one hazard Social vulnerability ■ ■ Income, poverty, economic into account but neglects another. activity and resources For example, installation of a heavy Depending on their level of roof to minimize cyclone damage ■ ■ Access to education, health care vulnerability, different groups and can result in greater earthquake communities are more or less able ■ Institutional capacity and ■ vulnerability. Unfortunately, the to respond during a disaster, cope governance, including political in its aftermath, and subsequently corruption and political stability consideration of multiple hazards, let

54 34 / 5. Drivers of Evolving Disaster Risk: Vulnerability ■ ■ Environment, particularly to provide resilience if another disposable household income is disaster occurs. Vulnerability can susceptibility to hazards too low to afford the premiums. In remain high following a shock such a case, vulnerability is likely to Infrastructure, including ■ ■ if appropriate reconstruction or evolve differently in some locations power, water, transportation, adaptation is not undertaken, or communication, sanitation than in others and to exacerbate if maladaptation occurs during any preexisting disparity. The evolution of social vulnerability unplanned or poorly planned Regarding the spatial dimensions can be gradual or almost development (Birkmann 2011). of vulnerability, it is important to instantaneous. Long-term trends In Haiti in 2010, for example, a note that when a person or group in vulnerability are influenced combination of factors—a long-term of people is considered vulnerable by population trends, such as situation of poor infrastructure and to one hazard, they may not be demographic skewing toward the health care, a major earthquake in equally vulnerable to another. As elderly or very young—segments January and hurricane in November hazard distributions change, some of the population that are more that caused further deterioration vulnerabilities in a given area may susceptible to injury or loss of life in in systems (Butler 2010), and slow decline and others become more a disaster (e.g., Cutter, Boruff, and recovery from the earthquake— important. For example, elderly Shirley 2003; Sorensen and Vogt- compounded vulnerability and Sorensen 2006; Guha-Sapir et al. populations in Europe are likely to contributed to the rapid spread of 2006; Brunkard, Namulanda, and become more vulnerable in future as cholera following its outbreak in Ratard 2008). Periods of political extreme heat events become more October of that year. instability, weak governance, or low frequent. Social vulnerability also has an institutional capacity may weaken Implementation of development important spatial dimension. economic resources, infrastructure, programs and infrastructure A study by Koks et al. (2015) health and education systems, emphasizes that the level of social projects can lessen vulnerability and social welfare, resulting in a vulnerability varies substantially by strengthening social safety nets, population with higher vulnerability. not only between countries, but enhancing income, and reducing Gradual environmental within the same country and even the proportion of the population improvement or degradation can on a subcity level; decisions on the in poverty. Recent evidence influence vulnerability by building implementation of disaster risk shows that global vulnerability to up or eroding a population’s management strategies need to flooding is declining, especially in resources or health. Rapid or take this variability into account. low- income regions, in response almost instantaneous changes in Neglecting social vulnerability vulnerability may occur in response to rising income per capita and in risk assessment or assuming to a disaster that destroys property adaptation efforts (Jongman 2014; homogeneous vulnerability may and livelihoods, increases poverty, Jongman et al. 2015). A key aim for lead to unsuitable or ineffective disrupts infrastructure, and disaster risk management strategies strategies. For example, a interrupts access to health care. is to similarly reduce vulnerability concentration of elderly people in the context of all hazards. To A high level of vulnerability created may not be easily evacuated achieve this, and to account for by a sudden shock may persist for from the hazard zone in case of a the influence of vulnerability on a short or long time, depending on rapidly occurring flood or tsunami, developing effective, equitable, the reconstruction and adaptation but may be better protected by and acceptable risk management processes in that location. physical infrastructure, vertical strategies, it is vital to improve the During the recovery period, when evacuation, or shelter-in-place way the evolution of vulnerability in resources, infrastructure, and strategies. Similarly, a homogenous time and space is incorporated into means of income generation are flood insurance scheme may not being restored, there may be little be viable in parts of the city where disaster risk assessment.

55 Making a riskier future: How our decisions are shaping future disaster risk / 35 Nepal, 2015. Earthquake damage in Bhaktapur, located 30 km east of Kathmandu, once rich with Buddhist and Hindu temples and a popular tourist spot. Photo credit: Julian Bound | Dreamstime.com

56 36 / Making a riskier future: How our decisions are shaping future disaster risk

57 Making a riskier future: How our decisions are shaping future disaster risk / 37 Quantifying the Evolution 6 of Disaster Risk ost disaster risk assessment tools developed to date focus on the static assessment of current risk. Thus most risk assessments do not accurately reflect longer-term dynamics, and decisions based upon M these assessments may not be optimal. Over time, disaster risk assessment has improved and grown more sophisticated (GFDRR 2014a), but due to the large uncertainties in projecting risk, and a focus on near-term time horizons for managing risk (particularly in the financial sector), efforts to model the evolution of risk have only recently been undertaken. A recent review of 80 open source and open access risk assessment tools (GFDRR 2014b) found none that included explicit modeling of future risk. However, risk models can be augmented with data representing future conditions (e.g., higher sea level, increased population density, or changing climatic conditions; see box Most risk assessments 6.1 for an account of how a set of emissions scenarios—the Representative Concentration Pathways—are used to model future climate). As more input do not accurately reflect data become available, and as hazard and exposure projection data are longer-term dynamics, developed, assessments are better able to consider evolving risk. Although an increasing number of analyses are using these projection data sets, the and decisions based upon current state-of-the-art in modeling of evolving disaster risk still has various these assessments may not limitations. This chapter considers these limitations and discusses some of be optimal. the key issues and challenges involved in projecting future disaster risk. FACING PAGE Temperatures soared to 47 degrees Celsius (116 Fahrenheit) in central Pakistan on May 21 and 22, 2004. Photo credit: NASA image courtesy Jacques Descloitres and Ana Pinheiro 37

58 38 / 6. Quantifying the Evolution of Disaster Risk Box 6.1 Representative Concentration Pathways The Representative Concentration Pathways are a set of emissions scenarios, each of which provides a trajectory of greenhouse gas (GHG) emissions and concentrations to 2100 (figure B6.1.1). Four RCPs have been developed from the many emissions scenarios in the published climate literature. The four RCPs are therefore representative of a wider base of emissions scenarios, and they provide a simplified basis from which to model future climate and a way to account for the uncertainty in the future trajectory of emissions. RCPs are used to input initial conditions for ocean-atmosphere climate models and to develop Shared Socioeconomic Pathways (SSPs), which are “reference pathways describing plausible alternative trends in the evolution of society and ecosystems over a century timescale” (O’Neill et al. 2014, 387). The RCPs do not include the impacts of socioeconomic change or climate policies. RCPs are labeled according to radiative forcing (the balance of incoming and outgoing energy in the earth-atmosphere system) at the year 2100, in watts per square meter (W/m2) (table B6.1.1). The higher the radiative forcing, the greater the climate warming; the warming occurs because there is more incoming solar energy and absorption of energy by GHGs than outgoing reflected energy. To summarize the assumptions of RCPs: Air pollution controls becomes more stringent, due to rising income levels, causing decline in air-polluting emissions (SO , NO , N ); GHG concentrations (CO O) match the emission trajectories of these GHGs. , CH 4 2 2 2 x Thus in all RCPs, radiative forcing continues on its current trajectory until 2025. For RCP8.5, radiative forcing continues to increase at the same rate throughout the 21st century; for RCP2.6, decline in radiative forcing begins at 2030; and for the other RCPs, radiative forcing increases at a slower rate than for RCP8.5. RCPs provide input to general circulation models, or global climate models (GCMs), which model atmospheric, ocean, or coupled atmosphere-ocean processes. These models simulate fluid motion in the atmosphere and oceans on three-dimensional grids through time in order to simulate the changes in and interactions between various climatic parameters: flow of air and water, surface pressure, temperature, water vapor, and radiation. GCMs produce spatial atmosphere, ocean, and land-surface data, such as monthly mean or time-dependent wind speed, humidity, air pressure, sea-level change, sea ice area/thickness, ocean heat flux, precipitation. These parameters can be incorporated into cyclone or flood models to simulate the effects of future climate on these hazards. GCMs operate at spatial resolutions in the tens of kilometers, and are unable to resolve features of the atmosphere finer than the model resolution. In order to resolve smaller features, GCMs are downscaled by nesting regional climate models (RCMs) within GCMs (using global variables as boundary conditions) to produce regional estimates of climate and weather. Alternatively, statistical downscaling can be used to relate global variables to regional or local variables. These downscaled regional and local variables are used as inputs to physical models (e.g., hydrological models, crop response models, and drought models) to generate hazard-specific event catalogs under future climate conditions. Comparison of RCPs Table B6.1.1 Temperature equivalent CO 2 (ppm) anomaly (°C) RCP Radiative forcing 2 RCP8.5 8.5 W/m in 2100 1,370 4.9 2 post 2100 850 3.0 6 W/m RCP6.0 2 650 2.4 post 2100 4.5 W/m RCP4.5 2 2 before 2100, declining to 2.6 W/m by 2100 490 1.5 3 W/m RCP2.6 Source: Moss et al. 2010, table 1. Note: ppm = parts per million.

59 39 The making of a riskier future: How our decisions are shaping future disaster risk / Box 6.1 Continues Figure B6.1.1. Trends in concentrations of greenhouse gases under each RCP. Grey area indicates the 98th and 90th percentiles (light/dark grey) of the recent EMF-22 study (Clarke et al. 2009). 500 1000 1000 900 400 4000 800 3000 300 700 600 RCP2.6 2000 200 RCP4.5 concentration (ppm) concentration (ppm) 2 4 O concentration (ppm) 500 2 RCP6 CO CH N RCP8.5 1000 100 400 0 0 300 2100 2025 2050 2050 2000 2000 2000 2075 2075 2100 2100 2025 2025 2050 2075 Source: Van Vuuren et al. 2011. comprise statistical distributions scenarios to provide the hazard Simple or complex to represent probability of hazard data. Probabilistic modeling approach intensity and damage, and they combines many thousands of Disaster risk assessments vary compute uncertainty at each step different events of varying frequency greatly in complexity. They can be of the modeling. In these models, (annual occurrence probability) and as simple as producing an order- the hazard component provides severity. The loss from each event in of-magnitude loss estimate by the georeferenced event severity a probabilistic event catalog can be overlaying exposure on a hazard (e.g., maximum wind speed, flow used to establish a loss exceedance scenario and assuming a damage depth, or ground-shaking intensity) probability and average annual loss ratio for each unit of exposure. and frequency (how often the (AAL), whether in terms of monetary Risk assessments can also be event is expected to occur) at each value, population, or asset units based on expert judgment to assess modeled location. Georeferenced (e.g., number of buildings). the likelihood of different risk exposure data provide population, Evolving hazard has been quantified components, or of overall loss. One asset characteristics, and value at in a number of studies investigating structured method of collecting each location. The vulnerability the effect of climate change on expert judgement is the Delphi component relates an event’s flood, cyclone, drought, wildfire, and method (e.g., Elmer et al. 2010), a intensity to its impact based on the extreme temperatures. The analyses weighted ranking approach based statistical relationship between have accounted for climate using on expert judgement, which can intensity and probability of damage, various methods (summarized in be employed to rank events or number of fatalities, or impact on figure 6.1), including a simple factor scenarios with a high degree of coping capacity and poverty. to increase frequency or intensity uncertainty in order to estimate Risk assessments can be of the hazard (at the less complex risks in the future. Complexity deterministic or probabilistic. end of the spectrum) and simulation increases through a range of approaches; the most complex Deterministic modeling uses event of multiple climate scenarios using

60 40 / 6. Quantifying the Evolution of Disaster Risk Features of the hazard component of models that seek to quantify Figure 6.1. Multiple influences on coastal evolving disaster risk, specifically for climate-related hazards. Complexity of flood risk analysis increases from top to bottom. Coastal flood risk is a good example of a risk that is affected Hierarchy of complexity in disaster risk analysis methods: Hazard by interrelated factors: sea-level rise and cyclone storm surge A simple uplift of hazard intensity or frequency is applied (which induce flooding from the to represent future increase in hazard coast), precipitation (which causes flash flooding or river flooding in the coastal city), and subsidence (which fundamentally changes the A low-resolution (global-scale) climate model is used to simulate topographic influence on flooding large-scale systems and key climate variables patterns). The coastal flood risk assessments described below include multiple factors—and show that the contribution of each factor Climate parameters are simulated using a high-resolution or regional can be quantified. climate model to represent smaller-scale features Analyzing flood hazard in Jakarta, Budiyono et al. (2015) estimated climate-affected precipitation Simultaion of climate parameters using a number of climate models, intensity using 20 combinations scenarios, or ensembles to better capture uncertainty of GCMs and RCPs, low and high scenarios of sea-level rise, and Modified from Bouwer 2013, table 1. Source: a scenario in which subsidence continued to 2025. The omission of subsidence alone would a number of regional-scale climate factors that influence the hazard, have resulted in a significant models to provide the range of for example flood assessments underestimation of damage, as this climate futures (at the more complex that include rising sea levels, factor contributed an increase in end). The benefit of the simple changing storm intensity, and damage of 173 percent—a significant approaches is their relatively low subsidence. A major limitation proportion of the total increase of computational cost, which makes of disaster risk assessments in 263 percent by 2030. The different them replicable across large areas general is that too few simulate approaches taken to account for or for numerous case studies. More interrelated—that is, cascading and complex modeling, while restricted to precipitation, sea-level rise, and coincident—hazards (see case study smaller study areas, is more suited to subsidence reflect the different levels F). Such interrelationships cause capturing important details required of data availability and uncertainty compounded risks under present for planning and implementing risk for each of the contributing factors. conditions, and they become reduction strategies. Sea-level rise is commonly accounted increasingly important when for as a series of scenario-based investigating future risk. The way increases, owing to uncertainty in interrelated risks are likely to evolve Modeling interrelated and the rate of future change. In the together in the future is uncertain evolving hazards absence of data on historical rates of (i.e., will they increase linearly or subsidence in many cities, Hanson et will we observe nonlinear effects?), Current risk assessments generally al. (2011) applied a uniform rise in so risk assessments should strive to deal with one hazard at a time, sea level of 0.5 m between 2005 and account for such interrelationships. although a few consider multiple

61 Making a riskier future: How our decisions are shaping future disaster risk / 41 increased storm activity and—in the adjusted surge probabilities, 2075 to all cities. They also applied a 10 percent increase in extreme high scenario—rapid ice melt. higher sea levels, and increased water levels to represent increased urban exposure in flood hazard storm intensity. In an update of that zones. These analyses were used Time dependency analysis, Hallegatte et al. (2013) to estimate the benefits of different assessed loss for two sea-level rise proposed defense schemes (that is, Many models assume that hazard scenarios (20 cm and 40 cm) and the extent to which they avoided events are time-independent of one subsidence scenario (40 cm). costs of surge-induced damage) over each other and that they form what a 100-year period. The inclusion is called a “Poisson process,” in The importance of combining of all factors had a significant which the probability of one event multiple factors in assessment impact on the results. When only occurring is not influenced by the was shown again in a cost- sea-level rise was included, none occurrence of any other event. benefit assessment of coastal of the proposed schemes was It is well known, however, that protection schemes in New York estimated to be cost-effective (costs geophysical and meteorological City (Aerts et al. 2014). The avoided did not exceed the cost of hazards can exhibit clustering, with authors probabilistically simulated implementation), but the schemes multiple events occurring close storm surge events under present became cost-effective in scenarios together in time and space (see conditions and under conditions that combined sea-level rise and at 2040 and 2080, incorporating text box 6.2). Time dependency can Box 6.2 Time-Dependent Hazards Cyclones can occur in clusters because the large-scale atmospheric conditions suitable for their formation and propagation— e.g., El Niño–Southern Oscillation (ENSO) and other naturally cyclical conditions—can persist for periods of several weeks (not just for the period of one storm). As a result, there may be periods of higher activity in which losses are more substantial than in periods of average activity. For example, in Europe, extratropical cyclone losses were particularly severe in 1990 (four events each caused over US$1.9 billion in losses) and 1999 (three events each caused over US$3 billion in losses). Earthquake clusters occur in the short term and long term. A short-term cluster is the series of foreshocks, mainshock, and aftershocks that comprises an earthquake sequence. Such sequences may last many months, as occurred in the 2010–2011 earthquake sequence in Canterbury, New Zealand. Long-term clusters are defined by the increased probability of large- magnitude earthquakes occurring on the same plate boundary as a result of increased stress transfer from an earlier earthquake. Time-dependent models of earthquake recurrence are used widely. River floods are often the result of large-scale weather systems, which may cause intense precipitation over large areas within a short time. In June 2013, for example, nine countries in Central and Eastern Europe were hit by a series of river floods causing over US$15 billion in damages. Jongman et al. (2014) showed that different parts of Europe are interconnected by these large- scale weather systems, and that failing to take into account these effects in continental-level risk assessment may strongly underestimate the risk.

62 42 / 6. Quantifying the Evolution of Disaster Risk if the resolution at which we can Hazard uncertainty monitor and investigate small-scale There is significant Hazard data availability varies atmospheric phenomena, such as uncertainty around between world regions and cloud formation, is low, a degree for different hazards, and in regional and local of uncertainty is introduced into many cases the instrumental or results that rely on that process. climate change impacts, historical record of observations As a result, there is significant is very short compared to the particularly around uncertainty around regional and long-term recurrence of events local climate change impacts, changes in frequency and and cycles of natural variability. particularly around changes For example, meteorological and intensity of precipitation in frequency and intensity of geophysical monitoring are now precipitation and cyclone winds. and cyclone winds. typically conducted with excellent These factors introduce uncertainty geographic coverage in developed into assessment of present-day countries using well-established influence disaster loss estimates hazard and, by extension, evolving and widespread or dense networks significantly; omitting time hazard. of monitoring stations; but in many dependency can underestimate developing countries, monitoring Use of climate projections the frequency of severe events, facilities are much sparser or only in disaster risk assessment thereby underestimating not only recently implemented, providing the losses from each event but also There is a high level of uncertainty fewer data points over a shorter the potential for multiple events to associated with many of the time period. Inhospitable conditions compound impacts that occur in a climate processes that contribute and limitations on resources mean short space of time. Expectations to meteorological hazards, and that some hazards remain poorly vary as to how climate change these are present in models that monitored; even now, only a might influence time-dependency attempt to represent future climate minority of active volcanoes around in meteorological hazards. In the conditions. Climate conditions the globe is monitored, limiting our quantification of evolving risk, it are already being influenced by knowledge on the eruptive history in is ever more important to simulate greenhouse gas emissions and many regions at risk. Paleoseismic disaster risk with time-dependent the atmospheric concentrations and paleoclimate studies provide hazard; thus the application of of greenhouse gas. Emissions are data from before the instrumental additional statistical methods will driven by many different factors record in tsunami, seismic, and be required. (including technological adaptation climate analyses through the and changes in consumption analysis of sediment and ice cores behaviors) in multiple sectors that record signatures of previous Uncertainty in risk (including energy, agriculture, conditions and events. However, assessment transport). Given the complex range in many cases the accuracy of There is uncertainty in all risk of influences, each of which is dating remains uncertain, and assessments, whether they are difficult to determine, uncertainty interpretation of what certain assessing present risk or projecting in long-term climate projections is paleo signatures represent is not future risk. Uncertainty arises in addressed by using emissions and straightforward (e.g., tsunami each of the hazard, exposure, and concentrations scenarios as well deposits are difficult to distinguish vulnerability components, as the as ensemble studies that apply from other high-energy marine result either of natural variability events in some sediment cores). multiple models. Exercises that (aleatory uncertainty) or of Technological capabilities can compare the results of multiple limitations in our knowledge and also limit our knowledge of certain models, such as the Coupled Model data (epistemic uncertainty). physical processes. For example, Intercomparison Project (CMIP5),

63 Making a riskier future: How our decisions are shaping future disaster risk / 43 demonstrate the ability of climate discharge data from the 20th- according to two of the five models models to reproduce current century simulation) is projected to used. The complex interaction of climate and historical climate increase significantly over North frequency and intensity means that trends, provide spatial patterns and South America, central and there are nonlinear effects on losses. of atmospheric circulation, and southern Africa, the Middle East, In terms of cyclone frequency, they consistently predict a warming southern Asia, and central and estimate more tropical depressions climate. western Australia. Li et al. (2009) and tropical storms, fewer cyclones used the results of 20 GCMs and of Category 1–4, and more cyclones Climate projections are widely used six emissions scenarios to assess of Category 5. as input to hazard modeling for future impact of drought on crop heat, drought, wind, and flood risk Flood modeling has recently yield. They estimated an increase assessments. In particular, drought begun to make frequent use of in drought-affected land area of in its various forms—meteorological, GCMs, specifically to estimate 15.4–44.0 percent by 2100, and a agricultural, hydrological, and precipitation in future climates yield reduction in major crops of socioeconomic—is a complex as input to hydrological models. >50 percent in 2050 and 90 percent hazard, driven by the interaction of Hirabayashi et al. (2013) showed in 2100. climatic and socioeconomic factors the importance of using a suite of over different time periods. To GCMs are also used to explore GCMs to determine the direction of simulate these factors, modeling of potential changes in cyclone future flood frequency in different drought risk under future climatic frequency. GCMs have been used regions, since any one model may and socioeconomic conditions to project atmospheric parameters predict an increase or decrease. requires the use of climate models. that can be downscaled to regional Arnell and Lloyd-Hughes (2014) also Analyses of the evolution of drought modeling to generate synthetic investigated the change in global risk in the past and the future have cyclone track catalogs. As part of the flood exposure in the 2050s and been conducted at varying scales. Pacific Catastrophe Risk Assessment 2080s compared to 1960–1990, Using four global climate models to and Financing Initiative (PCRAFI), using four RCP scenarios modeled drive six regional climate models, Arthur, Woolf, and Dailey (2014) in 19 GCMs. They showed that there Jeong, Sushama, and et al. (2014) analyzed present and future tropical is little difference in estimated generated drought scenarios based cyclone risk for 15 Pacific countries. on the simulated effects of future They applied the most extreme RCP temperature and evapotranspiration scenario, RCP8.5, in which annual Given the complex range in North America to 2069. Projected global temperature anomalies increases of more than 2°C result in reach +4°C by 2100. Using GCM of influences, each of increased future risk of long-term projections of future climate to which is difficult to and extreme drought in the United condition tropical cyclone catalogs, States and southern Canada. Risk they modeled future cyclone determine, uncertainty of short-term and moderate drought activity for 2050 and 2081–2100. in long-term climate is also increased, but to a lesser Their analysis found that the only projections is addressed extent. Hirabayashi et al. (2008) significant change (greater than used GCMs to assess low-resolution intermodel standard deviation) in by using emissions and (1.1 degree) global change in parameters is an eastward shift concentrations scenarios precipitation, evapotranspiration, in cyclone genesis, of 10 degrees and mean surface temperature to longitude. This shift results in an as well as ensemble 2100. The change in number of increase in 1-in-250-year loss in studies that apply multiple annual drought days (days when most of the studied countries, daily discharge was lower than but the potential total loss for the models. the 10th percentile of all river entire region may in fact decrease,

64 44 / 6. Quantifying the Evolution of Disaster Risk in hazard-prone areas), especially flood-prone population for RCP2.6, While the evolution of flood risk RCP4.5, and RCP6.0 at 2050, and under future climate conditions is in data-scarce areas in low-income for RCP4.5 and RCP6.0 at 2080. receiving considerable attention, countries. Additional uncertainty They also showed the considerable flood risk is also influenced arises from projecting spatial and uncertainty involved in projecting by nonstationary interannual temporal changes in exposure spatial and seasonal patterns in variability and climate cycles, ENSO into the future. The availability climate change—globally, between (Ward et al. 2014; Ward et al. 2013; of current exposure data is being 100 million and 580 million people . Ward et al. 2010) With ENSO a addressed through the use of are expected to experience an significant influence on the intensity open data and crowd-sourced increase in flood frequency by 2050; of annual floods—indeed affecting mapping (see box 6.3), and several between 80 million and 310 million flood risk across major parts of spatial data sets now provide people are expected to experience the world (Ward et al. 2014)—it global coverage of population and a decrease in flood frequency in is important to develop methods human settlement (see box 6.4), the same period. One other flood of assessing future flood risk that which provide baseline data on modeling project relevant here is incorporate this factor. a recent World Bank assessment present exposure. Exposure data of current and future flood risk in are projected from these baseline Uncertainty in exposure data Europe and Central Asia, which uses data to the current year and future and projections the GLOFRIS model in conjunction years using growth projections from There is significant uncertainty with multiple climate scenarios and national data and economic models. about current exposure (i.e., people, socioeconomic developments (see The methods used to project data infrastructure, and assets located case study G). from the past to present day, or from present day into the future, can be a source of uncertainty, as the relationships used may not accurately represent past or future growth in such a complex system of population changes and movement. For example, the majority of current global disaster risk projections have so far relied on the extrapolation of current spatial population density based on national-level population and/or gross domestic product (GDP) growth figures (Hinkel et al. 2014; Jongman, Ward, and Aerts 2012; UNISDR 2011). Such studies therefore assume that the distribution of people and cities will remain stable going forward, an assumption that has a strong effect on the outcomes of any projection, and any risk assessment that Favela Rocinha, largest in Rio de Janeiro, Brasil. Photo credit: Thinkstock.com incorporates that projection.

65 45 The making of a riskier future: How our decisions are shaping future disaster risk / Box 6.3 Open Cities Mapping and Development Timely collection and sharing of exposure data are vital for generating data that are as accurate and up-to-date as possible. Collecting data in the same area continuously over long periods of time can help to improve the temporal resolution of exposure data and to capture changes in the ongoing development of urban areas. The World Bank/Global Facility for Disaster Reduction to conduct community and Recovery (GFDRR) Open Data for Resilience Initiative (OpenDRI) uses tools such as OpenStreetMap Open Cities project ( http://www.worldbank.org/en/region/sar/publication/planning-open-cities- mapping initiatives under its mapping-project ). This type of community mapping makes it possible to update exposure data more frequently. Ultimately, these data can be incorporated in disaster risk assessments and inform projections of exposure for assessing future disaster risk. Case Study H provides insight into the benefits of an OpenDRI project in Malawi. Maps of Kathmandu, developed by OpenStreetMap before (left) and after (right) the MW 7.8 2015 Figure B6.2.1. Gorkha earthquake in Nepal. The left panes show a view of Kathmandu city, the right panes show greater detail at the building level. These images suggest how substantial increases in mapped information produced by the OSM community can improve maps when the need arises. © OpenStreetMap contributors (CC BY-SA, ). Source: https://creativecommons.org/licenses/by-sa/3.0/ Maps of Monrovia, Liberia, developed by OpenStreetMap before (left) and after (right) Figure B6.2.2. the 2014–2015 Ebola crisis in West Africa. The left panes show a view of Monrovia city, the right panes show greater detail at the building level. https://creativecommons.org/licenses/by-sa/3.0/ Source: © OpenStreetMap contributors (CC BY-SA, ). The Open Cities Project was launched in November 2012 to create open data ecosystems that will facilitate innovative, data- driven urban planning and disaster risk management in South Asian cities. Open Cities represents a scalable approach to developing open, accurate, up-to-date spatial data on the characteristics and location of built and natural environments. continues

66 / 6. Quantifying the Evolution of Disaster Risk 46 Box 6.3 Continues Since its start, Open Cities has brought together stakeholders from government, donor agencies, the private sector, universities, and civil society groups to create usable information through community mapping techniques, to build applications and tools that inform decision making, and to develop the networks of trust and social capital necessary for these efforts to become sustainable. The Open Cities Project launched its efforts in three cities: Batticaloa, Sri Lanka; Dhaka, Bangladesh; and Kathmandu, Nepal. In these cities, the project has led to development of comprehensive and accessible databases of the built environment. For instance, Batticaloa now has a detailed structural database of every building, and Kathmandu has a database of all schools and hospitals that can be used for risk assessment. The Open Cities Project has improved in-country capacity to update, maintain, and use key data sets; it has created innovation spaces (such as the Kathmandu Living Labs), internship opportunities, and university curricula that provide students with employable skills; and it has mainstreamed open data use and strengthened data collection and management processes at different levels of government. The Sri Lanka Survey Department, for example, asked for support to start incorporating crowd-sourcing and community mapping approaches into its regular work flow, and the government of Sri Lanka has sought support for the creation of an Open and Spatial Data Infrastructure. Another outcome of Open Cities is the adoption of new applications by multiple levels of government and World Bank– financed projects, as well as development of complementary new partnerships and increased collaboration. New partners to implement projects include the U.S. Department of State, the United States Agency for International Development (USAID), the Humanitarian OpenStreetMap Team (HOT), and the American Red Cross. protection on the local scale where wider availability of high-resolution Producing detailed risk these data are available for small topography data such as LIDAR assessments study areas, but these studies face has made possible the analysis The end goal of a disaster risk other uncertainties. For example, of coastal flood risk. Figure 6.2 assessment often determines in order to represent evolving demonstrates the detail that can the resolution of modeling that risk at a local scale, one needs to be obtained from high-resolution is required. A national-level risk translate changes in global climate data sets such as LIDAR for profile used for identifying hot spots into analysis of changes in local topographically sensitive analyses, on a large scale can be prepared flood frequency and intensity. To such as analysis of coastal flooding using lower-resolution data than obtain high-resolution estimates due to sea-level rise. With the recent would be required to better assess of future flood risk, it is necessary release of WorldDEM, a new digital the impact of mitigation strategies. to downscale projections of elevation model (DEM) product with A range of limitations exists in precipitation to the local level improved vertical accuracy, the producing detailed estimates of and implement those inputs into accuracy of coastal (and river) flood current and future risk, especially detailed hydrologic and hydraulic modeling is set to improve further. in data-scarce areas. For flood models. Furthermore, given the Because of the effects of local risk, for example, information on strong topographic effects on flood environmental factors and small- the status of flood management depth, improved elevation data are scale physical processes, high- (such as flood protection standards also required at this detailed level. resolution modeling is also required and early warning systems) is The availability of high-resolution to fully define the local effects not yet available globally (Ward elevation data is one of the most of temperature extremes. One of et al. 2015), precluding its use in significant limitations on accurate the effects of climate change in global models. Local coastal flood analyses of flood and sea-level rise, conditions of extreme heat is a and river flood assessments have including those that incorporate the advantage of including such surface moisture feedback, which important information about flood flood management strategies. The contributes to amplified heat

67 47 The making of a riskier future: How our decisions are shaping future disaster risk / Box 6.4 Global Population Data Sets An increasing number of spatial data sets provide estimates of human settlement through absolute population values, population density, characterization of land use, and delineation of urban/rural extents, and they therefore have the potential to be used in disaster risk assessment. These data sets are generally derived from census data and satellite imagery, and vary in available resolution. They can be used as baseline data sets for projecting exposure into the future. Among the most commonly used global data sets are the following: www.ornl.gov/landscan/ Landscan ■ ■ ) offers annually updated global population distribution at a spatial resolution of 30 arc ( seconds (c. 1 km2 at the equator), generated using census data, administrative boundaries, high-resolution land-use data, and topographic data to identify areas of land unsuitable for habitation or development, and aerial imagery to identify settlement patterns. ■ ■ The Global Rural-Urban Mapping Project (GRUMP) ( http://sedac.ciesin.columbia.edu/data/collection/grump-v1/methods ) has generated gridded population at 30 arc seconds resolution for 1990, 1995, and 2000 using census data and satellite data. Urban extents have been derived from NOAA’s night-time lights data set, and this project also provides a point data set of all urban areas with populations of > 1,000. ■ ■ provides a 30 arc-second (1 km http://www.ciesin.columbia.edu/data/gpw-v4/) (GPWv4) ( Gridded Population of the World The at the equator) resolution population data set, consisting of population estimates at five-year intervals between 2005 and 2020. ■ ■ WorldPop ( http://www.worldpop.org.uk/) provides freely available gridded population data at 100 m resolution for all low- and middle-income countries. The data are developed using high-resolution land cover, settlement, and census data . This level of detail enables the mapping of rural settlements and (Linard, Gilbert, and Tatem 2011; Tatem et al. 2007) provides information on the accessibility of population centers to rural populations. ) is an open database of ■ ■ The Global Earthquake Model (GEM) Global Exposure Database ( http://www.globalquakemodel.org/ global building stock and population distribution for earthquake vulnerability assessments. It provides multi-scale data (national to per-building scale) derived using multiple sources and homogenized to form a consistent data set (Dell’Acqua, Gamba, and Jaiswal 2012). ■ The Global Human Settlement Layer (GHSL) ( ■ http://ghslsys.jrc.ec.europa.eu/ ) is the first attempt to produce a high-resolution global data set of human settlement, through automatic image information retrieval of very high-resolution (0.5–10 m) remotely sensed image data input (Pesaresi et al. 2013). In places where no high-resolution imagery is available, the GHSL presents best estimates of human settlements using Landscan population and Modis 500 m urban extent data. (IMAGE) ( http://themasites.pbl.nl/models/image/index.php/Welcome_ The Integrated Model to Assess the Global Environment ■ ■ to_IMAGE_3.0_Documentation ) provides global downscaled model output data for a wide range of environmental and socioeconomic indicators, including global population and GDP per capita projections. The IMAGE model includes results from the HYDE history database on the global environment (Klein Goldewijk et al. 2011), which contains freely available raster data layers on estimated population, GDP, land use, greenhouse emissions, industrial production, and several agricultural indicators for the period 10,000 BCE–2005 CE. waste heat emission from buildings stress—and such an effect can be areas, and fine-scale models are required to simulate onshore/ and transport, and because of urban captured only in complex, land- offshore winds, which could affect construction materials’ thermal atmosphere coupled models that temperature response to climate properties (McCarthy, Best, and capture evapotranspiration as well change in coastal areas (Diffenbaugh Betts 2010, and McCarthy et al. as changes in temperature. Moreover, et al. 2007). 2012). Simulation of urban effects ocean-atmosphere coupling is involves the representation of urban required to show influence of sea Urban centers are susceptible to amplified heat extremes because of land cover at a resolution finer surface temperatures on coastal

68 48 / 6. Quantifying the Evolution of Disaster Risk Figure 6.2. The effect of projected sea-level rise between 2010 (top) and 2100 (bottom) at Cité de Soleil, Port-au-Prince, Haiti. Source: World Bank; Imagecat Inc.; RIT Haiti earthquake LIDAR data set (http://opentopo.sdsc.edu/) overlaid with OpenStreetMap data. Sea-level rise scenarios are based on IPCC data in Church et al. (2013).

69 Making a riskier future: How our decisions are shaping future disaster risk / 49 than GCM or RCM grid cells, and Features of the exposure component of models that seek to quantify Figure 6.3. evolving disaster risk. Complexity of analysis increases from top to bottom. thus requires downscaling of GCM results to the urban scale. Without Hierarchy of complexity in disaster risk analysis methods: Exposure the detailed representation of urban effects, local analyses of heat No scenario used for future conditions; static view extremes may result in a lower daily of present day population or asset value is assumed. minimum and lower daily maximum temperature than analysis with urban land cover. Single factor to project conditions: e.g., national population or asset Complexities in modeling growth applied to present distribution evolving exposure Many disaster risk assessments use a static view of exposure: a Multiple factors used to project future conditions, including changes in snapshot of data, taken from the population, capital, and distribution of assets. present time or from the point in the past when the data were Modified from Bouwer 2013, table 1. Source: collected. Recently, however, new methodologies have been developed such as low-lying coastal areas or exposure. At best, a static exposure for representing trends in exposure assessment might use estimated Small Island Developing States, change, so that both past and future current population and asset which are highly susceptible to changes in population and economic values generated by scaling past rising sea levels. Evolving exposure activity in hazard-prone areas are population and GDP change to the can be incorporated into existing taken into account, to different present day. Such assessments model frameworks by projecting levels of complexity (figure 6.3). present an estimate of potential spatial trends in land use (indicating losses for the current or past urbanization), population growth, A common example of a static situation, but do not incorporate and economic assets. Whereas exposure assessment is the use of projected risk. The use of a selection detailed projections of exposure census data or household surveys of data sets also presents the issue change are integrated in local-scale to establish the population at of nonstationarity in the data. This risk assessments and in national risk, or use of current estimates issue arises when two or more data risk assessments in a few high- of asset value and replacement sets of different exposure indicators cost to produce static views of income countries, they are more are created at different points in residential or commercial exposure. difficult to include at the global time and therefore do not represent National censuses are generally scale or in data-scarce areas. the same baseline situation. In carried out on a regular cycle, once these cases, projection of combined every 5 to 10 years, and are more Using socioeconomic exposure to the current situation comprehensive than household scenarios to project will have begun from different surveys (though the latter often population points in time. include data not contained in The collaborative development of Constantly evolving exposure can a census and can be seen as future socioeconomic scenarios have particularly important impacts complementary). This temporal (first SRES and later SSPs) has in areas of rapidly expanding resolution, combined with typical established a common framework population and urban development, delays in publishing census data, for implementing socioeconomic or in areas that are particularly means that risk assessments susceptible to changes in hazard, projections in disaster risk always have an outdated view of

70 50 / 6. Quantifying the Evolution of Disaster Risk assessments. These scenarios are socioeconomic development, and urbanization on flood risk is not relevant only to exposure— and they quantify development, demonstrated by assuming current socioeconomic evolution should independent of climate change or climate conditions continue also be taken into account in climate policy. Implementation of while population increases. The considering how future hazard the SSPs and the development of population living in flood-prone might be influenced by certain increasingly sophisticated methods areas globally could increase by changes in society, such as climate for projecting global population, 33–64 percent by 2050, and by policy or economic activities, which urban extent and economic activity, 20–91 percent by 2080 (table 6.1). affect future climate conditions. provide new exposure scenarios In projecting global coastal flood According to the IPPC (Field et al. for incorporation into disaster exposure in port cities, Hanson 2014, 56), risk assessments. These scenarios et al. (2011) defined population take into account heterogeneous Uncertainties about future distribution at 2005 from Landscan development patterns, based vulnerability, exposure, and data and mapped this to SRTM on improved understanding of responses of interlinked human (NASA’s Shuttle Radar Topography historical trends in population and natural systems are large Mission) topography data to growth and urban development. (high confidence). This motivates obtain population at different The studies described below exploration of a wide range elevations. Population distribution demonstrate integration of evolving of socioeconomic futures in was projected to 2075 using exposure in global-scale flood, assessments of risks. regional population scenarios cyclone, and drought modeling and from the projected urbanization The IPCC Special Report on Emissions reiterate the important influence of rate (extrapolated from the 2005– Scenarios (Nakicenovic et al. 2000) evolving exposure on disaster risk 3030 rate) of the Organisation developed emission scenarios that losses. for Economic Co-operation and included socioeconomic evolution Arnell and Lloyd-Hughes (2014) Development (OECD). The analysis as one of several drivers of change estimated future population assumed that any new urban areas in future emissions, along with exposed to water scarcity and in each city would have the same changing population and land use, flood hazard under future climate proportion of buildings exposed as economic and social development, and socioeconomic conditions. existing urban areas in that city. and technological development in They projected from a baseline GDP growth rate was based on the agricultural and energy sectors. of population at 2000, using the OECD projections of national GDP, Four scenario “families” containing GRUMP data set to provide spatial with all cities assumed to grow at 40 scenarios of theoretical futures distribution of population. National the national rate. Socioeconomic were developed based on storylines population was projected to 2050 change was shown to be the most for the future situation in each of and 2080 using the five SSPs. significant driver of population and the above drivers. These theoretical Projected population was rescaled assets exposed to the 100-year futures are each associated with to a higher-resolution grid using coastal flood hazard. future levels of GHG emissions, a single urbanization projection which are used as inputs to climate Jongman, Ward, and Aerts (2012) from SRES scenario A1B. The modeling. studied the socioeconomically authors acknowledge that the use driven evolution of global river More recently, the Shared of a single urbanization projection ́ ́ and coastal flood risk between Socioeconomic Pathways were may affect their estimates of the present day and 2050. They developed as part of the shared flood-prone populations, as the demonstrated the significant scenario framework (along with various growth scenarios in the increase in exposure in developing the RCPs); these are described SSPs may not occur with the countries even without climate by O’Neill et al. (2014). The SSPs same spatial distribution. The include a narrative storyline of impact of population growth change factors. Using World Bank

71 Making a riskier future: How our decisions are shaping future disaster risk / 51 Population (millions) in Flood-Prone Areas Resulting from Tokyo produced a Landsat-based Table 6.1. Socioeconomic Change, 2050 and 2080 global urban area map for five- year intervals between 1990 and SSP4 SSP5 SSP1 SSP2 SSP3 2010; examples are shown in figure 1041 (64) 847 (34) 2050 907 (43) 846 (33) 931 (47) 6.4. Satellite-derived night-time 2080 763 (20) 931 (47) 1213 (91) 936 (48) 768 (21) light information was used by Arnell and Lloyd-Hughes (2014). Source: Ceola, Laio, and Montanari (2014) Population is shown for the five SSPs. Numerals in parentheses show percentage increase in Note: to analyze changes in human population relative to the year 2000 flood-prone population of 634 million. settlement along rivers worldwide between 1992 and 2012. population and GDP projections not remain confined to its present Concerning the projection of urban based on baseline data from the footprint. Different urbanization expansion, Seto, Güneralp, and HYDE database, the study made patterns will influence the locations Hutyra (2012) demonstrated that projections of global population in which population growth and the analysis of an historical time and assets by projecting current economic activity occur, and series of satellite images can be population density and land use. therefore influence the evolution used to derive regionally specific The proportion of urban land use of disaster risk. Thus it is just probabilistic urban expansion per country was projected in line as important for assessments to patterns; the study goes on to with population increase. Two include projections of how and apply these patterns to develop a methods were used to obtain where urban development occurs as global data set of urban land cover projected exposure: GDP per to include the projected change in in 2030. The authors expect that capita based on population, and a population and asset values. between 2000 and 2030, the area commonly used depth-damage ratio In order to project urban of urban land use in developing combined with value of maximum development into the future, countries will triple, while damage per unit area, dependent past urban development must population is expected to double. on land-use type. Like other global be characterized. On a global There is likely to be more urban risk assessments, this study did scale and for data-scarce areas, expansion in the period 2000–2030 not account for detailed data analysis of past and future than ever before, and—based on such as flood defenses. Estimated human settlement has relied on probabilistic modeling of population global population exposed to satellite data. Angel et al. (2005) densities and location of new urban river and coastal flood is expected characterized urban area based to increase from 992 million in land—this expansion will likely on 30 m resolution Landsat 2010 to 1.3 billion in 2050, with be highly variable in magnitude imagery combined with census corresponding assets increasing and location within countries. The data from 1990 and 2000, and from US$46 trillion to US$158 probabilistic global urban expansion highlighted a gradual decline in trillion. Urban land exposed to model developed in this study has urban density globally. The Global floods increases from 44,000 been applied to estimate trends Urban Footprint, developed by 2 2 in in 2010 to 72,000 km km in global exposure to floods and the German Aerospace Center 2050, with corresponding damage droughts (Güneralp, Güneralp, and (DLR), used synthetic aperture increasing from US$27 trillion to Liu 2015) and used for probabilistic radar (SAR) and optical satellite US$80 trillion in that period. risk assessment on a national scale data to map the urbanized areas in Indonesia (Muis et al. 2015). of megacities for 1975, 1990, Projecting urban expansion An ongoing challenge is that global 2000, and 2010 (Taubenböck et assessments, as well as several al. 2012). The Earth Observation As populations grow and economic studies of developing countries, activity increases, urban areas Data Integration & Fusion Initiative extend; the built environment does (EDITORIA) of the University of estimate exposure changes using

72 52 / 6. Quantifying the Evolution of Disaster Risk Figure 6.4. Expansion of urban land-use from 1990 (orange) to 2010 (purple) in Shanghai, China (top) and Kampala, Uganda (bottom). Source: World Bank based on analysis by EDITORIA, University of Tokyo, using Landsat data.

73 Making a riskier future: How our decisions are shaping future disaster risk / 53 relatively low-resolution globally vulnerability might evolve over Evolving vulnerability: available data (> 1 km x 1 km) on time and to incorporate changing An ongoing challenge population and land use; more vulnerability into disaster risk Compared to hazard and exposure, detailed spatial information is assessments. This remains a major vulnerability has, to date, been not available. In several countries challenge to quantifying evolving quantified to a very limited extent in and cities, changing exposure risk, but it is being tackled by an the context of evolving risk. Global has been mapped at a much increasing number of studies. changes in vulnerability and their higher level of detail, using fine- Changes in vulnerability are linked effects on disaster risk therefore resolution land-use data or even closely to socioeconomic scenarios remain highly uncertain. Some building-level information (Aerts and policy decisions. Communities methodologies have been developed et al. 2014; Jongman et al. 2014). can decrease vulnerability by raising that project social vulnerability in Box 6.5 describes an approach to hazard awareness, developing terms of socioeconomic conditions urban expansion at the city level appropriate responses to hazards and structural vulnerability based that is based on historical trends (e.g., evacuation planning and provided in the Atlas of Urban on development of the building exercises), implementing warnings Expansion (Angel et al. 2013). The stock (figure 6.5), but these systems, constructing properties Atlas of Urban Expansion provides approaches have been implemented in a hazard-resistant way, and measures of population growth, in a few cases only. promoting household/institutional annual expansion of the urban area, Since vulnerability is influenced preparedness. A country’s levels fragmentation, compactness, and by a wide range of factors, it is of income and development have annual change in population density a complex task to estimate how a strong relation with the level of along with maps and spatial data for urban land-use expansion between around 1990 and 2000 for 120 cities globally, and between 1800 and Features of the vulnerability component of models that seek Figure 6.5. to quantify evolving disaster risk. Complexity of analysis increases from 2000 at 25-year intervals (for 30 top to bottom. cities). Metrics are provided for the study city, the regional average, and Hierarchy of complexity in disaster risk analysis methods: Vulnerability the global average. Box 6.5 describes a method that A single vulnerability relationship for an entire study area. uses past urbanization trends to characterize relationships between urban features, which are then used to project expansion forward. This methods avoids basic extrapolation of past growth trends, which are Simple or low-resolution impact model: vulnerability relationship valid for short time horizons of determined by land use, asset type, or population group. 20–30 years, but not for longer time horizons (Masson et al. 2014). Modeling of urban development on longer time horizons benefits from economic models that Full impact model, including the influence of multiple structural or social include behavior of residents and characteristics on vulnerability. developers as well as construction Modified from Bouwer 2013, table 1. Source: and rental markets.

74 54 / 6. Quantifying the Evolution of Disaster Risk Box 6.5 Spatial Patterns of Urban Growth in Africa Urbanization has profound social, environmental, and epidemiological implications. Spatial and quantitative estimations of urban change and population density are valuable information for vulnerability assessment. A model has been developed to predict the spatial pattern of urban growth in African cities to 2020 and 2030, based on the observed growth of 20 large African cities between 1990 and 2000 (Angel et al. 2013). The model combined a parsimonious set of generalizable factors that influence spatial patterns of urban growth: slope angle derived from a digital elevation model; accessibility represented by travel time to the central business district along the transport network; and neighborhood indexes such as the proportion of urbanized land within a given buffer distance (150 m, 1 km, and 5 km). Boosted regression trees (BRTs) were developed using classification of Landsat images into urban and nonurban pixels (30 m resolution) between 1990 and 2000 for 20 African cities as training data. The BRT model was then used to generate predictions of the rural to urban conversion probability for every 100 m pixel in the study cities (figure B6.5.1A) and predict their urban growth pattern (figure B6.5.2). Figure B6.5.1. Rural to urban conversion probability of 100 m pixels in Kampala, Uganda (A) and the two main urban expansion predictors: travel time to central business district (B) and proportion of urban land within 1 km (C). B A C C A B Conversion probability Travel time (hours) Proportion of urban land High: 0,31 High: 6 High: 1 0 5 5 5 10 km 0 10 km 10 km 0 Low: 0 Low: 0 Low: 0 Source: Catherine Linard.

75 55 The making of a riskier future: How our decisions are shaping future disaster risk / Box 6.5 Continued Figure B6.5.2. Predicted urban extents in Kampala, Uganda, in 2010, 2020, and 2030. 20 10 0 km ■ Undeveloped ■ Developed Source: Linard et al. 2014. Results showed that accessibility (figure B6.5.1B) and proportion of urban land within 1 km (figure B6.5.1C) were the most influential predictors of urban expansion. BRT models were found to have greater predictive power than a simple distance- based model (i.e., a model in which the rural to urban conversion probability is proportional to the distance from the nearest urban pixel, resulting in spatially uniform urban growth). Predictive power was low overall, however. The model predicted spatial growth well for small, rapidly growing cities, but it performed less well for large, slowly expanding cities—i.e., cities in later phases of urbanization. It is difficult to adequately capture all spatial heterogeneities of cities and temporal influences on development in a statistical model, and further models need to be developed to account for urban growth patterns in their different phases. The simple and generalizable model developed in this work is now being used to produce the most detailed Africa-wide urban expansion predictions that have yet been made, and it will provide realistic scenarios of urban growth to 2020 and 2030. Future work will use a version of the model presented here to simulate the urban expansion of every large African city to 2020 and 2030 and to produce projected population distribution data sets under a range of growth scenarios following AfriPop/ ). www.worldpop.org.uk WorldPop methods (Linard et al. 2012; Catherine Linard, Université Libre de Bruxelles. Source:

76 / 6. Quantifying the Evolution of Disaster Risk 56 that vulnerability levels in low- Jongman et al. (2015) analyzed vulnerability to disasters, as has income countries decline as their the differences in vulnerability been emphasized in a number income converges to the income between countries as well as of statistical analyses. Toya and level of high-income countries, changes over time. Using high- Skidmore (2007) analyzed the these projections show a possible resolution global flood inundation relationship between disaster strong reduction in future global and exposure maps, they showed impacts (mortality, losses as a vulnerability. However, if the that vulnerability to global flood share of GDP), GDP, education, and effective adaptation that contributes declined between 1980 and 2010, the level of government for 151 to lessening vulnerability in low- in terms of mortality and losses as countries. They found evidence that income countries does not happen, a share of the population and GDP countries with a high GDP and high future losses and fatalities could exposed to inundation. This decline levels of education and government increase very steeply. The authors coincided with rising per capita have significantly lower disaster conclude that reducing vulnerability income globally and converging impacts. This relationship between could counteract a large part of the levels of vulnerability in low- and disaster impacts on the one hand increase in exposure and hazard high-income countries (a function of and income and governmental under socioeconomic growth and declining vulnerability in developing strength on the other was later climate change. countries). Projections of future reestablished for overall disaster losses and fatalities were made impacts (Felbermayr and Gröschl Hallegatte (2012) argues for caution using a combination of climate 2014), and specifically for floods in making assumptions about models, emission scenarios, (Ferreira, Hamilton, and Vincent converging vulnerability levels socioeconomic pathways, and 2011) and tropical cyclones in high-income and low-income adaptation scenarios. Assuming (Bakkensen 2013). countries as income rises in the latter. He considers it questionable that Bangladesh would have the same level of vulnerability as Sweden in case these two countries reached the same level of income at some point in the future, and argues that other factors such as geography may also affect the relationship between income and losses. In terms of structural vulnerability, Lallemant, Wong, and Kiremidjian (2014) demonstrated a potential framework for evolution of exposure and vulnerability using simulations of 2,500 equally likely scenarios of an historical earthquake in Kathmandu, Nepal. Exposure was projected to 2015, 2020, and 2025 on the basis of a quadratic fit of census data (1991, 2001, 2011). To account for evolution of vulnerability, the study applied three examples of structural expansion typical of the case study area to represent Varanasi, India, flash flood. Photo credit: Danielrao/Thinkstock.com

77 Making a riskier future: How our decisions are shaping future disaster risk / 57 river floods in Indonesia, where they at current heights. With raised dike incremental construction over time. find that increasing flood protection heights, the global annual cost of With each expansion of the structure, to a 1-in-100-year standard could adaptation plus the annual flood the vulnerability curve changed to prevent 93 percent of all flood losses. cost are much lower than the annual relfect the new vulnerablity. The cost if dike heights are maintained projected changes in exposure and With respect to changing social at the present height. vulnerability were shown to increase vulnerability, several multifactor risk significantly. This framework indexes have been developed to Hallegatte et al. (2013) used is extended into a more detailed quantify this on a local to national estimates of capital production analysis of the evolution of structural level, specifically for the United per person to estimate AAL due to vulnerability in case study E. States (Cutter, Boruff, and Shirley coastal flood. They also included the 2003), the Netherlands (Koks et al. effects of evolving vulnerability on Hinkel et al. (2014) investigated 2015), and China (Zhou et al. 2014). annual flood loss by implementing the influence of dike protection on To determine spatial and temporal two scenarios of flood protection projected vulnerability to coastal patterns in social vulnerability, and assumptions about levels of flood damage under scenarios of the U.S. social vulnerability index adaption in the future. Their analysis sea-level rise and socioeconomic was applied to county-level data showed that socioeconomic change changes using the RCPs and SSPs. from the four decades 1960–2000 led to an increase in annual global Two scenarios of adaptation were (Cutter and Finch 2008). The flood loss in the 136 coastal cities, applied: dikes are maintained at majority (85 percent) of counties from US$6 billion to US$50 billion their present height into the future; showed no statistically significant in 2050; when the additional effects and dikes are raised as the demand change in vulnerability over the of climate change and subsidence for safety increases with growing four decades; only 2 percent are included, the annual global affluence and increasing population showed a statistically significant loss in 2050 is over US$1 trillion. density. The analysis showed that and clear increase or decrease Under an adaptation scenario the number of people flooded in vulnerability. Cutter and Finch assuming that flood protection will each year rises significantly with (2008) used the baseline data to be increased in height to maintain each degree of global temperature project vulnerability forward to the probability of flooding at present increase if dikes are maintained at 2010, based on linear trends in levels, estimated losses by 2050 are their present height (for all RCPs county-level vulnerability. While limited to US$60 billion to US$63 and SSPs). If dike height is raised, this is a simple approach, based on billion. The authors therefore argue the number of people flooded would relatively few (four) data points, it that a future protection strategy that decrease relative to the present demonstrates a possible method reduces annual flood probability is day. Expected annual flood cost for producing projections of social required to avoid an increase in risk. would rise with increasing global vulnerability into the future for Muis et al. (2015) also emphasize the temperature, but would rise by a incorporation into disaster risk importance of flood protection in a much smaller amount if dike heights assessment. national-level study of coastal and are raised rather than maintained Compared to hazard and exposure, vulnerability has, to date, been quantified to a very limited extent in the context of evolving risk. Global changes in vulnerability and their effects on disaster risk therefore remain highly uncertain.

78 58 / Making a riskier future: How our decisions are shaping future disaster risk

79 Making a riskier future: How our decisions are shaping future disaster risk / 59 Identifying Effective Policies 7 for a Resilient Future he preceding chapters have shown that currently available disaster risk assessment methodologies can provide detailed insights into past, current, and future disaster risk. Existing models and data are able to T incorporate the evolution of hazard from the simulation of climate change scenarios in global- and regional-scale climate models; they can incorporate the evolution of exposure through the projection of population growth and socioeconomic change, and the resulting patterns of urbanization and urban expansion. Models have incorporated evolving vulnerability to a lesser extent, but methods to project future levels of adaptation and structural vulnerability are being developed and applied. Increases in disaster risk can be limited by a number of disaster risk management (DRM) policy tools and strategies related to data improvements, Methods to project future risk analysis methods, planning and development, and design of mitigation levels of adaptation and and adaptation programs. There are also policies that spread the financial consequences of disasters when they do occur. From the wide range of DRM structural vulnerability tools available, this chapter selects and describes several key interventions are being developed and that can either improve risk assessment or directly inform policy decisions. applied. FACING PAGE Bang Kachao: Bangkok’s Green Lung. In the heart of Thailand’s most populous city, an oasis stands out from the urban landscape like a great “green lung.” That’s the nickname given to Bang Kachao—a lush protected area that has escaped the dense development seen elsewhere in Bangkok. Photo credit: NASA, acquired February 2, 2014 59

80 60 / 7. Identifying Effective Policies for a Resilient Future and communities more susceptible Mitigate climate change Manage urbanization to loss from some hazards (e.g., Mitigating emissions to limit extreme temperatures) while Limit harmful land-use change the continued increase in global focusing on reducing vulnerability and resource consumption temperatures that is expected in the to other hazards (e.g., earthquake). Land-use changes related to next decades is key to mitigating In large established cities such urbanization—deforestation, more disaster risk (manifested as changes as Bangkok and Tokyo, policies to extensive impermeable surfaces, in the rate of sea-level rise and the restrict groundwater extraction have increased groundwater extraction— intensity, frequency, and spatial been shown to effectively reduce have an important impact on distribution of cyclone, flooding, the rate of subsidence and restore disaster risk. Deforestation and and drought). There is a wide body groundwater levels (case study C). impermeable surfaces lead to of literature on the mitigation of Where high rates of subsidence faster run-off of precipitation and climate change and the strategies have been identified, restrictions increased surface flood hazard; that decision makers can use can be applied in conjunction with groundwater extraction leads to to reduce emissions, such as artificial recharge of aquifers and subsidence in coastal cities; and implementing new technologies and development of alternative supply new human settlements in hazard- changing consumption behaviors solutions. Planners and policy prone areas put more and more through taxation and regulation. makers in rapidly growing urban people at risk. Even changes in the Mitigation policies can operate at centers have the opportunity to use of existing developments can the national economy level and address the potential for subsidence change disaster risk, for example by within specific sectors (IPCC 2014; before it becomes an issue by increasing a building’s capacity or OECD 2008). For example, the establishing good management its vulnerability. Too often, planning energy sector could move from of water resources as part of decisions are made without investment in extraction of fossil integrated urban floodwater and considering the implications for fuels to investment in renewables, pollution management plans; this local hazard. Changes in upper river nuclear energy, and carbon approach will ensure a sustainable catchments that increase the speed water supply without incurring the capture and storage technologies. of water flow into swollen rivers, for detrimental effects of subsidence. In agriculture and forestry, example, may reduce flood hazard conservation and management in the upper catchment, but they Control increases in exposure of land and food resources could increase the hazard downstream. decrease deforestation and Thus catchment-level analyses Exposure change is shown to are often required to investigate maximize supply from agricultural be responsible for the majority changes in the disaster risk of the land while reducing emissions of increase in disaster risk. whole catchment. (FAO 2013). The Infrastructure and In Indonesia, for example, settlement planning sector should urbanization is estimated to lead The impacts of increased urban also incorporate climate action to at least a doubling of flood risk expansion must be considered and plans at the urban scale to ensure between 2010 and 2030, regardless accounted for in effective urban energy and transport infrastructure of the uncertain effects of climate planning and resource management. are effective in providing required change (Muis et al. 2015). Where These impacts include subsidence, services with least environmental there is rapid urbanization and which is a very important factor in cost. This sector is considered migration, risk evolves most rapidly relative sea-level change, as well as particularly important in rapidly in response to changes in exposure expansion of impermeable surfaces urbanizing areas, which are in and vulnerability. Land-use and land-use changes that alter the the process of developing new planning policies that incorporate risk environment. Effective planning risk are important to controlling must also avoid making structures infrastructure systems.

81 61 The making of a riskier future: How our decisions are shaping future disaster risk / Box 7.1 Land-Use Planning Land-use planning is the primary tool for controlling exposure to hazards. Land-use planning tools can be used to prevent new development in hazardous areas, relocate assets to less hazardous locations (“managed retreat”), or restrict the types of land use that can be permitted in hazard zones. The absence of urban planning in many areas of the world, particularly in developing countries, has led to uncontrolled development in hazardous areas (such as on landslide-prone hillsides) and to rapid development into areas of high flood hazard (such as Jakarta, Manila, and Bangkok). Where unplanned or poorly planned development occurs in hazardous areas, exposure and vulnerability increase significantly. Policies and regulations can undoubtedly be designed to limit exposure in hazard-prone areas. It is the enforcement of such policies that remains a big challenge. In many high-income countries it can be difficult, even with regulation effective by law, to prevent increasing exposure, either due to development or land-use changes, see Case Study I. In many low-income countries, the enforcement is even more limited, not only because governmental capabilities for enforcement are weak but because the areas themselves tend to be attractive in terms of jobs and services (Hallegatte et al. 2015). In a national-level analysis of flood risk and adaptation options in Indonesia, Muis et al. (2015) show that land-use planning can be a key policy tool for reducing flood risk in rapidly urbanizing countries. The authors show that if no new cities were constructed in Indonesia’s flood prone-areas between 2010 and 2030, annual expected losses from river and coastal floods would be 50–80 percent lower by the end of that time period than if cities were built. Without such limits on urban construction, it is estimated that flood risk may increase by as much as 166 percent (river floods) and 445 percent (coastal floods) over the three decades due to urbanization alone, with additional increases expected as a result of climate change and economic growth. the evolution of disaster risk, carbon sequestration. In coastal Reduce vulnerability through areas, green infrastructure can also primarily by providing a mechanism urban design be used to combat effects of rising to prevent new development Climate extremes pose serious sea levels (see the section below on or detrimental change of use in health, safety, and financial ecosystem-based risk management). hazard-prone areas (see box 7.1). risks to cities, where people and For example, land-use planning As cities generally suffer from a lack socioeconomic activity cluster policies can help to ensure that of space, the implementation and together. Urban design can vulnerable or high-value assets and design of green infrastructure needs incorporate green infrastructure— heavily occupied buildings (e.g., to be well thought out. First of all, eco-roofs, green spaces (parks and business or residential) are not there is no single recipe for reducing wetlands), and tree planting—to located on hazard-prone land, and vulnerability through urban design: manage storm water and flooding can seek to reduce exposure by adaptation measures need to be and reduce ambient temperatures placing low-density usage activities tailored to the local context. A and the urban heat island effect. (agriculture, parks and recreational neighborhood-specific rather than Green infrastructure, which land) in those areas. Plans for a citywide approach is preferable moderates expected increases designing structures and locating because it can account for the in extreme precipitation or assets should also consider multiple biophysical and sociodemographic temperature by its infiltration, interrelated hazards and should differences that exist within cities shading, and evaporative capacities, account for the impact of structures (Derkzen, van Teeffelen, and Verburg has been cited as having multiple (e.g., impermeable surfaces) on the 2015b). Neighborhoods that are benefits in climate adaptation local environment. Building design most vulnerable from a biophysical (Derkzen, van Teeffelen, and should also aim for habitability in perspective may not necessarily Verburg 2015b; Foster, Lowe, and future climates as well as in the benefit from or wish to implement Winkelman 2011). Trees planted present climate. the most effective adaptation in urban areas can contribute to

82 62 / 7. Identifying Effective Policies for a Resilient Future measures. The importance of design, construction practices, and earthquake sequence. construction materials will affect recognizing residents’ needs and Land-use planning decisions disaster risk in both current and preferences leads to a second related to hazards that can evolve future climates. consideration in designing green in future climates must take future infrastructure for risk reduction: conditions into consideration. Building practices informed decision making. For This requirement is exemplified by a legitimate implementation of Controlling building practices land-use restrictions within riverine adaptation measures, city planners through legislation or nonstatutory or coastal flood hazard zones. need public support. Derkzen, means influences the evolution of Rising sea levels and more extreme van Teeffelen, and Verburg vulnerability into the future. One precipitation should be accounted (2015a) suggest several ways to approach to limiting vulnerability for in development being planned or enhance public support, ranging is regulating the type and design of approved now; this step will ensure from the promotion of popular buildings that can be constructed, that structures built today—and green infrastructure benefits based on the hazards likely to be considered not at risk of flooding— such as pollution control, to the faced by those buildings in their continue to be found not at risk in prioritization of preferred measures lifetime (see box 7.2). several decades. on different scales, e.g., eco-roofs and gardens, small neighborhood Several key considerations can In the aftermath of a disaster, there parks, and canals along main roads. help to reduce vulnerability. is often a window of opportunity Green infrastructure designs should The first is whether adopting when decision makers can increase always incorporate recreational and building practices from a different resilience to future events through aesthetic functions. Finally, it is region and using nontraditional land-use planning, specifically essential to invest in raising public approaches is appropriate in the by relocating assets or critical awareness—not only about climate context of disaster risk. Builders infrastructure out of hazard zones. change impacts, but also about should consider, for example, what For example, reconstruction the role of green infrastructure in happens when stone walls and plans for Tohoku, Japan, relocate limiting these impacts. heavy tiled roofs are used in areas residential buildings, schools, of high seismic hazard instead and hospitals out of the tsunami Even in countries with well- of the traditional timber frame hazard zone, to be replaced with developed planning policies, the construction that is less susceptible low-density activities (such as extent to which disaster risk is to collapse due to ground shaking. light industry), with activities that integrated into policy varies widely. The second is the need for structural need to be at the coast, or with Furthermore, planning policies design and construction to consider open space that could be sacrificed are not always well enforced, all hazards present, since efforts with minimal economic and life and multi-hazard contexts may to reduce vulnerability to one loss in future events. Similarly, not be properly considered (see hazard can potentially increase reconstruction in Christchurch, New case study I). Existing well- vulnerability to another. Both of Zealand, is reserving large areas of known hazards, moreover, may be these considerations are part of the city for use as green space due ignored in contemporary planning good practice in any region. A third to the high liquefaction hazard. decisions. Some urban development consideration is the need to account of Christchurch, New Zealand, for evolving hazard in order to went ahead in recent decades Manage risk through address expected climate extremes without ground remediation, construction and new hazards that may affect the despite official knowledge of the location in the future. The construction of buildings, liquefaction hazard; the result was infrastructure, and urban Resilience in construction is another significant liquefaction damage to developments should consider how important consideration. Some several suburbs in the 2010–2011

83 63 The making of a riskier future: How our decisions are shaping future disaster risk / and some to be resilient enough to buildings are intended to provide life safety in the event of a disaster, Box 7.2 Reducing Building Vulnerability through Construction Legislation The vulnerability of building stock can be reduced by adhering to building design and construction standards that consider the forces imparted during events like earthquakes and floods. The history of building standards in New Zealand, and the occurrence of the 2010–2011 Canterbury earthquake sequence, demonstrate the important influence that building codes can have. There were estimated to be 3,750 unreinforced masonry (URM; generally stone or clay brick) buildings in New Zealand in 2010 (Russell and Ingham 2010), the majority of which had been constructed prior to 1940. Construction in URM was regionally variable, driven by availability of other building material or occurrence of earthquakes. URM buildings are stiff, heavy, and brittle structures that are likely Christchurch, New Zealand. Photo credit: Nigel Spiers | Dreamstime.com to suffer damage during ground shaking. Specific structural characteristics (e.g., height and configuration) affect the seismic resistance of different buildings within the general URM category, but overall these building are less seismically resistant than other construction types. They have little capacity to deform once the strength of their elements has been exceeded, leading to abrupt failure. In 1931, a magnitude 7.8 earthquake destroyed many URM buildings in the city of Napier in Hawke’s Bay, New Zealand. Subsequently, construction of URM buildings was discouraged and then finally prohibited by legislation. In 1935 a building standard was created that required buildings in New Zealand to withstand horizontal acceleration of 0.1 g, and that recommended reinforced concrete or steel frame for construction of public buildings (New Zealand Standards Institute 1935). In 1965, New Zealand standards prohibited the use of URM to various extents, depending on the seismic zone: entirely in zones of highest seismic risk; for buildings of more than one story in zones of moderate seismic risk; and for buildings of more than two stories in zones of low seismic risk (New Zealand Standards Institute 1965). In 1976, a more advanced loadings code explicitly prohibited the use of URM throughout the whole of New Zealand (Standards Association of New Zealand 1976; Russell and Ingham 2010). While the New Zealand legislation applied to new buildings, from 1968 the government had powers to classify existing buildings as “earthquake prone” and require owners to reduce or remove the danger (Russell and Ingham 2010). Many earthquake-prone buildings were strengthened between 1968 and 2003. When the new Building Act came into force in 2004, strengthening of earthquake-prone buildings was required to achieve one-third or two-thirds of the new building standard. During the 2010–2011 earthquake sequence in the Canterbury region of New Zealand, a M 6.3 earthquake struck the city of w Christchurch. The building stock in Christchurch in 2011 was primarily timber for residential buildings and reinforced concrete in commercial areas, with additional reinforced masonry buildings (Wilkinson et al. 2013) and a number of URM buildings. Thirty-nine of 185 fatalities in the February 2011 earthquake were attributed to the failure of URM construction, primarily in the central business district. Seismic retrofit was shown to be important in mitigating the damage: URM buildings strengthened to 100 percent of the new building standard performed well, those strengthened to 67 percent performed moderately well, and those strengthened to less than 33 percent did not perform significantly better than those that had not been strengthened. Ingham and Griffith (2011) showed that the risk to building occupants and public space occupants (those in the street near the building) was higher for buildings that received no strengthening than for those where walls, connections, or the entire structure had been strengthened, or elements (gables, parapets) secured. Another study showed that not all strengthening systems achieved the level of damage mitigation expected, partially due to the quality of the original construction material, and partially due to shortfalls in design and implementation of the strengthening mechanisms (Wilkinson et al. 2013). continues

84 64 / 7. Identifying Effective Policies for a Resilient Future Box 7.2 Continued Based on the 130 percent increase in the population of Canterbury, New Zealand, between 1930–1940 and 2010, a projection of the number of potential URM buildings in Canterbury suggests that there could have been an additional 275 URM buildings in the region in 2010 had legislation not prohibited their construction (Figure B7.2.1). All other thing being equal (including other trends in construction practices and rates of seismic retrofit), it would follow that the number of casualties in the 2011 Christchurch earthquake would also have been higher. The patterns of damage also suggest that had a smaller proportion of URM building been strengthened, the number of fatalities due to URM damage or collapse could have been higher. This short example thus demonstrates how disaster risk can be mitigated by prohibiting (or requiring retrofit and structural strengthening of) construction types with high seismic vulnerability. Figure B7.2.1. The projected number of URM buildings that might have existed in Canterbury, New Zealand, without building legislation to prevent their construction. 600,000 600 500 500,000 400 400,000 300,000 300 200,000 200 Canterburry population Number of URM buildings 100 100,000 – – 1900–1910 Pre-1900 1930–1940 1910–1920 2010 1920–1930 Population URM buildings – actual URM buildings – projected with no legislation Based on data from Russell and Ingham 2010. Source: continued function in hospitals, continue normal function. Generally, regions, resulting in more hot days for example. Thus in a flood hazard performance-based building and fewer cold days. Regions with codes require critical facilities high temperatures tend to use zone, the facility should not rely on (e.g., schools or hospitals) to traditional construction techniques power systems and communications maintain functionality in the event that allow buildings to remain cool, equipment located on the ground of a disaster; less vital buildings including building orientation, floor or basement. (e.g., shops or offices) prioritize thickness of walls, curved exterior Continuing habitability occupants’ ability to get out alive, surfaces (e.g., domes), height of of structures and they would likely require rooms, presence of courtyards, The habitability of structures is significant repair or rebuilding. areas shielded from direct sunlight, an important issue for a future Resilience also extends to critical features that funnel cool airflow contents of buildings—power supply into the building, and shutters in which extreme temperatures and equipment that is crucial to (Khalili and Amindeldar 2014). are expected to shift in several

85 Making a riskier future: How our decisions are shaping future disaster risk / 65 Where traditional construction has Techniques to Achieve Passive Cooling of Buildings in a Warm Climate Table 7.1. been replaced by other modes of Maximizing heat loss construction that discard these through natural cooling Minimizing heat gain cooling principles, buildings either Take advantage of the following: Shade windows, walls, and roofs ■ ■ become uninhabitable or require from direct solar radiation Air movement ■ ■ another (often technological) Use light-colored roofs to reflect ■ ■ Cooling breezes ■ ■ means of cooling the interior, such heat Evaporation ■ ■ as air conditioning. Reliance on ■ ■ Use insulation and buffer zones to ■ ■ Earth coupling technological means of cooling minimize conducted and radiated heat gains ■ ■ Reflection of radiation significantly increases power consumption and generation, as ■ ■ Make selective or limited use of thermal mass to avoid storing the experience in the United Arab daytime heat gains Emirates over the last few decades makes clear (Radhi 2009); it can Government of Australia 2013. Source: thus present a feedback of increased river network, and thus transfer often not as effective as other emissions into the climate change flood risk to, or exacerbate it in, options at reducing the impact process. Policy makers should downstream locations. A second of the hazard. The evidence-base consider such indirect impacts and concern is that construction of to support these options tends to include them when commissioning “hard” defenses at the coastline and be weaker so there is uncertainty buildings and developments; construction of dams on rivers can regarding their effectiveness” (Royal construction that promotes passive compromise the coastal sediment Society 2014, 62). cooling techniques to minimize heat budget and lead to increased gain and maximize heat loss (such Ecosystem-based approaches to coastal erosion. A third concern as those shown in table 7.1) will last managing the risk of urban, riverine, about engineered defenses is the for decades. and coastal flooding include need to ensure that investment in maintenance of floodplains and them remains effective into the increase in vegetation—specifically, Consider ecosystem- future; they must be maintained forestation of landslide-susceptible based risk management (which can be costly) to an effective slopes and river catchments prone standard of performance in terms Engineered structures such as to flash flooding, the greening of of strength and height, and should dikes, dams, and flood retention urban areas, use of vegetation account for expected sea-level rise areas are commonly installed for coastal protection instead of and increased flood levels. Note along riverbanks and coastlines to sea walls, and setting aside of that engineered solutions are also provide defense against flooding. box 7.3 land in floodplains ( ). The considered for tackling drought Engineered solutions can provide expectation is that such approaches (e.g., irrigation, wells, and drought- a high level of protection against will be able to adapt in an evolving resilient crops) and heat waves (e.g., floods, but they often harm climate, maintaining their ability air-conditioning, urban planning). natural processes—for example, by to mitigate evolving risk without disturbing ecosystem function, and Engineered approaches may be incurring high maintenance and in turn reducing the well-being of complemented by nature-based modification costs. For example, local communities (van Wesenbeeck approaches, or by taking a hybrid natural shorelines evolve on their et al. 2014). One concern is that approach, which can provide a own in response to changing altering a river channel to smooth balance of cost and effectiveness: conditions and require less the channel or increase capacity “Ecosystem-based options are the maintenance than traditional at one point may have the effect of most affordable and have positive protection structures (van channeling flow faster through the additional consequences, but are Wesenbeeck 2013).

86 66 / 7. Identifying Effective Policies for a Resilient Future Box 7.3 Nonengineered Solutions to Flood Protection Sustainable drainage systems (SuDS) are a means of reducing runoff from a site, encouraging settlement and infiltration of water, and treating surface water before it discharges into watercourses. These systems help to mitigate flood risk, and they also protect water quality, particularly in urban areas where surface water can be polluted by activities on roads and other paved surfaces (Charlesworth, Harker, and Rickard 2003). Relying on permeable rather than impermeable surfaces and on green vegetation-based treatment of water, SuDS make use of soakaways, retention ponds, or wetland areas. They are a form of infrastructure such as piped drainage and conventional water treatment grey infrastructure , offering an alternative to traditional systems (Andoh 2011). Coastal vegetation plays an important role in flood protection. Previous studies have suggested that coastal forests, including mangroves, can help to reduce losses due to cyclones (Badola and Hussain 2005) and tsunami (e.g., Dahdouh-Guebas et al. 2005). While the trees may suffer damage, the presence of tree trunks in the water increases friction and slows the flow. Vegetation such as dune grasses can stabilize coastal dunes, which because of their high elevation form a physical barrier to flow from the coast; the grasses bind the dune and mitigate erosion due to storm waves and rising sea levels. Not only can coastal vegetation mitigate the impact of storm and tsunami waves, it can also provide ecosystems that support residents’ livelihoods, for example through provision of timber and fisheries, or via social amenities and tourist activities. accurately quantify risk, improved collection of such data, as can Improve data for risk advances in the analysis of large and ongoing data collection is modeling amounts of earth observation key. In environments with rapidly Improving the accuracy of data used data and subsequent projection of changing exposure data (e.g., in risk models and reducing data’s changing population, land use, and developing countries with rapidly uncertainty are key to improving economic activity. growing urban populations), the the results of each component of use of snapshots of data from the the model, from modeled hazard High-resolution elevation data past renders risk assessments out of intensity to calculation of loss. date. In terms of vulnerability, there To accurately model localized, Among the data challenges that is a dearth of data about peoples’ topographically sensitive hazards modelers confront are the static and coping strategies in post-event such as river flooding, high- incomplete nature of exposure and situations; this must be addressed resolution elevation data are vulnerability data, the resolution to better understand coping crucial. Without these data, flood of available topography data, the capacity and adaptive capacities. risk assessments retain significant availability of flood protection uncertainty in depth values, which Incomplete data are a major barrier data, and the uncertainty in climate makes vulnerability analyses, as both to understanding patterns of projections. As data improve, a well as quantification of damage and socioeconomic development and to greater number of disaster risk losses, less reliable. As a result, poor modeling exposure and vulnerability assessments will ideally adopt the resolution also hampers the analysis changes for assessment of future more robust methods for including of individual DRM strategies. disaster risks. Collecting exposure evolving hazard, exposure, and and vulnerability information in The recent launch of the near-global vulnerability that studies cited in a timely manner and at suitable 30 m resolution Shuttle Radar this publication have described. and temporal resolution is spatial Topography Mission (SRTM) digital vital; this allows development of elevation data set (Simpson 2014) Dynamic exposure and robust baseline distributions and shows that there continue to be vulnerability data trends in information, which are improvements in the horizontal To improve our understanding of needed to improve projections. resolution of digital elevation models (Ward et al. 2015). However, further trends in disaster impacts and Crowd-sourcing can aid the

87 Making a riskier future: How our decisions are shaping future disaster risk / 67 refinement in vertical resolution the first continent-wide flood is required to really improve the protection database based on a One of the biggest accuracy of elevation data for modeling approach, which assigned contributors to uncertainty flood risk assessment (Schumann expected protection values to river et al. 2014). Useful data are often basins as a function of potential in flood risk analysis collected during the post-disaster risk in combination with a number remains the availability response phase, and they should of available empirical data points. and quality of information be integrated into disaster risk The authors then successfully assessment wherever possible, included these protection estimates on flood protection. to improve assessments moving in a probabilistic continental risk forward. LIDAR topography data model. 1. “No-regret” strategies. These that was collected in Haiti following While these modeled estimates of provide benefits regardless of the 2010 earthquake, for example, flood protection standards indeed whether the disaster risk evolves is now readily available for detailed lead to improved validation results due to a changing climate. modeling of future inundation due to of flood damage simulations, They include improved building sea-level rise. estimates of flood protection for all insulation to provide energy- river basins have not been extended saving benefits from day one, Flood protection data beyond Europe, and the required and land-use planning to reduce available empirical information on One of the biggest contributors to losses under current and future protection levels is still extremely uncertainty in flood risk analysis climate conditions. limited. An improved global remains the availability and Reversible and flexible options. 2. database for flood protection would quality of information on flood These options can be halted or be extremely valuable because protection measures that are adjusted at short notice, with it would enable more accurate in place in the area of interest. little or no sunk cost. They modeling of flood risk in present Presently, the availability of such include climate-proofing new conditions and improve cost- data is limited. Thus current flood buildings and erecting flood benefit analysis of flood protection risk assessments, on national to defenses that can easily be made measures for future disaster risk global scales, often assume either higher and stronger at little cost. management. highly simplified flood protection standards or assume no protection. Safety margins in investments. 3. As a result, they overestimate Design of infrastructure systems Implement robust, exposure, and therefore risk. On and structures should account flexible adaptation a global scale, Ward et al. (2013) for worst-case scenarios, rather According to Hallegatte (2009), found that expected annual than rely on later modification. one problem for adapting to damage was about 40 percent For example, drainage systems climate change is the rate at lower than in the absence of should be designed with which conditions are changing: protection, assuming that all areas sufficient capacity to cope with infrastructure and investments being were protected against a flood anticipated runoff. implemented now must be robust with a return period of only five 4. Appropriate adaptation strategies . enough to cope with a wider range years. Faced with this dearth of These include “soft” adaptation of climate conditions in the future. information, global models rely on strategies—such as early warning This need incurs additional costs an estimate of protection levels systems, evacuation plans, and for designing that infrastructure. based on a region’s or country’s insurance schemes—and long- Hallegatte cites five methods to socioeconomic conditions, income term planning horizons with promote effective adaptation in an level, or land use. Jongman et shorter-term revisions of plans. uncertain future climate: al. (2014) attempted to produce

88 68 / 7. Identifying Effective Policies for a Resilient Future Box 7.4 Social Safety Nets Social safety nets are “non-contributory transfers designed to provide regular and predictable support to targeted poor and vulnerable people” (World Bank 2014, xiii). They include cash transfers (e.g., school stipends and cash to the elderly or orphans) and in-kind transfers (e.g., school meals and food supplements or vouchers). Transfers may be unconditional or they may be conditional on attendance at health centers, school, or skills programs. Public works programs, which engage people in manual work such as building community assets and infrastructure, may also be part of a social safety net. World Bank 2014. Source: 5. . Shorter lifetime of investments The capacity to enhance resilience have been affected by a shock and adapt to climate change is This approach reduces (Hallegatte et al. 2015). not equal across all societies uncertainty about climate in Disaster risk financing can help (van Aalst and Burton 2000). decision making. to increase resilience at both Capacity comprises financial and Cost-benefit assessment of national and community levels by technical resources as well as investments should account for contributing to a proactive DRM governance to implement and use future losses and costs as well strategy. Risk financing involves resources effectively. Capacity as current costs; this approach is assessing a government’s contingent is undermined by lack of skills, particularly important for long-term liability to disasters, establishing poverty, and undeveloped social investments. catastrophe insurance programs institutions. Social safety nets in country or across regions, and (box 7.4) have been effective in putting mechanisms in place for reducing poverty, improving food Enhance disaster governments to fund post-disaster security and nutrition, stimulating resilience relief and reconstruction (Cummins local economies, and improving and Mahul 2009). Insurance is a Resilience determines the degree social cohesion (World Bank mechanism for risk transfer that to which affected groups of people 2014, table 6), all of which can operates by sharing the burden are able to bounce back—or, contribute to enhanced resilience. of risk (and losses when they preferably, bounce forward—after a The World Bank (2014) reports occur) across a large number of disaster hits (Manyena et al. 2011). that drought resilience increased policyholders— e.g., homeowners, Strengthening resilience is therefore in Zambia when households used businesses, and farmers. In the crucial for ensuring that recovery from unconditional cash transfers to event of a disaster, it mitigates the disasters occurs quickly, incorporates diversify into a nonagricultural detrimental impacts of a large loss effective adaptation, and reduces business, and in Ethiopia on each person—but premiums vulnerability to ongoing hazards and after a public works program must be affordable enough to the next disaster. But a community’s allowed farmers to invest in land encourage many people to become resilience cannot be strengthened improvements and fertilizer. policyholders and fund potential unless it is understood. Resilience Coverage of some types of social payouts. Market-based catastrophe is a product of a range of factors and protection is increasing, but risk financing can be supported by has social, infrastructural, community improvements are still needed; donor and international financial capital, economic, institutional, and access to social protection should institutions, which can help build environmental dimensions (Cutter, be expanded, the value of some technical capacity and develop Ash, and Emrich 2014). Measures that transfers should be increased, and complex financial products (Cummins seek to increase resilience therefore distribution of transfers should and Mahul 2009). Catastrophe need to address one or several of not only be prompt but should these dimensions. insurance schemes (see box 7.5) can more effectively target those who

89 Making a riskier future: How our decisions are shaping future disaster risk / 69 be set up to enable sharing of risk are conducted in the required Plan recovery and by several governments (e.g., the time scales, they should include reconstruction before Pacific Catastrophe Risk Assessment environmental or social change the event and Financing Initiative [PCRAFI] due to the event (e.g., permanent By anticipating disaster impacts, and the Caribbean Catastrophe ground displacement or relocation authorities can devise a recovery , [CCRIF]) Risk Insurance Facility of exposure). If these changes are and reconstruction strategy that or schemes can be funded via ignored, reconstruction activities addresses the areas likely to be international reinsurance markets to may not achieve the full potential of affected, as well as the resources offer additional diversification, thus resilience or sustainability, and may and investment needed to repair or making premiums more affordable even be detrimental to resilience or replace damaged infrastructure. If for individuals (e.g., the Turkish sustainability. ex ante recovery planning is carried Catastrophe Insurance Pool [TCIP]). out, recovery can be actioned Programs may be focused on insuring In general, ex ante approaches are more quickly (reducing short-term a particular type of risk (TCIP focuses preferred: “Emergency loans for shock-induced vulnerability), and on property; African Risk Capacity disaster recovery and rehabilitation reconstruction can make use of [ARC] focuses on agriculture). Payout tend to focus on the restoration of prior plans to incorporate effective from a scheme may be activated conditions to the pre-disaster state. adaptation strategies (Becker et al. when a certain loss is incurred, or They thus miss the opportunity 2008)—that is, embrace the “build when a proxy parameter is achieved to reduce vulnerabilities to future back better” concept to reduce (e.g., a certain category of cyclone, events, including increased risk future disaster risk. or level of drought index). The latter from climate change” (van Aalst and is predefined and measured by an Ex ante reconstruction strategies Burton 2000, 97). independent agency, facilitating should be based on risk assessments transparent settlement and rapid that include evolving disaster disbursement of funds. risk; and where ex post analyses Box 7.5 Catastrophe Insurance Schemes Caribbean Catastrophe Risk Insurance Facility The (Cummins and Mahul 2009) provides immediate funding to Caribbean governments in the event of a major hurricane or earthquake. The facility allows each participating country government to aggregate its risk into one portfolio. This diversifies the risks, and transfers some of the risk to the international reinsurance market, which reduces the premium each government pays to obtain insurance. Claims by participating governments are paid according to the occurrence of a predefined event (e.g., a hurricane of a given category within a predefined spatial extent). The Turkish Catastrophe Insurance Pool (GFDRR 2011) is a public entity that provides compulsory property earthquake and fire insurance to homeowners through multiple insurance companies. Affordable premiums are offered through the pool by aggregating risks from policies across Turkey into one portfolio. The pool transfers a portion of risk to the international reinsurance markets. The pool has succeeded in growing the catastrophe insurance market in Turkey; 3.5 million policies were sold in 2010 compared to 600,000 before the TCIP was established in 2000. The Pacific Catastrophe Risk Assessment and Financing Initiative includes the Catastrophe Risk Insurance Pilot, which allows Pacific countries to buy catastrophe insurance as a single group (pooling their risks into a single portfolio) (GFDRR 2015). Like the CCRIF, it uses predefined parametric triggers. The pilot provides an immediate payout to a participating government affected by an event meeting the predefined criteria. African Risk Capacity (ARC) (African Risk Capacity 2013) is a parametric-based pan-African funding mechanism for extreme weather events, covering drought initially but with plans to also cover flood. By pooling risks from governments across Africa, those risks are diversified, with the pool paying out on some events and transferring some risk to the international markets. Governments may choose to retain low-level risk, which requires them to cover losses from frequent or small events themselves.

90 70 / 7. Identifying Effective Policies for a Resilient Future Edited by C. B. Field, Climate Change. http://www. University Press. References V. Barros, T. F. Stocker, D. Qin, D. J. climatechange2013.org/images/report/ Executive Summary WG1AR5_Chapter13_FINAL.pdf. Dokken, K. L. Ebi, M. D. Mastrandrea, et al. Cambridge and New York: Cambridge Dai, A., K. E. Trenberth, and T. Qian. Bouwer, L. M., R. P. Crompton, E. University Press. 2004. “A Global Data Set of Palmer Faust, P. Hoppe, and R. A. Pielke Jr. 2007. “Disaster Management: Drought Severity Index for 1870–2002: Royal Society. 2014 . Resilience to Extreme Science Confronting Disaster Losses.” Relationship with Soil Moisture and Weather. London: Royal Society. 318, no. 5851: 753. doi:10.1126/ Effects of Surface Warming.” Journal of https://royalsociety.org/policy/ science.1149628. Hydrometeorology 5, no. 6: 1117–30. . projects/resilience-extreme-weather/ doi:10.1175/JHM-386.1. Hallegatte, S., M. Bangalore, L. Bonzanigo, United Nations. 2015. Sendai Framework M. Fay, T. Kane, U. Narloh, J. Deltares. 2014. “Sinking Cities.” Deltares, for Disaster Risk Reduction 2015–2030. Rozenberg, D. Treguer, and A. Vogt- https://www.deltares.nl/app/ Utrecht. Geneva, Switzerland. http://www. Schilb. 2015. “Shock Waves: Managing uploads/2015/01/Subsidence-Sinking- preventionweb.net/files/43291_ the Impacts of Climate Change cities_Deltares.pdf . sendaiframeworkfordrren.pdf . on Poverty.” World Bank Group, Deryng, D., D. Conway, N. Ramankutty, Washington, DC. 3. Drivers of Evolving Disaster Risk: J. Price, and R. Warren. 2014. “Global Hazard Hallegatte, S., C. Green, R. J. Nicholls, Crop Yield Response to Extreme Heat and J. Corfee-Morlot. 2013. “Future Stress under Multiple Climate Change Association of British Insurers. 2005. Flood Losses in Major Coastal Cities.” Futures.” Environmental Research Letters Financial Risks of Climate Change. Nature Climate Change 3, no. 9: 802–6. 9, no. 3: 034011. doi:10.1088/1748- London: Association of British Insurers. doi:10.1038/nclimate1979. 9326/9/3/034011. Briffa, K. R., G. van der Schrier, and P. D. Jongman, B., P. J. Ward, and J. C. J. H. Aerts. Elsner, J. B., J. P. Kossin, and T. H. Jagger. Jones. 2009. “Wet and Dry Summers 2012. “Global Exposure to River and 2008. “The Increasing Intensity of the in Europe Since 1750: Evidence of Coastal Flooding: Long Term Trends Strongest Tropical Cyclones.” Nature Increasing Drought.” International and Changes.” Global Environmental http://dx.doi. 455, no. 7209: 92–95. 29, no. 1: Journal of Climatology 22, no. 4: 823–35. Change org/10.1038/nature07234 . 1894–1905. doi:10.1002/joc.1836. doi:10.1016/j.gloenvcha.2012.07.004. Field, C. B., V. R. Barros, K. J. Mach, M. Budiyono, Y., J. C. J. H. Aerts, D. Tollenaar, Muis, S., B. Güneralp, B. Jongman, J. C. J. D. Mastrandrea, M. van Aalst, W. N. and P. Ward. 2015. “River Flood H. Aerts, and P. J. Ward. 2015. “Flood Adger, D. J. Arent, et al. 2014. “Technical Risk in Jakarta under Scenarios of Risk and Adaptation Strategies under Summary.” In Climate Change 2014: Natural Hazards and Future Change.” Climate Change and Urban Expansion: Impacts, Adaptation, and Vulnerability. Earth System Sciences Discussions A Probabilistic Analysis Using Global Part A: Global and Sectoral Aspects. 3, no. 7: 4435–78. doi:10.5194/ Data.” Science of the Total Environment Contribution of Working Group II to nhessd-3-4435-2015. 538: 445–57. the Fifth Assessment Report of the Mayoral Christchurch City Council. 2014. Intergovernmental Panel on Climate 1. Introduction Flood Taskforce Temporary Flood edited by C. B. Field, V. R. Change, Defence Measures Final Report—Part GFDRR (Global Facility for Disaster Barros, D. J. Dokken, K. J. Mach, M. D. A: Key Findings and Recommendations. Reduction and Recovery). 2014. Mastrandrea, T. E. Bilir, M. Chatterjee, Christchurch, New Zealand. Understanding Risk in an Evolving et al., 35–94. Cambridge and New York: World: Emerging Best Practices in http:// Cambridge University Press. Church, J. A., P. U. Clark, A. Cazenave, J. Natural Disaster Risk Assessment. www.ipcc.ch/pdf/assessment-report/ M. Gregory, S. Jevrejeva, A. Levermann, https:// Washington, DC: World Bank. ar5/wg2/WGIIAR5-TS_FINAL.pdf . M. A. Merrifield, et al. 2013. “Sea Level www.gfdrr.org/sites/default/files/ Change.” In Climate Change 2013: The Flannigan, M. D., M. A. Krawchuk, W. J. de publication/Understanding_Risk-Web_ Physical Science Basis. Contribution of Groot, B. M. Wotton, and L. M. Gowman. Version-rev_1.8.0.pdf . Working Group I to the Fifth Assessment 2009. “Implications of Changing Report of the Intergovernmental Panel on IPCC (Intergovernmental Panel on Climate Climate for Global Wildland Fire.” , edited by T. F. Stocker, Climate Change Change). 2012. Managing the Risks 18, International Journal of Wildland Fire D. Qin, G.-K. Plattner, M. Tignor, S. K. of Extreme Events and Disasters to no. 5: 483. doi:10.1071/WF08187. Allen, J. Boschung, A. Nauels, Y. Xia, Advance Climate Change Adaptation. A Special Report of Working Groups I and V. Bex, and P. M. Midgley, 1137–1216. Giovinazzi, S., T. Wilson, C. Davis, D. II of the Intergovernmental Panel on Cambridge and New York: Cambridge Bristow, M. Gallagher, A. Schofield, M.

91 Making a riskier future: How our decisions are shaping future disaster risk / 71 Villemure, J. Eidinger, and A. Tang. 2011. Australia.” International Journal “A Globally Consistent Reanalysis of of Climatology 33, no. 1: 33–47. “Lifelines Performance and Management Hurricane Variability and Trends.” doi:10.1002/joc.3405. Following the 22 February 2011 34. Geophysical Research Letters Christchurch Earthquake , New Zealand: doi:10.1029/2006GL028836. Mousavi, M., J. Irish, A. Frey, F. Olivera, Bulletin of Highlights Of Resilience.” and B. Edge. 2011. “Global Warming Kundzewicz, Z. W., S. Kanae, S. I. the New Zealand Society for Earthquake and Hurricanes: The Potential Impact Seneviratne, J. Handmer, N. Nicholls, P. 44, no. 4: 402–17. Engineering of Hurricane Intensification and Peduzzi, R. Mechler, et al. 2014. “Flood Sea-Level Rise on Coastal Flooding.” Risk and Climate Change: Global and Hallegatte, S., N. Ranger, O. Mestre, P. Climatic Change 104, no. 3-4: 575–97. Hydrological Regional Perspectives.” Dumas, J. Corfee-Morlot, C. Herweijer, doi:10.1007/s10584-009-9790-0. Sciences Journal , 59, no. 1: 1–28. doi:10. and R. M. Wood. 2011. “Assessing 1080/02626667.2013.857411. Climate Change Impacts, Sea-Level Rise Murnane, R., and J. B. Elsner. 2012. and Storm Surge Risk in Port Cities: A “Maximum Wind Speeds and US Leckebusch, G. C., U. Ulbrich, L. Fröhlich, Case Study on Copenhagen.” Climatic Hurricane Losses.” Geophysical and J. G. Pinto. 2007. “Property Loss 104, no. 1: 113–37. doi:10.1007/ Change 39: L16707. Research Letters Potentials for European Midlatitude s10584-010-9978-3. doi:10.1029/2012GL052740 Storms in a Changing Climate.” 34, no. 5: Geophysical Research Letters Hirabayashi, Y., S. Kanae, S. Emori, T. ́ ́ Nakicenovic, N., J. Alcamo, G. Davis, B. de L05703. doi:10.1029/2006GL027663. Oki, and M. Kimoto. 2008. “Global Vries, J. Fenhann, S. Gaffin, K. Gregory, Projections of Changing Risks of Floods et al. 2000. Special Report on Emissions Liu, Y., J. Stanturf, and S. Goodrick. 2010. and Droughts in a Changing Climate.” Scenarios: A Special Report of Working “Trends in Global Wildfire Potential in Hydrological Sciences Journal 53, no. 4: Group III of the Intergovernmental a Changing Climate.” Forest Ecology 754–72. doi:10.1623/hysj.53.4.754. . New York: Panel on Climate Change and Management 259, no. 4: 685–97. Cambridge University Press. doi:10.1016/j.foreco.2009.09.002. Hirabayashi, Y., R. Mahendran, S. Koirala, L. Konoshima, D. Yamazaki, NOAA National Climatic Data Center. Luber, G., and M. McGeehin. 2008. S. Watanabe, H. Kim, and S. Kanae. 2014. “NOAA NCDC State of the “Climate Change and Extreme 2013. “Global Flood Risk under Climate Climate—Global Analysis Annual Heat Events.” American Journal of 3, no. Nature Climate Change Change.” 2014.” http://www.ncdc.noaa.gov/sotc/ 35, no. Preventive Medicine 5: 429–35. 9: 816–21. doi:10.1038/nclimate1911. global/2014/13. doi:10.1016/j.amepre.2008.08.021. Holland, G., and C. L. Bruyère. 2014. Park, D.-S. R., C.-H. Ho, and J.-H. Kim. Majer, E. L., R. Baria, M. Stark, S. “Recent Intense Hurricane Response 2014. “Growing Threat of Intense Oates, J. Bommer, B. Smith, and to Global Climate Change.” Climate Tropical Cyclones to East Asia over H. Asanuma. 2007. “Induced : Dynamics 617–27. doi:10.1007/ 42 the Period 1977–2010.” Environmental Seismicity Associated with Enhanced s00382-013-1713-0. Research Letters 9: 014008. Geothermal Systems.” Geothermics doi:10.1088/1748-9326/9/1/014008. 36, no. 3: 185–222. doi:10.1016/j. IPCC (Intergovernmental Panel on Climate geothermics.2007.03.003. Change). 2012. Managing the Risks of Petley, D. N., S. A. Dunning, and N. Extreme Events and Disasters to Advance J. Rosser. 2005. “The Analysis of McCarthy, M. P., M. J. Best, and R. A. Betts. Climate Change Adaptation . A Special Global Landslide Risk through the 2010. “Climate Change in Cities due Report of Working Groups I and II of the Creation of a Database of Worldwide to Global Warming and Urban Effects.” Intergovernmental Panel on Climate Landslide Fatalities.” In Landslide Risk Geophysical Research Letters 37, no. 9. . Edited by C. B. Field, V. Barros, Change , edited by O. Hungr, R. Fell, Management doi:10.1029/2010GL042845. T. F. Stocker, D. Qin, D. J. Dokken, K. L. R. Couture, and E. Eberhardt . London: McCarthy, M. P., C. Harpham, C. M. Ebi, M. D. Mastrandrea, et al. Cambridge Taylor & Francis. Goodess, and P. D. Jones. 2012. and New York: Cambridge University Phien-wej, N., P. H. Giao, and P. Nutalaya. “Simulating Climate Change in UK Press. 2006. “Land Subsidence in Bangkok, Cities Using a Regional Climate Model, Karoly, D. 2010. “The Recent Bushfires Engineering Geology Thailand.” HadRM3.” International Journal of and Extreme Heat Wave in Southeast 82, no. 4: 187–201. doi:10.1016/j. Climatology 32, no. 12: 1875–88. Australia.” Bulletin of Australian enggeo.2005.10.004. doi:10.1002/joc.2402. Meteorological and Oceanographical Pielke Jr., R. A. 2007. “Future Economic McInnes, K. L., I. Macadam, G. Hubbert, 22: 10–13. http://www.amos. Societies Damage from Tropical Cyclones: and J. O’Grady. 2013. “An Assessment org.au/documents/item/165. Sensitivities to Societal and Climate of Current and Future Vulnerability Kossin, J. P., K. R. Knapp, D. J. Vimont, R. Changes.” Philosophical Transactions. to Coastal Inundation due to Sea- J. Murnane, and B. A. Harper. 2007. Level Extremes in Victoria, Southeast Series A, Mathematical, Physical, and

92 72 / Making a riskier future: How our decisions are shaping future disaster risk Scale.” Hydrology and Earth System New York: Cambridge University Press. 365, no. 1860: Engineering Sciences Sciences 18, no. 1: 47–66. doi:10.5194/ . http://www.ipcc.ch/report/ar5/wg1/ 2717–29. doi:10.1098/rsta.2007.2086. hess-18-47-2014. Sun, Y., X. Zhang, F. W. Zwiers, L. Song, H. http:// Propublica. 2014. “Losing Ground.” Wan, T. Hu, H. Yin, and G. Ren. 2014. Winn, P., R. Young, and A. Edwards. 2003. projects.propublica.org/louisiana/ . “Rapid Increase in the Risk of Extreme “Planning for the Rising Tides: The Rhodium Group LLC. 2014. American Summer Heat in Eastern China.” Nature Humber Estuary Shoreline Management Climate Prospectus: Economic Risks 4, no. 12: 1082–85. Climate Change Plan.” Science of The Total Environment http://rhg.com/ . in the United States doi:10.1038/nclimate2410. 314-316: 13–30. doi:10.1016/S0048- reports/climate-prospectus . 9697(03)00092-5. Tebaldi, C., B. H. Strauss, and C. E. Zervas. Rodolfo, K. S., and F. P. Siringan. 2006. 2012. “Modelling Sea-Level Rise World Bank. 2011a. “Climate Change “Global Sea-Level Rise Is Recognised, Impacts on Storm Surges along US Adaptation and Natural Disasters but Flooding from Anthropogenic Land Coasts.” Environmental Research Letters Preparedness in the Coastal Cities of Subsidence Is Ignored around Northern 7, no. 1: 014032. doi:10.1088/1748- North Africa.” Slide presentation. World Disasters Manila Bay, Philippines.” 9326/7/1/014032. http://resilient- Bank, Washington, DC. 30, no. 1: 118–39. doi:10.1111/j.1467- cities.iclei.org/fileadmin/sites/resilient- Trewin, B., and H. Vermont. 2010. 9523.2006.00310.x. cities/files/Resilient_Cities_2011/ “Changes in the Frequency of Record Presentations/F/F4_Multiple_Presenters. Roesner, L. A. 2014. “CSU’s Urban Temperatures in Australia, 1957- pdf . Stormwater Program.” Colorado Water Australian Meteorological 2009.” 31, no. 2 (March-April): 2–6. 60 (July): and Oceanographic Journal ———. 2011b. “Jakarta Case Study.” 113–19. In Climate Change, Disaster Risk Romps, D. M., J. T. Seeley, D. Vollaro, and and the Urban Poor: Cities Building J. Molinari. 2014. “Projected Increase 2009 Victorian Bushfires Royal .” Resilience for a Changing World in Lightning Strikes in the United Commission. 2010. Victorian Bushfires ttps:// World Bank, Washington, DC. h States due to Global Warming.” Science http:// Royal Commission Final Report . openknowledge.worldbank.org/ 346, no. 6211: 851–54. doi:10.1126/ www.royalcommission.vic.gov.au/ . handle/10986/6018 science.1259100. Commission-Reports/Final-Report.html . Turn Down the Heat: Why a ———. 2012. Savenije, H. H. G. 2004. “The Importance Veldkamp, T. I. E., Y. Wada, H. de Moel, M. 4 ° C Warmer World Must Be Avoided . of Interception and Why We Should Kummu, S. Eisner, J. C. J. H. Aerts, and P. Washington, DC: World Bank. http s:// Delete the Term Evapotranspiration J. Ward. 2015. “Changing Mechanism of openknowledge.worldbank.org/ from Our Vocabulary.” Hydrological Global Water Scarcity Events: Impacts handle/10986/11860 . Processes 18, no. 8: 1507–11. of Socioeconomic Changes and Inter- doi:10.1002/hyp.5563. annual Hydro-climatic Variability.” Turn Down the Heat: ———. 2014. Global Environmental Change 32: 18–29. Confronting the New Climate Normal . Sheffield, J., E. F. Wood, and M. L. doi:10.1016/j.gloenvcha.2015.02.011. Washington, DC: World Bank. http:// Roderick. 2012. “Little Change in Global documents.worldbank.org/curated/ Drought over the Past 60 Years.” Nature Visser, H., A. C. Petersen, and W. Ligtvoet. en/2014/11/20404287/turn-down-heat- 491, no. 7424: 435–38. doi:10.1038/ 2014. “On the Relation between confronting-new-climate-normal-vol-2- nature11575. Weather-Related Disaster Impacts, 2-main-report . Vulnerability and Climate Change.” Simpson, D. W., S. Leith, and C. H. Scholz. Climatic Change 125, no. 3-4: 461–77. Yan, J., and K. Kishore. 2014. “Detailed 1988. “Two Types of Reservoir-Induced doi:10.1007/s10584-014-1179-z. Island Risk Assessment in Maldives Seismicity.” Bulletin of the Seismological to Inform Disaster Risk Reduction Society of America 78, no. 6: 2025–40. Wang, X. L., Y. Feng, G. P. Compo, V. R. and Climate Change Adaptation.” In Swail, F. W. Zwiers, R. J. Allan, and P. D. Stocker, T. F., D. Qin, G.-K. Plattner, L. V. Understanding Risk in an Evolving World: Sardeshmukh. 2013. “Trends and Low Alexander, S. K. Allen, N. L. Bindoff, Emerging Best Practices in Natural Frequency Variability of Extra-tropical F.-M. Bréon, et al. 2013. Technical Disaster Risk Assessment , edited by Cyclone Activity in the Ensemble of Climate Change 2013: The Summary. In Global Facility for Disaster Reduction Climate Twentieth Century Reanalysis.” Physical Science Basis. Contribution of and Recovery, 132–35. Washington, Dynamics 40: 2775–2800. doi:10.1007/ Working Group I to the Fifth Assessment https://www.gfdrr. DC: World Bank. s00382-012-1450-9. Report of the Intergovernmental Panel on org/sites/default/files/publication/ , edited by T. F. Stocker, Climate Change Ward, P. J., S. Eisner, M. Flörke, M., D. Understanding_Risk-Web_Version- D. Qin, G.-K. Plattner, M. Tignor, S. K. Dettinger, and M. Kummu. 2014. . rev_1.8.0.pdf Allen, J. Boschung, A. Nauels, Y. Xia, V. “Annual Flood Sensitivities to El Nino- Southern Oscillation at the Global Bex, and P. M. Midgley. Cambridge and

93 Making a riskier future: How our decisions are shaping future disaster risk / 73 Noor, A. M., V. A. Alegana, P. W. Gething, Hallegatte, S., C. Green, R. J. Nicholls, 4. Drivers of Evolving Disaster Risk: A. J. Tatem, and R. W. Snow. 2008. and J. Corfee-Morlot. 2013. “Future Exposure “Using Remotely Sensed Night-Time Flood Losses in Major Coastal Cities.” Barredo, J. I. 2009. “Normalised Flood Light as a Proxy for Poverty in Africa.” 3, no. 9: 802–6. Nature Climate Change Losses in Europe: 1970–2006.” 6, no. 5. Population Health Metrics doi:10.1038/nclimate1979. Natural Hazards and Earth System doi:10.1186/1478-7954-6-5. Hammer, R. B., S. I. Stewart, and V. 9, no. 1: 97–104. doi:10.5194/ Science United Nations, Department of Economic C. Radeloff. 2009. “Demographic nhess-9-97-2009. and Social Affairs, Population Division. Trends, the Wildland-Urban Interface, ———. 2010. “No Upward Trend in Nor- World Urbanization Prospects: 2014. Socety and Wildfire Management.” malised Windstorm Losses in Europe: . CD-ROM edition. The 2014 Revision 22, no. 8: 12. and Natural Resources Natural Hazards and 1970–2008.” http://esa.un.org/unpd/wup/CD-ROM/ . doi:10.1080/08941920802714042. Earth System Science , 10, no. 1: 97– Veldkamp, T. I. E., Y. Wada, H. de Moel, M. IPCC (Intergovernmental Panel on Climate 104. doi:10.5194/nhess-10-97-2010. Kummu, S. Eisner, J. C. J. H. Aerts, and Change). 2012. Managing the Risks P. J. Ward. 2015. “Changing Mecha- Bouwer, L. M., R. P. Crompton, E. Faust, P. of Extreme Events and Disasters to nism of Global Water Scarcity Events: Hoppe, and R. A. Pielke Jr. 2007. “Di- Advance Climate Change Adaptation. A Impacts of Socioeconomic Changes saster Management: Confronting Di- Special Report of Working Groups I and and Inter-Annual Hydro-Climatic 318, no. 5851: Science saster Losses.” II of the Intergovernmental Panel on Global Environmental Variability.” 753. doi:10.1126/science.1149628. . Edited by C. B. Field, Climate Change Change 32: 18–29. doi:10.1016/j. V. Barros, T. F. Stocker, D. Qin, D. J. CLUVA (Cimate Change and and Urban gloenvcha.2015.02.011. Dokken, K. L. Ebi, M. D. Mastrandrea, Vulnerability in Africa). 2015. “Intro- Visser, H., A. C. Petersen, and W. Ligtvoet. et al. Cambridge and New York: Cam- duction.” http://www.cluva.eu/index. 2014. “On the Relation between bridge University Press. doi:10.1017/ php?option=com_content&view=arti- Weather-Related Disaster Impacts, CBO9781139177245. cle&id=46&Itemid=53 . Vulnerability and Climate Change.” Jongman, B., P. J. Ward, and J. C. J. H. Elvidge, C. D., K. E. Baugh, E. A. Kihn, H. 125, no. 3-4: 461–77. Climatic Change Aerts. 2012. “Global Exposure to River W. Kroehl, and E. R. Davis. 1997. “Map- doi:10.1007/s10584-014-1179-z. and Coastal Flooding: Long Term ping City Lights With Nighttime Data Wang, W., H. Cheng, and L. Zhang. 2012. Trends and Changes.” Global Envi- from the DMSP Operational Linescan “Poverty Assessment Using DMSP/OLS 22, no. 4: 823–35. ronmental Change Photogrammetric Engineering System.” Night-Time Light Satellite Imagery at doi:10.1016/j.gloenvcha.2012.07.004. and Remote Sensing 63, no. 6: 727–34. a Provincial Scale in China.” Advances http://www.asprs.org/a/publications/ Ligtvoet, W., H. Hilderink, A. Bouwman, P. 49, no. 8: 1253–64. in Space Research pers/97journal/june/1997_jun_727- - van Puijenbroek, P. Lucas, and M. Wit doi:10.1016/j.asr.2012.01.025. . 734.pdf mer. 2014. Towards a World of Cities Winsemius, H. C., L. P. H. Van Beek, B. in 2050: An Outlook on Water-Related Elvidge, C. D., K. Baugh, M. Zhizhin, and Jongman, P. J. Ward, and A. Bouwman. Challenges. Background Report to the F. C. Hsu. 2013. “Why VIIRS Data 2013. “A Framework for Global River UN-Habitat Global Report . The Hague: Are Superior to DMSP for Mapping Flood Risk Assessments.” Hydrology PBL Netherlands Environmental Nighttime Lights.” Proceedings of the 17, no. 5: and Earth System Sciences http://www.pbl. Assessment Agency. 35: Asia-Pacific Advanced Network 1871–92. doi:10.5194/hess-17-1871- nl/publicaties/leven-met-water-in-ex - 62–69. doi:10.7125/APAN.35.7. 2013. plosief-groeiende-steden . Freire, S., and C. Aubrecht. 2012. “Inte- Zhang, Q., and K. C. Seto. 2011. “Mapping Mohleji, S., and R. Pielke. 2014. “Reconcil- grating Population Dynamics into Urbanization Dynamics at Regional iation of Trends in Global and Regional Mapping Human Exposure to Seismic and Global Scales Using Multi-tem- Economic Losses from Weather Events: Natural Hazards and Earth Hazard. poral DMSP/OLS Nighttime Light Natural Hazards Review 1980–2008.” 12, no. 11: 3533–43. System Science Remote Sensing of Environment Data.” 15, no. 4: 4014009. doi:10.1061/ doi:10.5194/nhess-12-3533-2012. 115, no. 9: 2320–29. doi:10.1016/j. (ASCE)NH.1527-6996.0000141. rse.2011.04.032. Gerland, P., A. E. Raftery, H. Sevcíková, N. NOAA National Centers for Environmental Li, D. Gu, T. Spoorenberg, L. Alkema, et 5. Drivers of Evolving Disaster Risk: Information. 2015. “Version 4 DM- al. 2014. “World Population Stabili- Vulnerability SP-OLS Nighttime Lights Time Series.” zation Unlikely This Century.” Science http://ngdc.noaa.gov/eog/dmsp/down- 346, no. 6206: 234–37. doi:10.1126/ Birkmann, J. 2011. “First- and Second- . loadV4composites.html Order Adaptation to Natural Hazards science.1257469.

94 74 / Making a riskier future: How our decisions are shaping future disaster risk and Extreme Events in the Context of Jongman, B. 2014. “Unravelling the Drivers doi:10.1007/978-0-387-32353-4_11. Climate Change.” Natural Hazards 58, of Flood Risk across Spatial Scales.” Suppasri, A., E. Mas, I. Charvet, R. no. 2: 811–40: doi:10.1007/s11069- PhD thesis, VU University, Amsterdam. Gunasekera, K. Imai, Y. Fukutani, Y. 011-9806-8. Jongman, B., H. C. Winsemius, J. C. J. Abe, and F. Imamura. 2013. “Building Brunkard, J., G. Namulanda, and R. Ratard. H. Aerts, E. Coughlan de Perez, M. Damage Characteristics Based on 2008. “Hurricane Katrina Deaths, van Aalst, W. Kron, and P. J. Ward. Surveyed Data and Fragility Curves of Louisiana, 2005.” Disaster Medicine 2015. “Declining Vulnerability to the 2011 Great East Japan Tsunami.” and Public Health Preparedness 2: River Floods and the Global Benefits 66, no. 2: 319–41. Natural Hazards 215–23. Proceedings of the of Adaptation.” doi:10.1007/s11069-012-0487-8. National Academy of Sciences of the Butler, D. 2010. “Cholera Tightens Grip UNISDR (United Nations Office for . doi:10.1073/ United States of America on Haiti.” Nature News 468, no. 7323: Disaster Risk Reduction). 2011. pnas.1414439112. 483–84. Global Assessment Report on Disaster Koks, E. E., B. Jongman, T. G. Husby, and Risk Reduction 2011: Revealing Risk, Cutter, S. L., B. J. Boruff, and W. L. W. J. W. Botzen. 2015. “Combining . Geneva: Redefining Development Shirley. 2003. “Social Vulnerability Hazard, Exposure and Social UNISDR. to Environmental Hazards.” Social Vulnerability to Provide Lessons Science Quarterly 84, no. 2: 242–61. Verner, D., ed. 2012. Adaptation to for Flood Risk Management.” http://www.colorado.edu/hazards/ a Changing Climate in the Arab 47: 42– Environmental Science & Policy resources/socy4037/Cutter Social Countries: A Case for Adaptation 52. doi:10.1016/j.envsci.2014.10.013. vulnerability to environmental Governance and Leadership in Building hazards.pdf . Lallemant, D., S. Wong, and A. Kiremidjian. . Washington, Climate Resilience 2014. “A Framework for Modelling DC: World Bank. License: Creative Cutter, S. L., C. T. Emrich, D. P. Morath, Future Urban Disaster Risk.” In Commons Attribution CC BY 3.0. and C. M. Dunning. 2013. “Integrating Understanding Risk in an Evolving Social Vulnerability into Federal Flood doi:10.1596/978-0-8213-9458-8. World: Emerging Best Practices in Risk Management Planning.” Journal Wisner, B., P. Blaikie, T. Cannon, and I. , Natural Disaster Risk Assessment 6, no. 4: of Flood Risk Management At Risk: Natural Hazards, Davis. 2004. 332–44. doi:10.1111/jfr3.12018. edited by Global Facility for Disaster . People’s Vulnerability and Disasters Reduction and Recovery, 191–96. Daniell, J. E. 2014. “Development of Socio- 2nd ed. Trowbridge, UK: Taylor & https:// Washington, DC: World Bank. economic Fragility Functions for Use Francis Group. www.gfdrr.org/sites/default/files/ in Worldwide Rapid Earthquake Loss publication/Understanding_Risk-Web_ World Bank and United Nations. Estimation Procedures.” PhD diss. Version-rev_1.8.0.pdf . Natural Hazards, UnNatural 2010. Karlsruhe Institute of Technology. Disasters: The Economics of Effective Marfai, M. A., A. B. Sekaranom, and P. J. Duncan, M. 2014. “Multi-hazard Prevention . Washington, DC: World Ward. 2014. “Community Responses Assessments for Disaster Risk Bank. http://econpapers.repec.org/ and Adaptation Strategies toward Reduction: Lessons from the RePEc:wbk:wbpubs:2512 . Flood Hazard in Jakarta, Indonesia.” Philippines and Applications for Non- 75, no. 2: 1127–44. Natural Hazards 6. Quantifying the Evolution governmental Organisations.” EngD doi:10.1007/s11069-014-1365-3. diss., University College London. of Disaster Risk Mechler, R., and L. M. Bouwer. 2015. Fekete, A. 2009. “Validation of a Social Aerts, J. C. J. H., W. J. W. Botzen, K. “Understanding Trends and Vulnerability Index in Context to River- Emanuel, N. Lin, and H. De. Moel. Projections of Disaster Losses and Floods in Germany.” Natural Hazards 2014. “Evaluating Flood Resilience Climate Change: Is Vulnerability the 9: 393–403. and Earth System Sciences Strategies for Coastal Megacities.” 133, no. Missing Link?” Climate Change doi:10.5194/nhess-9-393-2009. no. Science 344, 6183: 473–75. 1: 23–35. doi: 10.1007/s10584-014- doi:10.1126/science.1248222. Guha-Sapir, D., L. V. Parry, O. Degomme, 1141-0. P. C. Joshi, and J. P. Saulina Arnold. Angel, S., J. Parent, D. L. Civco, and A. M. Sorensen, J. H., and B. Vogt-Sorensen. 2006. “Risk Factors for Mortality and . Atlas of Urban Expansion Blei. 2013. 2006. “Community Processes: Injury: Post-Tsunami Epidemiological Cambridge, MA: Lincoln Institute of Warning and Evacuation.” In Handbook Findings from Tamil Nadu.” Centre Land Policy. of Disaster Research , edited by H. for Research on the Epidemiology of Rodríguez, E. L. Quarantelli, and R. R. Angel, S., S. C. Sheppard, D. L. Civco, Disasters (CRED), School of Public Health, Catholic University of Louvain, Dynes, 183–99. New York: Springer R. Buckley, A. Chabaeva, L. Gitlin, Brussels, Belgium. Science + Business Media, LLC. A. Kraley, J. Parent, and M. Perlin.

95 Making a riskier future: How our decisions are shaping future disaster risk / 75 2005. The Dynamics of Global Urban M. A. Merrifield, et al. 2013. “Sea Level Method Expert Survey to Derive Climate Change 2013: The Change.” In Washington, DC: World Expansion. Standards for Flood Damage Data Physical Science Basis. Contribution of 30, no. Collection.” Risk Analysis Bank. Working Group I to the Fifth Assessment 1: 107–24. doi:10.1111/j.1539- Arnell, N. W., and B. Lloyd-Hughes. Report of the Intergovernmental 6924.2009.01325.x. 2014. “The Global-Scale Impacts of Panel on Climate Change , edited by Felbermayr, G., and J. Gröschl. 2014. Climate Change on Water Resources T. F. Stocker, D. Qin, G.-K. Plattner, “Naturally Negative: The Growth and Flooding under New Climate M. Tignor, S. K. Allen, J. Boschung, Journal of Effects of Natural Disasters.” and Socio-economic Scenarios.” A. Nauels, Y. Xia, V. Bex, and P. M. Development Economics 111: 92–106. Climatic Change 122, no. 1-2: 127–40. Midgley, 1137–1216. Cambridge and doi:10.1016/j.jdeveco.2014.07.004. doi:10.1007/s10584-013-0948-4. New York: Cambridge University Press. https://www.ipcc.ch/pdf/assessment- Ferreira, S., K. Hamilton, and J. R. Vincent. Arthur, W. C., H. M. Woolf, and P. Dailey. report/ar5/wg1/WG1AR5_Chapter13_ 2011. “Nature, Socioeconomics and 2014. “Delivering Risk Information FINAL.pdf . Adaptation to Natural Disasters: for a Future Climate in the Pacific.” New Evidence from Floods.” Policy In Understanding Risk in an Evolving Clarke, L., J. Edmonds, V. Krey, R. Research Working Paper, World Bank, World: Emerging Best Practices in Richels, S. Rose, and M. Tavoni. http://elibrary. Washington, DC. Natural Disaster Risk Assessment , 2009. “International Climate Policy worldbank.org/doi/abs/10.1596/1813- edited by Global Facility for Disaster Architectures: Overview of the EMF . 9450-5725 Reduction and Recovery, 185–90. Energy 22 International Scenarios.” https:// Washington, DC: World Bank. doi:10.1016/j. 31: S64–S81. Economics Field, C. B., V. R. Barros, K. J. Mach, M. D. www.gfdrr.org/sites/default/files/ eneco.2009.10.013 . Mastrandrea, M. van Aalst, W. N. Adger, publication/Understanding_Risk-Web_ D. J. Arent, et al. 2014. “Technical Cutter, S. L., B. J. Boruff, and W. L. . Version-rev_1.8.0.pdf Summary.” In Climate Change 2014: Shirley. 2003. “Social Vulnerability Impacts, Adaptation, and Vulnerability. Bakkensen, L. A. 2013. “Adaptation Social to Environmental Hazards.” Part A: Global and Sectoral Aspects. and Natural Disasters: Evidence 84, no. 2: 242–61. Science Quarterly Contribution of Working Group II to from Global Tropical Cyclone http://www.colorado.edu/hazards/ the Fifth Assessment Report of the Damages and Fatalities.” EAERE resources/socy4037/Cutter Social Intergovernmental Panel on Climate Papers. http://webmeets.com/files/ vulnerability to environmental Change , edited by C. B. Field, V. R. papers/EAERE/2013/1124/Cyclone_ hazards.pdf. Barros, D. J. Dokken, K. J. Mach, M. D. Adaptation.pdf. Cutter, S. L., and C. Finch. 2008. Mastrandrea, T. E. Bilir, M. Chatterjee, Bouwer, L. M. 2013. “Projections of “Temporal and Spatial Changes et al., 35–94. Cambridge and New York: Future Extreme Weather Losses in Social Vulnerability to Natural Cambridge University Press. http:// Under Changes in Climate and Hazards.” Proceedings of the National www.ipcc.ch/pdf/assessment-report/ Risk Analysis Exposure.” 33, no. Academy of Sciences of the United ar5/wg2/WGIIAR5-TS_FINAL.pdf. 5: 915–30. doi:10.1111/j.1539- States of America 105, no. 7: 2301–6. GFDRR (Global Facility for Disaster 6924.2012.01880.x. doi:10.1073/pnas.0710375105. Reduction and Recovery). 2014a. Budiyono, Y., J. C. J. H. Aerts, D. Tollenaar, Dell’Acqua, F., P. Gamba, and K. Jaiswal. Understanding Risk in an Evolving and P. Ward. 2015. “River Flood 2012. “Spatial Aspects of Building and World: Emerging Best Practices in Risk in Jakarta under Scenarios of Population Exposure Data and Their Natural Disaster Risk Assessment . Natural Hazards and Future Change.” Implications for Global Earthquake Washington, DC: World Bank. https:// Earth System Sciences Discussions Natural Hazards Exposure Modeling.” www.gfdrr.org/sites/default/files/ 3, no. 7: 4435–78. doi:10.5194/ 68, no. 3: 1291–1309. doi:10.1007/ publication/Understanding_Risk-Web_ nhessd-3-4435-2015. s11069-012-0241-2. . Version-rev_1.8.0.pdf Ceola, S., F. Laio, and A. Montanari. Diffenbaugh, N. S., J. S. Pal, F. Giorgi, ———. 2014b. Understanding Risk: Review 2014. “Satellite Nighttime Lights and X. Gao. 2007. “Heat Stress of Open Source and Open Access Reveal Increasing Human Exposure Intensification in the Mediterranean Software Packages Available to to Floods Worldwide.” Geophysical Geophysical Climate Change Hotspot.” . Quantify Risk from Natural Hazards 41, no. 20: 7184–90. Research Letters 34, no. 11: 1–6. Research Letters Washington, DC: World Bank. doi:10.1002/2014GL061859. doi:10.1029/2007GL030000. Güneralp, B., I. Günera lp, and Y. Liu. Church, J. A., P. U. Clark, A. Cazenave, J. M. Elmer, F., I. Seifert, H. Kreibich, and 2015. “Changing Global Patterns of Gregory, S. Jevrejeva, A. Levermann, A. H. Thieken. 2010. “A Delphi Urban Exposure to Flood and Drought

96 76 / Making a riskier future: How our decisions are shaping future disaster risk D. Qin, D. J. Dokken, K. L. Ebi, M. D. Hazards.” Global Environmental World: Emerging Best Practices in 31: 217–25. doi:10.1016/j. Change Mastrandrea, et al. Cambridge and , Natural Disaster Risk Assessment gloenvcha.2015.01.002. New York: Cambridge University Press. edited by Global Facilty for Disaster doi:10.1017/CBO9781139177245. Reduction and Recovery, 191–96. Hallegatte, S. 2012. “Economics: The Washington, DC: World Bank. https:// Rising Costs of Hurricanes.” Nature Jeong, D. I., L. Sushama, and M. www.gfdrr.org/sites/default/files/ Climate Change 2, no. 3: 148–49. Naveed Khaliq. 2014. “The Role of publication/Understanding_Risk-Web_ doi:10.1038/nclimate1427. Temperature in Drought Projections Version-rev_1.8.0.pdf. over North America.” Climatic Change Hallegatte, S., C. Green, R. J. Nicholls, 127, no. 2: 289–303. doi:10.1007/ Li, Y., W. Ye, M. Wang, and X. Yan. 2009. and J. Corfee-Morlot. 2013. “Future s10584-014-1248-3. “Climate Change and Drought: A Risk Flood Losses in Major Coastal Cities.” Assessment of Crop-Yield Impacts.” Nature Climate Change 3, no. 9: 802–6. Jongman, B., E. E. Koks, T. G. Husby, Climate Research 39 (June): 31–46. doi:10.1038/nclimate1979. and P. J. Ward. 2014. “Increasing doi:10.3354/cr00797. Flood Exposure in the Netherlands: Hanson, S., R. J. Nicholls, N. Ranger, Implications for Risk Financing.” Linard, C., M. Gilbert, A. E. Gaughan, F. R. S. Hallegatte, J. Corfee-Morlot, C. Natural Hazards and Earth System Stevens, and A. J. Tatem. 2014. “Urban Herweijer, and J. Chateau. 2011. “A 14: 1245–55. doi:10.5194/ Sciences Expansion Forecasts and Changing Global Ranking of Port Cities with nhess-14-1245-2014. Human Population Distribution in High Exposure to Climate Extremes.” Africa.” Paper presented at the Global Climatic Change 104, no. 1: 89–111. Jongman, B., P. J. Ward, and J. C. J. H. Aerts. Land Project Open Science Meeting, doi:10.1007/s10584-010-9977-4. 2012. “Global Exposure to River and “Land Transformations: Between Coastal Flooding: Long Term Trends Hinkel, J., D. Lincke, A. T. Vafeidis, M. Global Challenges and Local Realities,” Global Environmental and Changes.” Perrette, R. J. Nicholls, R. S. J. Tol, B. Berlin, March 19–21. Change 22, no. 4: 823–35. Marzeion, X. Fettweis, C. Ionescu, doi:10.1016/j.gloenvcha.2012.07.004. Linard, C., M. Gilbert, R. W. Snow, A. and A. Levermann. 2014. “Coastal M. Noor, and A. J. Tatem. 2012. Flood Damage and Adaptation Costs Jongman, B., H. C. Winsemius, J. C. J. “Population Distribution, Settlement under 21st Century Sea-Level Rise.” H. Aerts, E. Coughlan de Perez, M. Patterns and Accessibility across Proceedings of the National Academy of van Aalst, W. Kron, and P. J. Ward. 7, no. Africa in 2010.” PloS One Sciences of the United States of America 2015. “Declining Vulnerability to 2: e31743. doi:10.1371/journal. . 9: 3292–97. doi:10.1073/ 111, no River Floods and the Global Benefits pone.0031743. pnas.1222469111. of Adaptation.” Proceedings of the National Academy of Sciences of the Linard, C., M. Gilbert, and A. J. Tatem. Hiraba yashi, Y., S. Kanae, S. Emori, T. United States of America .doi: 10.1073/ 2011. “Assessing the Use of Global Oki, and M. Kimoto. 2008. “Global pnas.1414439112. Land Cover Data for Guiding Large Projections of Changing Risks of Area Population Distribution Floods and Droughts in a Changing Klein Goldewijk, K., A. Beusen, G. Van 76, no. 5: Modelling.” GeoJournal Hydrological Sciences Journal Climate.” Drecht, and M. De Vos. 2011. “The 525–38. doi:10.1007/s10708-010- 53, no. 4: 754–72. doi:10.1623/ HYDE 3.1 Spatially Explicit Database 9364-8. hysj.53.4.754. of Human-Induced Global Land-Use Change over the Past 12,000 Years.” Masson, V., C. Marchadier, L. Adolphe, R. Hirabayashi, Y., R. Mahendran, S. Koirala, 20, Global Ecology and Biogeography Aguejdad, P. Avner, M. Bonhomme, L. Konoshima, D. Yamazaki, S. no.1: 73–86. G. Bretagne, et al. 2014. “Adapting Watanabe, H. Kim, and S. Kanae. 2013. Cities to Climate Change: A Systemic “Global Flood Risk under Climate Koks, E. E., B. Jongman, T. G. Husby, and Modelling Approach.” Urban Climate 3, no. Nature Climate Change Change.” W. J. W. Botzen. 2015. “Combining 10: 407–429. doi:10.1016/j. 9: 816–21. doi:10.1038/nclimate1911. Hazard, Exposure and Social uclim.2014.03.004 Vulnerability to Provide Lessons IPCC (Intergovernmental Panel on Climate for Flood Risk Management.” McCarthy, M. P., M. J. Best, and R. A. Betts. Change). 2012. Managing the Risks Environmental Science & Policy 47: 42– 2010. “Climate Change in Cities due of Extreme Events and Disasters to 52. doi:10.1016/j.envsci.2014.10.013. to Global Warming and Urban Effects.” Advance Climate Change Adaptation. 37, no. 9. Geophysical Research Letters A Special Report of Working Groups Lallemant, D., S. Wong, and A. Kiremidjian. doi:10.1029/2010GL042845. I and II of the Intergovernmental 2014. “A Framework for Modelling Panel on Climate Change . Edited by Future Urban Disaster Risk.” In McCarthy, M. P., C. Harpham, C. M. C. B. Field, V. Barros, T. F. Stocker, Goodess, and P. D. Jones. 2012. Understanding Risk in an Evolving

97 Making a riskier future: How our decisions are shaping future disaster risk / 77 ñ o-Southern Oscillation at the Ni pnas.1211658109. “Simulating Climate Change in UK Cities Using a Regional Climate Model, Hydrology and Earth Global Scale.” Tatem, A. J., A. M. Noor, C. von Hagen, A. HadRM3.” International Journal of System Sciences 18, no. 1: 47–66. Di Gregorio, and S. I. Hay. 2007. “High Climatology 32, no. 12: 1875–88. doi:10.5194/hess-18-47-2014. Resolution Population Maps for Low doi:10.1002/joc.2402. Ward, P. J., B. Jongman, P. Salamon, A. Income Nations: Combining Land Moss, R. H., J. A. Edmonds, K. A. Hibbard, Simpson, P. Bates, T. De Groeve, S. Cover and Census in East Africa.” PloS M. R. Manning, S. K. Rose, D. P. van Muis, et al. 2015. “Usefulness and 2, no. 12: e1298. doi:10.1371/ One Vuuren, T. R. Carter, et al. 2010. Limitations of Global Flood Risk journal.pone.0001298. “The Next Generation of Scenarios Models.” Nature Climate Change 5, no. Taubenböck, H., T. Esch, A. Felbier, for Climate Change Research and 8: 712–15. doi:10.1038/nclimate2742. M. Wiesner, A. Roth, and S. Dech. Assessment.” 463, no. 7282: Nature Zhou, Y., N. Li, W. Wu, J. Wu, and P. Shi. 2012. “Monitoring Urbanization in 747–56. doi:10.1038/nature08823. 2014. “Local Spatial and Temporal Remote Mega Cities from Space.” Muis, S., B. Güneralp, B. Jongman, J. C. J. Factors Influencing Population Sensing of Environment 117: 162–76. H. Aerts, and P. J. Ward. 2015. “Flood and Societal Vulnerability to doi:10.1016/j.rse.2011.09.015. Risk and Adaptation Strategies under Natural Disasters.” Risk Analysis: Toya, H., and M. Skidmore. 2007. Climate Change and Urban Expansion: An Official Publication of the Society “Economic Development and the A Probabilistic Analysis Using Global 34, no. 4: 614–39. for Risk Analysis Impacts of Natural Disasters.” Science of the Total Environment Data.” doi:10.1111/risa.12193. Economics Letters 94 no. 1: 20–25. 538: 445–57. 7. Identifying Effective Policies doi:10.1016/j.econlet.2006.06.020. Nakicenovic, N., J. Alcamo, G. Davis, B. for a Resilient Future de Vries, J. Fenhann, S. Gaffin, K. UNISDR (United Nations Office for African Risk Capacity. 2013. “African Gregory, et al. 2000. Special Report Disaster Risk Reduction). 2011. Risk Capacity: Sovereign Disaster on Emissions Scenarios: A Special Global Assessment Report on Disaster Risk Solutions: A Project of Report of Working Group III of the Risk Reduction 2011. Revealing Risk, the African Union.” http:// Intergovernmental Panel on Climate Geneva: Redefining Development. www.africanriskcapacity. Change , New York: Cambridge UNISDR. org/c/document_library/get_ University Press. file?uuid=79be2055-4576-4157-b7ff- Van Vuuren, D. P., J. Edmonds, M. Kainuma, O’Neill, B. C., E. Kriegler, K. Riahi, K. L. . 396970d8a512&groupId=350251 K. Riahi, A. Thomson, K. Hibbard, G. C. Ebi, S. Hallegatte, T. R. Carter, R. Hurtt, et al. 2011. “The Representative Andoh, R. 2011. “Blue, Green and Mathur, and D. P. van Vuuren. 2014. “A Concentration Pathways: An Grey Infrastructure: What’s the New Scenario Framework for Climate 109, no. Climatic Change Overview.” Difference—and Where Do They Change Research: The Concept of 1: 5–31. doi:10.1007/s10584-011- Engineering Nature’s Way Overlap?” Shared Socioeconomic Pathways.” 0148-z. (blog). September 2. http://www. Climatic Change 122, no. 3: 387–400. engineeringnaturesway.co.uk/2011/ Ward, P. J., W. Beets, L. M. Bouwer, doi:10.1007/s10584-013-0905-2. blue-green-and-grey-infrastructure- J. C. J. H. Aerts, and H. Renssen. what%E2%80%99s-the-difference- Pesaresi, M., H. Guo, X. Blaes, D. Ehrlich, 2010. “Sensitivity of River %E2%80%93-and-where-do-they- S. Ferri, L. Gueguen, M. Halkia, et al. Discharge to ENSO.” Geophysical overlap// . 2013. “A Global Human Settlement 37, no. 12: 1–6. Research Letters Layer From Optical HR/VHR RS Data: Badola, R., and S. A. Hussain. 2005. doi:10.1029/2010GL043215. Concept and First Results.” IEEE “Valuing Ecosystem Functions: Ward, P. J., M. D. Dettinger, B. Jongman, Journal of Selected Topics in Applied An Empirical Study on the Storm M. Kummu, F. Sperna Weiland, and ́ ́ Earth Observations and Remote Sensing Protection Function of Bhitarkanika H. C. Winsemius. 2013. “Flood Risk 6, no. 5: 2102–31. doi:10.1109/ Mangrove Ecosystem, India.” Assessment at the Global Scale: The JSTARS.2013.2271445. Environmental Conservation Role of Climate Variability.” In EGU 32, no. 1: 85–92. doi:10.1017/ Seto, K. C., B. Güneralp, and L. R. Hutyra. General Assembly Conference Abstracts . S0376892905001967. 2012. “Global Forecasts of Urban http://www.geophysical-research- Expansion to 2030 and Direct Impacts Becker, J. S., W. S. A. Saunders, L. Hopkins, abstracts.net/volumes.html. on Biodiversity and Carbon Pools.” K. Wright, and J. Kerr. 2008. “Pre- Ward, P. J., S. Eisner, M. Flörke, M., D. Proceedings of the National Academy of event Recovery Planning for Land Dettinger, and M. Kummu. 2014. Sciences of the United States of America Use in New Zealand: An Updated “Annual Flood Sensitivities to El 109, no. 40: 16083–88. doi:10.1073/ Methodology.” GNS Science Report

98 78 / Making a riskier future: How our decisions are shaping future disaster risk Infrastructure for Urban Climate 2008/11. Lower Hutt, New Zealand: , edited by C. B. Field, V. R. Change Adaptation.” Center for Clean Air GNS Science. Barros, D. J. Dokken, K. J. Mach, M. D. Policy, Washington, DC. Mastrandrea, T. E. Bilir, M. Chatterjee, Charlesworth, S. M., E. Harker, and et al., 1–31. Cambridge and New York: GFDRR (Global Facility for Disaster S. Rickard. 2003. “A Review of Cambridge University Press. Reduction and Recovery). 2011. Sustainable Drainage Systems (SuDS): “Turkish Catastrophe Insurance Pool: A Soft Option for Hard Drainage Jongman, B., S. Hochrainer-Stigler, L. Disaster Risk Financing and Insurance Questions?” Geography 88, no. Feyen, J. C. J. H. Aerts, R. Mechler, W. J. Case Study.” GFDRR, Washington, DC. 2: 99–107. http://www.jstor.org/ W. Botzen, L. M. Bouwer, et al. 2014. http://www.gfdrr.org/sites/gfdrr.org/ stable/40573828 . “Increasing Stress on Disaster-Risk files/documents/DFI_TCIP__Jan11.pdf . Finance Due to Large Floods.” Nature Cummins, J. D., and O. Mahul. 2009. Climate Change 4, no. 4: 264–68. ———. 2015. Advancing Disaster Risk Catastrophe Risk Financing in doi:10.1038/nclimate2124. . Financing & Insurance in the Pacific Developing Countries: Principles for Washington, DC: World Bank. https:// . Washington, DC: Public Intervention Khalili, M., and S. Amindeldar. 2014. www.gfdrr.org/sites/default/files/ World Bank. “Traditional Solutions in Low Energy publication/2015.06.25_PCRAFI_ Buildings of Hot-Arid Regions of Iran.” Cutter, S. L., K. D. Ash, and C. T. Combined-%5BCompressed%5D-rev- Sustainable Cities and Society 13: 171– Emrich. 2014. “The Geographies of 0.9.pdf. 81. doi:10.1016/j.scs.2014.05.008. Community Disaster Resilience.” Government of Australia. 2013. Global Environmental Change 29: Manyena, S. B., G. O’Brien, P. O’Keefe, and “Your Home—Australia’s Guide to 65–77. http://www.sciencedirect. J. Rose. 2011. “Disaster Resilience: Environmentally Friendly Sustainable com/science/article/pii/ A Bounce Back or Bounce Forward http://www. Homes: Passive Cooling.” . S0959378014001459 16, no. 5: Ability?” Local Environment yourhome.gov.au/passive-design/ 417–24. http://dx.doi.org/10.1080/135 Dahdouh-Guebas, F., L. P. Jayatissa, D. Di . passive-cooling 49839.2011.583049 . Nitto, J. O. Bosire, D. Lo Seen, and N. Hallegatte, S. 2009. “Strategies to Adapt Koedam. 2005. “How Effective were Muis, S., B. Güneralp, B. Jongman, J. C. J. to an Uncertain Climate Change.” Mangroves as a Defence against the H. Aerts, and P. J. Ward. 2015. “Flood 19, Global Environmental Change Recent Tsunami?” Current Biology Risk and Adaptation Strategies under no. 2: 240–47. doi:10.1016/j. 15, no. 12: R443–7. doi:10.1016/j. Climate Change and Urban Expansion: gloenvcha.2008.12.003. cub.2005.06.008. A Probabilistic Analysis Using Global Data.” Science of the Total Environment Hallegatte, S., M. Bangalore, L. Bonzanigo, Derkzen, M. L., A. J. A. van Teeffelen, 538: 445–57. M. Fay, T. Kane, U. Narloh, J. and P. H. Verburg. 2015a. “Green Rozenberg, D. Treguer, and A. Vogt- Infrastructure for Climate Adaptation: New Zealand Standards Institute. 1935. Schilb. 2015. “Shock Waves: Managing A study of Awareness, Perceptions, “NZSS No. 95:1935, Model Building the Impacts of Climate Change and Preferences in Rotterdam, the By-Law.” New Zealand Standards on Poverty.” World Bank Group, Netherlands.” Paper presented at Institute, Wellington, New Zealand. Washington, DC. eighth conference of the Ecosystem ———. 1965. “NZSS 1900:1965, Model Services Partnership, Stellenbosch, Ingham, J. M., and M. C. Griffith. 2011. Building By-Law. Chapter 8: Basic South Africa, November 9–13. The Performance of Earthquake Design Loads.” New Zealand Standards Strengthened URM Buildings in the ———. 2015b. “Quantifying Urban Institute, Wellington, New Zealand. Christchurch CBD in the 22 February Ecosystem Services Based on OECD (Organisation for Economic Co- 2011 Earthquake: Addendum High-Resolution Data of Urban operation and Development). 2008. Report to the Royal Commission of Green Space: An Assessment for “Climate Change Mitigation: What Do . Christchurch, New Zealand: Inquiry Journal Rotterdam, the Netherlands.” We Do?” http://www.oecd.org/env/ Canterbury Earthquakes Royal of Applied Ecology 52: 1020–32. cc/41751042.pdf. Commission. doi:10.1111/1365-2664.12469. Radhi, H. 2009. “Evaluating the IPCC (Intergovernmental Panel on FAO (Food and Agriculture Organization). Potential Impact of Global Warming Climate Change). 2014. “Summary Climate-Smart Agriculture 2013. on the UAE Residential Buildings: for Policymakers.” In Climate Change Sourcebook . http://www.fao.org/ A Contribution to Reduce the CO2 2014: Mitigation of Climate Change. docrep/018/i3325e/i3325e.pdf. Emissions.” Building and Environment Contribution of Working Group III to Foster, J., A. Lowe, and S. Winkelman. 44, no. 12: 2451–62. doi:10.1016/j. the Fifth Assessment Report of the 2011. “The Value of Green buildenv.2009.04.006. Intergovernmental Panel on Climate

99 Making a riskier future: How our decisions are shaping future disaster risk / 79 Ward, P. J., B. Jongman, F. S. Weiland, Van Aalst, M. K., and I. Burton. 2000. Royal Society. 2014. Resilience to Extreme A. Bouwman, R. van Beek, M. F. “Climate Change from a Development . London: Royal Society. Weather P. Bierkens, Ligttvoegt. W., et al. Perspective.” In Managing Disaster https://royalsociety.org/policy/ 2013. “Assessing Flood Risk at the , edited Risk in Emerging Economies projects/resilience-extreme-weather/ . Global Scale: Model Setup, Results, by A. Kreimer and M. Arnold, 91–98. Russell, A. P., and J. M. Ingham. 2010. Environmental and Sensitivity.” Disaster Risk Management Series no. “Prevalence of New Zealand’s 8, no. 4: 044019. Research Letters 2. Washington, DC: World Bank. Unreinforced Masonry Buildings.” doi:10.1088/1748-9326/8/4/044019. Van Wesenbeeck, B. K. 2013. “Nature- Bulletin of the New Zealand Society Wilkinson, S., D. Grant, E. Williams, Based Coastal Defences: Can for Earthquake Engineering 43, no. 3: S. Paganoni, S. A. Fraser, D. Boon, Biodiversity Help?” In Encyclopedia 182–202. A. Mason, and M. Free. 2013. of Biodiversity , 2nd ed., 451–58. Schumann, G. J-P., P. D. Bates, J. C. “Observations and Implications of Amsterdam: Academic Press. Neal, and K. M. Andreadis. 2014. Damage from the Magnitude M 6.3 w Van Wesenbeeck, B. K., J. P. Mulder, M. “Technology: Fight Floods on a Christchurch, New Zealand Earthquake Marchand, D. J. Reed, M. B. de Vries, Nature 507, no. 169. Global Scale.” of 22 February 2011.” Bulletin of H. J. de Vriend, and P. M. J. Herman. doi:10.1038/507169e. Earthquake Engineering 11, no. 1: 2014. “Damming Deltas: A Practice of 107–40. doi:10.1007/s10518-012- Simpson, A. 2014. “Better Understanding the Past? Towards Nature-Based Flood 9384-5. Disaster Risk: A New Dataset Is Set Defenses.” Estuarine, Coastal and World Bank to Make a Difference.” 140: 1–6. doi:10.1016/j. Shelf Science World Bank. 2014. “State of Social Safety Voices Blog . September 24. http:// ecss.2013.12.031. Nets 2014.” Washington, DC, World blogs.worldbank.org/voices/better- Bank. http://documents.worldbank. Ward, P. J., B. Jongman, P. Salamon, A. understanding-disaster-risk-new- org/curated/en/2014/05/19487568/ Simpson, P. Bates, T. De Groeve, S. dataset-set-make-difference. . state-social-safety-nets-2014 Muis, et al. 2015. “Usefulness and Standards Association of New Zealand. Limitations of Global Flood Risk 1976. “NZS 4203: Code of Practice 5, no. Nature Climate Change Models.” for General Structural Design and 8: 712–15. doi:10.1038/nclimate2742. Design Loadings for Buildings.” Standards Association of New Zealand, Wellington, New Zealand.

100 80 / Making a riskier future: How our decisions are shaping future disaster risk

101 Making a riskier future: How our decisions are shaping future disaster risk / 81 Case Studies 8 CASE STUDY A World Weather Attribution Erin Coughlan de Perez (Red Cross/Red Crescent Climate Centre; Institute for Environmental Studies, VU University; International Research Institute for Climate and Society), Heidi Cullen (Climate Central), David Karoly (ARC Centre of Excellence for Climate System Science, University of Melbourne), Andrew King (ARC Centre of Excellence for Climate System Science, University of Melbourne), Friederike Otto (Environmental Change Institute, University of Oxford), Roop Singh (Red Cross/Red Crescent Climate Centre), Dina Sperling (Climate Central), Maarten van Aalst (Red Cross/Red Crescent Climate Centre; International Research Institute for Climate and Society), and Geert Jan van Oldenborgh (Royal Netherlands Meteorological Institute) The continual question, ne of the most significant effects of climate change is its impact on extreme weather. Changes are projected in the frequency and intensity therefore, is whether of floods, droughts, and heat waves around the world, but extreme O climate change plays a role weather is not only a future concern. We already live in a climate that has in each specific extreme changed, and the risks of extreme weather events have already been altered. event that we observe The continual question, therefore, is whether climate change plays a role in each specific extreme event that we observe today (Trenberth, Fasullo, today (Trenberth, Fasullo, and Shepherd 2015). During and after a disaster, the media and impacted and Shepherd 2015). stakeholders continually speculate about the link to climate change. Between 2011 and 2014, for example, 42 articles about the California drought mentioned the possible connection to climate change, and within those articles there was no agreement about whether climate change did or did not play a role in the drought. PHOTO The Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite captured this image of cloud streets over the Black Sea on January 8, 2015. Credit: NASA Earth Observatory image by Jesse Allen 81

102 CASE STUDY A 82 / World Weather Attribution Until recently, scientists did not have members estimate the probability how specific events can be examined an answer to this question. Certainly of the event occurring in both the in the context of climate change and many studies showed that, from a current and the pre-industrial climate, analyzed several examples, such as global perspective, the frequency using several independent methods to the 2003 heat wave in Europe (e.g., and intensity of extreme events like determine whether the event occurs Stott, Stone, and Allen 2004). more frequently in one case than the heat waves and floods were rising or other. Methods include a comparison projected to rise; but such findings Operationalization of observations from the past as well are not applicable to individual as many simulations of a world with extreme events. This is because While the scientific community is and without climate change. Finally, computing how the probability of now able to determine whether an carefully calibrated statements about an extreme event has changed is not event was influenced by climate the results are issued to the public. easy; indeed, it is even harder than change, findings are not immediately The partnership carefully considers making regional climate projections. available; because of the time scale the uncertainties in the analysis, and Human-induced alterations of the of academic publishing, studies communicates these openly as part of atmosphere through greenhouse gas usually become available a year the results. The methods and protocol emissions not only lead to warming or longer after an event has taken are reviewed by a Science Oversight and hence increased moisture in the place. To encourage event attribution Committee that is composed of air, but also induce changes in the Bulletin of the American analyses, the leading researchers in the field of atmospheric circulation. Regionally, has published Meteorological Society extreme event attribution and risk or in specific seasons, such changes a yearly collection of attribution management. can have opposing effects on weather studies since 2011; each issue events and lead, for example, to focuses on events of the previous Based on the results of the team’s a decrease in the risk of extreme year. However, these studies do not analysis, we are able to compute precipitation instead of an increase. provide answers to the questions to what extent, if any, the risk of an Thus in order to assess the true risks asked during and immediately after extreme event has changed due to of harmful extreme events in regional an event. anthropogenic climate change. In contexts, and to assess as well the the case of major disasters, this is a Recognizing that scientific current impacts of climate change, crucial question: have the risks been advancements coupled with an the full role of human-induced changing, and if so, why? operational setup would provide climate change in individual extreme answers more quickly, a group of events needs to be explored. organizations formed a partnership Attribution in Brazil In the past, we did not have the called World Weather Attribution tools to explain how climate change One of the first events analyzed by (WWA). This initiative brings might have impacted a specific the WWA group was the 2014–2015 together Climate Central, the event. Hence many people around drought in Brazil (Otto et al. 2015; University of Oxford Environmental the world see “climate change” the result was published well Change Institute, the Royal as a problem of the future, not as after the analysis). In early 2015, Netherlands Meteorological Institute something that is already happening Southeast Brazil was suffering (KNMI), the University of Melbourne, today. But over the past decade, a from major water shortages. and the Red Cross/Red Crescent new field of science called “extreme From January 2014 to February Climate Centre to analyze extreme event attribution” has emerged, 2015—including most of two rainy events in real time using a set of seasons—the region received very which addresses the gap in our complementary methods. little precipitation. The affected knowledge and answers the question: The team begins by defining the area included Greater São Paolo, did climate change play a role in “event” based on observations the largest city in the country, with a this specific extreme event? Early population of over 20 million. and reports of impacts. Next, team breakthroughs both characterized

103 Making a riskier future: How our decisions are shaping future disaster risk / 83 The goal was to characterize how Population and water consumption in São Paulo. The figure Figure A.1. shows São Paulo’s metropolitan population from 1960 to 2010 (red line) drought risk is changing over time, and estimated water use over the same period (blue line); actual water use and identify the main drivers that in Greater São Paulo (defined slightly differently) is shown for the period are contributing to those changes. 1999–2013 (aqua line). The risk of this drought event is a function of the hazard, vulnerability, Population and water consumption and exposure in the area, and the 35 1800 WWA group set out to examine 1600 30 how each of these components had 1400 changed over time. 25 1200 Did the hazard change? 20 1000 The WWA group determined that 800 15 the probability of a rainfall deficit as 600 experienced by Southeast Brazil in 10 Population Population (million people) 2014–2015 had not changed much 400 Estimated water consumption 5 Water demand Imillion cubic meter) due to climate change. There are 200 actual water consumption several examples of similar events 0 0 in the historical record, including 1980 1960 1990 2000 1970 2010 1953–1954, 1962–1963, 1970–1971, Year and 2001. In the model data, the likelihood of this drought happening Source: Otto et al. 2015. Data on actual water use are from São Paulo state water/waste management now is not appreciably different from company (SABESP). the likelihood of it happening in a world without climate change. In fact, not as catastrophic. But information water usage per person has increased. in the observations-based approach is needed to guide the size and type Combined with the population boom, and one of the two modeling studies of investment. During the 1953–1954 the total water usage has increased used in the analysis, the risk of a drought, Brazil constructed its largest substantially, and this has put a great precipitation deficit decreased slightly water supply system, Cantareira, to strain on water supplies (see figure under current conditions. The analysis provide water to the people of São A.1). As a result of major public health also took into account the fact that Paulo. The attribution analysis of the investments between 1980 and 2005, in a warming world evaporation 2014–2015 drought shows that it however, vulnerability to cholera increases, and in this example the would not be necessary to take into impacts from drought has essentially combination of effects—fewer rainfall account more frequent precipitation vanished. Indeed, there was no deficits and increased evaporation— deficits in the design of such a system. cholera reported during this drought. led to no change in the likelihood of the overall drought hazard occurring. In the case of Hurricane Sandy in New York, scientists provided a Building back better Did the exposure change? clear partial attribution statement Ultimately, an analysis of trends in about the storm surge, explaining Yes. Analysis of population trends each of the components of disaster that because of sea-level rise, the showed that São Paulo had risk is key to making good decisions. quadrupled in size since 1960. huge waves that crashed down Extreme events can catalyze game- on the city were higher than they Did vulnerability change? changing investments in “building would otherwise have been. Climate back better,” reducing exposure and change had played an appreciable Vulnerability to water shortages vulnerability so that the next event is role in this event; much of the certainly increased over time, as

104 CASE STUDY A 84 World Weather Attribution / damage from the storm was due published (many months after the to happen now than in the past in event itself), interest has waned, this part of the world. In fact, many to the storm surge. Increased communication opportunities of the extremes were found to be sea surface temperatures were at least twice as likely to happen have closed, and critical decisions also shown to have increased the today as they would have been in have already been made about intensity of the storm (Magnusson a world without climate change. how to rebuild. By committing et al. 2014), but a full analysis Note that attribution studies tend to set up models in advance, the including all factors has not yet to report the lower boundary of the WWA team has positioned itself to been performed. often large uncertainty range, as provide information when it is most After Sandy, New Yorkers and it is easier to compute and society needed—in the immediate aftermath politicians demonstrated a marked demands conservative numbers. of the event. shift in their commitment to The best estimate of the increase is climate change adaptation. While much larger than a factor two. Attribution in real time: information about sea-level rise had Europe been available before the storm, Conclusion attributing a portion of the storm In July 2015, extreme heat waves surge to climate change catalyzed Ultimately, understanding trends set in across the Netherlands, new policies to build back better in disaster risk is crucial for better Spain, Germany, France, and and take into account this pattern decision making, and trends in Switzerland (figure A.2). Heat of rising risks. For example, the hazards are an essential component waves disproportionately affect the Hurricane Sandy Rebuilding Task of risk. Extreme event attribution elderly, the sick, and infants, and Force (2013) acknowledges that offers the opportunity to analyze each country put in place measures “it is important not just to rebuild how hazard events might have to reduce the vulnerability of its but to better prepare the region been influenced by climate change, population (largely in reaction to for the existing and future threats and to dissect the components of the heat waves of 2003 and 2006, exacerbated by climate change. events to inform efforts to “build when lack of preparedness led to President Obama’s Climate Action back better.” This type of analysis thousands of deaths). As the heat Plan clearly states that ‘climate can reveal what steps are needed waves were occurring, the WWA change is no longer a distant for successful adaptation to climate team carried out an analysis of the threat—we are already feeling its change. After the 2003 heat wave extreme temperatures and provided impacts across the country’” (3). in France, for example, heat-health up-to-the-moment scientific analysis In light of these changing risks, early warning plans and procedures to the public. Detailed graphics the task force “is developing 21st were put in place to prevent the and analysis were made available century solutions to the 21st century loss of life in future, and these were online ( http://www.climatecentral. challenges facing our Nation” (4). shown to be effective in the 2006 org/europe-2015-heatwave-climate- Updated flood risk maps have heat wave that followed (Fouillet et change ) for the public to access now been issued for the area, and al. 2008). during the event. rebuilding is taking into account the In this case, the evidence was Attribution of extreme events makes changed risks. overwhelming: climate change it easier for society to accept the As the experience during increased the likelihood of each reality of climate change and helps Hurricane Sandy showed, a major of the heat waves. France and to identify whether climate change breakthrough of the WWA team is Germany set records for the hottest is playing a role in specific events or the ability to carry out attribution day ever observed, and the WWA not. Projections then guide policy analyses in real time—when team is “virtually certain” that makers and the public in selecting everyone is listening. By the time because of climate change, heat and implementing the adaptations most event attribution studies are waves of this type are more likely needed to reduce exposure and

105 Making a riskier future: How our decisions are shaping future disaster risk / 85 Figure A.2. Observed/forecast three-day maximum temperature in Europe in summer 2015 as departure from average June-July-August maximum (1981–2010). This plot was available to the public during the heat wave of July 2015 in Europe. max_tmax–clim8100 JJA2015 ERA–int+ seasonal max of saily Tmax 60N 57N 54N 51N 48N 45N 42N 39N 36N 25E 10E 5W 15W 30E 15E 0 20E 5E 10W 1 –2 –4 4 2 –1 –3 –5 3 5 Source: Climate Central, http://www.climatecentral.org/europe-2015-heatwave-climate-change. Main Drivers of 2014/15 Water Hurricane Sandy Rebuilding Task vulnerability to changing hazards, Force. 2013. “Hurricane Sandy Shortage in Southeast Brazil.” Bulletin and in this way keep the risk at an Rebuilding Strategy.” http://portal. of the American Meteorological acceptable level. hud.gov/hudportal/documents/ Society 96, no. 8 (September). huddoc?id=hsrebuildingstrategy.pdf. doi:10.1175/BAMS-D-15-00120.1. References Magnusson, L., J.-R. Bidlot, S. Lang, A. Stott, P. A., D. A. Stone, and M. R. Allen. Thorpe, N. Wedi, and M. Yamaguchi. 2004. “Human Contribution to Fouillet, A., G. Rey, V. Wagner, K. Laaidi, 2014. “Evaluation of Medium-Range P. Empereur-Bissonnet, A. Le Tertre, the European Heatwave of 2003.” Forecasts for Hurricane Sandy.” P. Frayssinet, et al. 2008. “Has the Nature 432: 610–14. doi:10.1038/ Monthly Weather Review 142: 1962– Impact of Heat Waves on Mortality nature03130. 81. doi:10.1175/MWR-D-13-00228.1. Changed in France Since the European Trenberth, K. E., J. T. Fasullo, and T. G. Otto, F. E. L., C. A. S. Coelho, A. King, E. Heat Wave of Summer 2003? A Study Shepherd. 2015. “Attribution of Coughlan de Perez, Y. Wada, G. J. van International of the 2006 Heat Wave.” Climate Extreme Events.” Nature Oldenborgh, R. Haarsma, et al. 2015. Journal of Epidemiology 37, no. 2: “Factors Other Than Climate Change, Climate Change 5: 725–30. 309–17. doi:10.1093/ije/dym253.

106 86 World Weather Attribution / CASE STUDY B losses experienced in recent events of the exposure as the model and CASE STUDY B and show that the loss exceedance user can support. For property probability distribution, at shorter exposures, for example, information return periods, is consistent with the such as construction type, Catastrophe Models past few decades of loss experience. occupancy, elevation, and presence to Assess Future of basements may be specified. The structure of catastrophe models Risk Regular updates to insured exposure, can be described as four related reflecting changes in an insurance but independently validated and Paul Wilson, Alison Dobbin, and portfolio, are often the biggest driver Alexandra Guerrero (RMS) calibrated components: hazard, of changes in catastrophe risk year- exposure, vulnerability, and loss. on-year for insurance companies and Evolving risk and The hazard component is used are closely monitored by users of catastrophe models to characterize the frequency, these models. On an industry-wide intensity, and spatial distribution of level, the changes in population, Catastrophe models are an a particular peril (which may also building stock, and urbanization established and critical component include secondary perils such as are all important factors reflecting of how catastrophe insurers and storm surge or inland flooding in the the dynamic and evolving nature of reinsurers manage their business. case of tropical storms). While this exposure. These models are routinely used component is often calibrated to to help answer key (re)insurance The vulnerability component the long-term climatology, for many questions, such as how much accounts for the response of the climate-related perils frequency and premium should be charged for a exposure to the hazard. For property severity show time dependence on risk, or how much capital should exposures, vulnerability functions multiyear to decadal time scales. be held against the potential for estimate the damage to structures To account for this, catastrophe extreme losses. Catastrophe models and their contents that result from modelers will periodically assess and help to answer such questions by a given hazard level, as well as update modeled event frequencies providing synthetic catalogs of the amount of time required for to reflect the current activity. Where extreme events, often representing rebuilding. The implicit assumption there is sufficient evidence to hundreds of thousands of years is a static time-invariant response to indicate that current activity rates of activity and thus reducing the hazard. In reality vulnerability differ from the long-term historical dependence on limited historical is far from a static quantity, and average, and forecasts can be made experience of catastrophic loss. sophisticated catastrophe models with sufficient skill, activity rates account for the evolution of the projecting the expected activity These stochastic catalogs are derived risk by making the vulnerability over the next few years may also from a combination of statistical dependent on time-varying factors be embedded within the model and physics-based models; this such as changes in building design as a recommended reference or basis ensures that the catalogs are codes, the age of the structure (i.e., alternative view. composed of physically realistic degradation), and other relevant events and that they accurately The exposure component quantifies regulatory changes. While the extrapolate the historical experience the people or property exposed to a burden is on the user to capture to encompass all physically possible particular hazard and is the primary detailed exposure information, the scenarios. Catastrophe models are user-defined input into catastrophe model framework is designed to extensively validated both internally model software. At a minimum, allow for this. by the vendor company and exposure-related information externally by users of the models. The final component is the loss includes the location and value of For a model to be accepted, it must component or financial model that exposed assets, but the information be able to both replicate the actual can be as detailed a representation is used to estimate the impacts—

107 Making a riskier future: How our decisions are shaping future disaster risk / 87 most often monetary costs of businesses and households alike. property damage—produced by the The Risky Business Project, Catastrophe models can combination of hazard, exposure, cochaired by former New York offer powerful business- and vulnerability. In commercial City mayor Michael Bloomberg, models this component will also and policy-relevant former U.S. Treasury secretary account for any insurance-related Henry Paulson, and Farallon Capital insights into future risk. factors or policy terms. founder Tom Steyer, was set up to quantify and publicize these Catastrophe models, particularly Mexico, are at risk of hurricanes and risks to the business and financial commercial vendor models, have other coastal storms, which inflict communities, so that decision not traditionally been used as billions of dollars of property and makers in business and government part of climate change impact infrastructure damage each year. would have information about the analysis. When suitably modified, Climate change will elevate these economic risks and opportunities however, these models can offer risks. If preventive measures are climate change poses. powerful business- and policy- not taken, rising sea levels will over relevant insights into future risk. For Led by Next Generation, a not- time inundate low-lying property example, as part of the World Bank’s for-profit think tank addressing and increase the amount of flooding Pacific Catastrophe Risk Assessment key challenges for the next that occurs during coastal storms. and Financing Initiative, AIR used generation of Americans, and Warmer sea surface temperatures its Pacific basin tropical cyclone the Rhodium Group, a policy and may also change the frequency and model, modified based on the econometric consultancy, the intensity of those storms. output from 11 general circulation project used meta-analysis of models provided by Geoscience In consultation with Dr. Robert microeconometric research and Australia, to assess how tropical Kopp, RMS sought to simulate detailed sector models, including cyclone risk would impact 15 Pacific the effects of future sea-level rise the RMS North Atlantic hurricane islands. In a similar manner, for the by adjusting the surge heights catastrophe model, in conjunction Risky Business Project (2014), RMS for each of the over 50,000 with the best available scientific was able to address the future risks events in our synthetic tropical evidence, including that of the arising from climate change along cyclone hazard catalog (Kopp et Intergovernmental Panel on Climate the U.S. coastline by partnering al. 2014); these adjustments were Change (IPCC) and the U.S. National with experts in the field of climate meant to reflect changes in local Climate Assessment. This approach change and hurricane risk to sea level for a range of climate made it possible to establish the integrate the latest projections of change projections as defined impact of potential changes in local sea-level rise and potential by the IPCC’s latest Coupled temperature, precipitation, sea hurricane activity changes into the Model Intercomparison Project level, and extreme weather events RMS North Atlantic hurricane model. Phase 5 (CMIP5) Representative on different sectors of the economy Concentration Pathways (RCPs). and regions of the country (Houser Integrating the modified catalogs et al. 2014). Risky business: The into RMS’s software allowed the economic risks of climate The U.S. coastline is a key to the financial impacts to be analyzed. change to the United U.S. economy. Counties touching Because there is considerable States the coast account for 39 percent uncertainty surrounding future of total U.S. population and 28 coastal development patterns, Given the importance of climate percent of national property accurately projecting exposure conditions to U.S. economic by value. These vast exposure is challenging. Over the past few performance, climate change concentrations, particularly on the decades, population and property presents meaningful risks to the values in coastal counties have financial security of American East Coast and along the Gulf of

108 88 World Weather Attribution / CASE STUDY B Figure B.1 shows the increase in expected annual property losses as a result local sea-level rise (see Kopp et al. of local sea-level rise, assuming no change in hurricane activity for three RCPs. 2014). The current annual average The distributions reflect the uncertainty in the climate response to each RCP. baseline of coastal storm damages to commercial and residential Billion 2011 USD property, including business interruption along the East Coast and Gulf of Mexico, is estimated to be roughly $27 billion. Taking this analysis one step further, the impact of projected changes in hurricane frequency and intensity was also investigated. There is considerable uncertainty about how climate change will influence the frequency and intensity of hurricanes going forward, but the impact of potential hurricane activity change is significant. For example, using ensemble projections from Professor Kerry Emanuel (2013) for changes in hurricane frequency and intensity under RCP 8.5 to further modify the RMS hazard catalog, the analysis showed that average annual damage from East Coast and Gulf of Mexico 5 35 15 25 0 20 10 40 30 hurricanes will likely grow by between $3.0 billion and $7.3 billion Risky Business Project 2014. © Rhodium Group. Reproduced with permission; further Source: by 2030, an 11–22 percent increase permission required for reuse. from current levels. By 2050, the combined impact of higher sea grown faster than the national level and storm activity relative to levels and modeled changes in the coastline as it exists today. average. The extent to which this hurricane activity will likely raise trend will continue is unclear, given annual losses by between $11 billion Figure B.1. Increase in expected constraints to further development and $23 billion, roughly twice annual property losses in billions of and expansion in many coastal as large an increase as that from U.S. dollars (shown along the x-axis) areas. The analysis therefore did changes in local sea levels alone. By averaged over the two-decade not attempt to predict how the the end of the century, the combined intervals 2020–2039, 2040–2059, built environment will evolve in likely impact of sea-level rise and and 2080–2099 as a result of the decades ahead; instead, it modeled changes in hurricane local sea-level rise, assuming no used RMS’s in-house database of activity raise average annual losses change in hurricane activity for current commercial and residential by between $62 billion and $91 three RCPs. The distributions reflect property exposures to calculate billion, three times as much as the uncertainty in the climate the impact of future changes in sea response to each RCP, specifically higher sea levels alone.

109 89 Making a riskier future: How our decisions are shaping future disaster risk / Kopp, R. E., R. M. Horton, C. M. Little, quantify the cost-benefit of possible Conclusions J. X. Mitrovica, M. Oppenheimer, D. mitigation and adaption measures— J. Rasmussen, B. H. Strauss, and C. Catastrophe models are an are also possible. Tebaldi. 2014. “Probabilistic 21st and established framework for 22nd Century Sea-Level Projections quantifying the cost of disasters. at a Global Network of Tide Gauge References Partnerships between catastrophe Sites.” Earth’s Future 2: 287–306. modeling companies and experts doi:10.1002/2014EF000239. Emmanuel, K. 2013. “Downscaling CMIP5 in the physical implications of Climate Models Shows Increased Risky Business Project. 2014. Risky Tropical Cyclone Activity over the 21st climate change can allow these Business: The Economic Risks of Climate Proceedings of the National Century.” models to be adjusted to represent Change in the United States. Risky Academy of Sciences of the United http://riskybusiness. Business Project. future climates and the elevated States of America 110: 12219–24. org/uploads/files/RiskyBusiness_ risks of catastrophic losses under a . Report_WEB_09_08_14.pdf Houser, T., R. Kopp, S. Hsiang, M. Delgado, changing climate. The collaboration A. Jina, K. Larsen, M. Mastrandrea, S. between Risky Business and RMS Mohan, R. Muir-Wood, D. J. Rasmussen, has highlighted just one such J. Rising, and P. Wilson. 2014. American application via modification of the Climate Prospectus: Economic Risks in hazard component of RMS’s North the United States. New York: Rhodium http://rhg.com/reports/climate- Group. Atlantic hurricane model. Further prospectus . modifications that would explore the combined impact of changes in exposure or vulnerability—i.e.,

110 90 Sinking Cities: An Integrated Approach to Solutions / CASE STUDY C of more sustainable and resilient manage subsidence and develop CASE STUDY C efficient and effective approaches urban development. for both the short and long term. There is abundant evidence that Urban (ground)water management, Sinking Cities: land subsidence causes major adaptive flood risk management, problems worldwide: An Integrated and related spatial planning strategies are just a few examples of In many coastal megacities Approach to 1 the options available. around the world, land subsidence Solutions increases flood vulnerability Figure C.1 illustrates the current Gilles Erkens (Deltares Research (frequency, inundation depth, and subsidence problems related to Institute; Utrecht University), Tom duration of floods), and hence socioeconomic development and Bucx (Deltares Research Institute), Rien contributes to major economic Dam (WaterLand Experts), Ger de Lange climate change. damage and loss of lives. Land (Deltares Research Institute), and John Currently, global mean absolute Lambert (Deltares Research Institute) subsidence is responsible for sea-level rise is around 3 mm/year significant economic losses in the In many coastal and delta cities, (table C.1), and projections until form of structural damage and land subsidence exceeds absolute 2100 based on Intergovernmental high maintenance costs; it affects sea-level rise up to a factor of 10. Panel on Climate Change scenarios roads and transportation networks, Without action, parts of Jakarta, expect a global mean absolute sea- hydraulic infrastructure (river Ho Chi Minh City, Bangkok, and level rise in the range of 3–10 mm/ embankments, sluice gates, flood numerous other coastal cities will year. However, currently observed barriers, and pumping stations), sink below sea level. Increased subsidence rates in coastal megacities sewage systems, buildings, and flooding and other widespread are in the range of 6–100 mm/year foundations. The total damage impacts of land subsidence result ( ), and projections until table C.2 associated with subsidence in damage totaling billions of 2025 expect similar subsidence worldwide is estimated at billions of dollars per year. A major cause rates, depending on what policies are dollars annually. of severe land subsidence is adopted ( ). figure C.2 the excessive groundwater Because of ongoing urbanization extraction that accompanies rapid and population growth in delta Monitoring urbanization and population areas, in particular in coastal growth. To deal with the hidden but megacities, there is and will To determine land subsidence rates, urgent threat of subsidence, the continue to be more economic accurate measuring techniques are problem must be thought about in development in subsidence-prone required. These are also essential new ways. The Deltares Research areas. Detrimental impacts will to validate subsidence prediction Institute presents a comprehensive increase in the near future, making models. Ongoing subsidence approach that addresses land it necessary to address subsidence- monitoring provides the necessary subsidence from the perspective related problems now. insight into changes—ranging from minor to very significant—in 1 Material from this case study may be The impacts of subsidence are the topography of the urban area. cited freely but must be attributed further exacerbated by extreme Such monitoring could be used to as follows: Erkens, Gilles, Tom Bucx, weather events (short term) and Rien Dam, Ger de Lange, and John develop a so-called dynamic digital rising sea levels (long term). Lambert. 2015. “Sinking Cities: An elevation model (DEM). This is not Integrated Approach to Solutions.” Subsidence is an issue that involves just a static, one-time (preferably In The Making of a Riskier Future: many policy fields, complex high-resolution) recording of the How Our Decisions Are Shaping Future technical factors, and potential local topography, but an elevation , edited by Global Facility Disaster Risk actors in governance. An integrated model that can be corrected and for Disaster Reduction and Recovery. updated from time to time, and that Washington, DC: World Bank. approach is needed in order to

111 Making a riskier future: How our decisions are shaping future disaster risk / 91 Drivers, impact, and causes of land subsidence in coastal cities from a multi-sectoral perspective. Figure C.1. Climate change Socioeconomic development • Accelerated sea-level rise • Urbanization and population growth • Extreme weather events • Increased water demand Impacts • Increased flood risk • Damage to buildings, infrastructure • Disruption of water management Causes • Groundwater extraction • Oil, gas, coal mining • Tectonics Source: Modified from Bucx, Ruiten, and Erkens 2013. Table C.1. Sea-Level Rise Cumulative mean sea- Maximum rate Possible additional Current rate level rise, 1900–2013 (mm/year) (mm/year) future sea-level rise (mm) until 2025 (mm) 195 86 Worldwide mean — 3 Sources: Church and White 2011; Slangen 2012. Note: — = not available. Table C.2. Subsidence in Sinking Cities Mean cumulative Mean current Maximum subsidence Estimated additional subsidence, 1900– subsidence rate rate mean cumulative 2013 (mm) (mm/year) (mm/year) subsidence until 2025 (mm) 75–100 179 1,800 Jakarta 2,000 200 80 Up to 80 300 Ho Chi Minh City 120 20–30 1,250 Bangkok 190 1,130 6 26 > 200 New Orleans 0 239 Around 0 Tokyo 4,250 2-10 275 70 > 17 West Netherlands MoNRE-DGR 2012 (Bangkok); Van Trung and Minh Dinh 2009 (Ho Chi Minh City); JCDS 2011 (Jakarta); Eco, Lagmay, and Bato 2011 (Manila); Van de Sources: Ven 1993 (West Netherlands); Kaneko and Toyota 2011 (Tokyo). ■ ■ Interferometric synthetic can be used in hydraulic models for ■ ■ Optical leveling flood prediction and urban water aperture radar (InSAR) satellite ■ Global Positioning System (GPS) ■ management. imagery surveys The following observation ■ Field observations (ground- ■ ■ Laser Imaging Detection and ■ methods are being used to monitor truthing of buildings and Ranging (LIDAR) subsidence: infrastructure, including through the use of extensometers)

112 92 Sinking Cities: An Integrated Approach to Solutions / CASE STUDY C polluted (Jakarta, Dhaka). In Dhaka Figure C.2. Global sea-level rise and average land subsidence for several coastal cities. Subsidence can differ considerably within a city area, depending continuous large-scale extractions on groundwater levels and subsurface characteristics. have caused groundwater levels to Year fall by on average 2.5 m per year in 1925 2000 1950 1900 1975 2025 recent years (Hoque, Hoque, and 1 Ahmed 2007). Moreover, in many Absolute sea-level rise rise (m) sea-level developing cities, foundation 0 West Netherlands excavations for multiple large Ho Chi Min City construction activities require -1 Bangkok site dewatering. This also causes Manila lowering of the groundwater level, -2 resulting in soil compression and Subsidence (m) land subsidence. -3 Jakarta Studies in many cities have revealed -4 a distinct relation between falling Tokyo groundwater levels and subsidence -5 (figure C.4). The resulting spatial Modified from Bucx, Ruiten, and Erkens 2013. Source: pattern of subsidence and its progress over time are strongly (0–20 m) by loading (with Following early work with related to the local composition of systematic optical leveling, buildings), or as a result of drainage the subsurface and the number and observation nowadays deploys and subsequent oxidation and location of groundwater wells. GPS surveys and remote sensing consolidation of organic soils and techniques (LIDAR and InSAR) with peat. Alluvial sediments consisting New Orleans is a prominent example impressive results. In contrast to of alternating layers of sand, of a city where shallow drainage surveys, LIDAR and InSAR images clay, and peat are specifically causes subsidence. After the organic give a spatially resolved subsidence compressible and vulnerable to rich soils are drained, they start to signal. InSAR images date back oxidation. This makes low-lying oxidize, which adds to the overall to the 1990s. Application of this coastal and delta areas very prone subsidence rate of 6 mm/year technique is for the moment limited to subsidence. In deeper layers (Dixon et al. 2006). This process, to the urban environment. subsidence is caused by extraction which will go on as long as organic of resources such as oil, gas, coal, material is available, contributes to Periodic and systematic surveys salt, and groundwater. the sinking of the already low-lying remain essential for ground-truthing coastal city. of subsidence rates derived from In most of the large delta cities remote sensing and for validating where subsidence is severe subsidence prediction models. (Jakarta, Ho Chi Minh City, Bangkok, State-of-the-art Dhaka, Shanghai, and Tokyo), subsidence modeling the main cause is extraction of Causes Land subsidence modeling groundwater (figure C.3 shows and forecasting tools are being the Jakarta situation). Rapidly Subsidence can have natural as developed that enable Deltares expanding urban areas require huge well as anthropogenic causes. The Research Institute to quantitatively amounts of water for domestic and natural causes include tectonics, assess medium- to long-term land industrial water supply. This need glacial isostatic adjustment, and subsidence rates, and to determine often leads to overexploitation of natural sediment compaction. groundwater resources, especially and distinguish between multiple Anthropogenic causes include when surface waters are seriously causes. Modeling tools are used as compression of shallow layers

113 Making a riskier future: How our decisions are shaping future disaster risk / 93 Cumulative land subsidence over the period 1974–2010 in Jakarta, Indonesia, based on GPS Figure C.3. (Institut Teknologi Bandung) and conventional benchmark measurements (Water Resources Management Study). 0 -3.2 -2.4 -4.0 -1.6 -0.8 Modified from JCDS 2011. Source: Figure C.4. Distinct relation between falling groundwater level (hydraulic head) and subsidence in Ho Chi Minh City, Vietnam. -20 0 0 Observed head -1 Simulated head 20 -2 40 60 -3 Head (m) 80 Subsidence (mm) -4 100 Subsidence (mm) -5 120 140 -6 Jan 2010 Jul 2007 Jan 2000 Jan 2005 Jul 1997 Jul 2002 Royal Haskoning-DHV and Deltares Research Institute 2013. Source:

114 94 CASE STUDY C / Sinking Cities: An Integrated Approach to Solutions These impacts will be aggravated part of our integrated approach and Impacts over the long term by future climate are complemented with monitoring Major impacts of subsidence include change impacts, such as sea-level techniques (i.e., GPS leveling, InSAR the following: rise, increased storm surges, and monitoring). The required primary changes in precipitation. monitoring data and analytical Increased flood risk (due to ■ ■ results (of the various modeling increased frequency, depth, and Subsidence leads to direct and tools) should if possible be stored in duration of inundation) and indirect damage. Direct effects a central database. more frequent rainfall-induced include loss of functionality or floods due to ineffective drainage integrity of structures like buildings, Because land subsidence is systems roads, and underground utility so closely linked to excessive networks (critical infrastructure). ■ Damage to buildings, founda- ■ groundwater extraction, Deltares The most common indirect effects tions, infrastructure (roads, Research Institute has developed of damage are related to changes bridges, dikes), and subsurface modeling tools that calculate land in relative water levels, both for structures (drainage, sewerage, subsidence—vertical compaction— groundwater and surface water. gas pipes, etc.) in regional groundwater flow The estimation of associated models (figure C.5). These models ■ Disruption of water management ■ costs is very complex. In practice, enable us to make predictions for and related effects (changing operational and maintenance costs land subsidence under different gradient of streams, canals, are considered in several short- and scenarios of groundwater usage, and drains; increased saltwater long-term policies and budgeting. intrusion; increased need for understand the environmental and The costs appear on financial sheets pumping) socioeconomic impacts of using as ad hoc investments or planned groundwater, and contribute to As available space for building and maintenance schemes, but not as integrated management of water development decreases, there is damage costs related to subsidence. resources. an increase in housing, industrial In China, the average total economic estates, and infrastructure situated The subsidence modeling approach loss due to subsidence is estimated in subsidence-prone (marginal) uses changes in groundwater at around US$1.5 billion per year, lands, such as floodplains storage in subsurface layers of which 80–90 percent is from and coastal marshes (Jakarta, (aquifers and aquitards) and New Orleans)—with obvious indirect losses. In Shanghai, over accounts for temporal and spatial consequences. the period 2001–2010, the total loss variability of geostatic and effective stresses to determine Figure C.5. The influence of creep, the slow and largely irreversible component layer compaction. The modeling of subsidence, as determined by Deltares’s new subsidence model. Specifically tool is a modified version of the in aquifers with many fine-grained interbeds, creep clearly adds to the total groundwater flow model (developed amount of settlement over time and should not be neglected. by the U.S. Geological Survey). It 0 has been used in several studies ) 0.1 (Jakarta, Ho Chi Minh City) to 0.2 assess the adverse consequences of groundwater extraction and 0.3 Settlement (m to determine medium- to long- 0.4 term land subsidence trends and 0 20 40 60 80 100 120 Year With creep Without creep consequences for urban flood Deltares Research Institute. Source: management and vulnerability.

115 Making a riskier future: How our decisions are shaping future disaster risk / 95 cumulates to approximately US$2 Measures to counteract component of an integrated flood anthropogenic subsidence are in management and coastal defense billion (Tiefeng 2012). In Bangkok, most cases initiated only when strategy. where many private and public the detrimental impacts become buildings, roads, pavements, levees, , regulation of and Bangkok In apparent, in the form of flooding or and underground infrastructure restrictions on groundwater serious damage to buildings and (sewerage, drainage) are severely extraction have successfully reduced infrastructure. Responses until now damaged by subsidence, proper extreme land subsidence. A specific have largely focused on restricting estimates of the costs of damage are law (the Groundwater Act) was groundwater extraction, making not available. enacted in 1977. The most severely some spatial planning adjustments, affected areas were designated as In 2006, the total cost of or locally raising the level of critical zones, and the government subsidence-related damage in the land. A comprehensive and was given more control over private the Netherlands was estimated integrated (multi-sectoral) approach and public groundwater activities at over €3.5 billion per year is often lacking. in these areas. Groundwater use (Muntendam-Bos et al. 2006). The Greater Jakarta area (figure In the charges were first implemented in majority of these costs will not C.3), metropolitan authorities and 1985 and have gradually increased. be recognized directly as damage technical agencies are advocating Currently, about 10 percent of the due to subsidence. Note that the the reduction of groundwater total water use in Bangkok is from construction site preparation and extraction in vulnerable areas. groundwater extraction. Subsidence construction costs in soft-soil areas The goal is to completely phase continues but at a much slower pace should be considered as subsidence- out the use of groundwater and than before. related costs, as these are mainly tax groundwater consumption, incurred to prevent consolidation. Although land subsidence in Ho Chi an approach that would require Because of ongoing economic and has been observed since Minh City developing an alternative water 1997, there is still considerable urban development, the potential supply for large industrial users disagreement about its causes and damage costs for subsidence will or relocating large groundwater impacts. This is partly due to poor increase considerably in the future, users outside the so-called critical monitoring data on land subsidence especially in subsidence-prone zones. The number of unregistered and groundwater extraction. areas such as floodplains. users is still a problem. Ongoing Restrictions on groundwater economic development and city extraction have been initiated, but expansion lead to the filling of Responses it is too early to observe effects. low-lying and flood-prone lands Besides the registered groundwater In pristine deltas, the naturally with mineral aggregates and (often) exploitation, which draws mainly occurring subsidence is compensated waste materials. To some extent, from the deeper aquifers, there is for by the sediment delivered by the spatial planning measures were significant unregistered extraction river. Nowadays, however, many river applied to avoid subsidence-prone for domestic water supply. The systems deliver much less sediment areas, but fast growth of informal total drawdown rate shows no sign to their deltas because sediment settlements has made many of of decreasing because of these is trapped by upstream dams or is these plans obsolete. Recently the unofficial activities and perhaps also extracted for building material. With Jakarta Coastal Defence Strategy because urbanization has reduced limited sediment supply, natural program integrated the results of the infiltration area, which in turn subsidence remains inadequately various subsidence studies and hinders recharge. compensated. In many delta cities, tried to obtain reliable figures for there is additional human-induced current and future subsidence (JCDS In New Orleans and the Mississippi subsidence, making these urban 2011). This subsidence prognosis delta, there is as yet no coordinated areas the delta subsidence hot spots. is regarded as an extremely vital strategy for mitigating subsidence.

116 / 96 Sinking Cities: An Integrated Approach to Solutions CASE STUDY C subsidence and its impacts are As the relationship between The extraction of oil and gas is of currently lacking. At present, 87 groundwater extraction and land great economic importance for the percent of the supplied water is from subsidence came to be better region, and economic pressures will groundwater extraction (Sengupta, understood, techniques were likely stimulate rather than limit Kang, and Jacob 2012), and it has to restore developed in Shanghai it. The debate on groundwater use been acknowledged that a shift to groundwater levels with active or in New Orleans has only recently using surface water is necessary. passive recharge. Although this started, as its contribution to However, treating surface water is approach reduced the further subsidence is so far unknown. much more technically complex and lowering of groundwater tables The recently published water expensive than using groundwater, and limited subsidence, it did management strategy for New in part because the large rivers not solve immediate problems, Orleans, however, recommends nearest to Dhaka are polluted by notably the effect of subsidence raising water levels in areas with the economically important textile on infrastructure, roads, and organic rich soils, reducing oxidation industry, among others. buildings. Further developments of organic matter, and mitigating in Shanghai have shown that subsidence. The Mississippi delta A flood event can lead to more active and substantial recharge is starved of sediment because of attention for subsidence. This makes sustainable groundwater construction of dams and erosion- happened in November 2007, for use possible, without severe prevention measures upstream in the example, when the northern part of subsidence, provided that average catchment. The Coastal Master Plan Jakarta, which is heavily subsided yearly pumping rates are in balance for the Mississippi delta includes and below sea level, was flooded with the average yearly recharge. plans to reintroduce sediment-loaded by the sea during an extremely floodwaters to the delta once more. high tide. For a long time, land Dhaka In , increasing problems subsidence was not really seen as with flooding and water supply In Tokyo, regulations restricting one of the root causes of flooding. are resulting in more attention to groundwater use were imposed in the Nowadays, there is increasing excessive groundwater extraction early 1960s. The groundwater levels awareness that land subsidence and subsidence. Although many began to increase as a result and after has to be integrated into long-term areas are subsidence prone in this around 10 years the subsidence was flood management and mitigation rapidly expanding city, data on stopped (see figure C6). strategies. Figure C.6. Land subsidence and groundwater level in Tokyo area. Year Integrated approach 1900 2000 1960 1980 1940 1920 0 Land subsidence is often literally a -10 hidden issue. Not only does it take place out of sight, but its complex, -20 cross-sectoral nature means Groundwater -30 that it is rarely fully recognized level (m) -40 (or acknowledged), especially 0 in the domain of governance Cumulative land -1 and institutional mandates and subsidence (m) -2 responsibilities. As yet, insufficient -3 account is taken of natural resource -4 management, regional (urban) development, and strategic spatial -5 planning, and in particular urban Modified from Kaneko and Toyota 2011. Source:

117 Making a riskier future: How our decisions are shaping future disaster risk / 97 Figure C.7. DPSIR approach to subsidence. Source: Bucx, Ruiten, and Erkens 2013. ■ about Develop in-depth knowledge ■ flood management, infrastructure to avoid repetitive problems design, and infrastructure the process of subsidence and and duplication of (research) maintenance. The detrimental develop models and tools to activities effects of subsidence are ignored assess and forecast subsidence Deltares Research Institute has until they become a serious and and to measure the effects of developed an integrated assessment costly issue, one causing significant mitigative efforts framework that can be applied to economic losses and posing a ■ Assess vulnerabilities, risks, and ■ any subsidence case. It is based on nuisance to millions of people. A regarding flooding, impacts the DPSIR (driving forces, pressures, further difficulty is that acquiring, buildings, infrastructure, roads, and state, impacts, and responses) processing, and disseminating land subsurface infrastructure, in the approach and on a spatial layer subsidence information so that it short and long term, including costs model (see figure C.7). The DPSIR reaches diverse stakeholders and elements cover the cause-effect- ■ in Develop responses and solutions ■ decision makers is a complicated response chain being elaborated for a context of sustainable natural and multifaceted task. three spatial layers: the occupation resources management, climate If proper attention is paid to layer (land and water use), network change scenarios, and socioeco- developing the required technical, layer (infrastructure), and base layer nomic development administrative, and institutional (natural resources subsurface). ■ Address governance ■ by means capabilities, the harmful impacts of The DPSIR assessment uses a set land subsidence can be mitigated of multi-sectoral policy blueprint to look at a city’s science and the process largely stopped. development and coordination; and policy activities in order to A comprehensive and integrated seek participation of all relevant address subsidence. It asks a series approach is therefore needed. It stakeholders; and develop of questions that are commonly would carry out the following: innovative financing structures relevant for developing a successful ■ Support decision makers ■ with about land Raise awareness ■ ■ subsidence coping strategy ( table models and tools for selecting subsidence, to involve relevant C.3 ): What are the main causes? the most appropriate adaptive stakeholders and to determine What is the current subsidence rate? ownership and responsibilities measures (best practices), What are future scenarios? What including their costs and benefits are the impacts and risks? How can ■ Organize systematic monitoring ■ adverse impacts be mitigated or ■ ■ Facilitate exchange of knowledge and ensure that data are reliable compensated for? Who is involved and easily accessible and best practices in order

118 98 CASE STUDY C / Sinking Cities: An Integrated Approach to Solutions (for instance Dhaka, Bangladesh) identification to planning and and responsible to act? As cities to a stage at the other end of the implementation of solutions and seek to answer these technical spectrum where the problem seems their evaluation. Every subsiding and governance questions, the more or less to have been solved city is somewhere along this integrated approach supports the (for instance Tokyo, Japan). development path (see table C.3), (policy) development path that ranging from an early analysis stage cities should follow, from problem Table C.3. Questions That Need to Be Addressed to Develop a Successful Coping Strategy for Subsidence City example (state of Steps Questions development) Technical aspects Governance aspects Dhaka Measurement data collection How much subsidence is Awareness raising Manila there? Data analyses to disentangle Stakeholder analysis and 1. Problem New Orleans subsidence causes What are the causes? identification of problem owners analysis Jakarta (Inverse) modeling to make Who is involved and predictions responsible? Capacity building and education Multi-sectoral planning, Scenario constructions participation, stakeholder Modeling/forecasting engagement, and commitment (4, 5) Damage assessments How much future subsidence is predicted? Political action; development Vulnerability and risk of policy, strategy, and legal assessments What are the current and Ho Chi Minh instruments future impacts (monetized)? 2. Planning Decision support systems (6) City Planning and design of buildings What are most vulnerable Cost-benefit analyses/ and infrastructure, including areas? Multicriteria analysis building codes (8) What are possible solutions? Selection of structural Decision making on measures in an integrated implementation (5) multi-sectoral perspective Selection of nonstructural measures Multi-sectoral cooperation and Installation of monitoring organizational structure systems (7) Implementation of nonstructural Establishment of pilot projects measures (1) Proposals for innovative Legal framework and (alternative) solutions (3) What will be done, how and Bangkok operational procedures/ 3. Implementation when and by whom? Implementation of structural guidelines mitigating and/or adapting Enforcement of laws and measures (1, 2, 3) regulations Exchange of knowledge and Financing mechanisms and asset best practices (10) management (9) Monitoring, remodeling Stakeholder evaluations Tokyo Compliance checking Is the problem under control? 4. Evaluation Public hearing Shanghai Assessment and outlook Note: The numbers in parentheses refer to the following key issues, discussed in more detail below: (1) Restriction of groundwater extraction; (2) natural and artificial recharge of aquifers; (3) development of alternative water supply (instead of groundwater); (4) integrated (urban) floodwater management; (5) improving governance and decision making; (6) decision support models and tools; (7) appropriate monitoring and database system; (8) integration of geotechnical aspects in planning and design of buildings and infrastructure; (9) asset management, financing, and public-private partnerships; (10) exchange of knowledge and best practices.

119 Making a riskier future: How our decisions are shaping future disaster risk / 99 3. Development of alternative 6. Decision support models and Key issues in subsidence tools water supply (instead of policy and research groundwater) To support good decision making, In the framework of an integrated To meet the increasing (urban) models and tools are needed. approach to subsidence, 10 key water demand, an alternative water It is especially important to issues are presented here along with supply for industry and domestic analyze the relationship between possible solutions. users is required. The process of groundwater level and subsidence, shifting to an alternative supply develop modeling and forecasting 1. Restriction of groundwater should include water demand capabilities, and implement an extraction assessments (water footprint) integrated groundwater-subsidence This measure is very important and cost/benefit assessments. monitoring and analytical model. for counteracting human-induced Addressing and reducing surface Moreover, it is essential that local subsidence. water pollution is vital for agencies have the expertise and In vulnerable areas, extraction of developing a sustainable alternative tools to conduct studies, and groundwater should be reduced or water supply. that they are engaged in ongoing completely phased out. Any relevant capacity building, training, and legislation or regulation, such as the 4. Integrated (urban) floodwater knowledge exchange. following, should be consistently management implemented and enforced: Improved groundwater management 7. Appropriate monitoring and and subsidence studies should be database system ■ ■ Designation of groundwater part of an integrated urban water Ongoing studies show that the regions and critical zones (resources) management strategy that weak spot in efforts to reduce Restricted licensing and ■ ■ includes the whole water-subsurface subsidence and related flood risk compliance checking for system. Water resources management is access to reliable ground-truth groundwater well drilling should be linked to flood mitigation. data. To strengthen this area of Ultimately, land subsidence is closely weakness and build a good database ■ Universal groundwater use ■ linked to integrated land and water with long-time measurements metering and charges for management, including surface as of subsidence, it is necessary to groundwater use well as subsurface resources and develop and maintain geodetic constraints. monitoring networks throughout 2. Natural and artificial recharge the metropolitan areas, with stable, of aquifers 5. Improving governance and precisely calibrated benchmarks and When addressed consistently decision making periodic leveling surveys. and effectively, the reduction of In many cases, current governance groundwater mining can eliminate is inadequate to address subsidence 8. Integration of geotechnical one of the primary causes of land through an integrated multi- aspects in planning and design of subsidence. However, the prolonged sectoral approach and to develop buildings and infrastructure effects of settlement, possibly taking sustainable short- and long-term In the planning and design of up to 10 years, are not immediately solutions. Improving governance (heavy) buildings and road solved. Natural and/or controlled involves raising (public) awareness, infrastructure, geotechnical research groundwater recharge may be encouraging (public) participation, and modeling of the subsoil should applied to speed up recovery, as fostering cooperation and be taken into account in order well as controlled aquifer storage coordination between stakeholders to avoid subsidence problems, and recovery, a practice currently at different scales and levels, and including differential settlements, being developed and implemented enabling good decision making in the short or long term. This in Shanghai and Bangkok. buttressed by decision support approach will avoid considerable models and tools. damage and high maintenance

120 100 Sinking Cities: An Integrated Approach to Solutions / CASE STUDY C Groundwater Resources). 2012. “The costs of infrastructure and buildings References Study of Systematic Land Subsidence (foundations). During underground Bucx, T., K. van Ruiten, and G. Erkens. Monitoring on Critical Groundwater construction activities (those for 2013. “An Integrated Assessment Used Area Project.” Study by Phisut deep parking lots or metro stations Framework for Land Subsidence in Technology, Bangkok, Thailand, for the Delta Cities.” Abstract EP34B-03 or involving tunneling), the effects Department of Groundwater Resources presented at American Geophysical of the Ministry of Natural Resources of dewatering should be minimized Union fall meeting, San Francisco, and Environment, Bangkok, Thailand. and, if necessary, monitored and/or December 5–9. Report number 2555. mitigated. Church, J. A., and N. J. White. 2011. Muntendam-Bos, A. G., I. C. Kroon, P. “Sea-Level Rise from the Late 19th A. Fokker, and G. de Lange. 2006. 9. Asset management, financing, to the Early 21st Century.” Surveys in “Bodemdaling in Nederland.” TNO and public-private partnerships 32, no. 4–5: 585–602. Geophysics (Dutch Geological Survey). To minimize damage caused by Dixon, T. H., F. Amelung, A. Ferretti, F. Royal Haskoning-DHV and Deltares subsidence, the main financial risks Novali, F. Rocca, R. Dokka, G. Sella, Research Institute. 2013. “Annex 3: associated with investments and and S. W. Kim. 2006. “Subsidence and Ho Chi Minh City Land Subsidence.” In Flooding in New Orleans: A Subsidence Flood and Inundation Management: maintenance of assets (buildings, Map of the City Offers Insight into the IFRM Strategy . Final Report . Vol. 2: infrastructure) should be assessed. Failure of the Levees During Hurricane Report number 9T4178.21 for the Client This approach, which will lead Katrina.” Nature 441: 587–88. Steering Centre for Urban Flood Control to improved design options, Program, Ho Chi Minh City, Vietnam. Eco, R. C., A. A. Lagmay, and M. P. programming, and prioritization of Bato. 2011. “Investigating Ground Sengupta, S., A. Kang, and N. Jacob. 2012. investments, involves determining Deformation and Subsidence in “Water Wealth: A Briefing Paper on the performance indicators, functional Northern Metro Manila, Philippines State of Groundwater Management in Using Persistent Scatterer Bangladesh.” Centre for Science and specifications, risk mitigation Interferometric Synthetic Aperture Environment Bangladesh. http://www. measures, and bonus/malus in Radar (PSInSAR).” Abstract G23A- cseindia.org/userfiles/groundwater_ (innovative) contracts. Moreover, 0822 presented at American management_bangladesh.pdf. public-private partnerships and Geophysical Union fall meeting, San Slangen, A. B. A. 2012. “Towards Regional Francisco, December 5–9. private financing approaches that Projections of Twenty-First Century build on sustainable business Hoque, M. A., M. M. Hoque, and K. M. Sea-Level Change Based on IPCC SRES Ahmed. 2007. “Declining Groundwater Scenarios.” 38: 5–6. Climate Dynamics models should be explored. Level and Aquifer Dewatering in Dhaka Tiefeng, Li. 2012. “Land Subsidence Metropolitan Area, Bangladesh: Causes 10. Exchange of knowledge and Monitoring, Prevention and and Quantification. Hydrogeology Controlling in Coastal Cities in China.” best practices Journal 15: 1523–34. Contribution to the Expert Meeting Through international conferences, JCDS (Jakarta Coastal Defence Strategy). on Land Subsidence in Coastal workshops, expert meetings, and 2011. “Atlas JCDS.” Jakarta, Ministry Megacities, Malaysia, November 9. courses, knowledge and best of Public Works, Deltares Research Van de Ven, G. P. 1993. Man-Made Lowlands: practices can be exchanged to Institute, and Urban Solutions. History of Water Management and Land extend the common knowledge Kaneko, S., and T. Toyota. 2011. “Long- . Utrecht: Reclamation in the Netherlands base efficiently and effectively. Term Urbanization and Land Uitgeverij Matrijs. Subsidence in Asian Megacities: This step can be further supported Van Trung, L., and H. T. Minh Dinh. 2009. An Indicators System Approach.” by development of collaborative “Monitoring Land Deformation In Groundwater and Subsurface Using Permanent Scatterer INSAR research projects, preferably in Environments: Human Impacts in Asian Techniques (Case Study: Ho Chi Minh the framework of international Coastal Cities, ed. Makoto Taniguchi, City).” Paper presented at the seventh 249–70.Tokyo: Springer. (research) networks and initiatives International Federation of Surveyors such as UNESCO and the Delta MoNRE-DGR (Ministry of Natural Resources (FIG) Regional Conference, Hanoi, and Environment, Department of Alliance. October 19 –22.

121 Making a riskier future: How our decisions are shaping future disaster risk / 101 As the world continues to evolve, the index to the capital and flow losses CASE STUDY D removal of historically vulnerable seen in natural disasters (Daniell, building stock and improvement of Wenzel, and Khazai 2010). Using capital will lead to a reduction in information for the period 1900– The Evolving Risk of losses as a total percentage of that 2014 on the global population as Earthquakes: Past, stock. Global changes will also affect well as the global death rate, which the economic flow processes of takes into account war and disaster Present, and Future production, so that in certain cases deaths as well as all non-disaster- James Edward Daniell (Karlsruhe services will be significantly affected. related deaths, figure D.1 shows the Institute of Technology) This study explores these trends, long-term averages of earthquake Earthquakes have always had the starting from the past and moving deaths from nearly 2,100 fatal power to shape nations and their through the present to the future. events as a percentage of worldwide path through history. The major deaths and population. earthquakes—such as those in Historical global trends The death rate from all causes Lisbon in 382 and 1755, Shemakha of earthquakes worldwide decreased as the average in 1667 and 1902, Tokyo in 1703 life expectancy worldwide was and 1923, Managua in 1972, the Information about countries’ increasing. A range of 48.1 million Indian Ocean in 2004, Hawkes Bay earthquake risk is available in the to 77.9 million deaths per year is in 1931, and Christchurch in 2011— natural disaster databases collected seen globally, with a maximum in cause major losses within seconds, in CATDAT, the largest global 1918 and minimum in 1972. Using but exert an influence on countries database of historical damaging a 10-year average for yearly deaths for years afterward. earthquake events. The data set for worldwide makes it possible to each new event is available in annual The world today is very different determine the general trend for releases on www.earthquake-report. from what it was 100 years earthquake deaths per year as a com, and as part of collaboration ago. Global trade makes it percentage of total global deaths. projects for subsets of data. CATDAT more interconnected; building The death rate is affected by the includes not only the historical loss standards and engineering quality major events and is periodic, but it estimates of over 13,000 damaging have improved; the impacts of is constant as a percentage of global earthquakes (more than 7,500 earthquakes are better understood; deaths per year. Although the since 1900) and footprints of each and populations and exposure have 10-year average has been earthquake, but also socioeconomic increased in certain locations. As increasing, the last four-year indicators through time, such as a result of these changes, some period since 2011 has been one of population, human development, aspects of the world are less the quietest on record, meaning economic inflation estimates, and vulnerable today than they once a current return to the long- other key characteristics that allow were, and some are more. In most term average. As a percentage earthquake trends to be examined. earthquake-prone countries, the of global population, the deaths Data in CATDAT on the economic traditional nonengineered masonry from earthquakes have also been loss and death toll from each of the structures are slowly being phased decreasing, meaning that even damaging earthquakes from 1900 out in response to better knowledge with increasing life expectancy, a to 2014 were used to calculate the of the way these structures react declining earthquake fatality rate temporal trend of disaster losses to earthquakes; however, in some is observed. Categorizing each of discussed below (Daniell et al. 2011). megacities, where rapid expansion the earthquake-related fatalities by is occurring due to uncontrolled The losses were adjusted to 2014 source of fatality shows that just population increase, nonengineered dollars using the HNDECI, a hybrid under 60 percent of fatalities have building is still occurring at an index of inflation metrics that is occurred as a result of masonry alarming rate. better suited than a consumer price failures (figure D.2).

122 CASE STUDY D 102 The Evolving Risk of Earthquakes: Past, Present, and Future / Fatalities from earthquakes as a percentage of global deaths and as a percentage of global population, summed Figure D.1. in each year. The trend relative to the population decreases, but the trend as a percentage of global deaths is constant. Year 1900 1969 1992 1946 2015 1923 1 Deaths as a % of global deaths (10-year average 0.1 Deaths as a % of global deaths (cumulative average) 0.01 Deaths as a % of global population (cumulative average) 0.001 Deaths as a % of worldwide deaths per year 0.0001 Deaths as a % of global deaths/population 0.00001 Source: Calculations based on data in CATDAT. The reason for fatalities from about 2,100+ fatal earthquakes in the period 1900–2014 (left), and the Figure D.2. disaggregated total economic costs cumulated from 7,500+ damaging earthquakes (right). Total economic costs Fatalities (1900–20014) (2.32 million deaths) ($US3.19 triillion) Other structural (0.05%) NaTECH (6.1%) Shaking (61.8%) RC/C1-5/steel (8.05%) Tsunami (12.5%) Fire (13.1%) URM/UCB/RM (13.1%) Landslides (11.4%) Fire (4.1%) Tsunami (10.3%) Heart attack (0.15%) Liquefaction indirect (0.1%) Nonstructural (2.4%) Liquefaction (3.6%) Timber/bamboo/wood (2.5%) Mud/adobe/earthen/ rubble masonry (38.8%) Stone wall/masonry (6.7%) Landslide (5.1%) Calculations based on data in CATDAT. Source: RC/C1-5 = reinforced concrete/concrete building typologies; URM/UCB/RM = unreinforced masonry/unreinforced concrete block masonry/reinforced Note: masonry; Lq = liquefaction; NaTECH = natural hazard triggering a technological disaster. Dollar amount in right-hand figure was adjusted to 2014 dollars using the HNDECI.

123 Making a riskier future: How our decisions are shaping future disaster risk / 103 these two components as well as a losses are increasing. This finding In contrast with the decreased third: the capital improvement as a seems to match the preconception fatality rate, absolute loss is result of the reconstruction from net that the building standards for life observed to increase through the (depreciated) to gross (new) capital safety improve with development period from 1900 to 2014, as seen stock. This analysis suggests that via performance-engineered in figure D.3. An order of magnitude buildings are becoming safer, but structures and better building change in baseline losses can be because safer building typologies standards globally, as seen from seen when the period is split into are more expensive to construct, the fatality trends in developed two component parts (1900–1956 damage to those buildings incurs countries from 1900 to 2014. and 1957–2014). The losses greater reconstruction costs. A key indicator of the economic increase as an absolute number, but damage ratio is building age. As there is a reduction in losses as a newer building stock replaces the percentage of global gross domestic The age of infrastructure old stock, the damage ratio will product (GDP) or gross capital stock. and the impact of continue to decrease over time. The resulting earthquake loss has building standards in This change is directly correlated two components associated with recent earthquakes to the Human Development Index it: the capital stock loss (building (HDI) (UNDP 2014), with the and infrastructure losses) and the The data suggest that the relative socioeconomic fragility functions GDP loss (split from capital). The losses from disasters are decreasing of Daniell (2014) showing highly cost of an earthquake includes slightly over time, while absolute Figure D.3. Economic losses and costs from earthquakes occurring 1900–2014, as well as the relative cost versus the global gross capital stock. A reduction over time can be seen as a percentage of gross capital stock or GDP. Capital improvement 1900 2014 450 1 GDP loss Capital stock loss 400 Cost as % of current gross capital stock (cumulative) 350 Cost as % of current gross capital stock (10-year average) 0.1 300 250 0.01 200 (HNDECI-adjusted to 2014) 150 Direct loss and costs in US$ billions % of current gross global capital stock 0.001 00 50 0.0001 0 1912 1972 1918 1978 1924 1942 1936 1954 2014 1930 1948 1984 1996 1966 1906 1960 1990 2002 1900 2008 CATDAT. Source:

124 104 CASE STUDY D / The Evolving Risk of Earthquakes: Past, Present, and Future for the older building stock. In total 99 percent were built before developed nations reducing globally, building stock replacement 1980, although pre-1980 buildings earthquake damage ratios from is occurring at a fast rate, with at represented only 64 percent of the major events over time. Daniell least 1–2 percent of capital being total building stock. This means (2014) correlates 7,200 individual replaced annually. When the ratio that the remaining 36 percent of events against the province and of gross capital stock to net capital stock, built after 1980, suffered subprovince HDI of the event, and stock in 1995—1.685—is used to only 1 percent of the destruction. against the damage ratios from calculate actual loss, the result is Clearly, lesser age, better building 1900 to 2012. As shown in figure US$66.5 billion, reduced from the standards, and greater earthquake D.4, the higher-HDI countries US$112 billion replacement cost/ knowledge are key parameters for generally have a higher loss-per- repair cost quoted post-disaster. better earthquake outcomes. fatality ratio, demonstrating the Based on the sum of the value of reduction in fatality rate (via The gross (replacement value of all buildings in Kobe, the average improved construction standards) assets) and net (depreciated value construction year of net capital and increase in economic loss as of assets at book value) capital stock stock was 1976 (meaning that HDI increases. loss ratios for Kobe are shown in buildings were on average 19 years figure D.6, with the striped portion A good example of the change over old at the time of the earthquake). indicating the loss and the entire time is the 1995 Kobe earthquake. When using the year of construction column indicating the percentage of As shown in figure D.5, older as the basis and using the weighted total building value. Losses for the buildings were far more vulnerable losses of each building, the average newer building stock (under code, in this event than newer ones. construction year of buildings and better built) represent a smaller Of the buildings destroyed or contributing to total loss in dollar share of the total value than losses demolished in the Kobe earthquake, Figure D.4. The effect of HDI versus the fatality and replacement cost ratios for each country (number of damaging earthquakes used indicated in parentheses). $1,000m per fatality $100m per fatality $m denotes US$ millions 10,000,000 $10m per fatality ■ ■ Very high HDI: 0.90–0.99 High HDI: 0.80–0.89 ■ ■ $1m per fatality 1,000,000 Moderate-high HDI: 0.65–0.79 ■ ■ Moderate-low HDI: 0.50–0.64 ■ ■ $100,000 per fatality 100,000 ■ ■ Low HDI: 0.00–0.49 $10,000 per fatality 10,000 1,000 (HNDECI-adjusted to 2012) 100 Total replacement cost in US$ millions 10 1 100,000 1000,000 10,000 1,000 100 10 0,1 1 Total fatalities 1900–2012 CATDAT. Source:

125 Making a riskier future: How our decisions are shaping future disaster risk / 105 Figure D.5. Outcomes for buildings in the 1995 Kobe earthquake by period of construction. 18,810 7, 1 9 0 Before 1945 9,000 5,000 1945–1950 ■ Remaining ■ 20,000 18,000 1951–1960 Destroyed ■ ■ 32,000 65,000 1961–1970 133,000 31,000 1971–1980 Period of construction 1,000 155,000 1981–1990 32,850 150 1991–1993 40 0 80 20 60 100 Outcome for Kobe building stock (percent) Source: Adapted from Kobe municipal government statistics. Net capital and gross capital stock estimates for the dwelling portion Figure D.6. of the losses/costs incurred in the 1995 Kobe earthquake. 35 30 25 ■■ ■ Net capital stcok (% of gross capital stock value) 20 ■ Net capital stock losses ■■ ■ ■■ Gross capital stock (%) 15 ■ ■■ Gross capital stock losses 10 % of building stock gross capital value 5 0 1981–1990 1961–1970 1945–1950 1971–1980 1951–1960 Before 1945 1991–1993 Source: CATDAT. on the influence of these factors Globally, building stock and thus values was 1966. This 10-year on differences between countries. vulnerability vary significantly, with difference between the average year Figure D.7 (bottom) shows that, many different factors at play, such of construction for net capital stock globally, relatively few buildings and as building materials, the quality and for buildings contributing to infrastructure have been built in the of the seismic hazard zonation total loss in dollar values indicates time that seismic-resistant codes used to define seismic-resistant that damage was proportionally have been in place in each country; codes (figure D.7, top), enforcement greater in older building stock. In thus countries tend to rely on of building standards, and the smaller earthquakes, or earthquakes better building quality, rather than age of buildings. A recent study where old and new buildings incur codes, to withstand earthquakes. (Daniell et al. 2014) sheds light equal losses, this effect will be nil. Source: CATDAT.

126 106 CASE STUDY D The Evolving Risk of Earthquakes: Past, Present, and Future / Kathmandu and Istanbul. The trends case. Following the trends into the Figure D.7 (bottom) shows the of future building stock losses will future, the percentage of buildings percentage of buildings built clearly be substantially influenced built under code is increasing in since the code implementations by countries’ political and developed nations. There is rapid for zones in the countries—but it socioeconomic climate (Ambraseys expansion in certain locations that cannot be assumed that engineering and Bilham 2011; Spence 2007). are at risk of earthquake, such as standards were adhered to in every Figure D.7. The quality of seismic hazard zonation, based on past earthquakes, which determines requirements of seismic design code (top); and the percentage of buildings that have been built since the implementation of seismic codes in each country within the hazardous zones (bottom). Code seismic hazard zonation quality 0–10 11–20 21–30 61–70 71–80 41–50 31–40 51–60 81–90 91–100 % of buildings built in seismic zones since codes were implemented 0–17.9 53.7–71.4 18.0–35.7 71.5–89.3 35.8–53.6 Daniell et al. 2014. Source:

127 Making a riskier future: How our decisions are shaping future disaster risk / 107 Figure D.8. Comparison of present losses to future losses (in 2030 and 2080) for The future risk of 33 nations in Eastern Europe and Central Asia for the probable maximum loss in earthquakes 200 years (PML200) scenario and for average annual loss. By studying the past, the absolute 35 and relative trends of earthquake losses can be seen. The capital 30 replacement, potentially better 25 building standards, and relative 20 frequency of earthquake occurrence ■■ Increase ■ have been combined together 15 Decrease ■■ ■ with the future population and 10 GDP estimates of the Shared Number of countries 2 5 to Socioeconomic Pathways calculate earthquake risk. 0 Year 2030 Year 2080 Year 2030 Year 2080 A study by Daniell and Schaefer with without with without protection protection protection protection (2014) looks at the risk of earthquake loss currently, in 2030, Source: Daniell and Schaefer 2014. and in 2080 for 33 countries in Eastern Europe and Central Asia, taking into account the improvement of building stock. earthquake-resistant standard shows that there are significant The study (the results of which quality). Figure D.8 summarizes the changes due to better building are shown in case study G below) loss results for the 33 countries for standards and materials or changing undertakes a stochastic risk fatalities and economic losses in the global patterns of economy and assessment that simulates all present compared to the future for population. Figure D.9 shows possible earthquake events over the 200-year return period value examples from the CATDAT catalog a 10,000-year period in each of (PML200). It shows the benefit of for Bosnia (the 1969 Banja Luka the countries using data about the adding protection to the building earthquake series) and Croatia frequency of earthquake events stock over time. In terms of the (the 1667 Dubrovnik event) for over the past 2,000 years as well as average annual loss, many more past, present, and future (with and geology and tectonics. benefits arise from adding greater without protection of stock). The analysis by Daniell and Schaefer protection immediately, with 32 of (2014) shows that some of the 33 countries indicating a reduction Conclusion 33 countries will have a future in loss by 2030, compared to 26 of reduction in risk simply due to 33 in terms of the PML200 value. The study of historical, present, reduction in population and GDP Some countries will naturally have and future earthquake footprints in vulnerable areas. The analysis varying patterns of socioeconomic in conjunction with socioeconomic also takes into account the effect change that benefit their earthquake loss analysis and indicators of adding protection—that is, risk, meaning that even without helps to highlight key trends. the effect of renewing 1 percent additional protection, they have a The distribution of development of building stock per year with reduction in risk in 2030 and 2080. throughout the world shows a a reduced vulnerability (to near changing climate of earthquake Footprint analysis of historic losses, where potential direct earthquake scenarios throughout SSP Database, 2012, 2 https://secure. hits on major urban centers may the region as part of CATDAT mimics iiasa.ac.at/web-apps/ene/SspDb, have huge consequences. As a 27.06.2014. the results; however, this analysis

128 108 CASE STUDY D The Evolving Risk of Earthquakes: Past, Present, and Future / Past, present, and future losses for the 1969 earthquake in Banja Luka, Bosnia (left), Figure D.9. and 1667 earthquake in Ragusa (Dubrovnik), Croatia (right). Legend Legend Ground motion (g) Ground motion (g) ■ 0.05 ■ 0.05 ■ 0.1 ■ 0.1 0.25 ■ 0.25 ■ ■ 0.5 0.5 ■ ■ 1.25 ■ 1.25 M6.4 at 16km depth M7.4 at 23km depth Original loss: 3,000 dead; fall of Republic of Ragusa 1969 population in Banja Luka: 90,000 Dubrovnik completely destroyed (5,000 homes). Fifty years of economic 2014 population in Banja Luka: 250,000 downturn due to multiple shocks; 1,100 injured: economic loss of US$50 Original loss: 14 dead due to multiple shocks; 1,100 injured; economic loss million (US$682 adjusted to 2014 dollars of U$50 million (US$682 adjusted to 2014 dollars) Loss today: 405 dead; economic loss of US$4.08 billion (22% of GDP) Loss today: 1,520 dead; US$7.02 billion (12.5% of GDP) Loss in 2030 with protection: US$6.73 billion (9.5% of GDP) Loss in 2030 with protection: US$5.64 billion (17.4% of GDP) Loss in 2030 without protection: US$7.68 billion (10.8% of GDP) Loss in 2030 without protection: US$6.40 billion (19.7% of GDP) Loss in 2080 with protection: US$4.42 billion (5.0% of GDP) Loss in 2080 with protection: US$4.52 billion (8.8% of GDP) Loss in 2080 without protection: US$9.05 billion (10.2% of GDP) Loss in 2080 without protection: US$8.88 billion (17.3% of GDP) CATDAT; Daniell and Schaefer 2014. Source: Note: With protection = 1% improved/code stock per year. Vulnerability Indices for Use in Daniell, J. E., B. Khazai, F. Wenzel, and percentage of total GDP, capital Earthquake Loss Estimation.” Paper A. Vervaeck. 2011. “The CATDAT stock, and population, the general no. 1400, 15th European Conference Damaging Earthquakes Database.” trend of losses and fatalities is Istanbul, , on Earthquake Engineering Natural Hazards and Earth System decreasing globally; however, in Turkey. 11, no. 8: 2235–51. Sciences absolute terms, the losses are doi:10.5194/nhess-11-2235-2011. Spence, R. J. S. 2007. “Saving Lives in increasing. Appropriate building Earthquakes: Successes and Failures Daniell, J. E., and A. M. Schäfer. 2014. standards, replacement of stock in Seismic Protection Since 1960.” “Eastern Europe and Central Asia with better enforcement, increased 5, Bulletin of Earthquake Engineering Region Earthquake Risk Assessment development, and distributed GDP no. 2: 139–251. Country and Province Profiling.” ECA Region Report, World Bank, and population over countries will UNDP (United Nations Development Washington, DC. allow for further reductions in the Programme). 2014. Human future. Development Report 2013. New York: Daniell, J. E., F. Wenzel, and B. Khazai. United Nations. 2010. “The Cost of Historic Earthquakes Today—Economic References Analysis Since 1900 through the Use Ambraseys, N. N., and R. Bilham. 2011. of CATDAT.” Paper no. 07, Australian “Corruption Kills.” 469, no. Nature Earthquake Engineering Society 7329: 153–55. Conference, Perth, Australia. Daniell, J. E. 2014. “Development of Socio- Daniell, J. E., F. Wenzel, B. Khazai, J. G. economic Fragility Functions for Use Santiago, and A. M. Schäfer. 2014. in Worldwide Rapid Earthquake Loss “A Worldwide Seismic Code Index, Estimation Procedures.” PhD diss. Country-by-Country Global Building Karlsruhe Institute of Technology. Practice Factor and Socioeconomic

129 109 Making a riskier future: How our decisions are shaping future disaster risk / the world, and presents a catalog rural dwellers. This dramatic CASE STUDY E of common building expansions. transformation has been described Using vulnerability curves developed as “one of the most powerful, through incremental dynamic irreversible, and visible anthropogenic Changing structural analysis for each possible forces on Earth” (IHDP 2005). By Earthquake building configuration, it presents a 2030, the global population will reach stochastic building expansion model 9 billion, of which 60 percent will Vulnerability Linked to simulate possible expansion reside in cities (United Nations 2006). to Informal Building sequences over the lifetime of a To put this into perspective, twice Expansion building. The model is then used to as many people will live in cities in simulate an entire neighborhood 2030 as there were total people living David Lallemant, Henry Burton, Luis Ceferino, Zach Bullock, Anne Kiremidjian in the Kathmandu valley area, in 1970. Most of this urban growth (Stanford University) and analyzed to understand will occur in cities in developing neighborhood-level risk over countries (United Nations 2006), This study investigates the impact time, based on a reproduction of where the pay-as-you-go process of on earthquake vulnerability of the 1934 Nepal-Bihar earthquake informal building expansion is the de incremental building expansion that destroyed the city. The study facto pattern of growth. Households in rapidly urbanizing areas in demonstrates that informal start with simple one- or two-story developing countries. Earthquake expansions significantly increase the shelters, which over time—and given engineers understand that collapse risk of buildings. It points to sufficient resources—are transformed incremental expansion—adding over the need to limit such expansions, or incrementally to multistory homes time to what were originally one- develop methods to safely construct and rental units, as can be seen in or two-story buildings—increases them. figure E.1. Indeed, the concept of a buildings’ vulnerability, but little “static” building—designed by an has been done to model and architect or engineer, constructed Background quantify this increase. according to plan, and subsequently This study aims to help fill this gap in remaining as such for its lifetime—is The year 2008 marked a significant knowledge. It focuses on infill frame the exception rather than the norm. threshold in the history of human buildings, which are ubiquitous in Buildings are not static but evolve settlement, when for the first time cities in developing countries around urban dwellers outnumbered over time, reflecting patterns of cash Diagram of the process of incremental building construction typical of cities throughout the world. Figure E.1. Pukka 2 storey Pukka 1.5 storey Kuccha 1 storey Pukka 2.5 storey Kuccha 1 storey Pukka 1 storey Kuccha 1 storey King 2011. © Julia King. Reproduced with permission; further permission required for reuse. Source:

130 110 Changing Earthquake Vulnerability Linked to Informal Building Expansion / CASE STUDY E this study is the first attempt buildings with masonry infill, flow, family expansions, investments at quantifying the increases in which represent a very common in home businesses, and other earthquake vulnerability linked construction type in developing factors. While structural building to common building expansions. countries around the world. It further types and construction materials It further looks at a case study in focuses on buildings that expand vary from context to context, the Kathmandu, Nepal, to explore the to no more than three stories. The basic incremental building process is impact of building expansion at catalog of common expansion ubiquitous in developing countries a neighborhood scale. The study morphologies presented in figure E.2 across the world. This bottom-up hints at possible approaches to includes 10 building morphologies, approach to city building has received reducing earthquake risk, such as from which numerous evolutionary increasing attention by researchers, a simple policy limiting building building sequences are possible. as it is one of the only ways for cities expansions, or linking expansions to respond to their massive housing In order to keep the study as with strengthening. and infrastructure needs. Researchers general as possible while reflecting are attempting to find ways to harness reality, building morphologies were this organic process and ensure developed that are emblematic of Incrementally expanding that it is coupled with adequate real buildings found in Kathmandu, building morphologies infrastructure and services. Nepal, as pictured in figure E.3. The two most common building Despite the fact that buildings expansions are vertical extensions are rarely static, one of the Building vulnerability (additional stories) and cantilevered assumptions implicit in current modeling horizontal extensions (additional risk assessment models is that stories cantilevered above vulnerability is constant over The earthquake vulnerability of sidewalks or streets). These two time. The current study proposes buildings is defined by fragility basic extensions can be combined a framework for incorporating curves (also called vulnerability to form a variety of building time-dependent fragility into curves). These describe the morphologies. large-scale risk assessment models, relationships between the intensity focusing on incremental building For the purposes of this study, of earthquake ground motion and expansion as a significant driver of a standard building layout was the probability of experiencing changes in vulnerability. Empirical developed for a typical residential or exceeding a particular level of evidence suggests that such building. This study focuses damage. expansions significantly increase specifically on concrete-frame the vulnerability of buildings to Ten common building morphologies. Figure E.2. natural hazards, particularly to earthquakes. Damage assessments conducted following the 2010 earthquake in Haiti reported that buildings expanded to two or more stories collapsed at a higher rate than others. This finding is expected, since the majority of such buildings were not designed anticipating the loads of additional stories, nor were they strengthened in the expansion process. While the greater vulnerability of expanded buildings is known,

131 Making a riskier future: How our decisions are shaping future disaster risk / 111 Figure E.3. Buildings in Kathmandu, Nepal, showing typical incrementally expanded building morphologies. Anne Sanquini. Reproduced with permission; further permission required for reuse. Source: © Sample building sequence and associated changes In this study, analytical collapse Figure E.4. in vulnerability curve. fragility curves were developed for each of the 10 common building morphologies. These relate the intensity of the earthquake shaking (measured in terms of acceleration 1.00 of the ground) to the probability of the building collapsing. Specific 0.75 Building structural parameters were defined morphology State 1 based on Nepal National Building 0.50 State 4 Code guidelines for reinforced State 5 concrete buildings with masonry 0.25 Probability of collapse infill (Government of Nepal 1994). The collapse performance 0.00 assessment was conducted using 4 3 1 2 0 the Incremental Dynamic Analysis PGA (in g) (IDA) technique (Vamvatsikos and Cornell 2002). The overall analysis approach is based on the Note: PGA = peak ground acceleration; g = acceleration of gravity. methodology developed by Burton given time increment, a building the probability of transitioning from and Deierlein (2014) for simulating may expand or may stay in its any state to another in a given time the seismic collapse of nonductile current state. In order to simulate period, and it can be calibrated reinforced concrete frame buildings this, a Markov chain process model to context-specific state-change with infill. Sample fragility curves was developed. Markov chains are rates based on observations of for a specific building sequence are used to simulate mathematical buildings over time. Because data shown in figure E.4. systems that transition from one from Kathmandu were not available, state to another in state space. the study assumed and tested Rate of building These models are “memoryless,” certain transition rates to check expansion such that the next state depends reasonable outcomes of building only on the current state, not on the states after 10-, 25-, and 50-year In order to model the expansion of sequence of events that preceded it. simulations. For any given starting buildings over time, a simulation algorithm was developed. For any A transition matrix is used to define state, an expansion sequence can be

132 112 CASE STUDY E / Changing Earthquake Vulnerability Linked to Informal Building Expansion Figure E.5. simulated and tracked over time, as Sample simulation of stochastic building expansion over time based on Markov chain process. demonstrated in figure E.5. Simulation State 1 State 4 Earthquake scenario #1 29 years 21 years Kathmandu is located in a seismically active region. It has a long history of earthquake, with 71 events of magnitude 5 or greater recorded State 2 State 3 State 1 Simulation between 1911 and 1991. The largest 23 years 10 years 17 years #2 earthquake in the recent history of the region, the Great Nepal-Bihar Earthquake, occurred on January 16, 1934. The event was estimated Spatially correlated earthquake ground motion field based on a Figure E.6: to be of magnitude 8.1 and caused reproduction of the 1934 Great Nepal-Bihar Earthquake. extensive damage in the region. A reproduction of the same earthquake was chosen for this scenario. Spatially correlated 2 7.74 earthquake ground motion fields were simulated, reflecting the fact that shaking at sites close to each other is expected to be similar in 2 7.7 2 PGA (g) intensity. This approach was used to 0.7 investigate the predicted loss for a 0.6 Latitude portfolio of buildings evolving and changing over time, based on the 0.5 2 7.7 0 same baseline earthquake scenario. 0.4 An example of a spatially correlated ground motion field simulation for Kathmandu is shown in figure E.6. 27.68 Neighborhood case study 85.300 85.325 85.350 85.375 85.275 In order to demonstrate the Longitude impact of incremental expansion Note: PGA = peak ground acceleration; g = acceleration of gravity. on vulnerability over time at a community scale, a hypothetical neighborhood was created The figure demonstrates that of buildings in the neighborhood consisting of 100 buildings on 25 percent of buildings could computed every three years based the outskirts of Kathmandu city. be expected to collapse if the on the Nepal-Bihar earthquake It is a “young” neighborhood, earthquake occurred in 2021, scenario. Figure E.7 shows the with all buildings of either one while 50 percent of buildings rate of building collapse over or two stories. The growth of this would collapse if it occurred in time, driven by the increasing neighborhood is simulated over 2045. The blue bands in the figure vulnerability of buildings as they 30 years, and the collapse rate indicate that significant uncertainty expand vertically and horizontally.

133 Making a riskier future: How our decisions are shaping future disaster risk / 113 can be combined with modern Building collapse rate over time in a neighborhood on the outskirts Figure E.7. of Kathmandu in Nepal, due to a reproduction of the 1934 Great Nepal-Bihar structural analysis tools, Earthquake. simulated building expansion, and other models to gain an understanding of the main trends in the disaster risk of 0.75 cities. As part of efforts to ensure that cities are resilient 0.50 to future disasters, these tools can serve as the basis for risk- informed urban planning and 0.25 policy analyses that place urban environments on a trajectory to 0.00 minimize future risk. 2030 2042 2033 2021 2024 2039 2027 2015 2036 2045 2018 Building collapse rate in overall neighborhood Year References Burton, Henry, and G. G. Deierlein. main conclusions can be drawn from surrounds these estimates; this 2014. “Simulation of Seismic Collapse this study’s findings: arises from uncertainty in the in Non-Ductile Reinforced Concrete intensity of ground shaking caused Frame Buildings with Masonry Infills.” Driven by informal building 1. Journal of Structural Engineering 140: by the fault rupture, uncertainty in expansion, risk increases with A4014016. the fragility curves, and uncertainty time. There is a significant Government of Nepal. 1994. “Mandatory in the building growth over time. earthquake risk linked with Rules of Thumb - Reinforced Concrete The trend however is clear. Note Ministry Buildings with Masonry Infill.” informal building expansion. that the increase in vulnerability of Planning and Works, Department The risk is easy to overlook is doubly troubling, because of Urban Development and Building for a single building or short Construction. Babar Mahal, it is linked with an increase in time frame, but given enough Kathmandu, Nepal. occupancy as buildings get larger. time and scaled to entire IHDP (International Human Dimensions neighborhoods, the incremental Programme). 2005. “Urbanization and Global Environmental Change.” http:// Conclusion expansion process can . www.ihdp.unu.edu/file/get/8556.pdf profoundly shift earthquake This study showcases a model for King, Julia. 2011. “Early Results from risk. Governments should understanding how the vulnerability Savda Ghevra Field Work, Delhi.” consider policies to control the of buildings changes over time Incremental Housing (website). http:// most dangerous expansions web.mit.edu/incrementalhousing/ due to typical expansions. Young and/or should develop design articlesPhotographs/pdfs/Julia-King- urban settlements grow over time ARTICLE-e.pdf. guidelines for expanding safely. through the informal expansions Both of these steps would have United Nations. 2006. World Urbanization of individual buildings. In many Prospects: The 2005 Revision . New significant impact on reducing parts of the world, including http://www. York: United Nations. the future risk of cities. the fast-growing urban centers un.org/esa/population/publications/ WUP2005/2005WUPHighlights_Final_ in developing countries, these 2. The change in risk is Report.pdf . informal expansions constitute predictable. The disaster Vamvatsikos, Dimitrios, and C. Allin the main process of city building. risk of rapidly changing Cornell. 2002. “Incremental Dynamic This study looks at the impact of cities is predictable, even if Earthquake Engineering Analysis.” such a process on the earthquake it has significant uncertainty. 31, no. 3: & Structural Dynamics vulnerability of neighborhoods. Two Probabilistic hazard models 491–514. doi:10.1002/eqe.141.

134 114 CASE STUDY F / An Interrelated Hazards Approach to Anticipating Evolving Risk of secondary hazards. Multi-hazard Despite growing recognition of the CASE STUDY F assessments should account for importance of these interrelations, these interrelations; but, in reality, there is no agreed-upon terminology assessments rarely consider the full for interrelated hazards (Kappes An Interrelated spectrum of hazards and even less et al. 2012). Interrelated hazards Hazards Approach the interrelations between hazards are often categorized by the (Kappes et al. 2012; Duncan 2014; process (e.g., one hazard triggering to Anticipating Gill and Malamud 2014). another), actual examples of Evolving Risk interaction (e.g., earthquake In the context of evolving risk, Dr. Melanie Duncan (University College triggering landslide), and/or the there is evidence to suggest that London Hazard Centre); Dr. Stephen effect (e.g., positive or negative humanitarian actors—particularly Edwards (University College London impact on the subsequent hazard). Hazard Centre); Dr. Christopher Kilburn international humanitarian and The coincidental occurrence of (University College London Hazard development nongovernmental hazards (“risk migration”) and the Centre); Dr. John Twigg (Centre for organizations (NGOs)—are Urban Sustainability and Resilience, triggering or cascade (“chains”) particularly preoccupied with University College London); Dr. Kate of hazards (“risk amplification”) climate change rather than the full Crowley (National Institute of Water and are generally the most considered 3 range of threats (Duncan 2014). Atmospheric Research Ltd). processes (UNISDR 2011; Kappes Unless approaches are strengthened et al. 2012; Marzocchi et al. 2012; Any disaster risk management to assess multiple and interrelated Mignan Wiemer, and Giardini 2014). strategy needs to account for the hazards, there is the possibility However, hazard interrelations dynamic nature of risk and its that decisions could be leading can be further differentiated into components over time, which can to maladaptation. The following (for instance) four interdependent interact and result in emergent discussion presents a brief overview categories (table F.1). threats. Risk interaction can of interrelated hazard assessment occur at the hazard, exposure, approaches, summarizes a study Each of these interrelated hazard and vulnerability level. This of nongovernmental organizations processes can occur during a discussion focuses on hazard (NGOs) in this context, and single disaster, depending on the interrelations, which include a examines findings of particular analytical spatial and temporal number of influences, including relevance to evolving risk. scale considered (Duncan 2014). interactions, between hazards. For instance, in the Philippines, the Evidence suggests that assessments 1991 eruption of Mount Pinatubo Interrelated hazards and that do not account for the is associated with the preceding evolving risk interrelations between hazards 1990 Luzon earthquake (Bautista might underestimate risk (e.g., et al. 1996). Moreover, during Multi-hazard assessments have long Marzocchi et al. 2012; Budimir, the eruption, the coincidental been advocated as an approach to Atkinson, and Lewis 2014; occurrence of Typhoon Yunya risk reduction, but little attention Mignan, Wiemer, and Giardini resulted in the saturation of has been given to what a multi- 2014). Thus strategies based on accumulating volcanic materials hazard approach requires (Duncan such assessments could actually with rainfall, the weight of which 2014). Most assessments described increase vulnerability by focusing caused the roofs of homes and as “multi-hazard” tend to account on primary hazards at the expense for more than one hazard in a place businesses to collapse, resulting in order to (ultimately) prioritize in most of the 200–300 deaths 3 Funding for the project on which this risks. However, since hazards are directly associated with the eruption study was based came from the UK related and can interact, these (Wolfe and Hoblitt 1996). After the Engineering and Physical Research assessments should also account for eruption, large lahars were triggered Council and the Catholic Agency for Overseas Development. the interrelations between hazards. by monsoon and typhoon rainfall

135 Making a riskier future: How our decisions are shaping future disaster risk / 115 Table F.1. Categories of Interrelated Hazard Processes Category Description Example Hazards generate secondary events, which may An earthquake-triggered landslide, which blocks a Causation occur immediately or shortly after the primary river and later leads to flooding from a dam burst hazard (including cascading hazards). Hazards increase the probability of secondary events, but it is difficult to quantify this link and Stress transfer along faults Association therefore confirm causation. The effect of coastal erosion from an earlier event (e.g., tsunami) on the subsequent impact of coastal Amplification (or alleviation) Hazards exacerbate (or reduce) future hazards. flooding and tsunami inundation The coincidence of a typhoon with a volcanic Hazards occur in the same place simultaneously eruption (lahar hazard) or a windstorm and an Coincidence (or closely timed), resulting in compounded effects earthquake (firestorm hazard) or secondary hazards. Source: Duncan 2014. hazards are considered, the focus (Newhall, Hendley, and Stauffer occurring hazards, vulnerability can also be time-variant; in other is more upon physical vulnerability 2005). words, the occurrence of the first (e.g., the vulnerability of a building Interrelated hazard processes event may increase vulnerability already covered by snow or volcanic can emerge over different time to the second. A study in Italy, ash to an earthquake; see Lee and periods. In the case of causation, for instance, demonstrated that Rosowsky [2006]) rather than the secondary hazard may occur volcanic and industrial risks are socioeconomic vulnerability. immediately or shortly after the underestimated if the link between event. Identifying this time window them is not considered. Although Methods for assessing makes constraint of disaster events a small accumulation of ash may interrelated hazards challenging, particularly for the not lead to building collapse, it insurance sector (Selva 2013). In could cause casualties through Understanding of hazard the context of assessing long-term an industrial accident, thereby interrelations has tended to emerge evolving risk, particularly changes increasing the risk posed by an from the assessment of discrete in the environment, association eruption when considering this cases. However, there have been between hazards (increased secondary effect (Marzocchi et recent attempts to establish probability) and the amplification al. 2012). The consideration of generic approaches to the analysis effect are of particular interest time-variant vulnerability owing of interrelated hazards. One such because the influence of these to the influence of hazards is in approach is the generic multi-risk processes may not be immediate. addition to the consideration of framework designed by Mignan, dynamic vulnerability (e.g., changes In addition to hazards’ direct Wiemer, and Giardini (2014), in poverty, physical changes in influence on other hazards, the which incorporates coinciding buildings over time), which should influence of interrelated hazards events, triggered chains of events, be considered in risk assessments on exposure, vulnerability, and and changes in vulnerability and regardless of whether they risk (loss) is increasingly being exposure over time. Another incorporate interrelated hazards. considered. Elements at risk can approach is the development have vulnerabilities specific to of global frameworks for the Addressing sequential damage and assessment and visualization different hazards, a fact that has the separation of the respective of interacting hazards, such as implications for the mitigation impact of each hazard is rare in risk the matrixes designed by Gill of coincidental hazards. In the assessments (Kappes et al. 2012). Furthermore, where interrelated and Malamud (2014) (discussed context of cascading or closely

136 116 An Interrelated Hazards Approach to Anticipating Evolving Risk / CASE STUDY F hazard interrelations is through matrixes, although this implies a combination of the review of a mutual influence between Multi-hazard assessments two processes when in fact literature and intuitive judgment have long been advocated some of these matrixes have (e.g., Mignan, Wiemer, and Giardini as an approach to risk been utilized only to identify 2014; Gill and Malamud 2014). For a sequential cause and effect. specific case studies, interrelations reduction, but little Matrixes are used to identify are recognized by identifying attention has been given the existence of interactions spatially and temporally overlapping (e.g., Tarvainen, Jarva, and hazards using a combination of to what a multi-hazard Grieving 2006) and, recently, geographical information systems approach requires. to quantify the frequency of (GIS) and matrixes. GIS can be used these interactions, the spatial to identify which hazards might overlap of interacting hazards interact and where interactions and in greater detail below). These and temporal likelihood of the coincidences might occur, but this generic and global frameworks triggered secondary hazard, information needs to be supported cannot account for the complexity and the intensity relationship by scenarios of the likely occurrence of assessment scenarios for actual between the primary hazard of these interrelations (Kappes et al. places. They may, however, help and secondary hazard (e.g., Gill 2012). Network analysis, matrixes, policy makers address evolving risk and Malamud 2014). This final and event trees have been utilized by providing information about the application is important, since to identify and predict interrelated potential for hazard interrelations, underestimating the intensity hazards. These are briefly described hazards’ spatial and temporal of the primary hazard has been here with examples of recent overlap, and the intensity of shown to result in unexpected applications. subsequent hazards. cascading disasters, such as Network analysis was used 1. There is a growing number of the 2011 Japanese earthquake by Gill and Malamud (2014) methods for the assessment of and subsequent tsunami and to identify the existence of interrelated hazards. These can be nuclear disaster. interrelated hazards based on distinguished by what scale they use, 3. emerged from Event trees the review of 200 papers. After whether they adopt a qualitative or volcanology. The move toward normalization, they found that quantitative approach, and whether more probabilistic approaches geophysical and atmospheric they anticipate the location, timing, in volcanic risk assessment and severity of the subsequent hazards were the predominant created two challenges: (a) hazard. In terms of statistical triggers of other hazards, the difficulty of assessing the analysis, the challenge of assessing but also that geophysical as relative likelihoods of different interrelated hazards is that they well as hydrological hazards ways in which a multi-hazardous cannot be treated in the same way are triggered by the most volcanic system could evolve in that a single hazard is treated in hazards. These initial rankings the future or during a real-time typical assessments. For instance, do not reflect the overall crisis; and (b) the difficulty of many studies consider hazard event extent of spatial overlap and communicating probabilistic sets as stochastic (random) and temporal likelihood of these information to decision makers independent, but secondary and interrelations. (Martí et al. 2008). To address cascading hazards are dependent on Matrixes are typically used to 2. these difficulties, event trees previous events and require the use identify hazard interrelations of impact scenarios were of conditional probabilities. in a qualitative or semi- developed for volcanic crisis In the case of global and generic quantitative manner. These and, more recently, for the assessments, the identification of are often termed “interaction” assessment of interrelated

137 Making a riskier future: How our decisions are shaping future disaster risk / 117 hazard, such as rock slides in and for event trees analyses, Norway (Lacasse et al. 2008). conditional probabilities can be Many studies consider Event trees are graphical proposed based on simulated hazard event sets as representations of events with changes in environment and stochastic (random) branches that represent logical hydrometeorological systems, in steps from a general event place of probabilities based on and independent, but through increasingly specific current conditions. secondary and cascading subsequent events and final In the long term, assumptions can outcomes (Newhall and Hoblitt hazards are dependent be made about the risk of secondary 2002). In contrast to event hazards assuming a constant rate on previous events trees for volcanic settings, those of primary hazards over time; for interrelated hazards might and require the use of however, these assumptions begin with a number of branches conditional probabilities. become irrelevant when changes before focusing toward a single in the environment—for instance outcome. Event trees tend to climate change—are taken into employ conditional probabilities are confined to case studies in consideration (Marzocchi et al. in order to account for the developed countries (e.g., De Pippo 2012). While some studies of influence of previous events. et al. 2008; Marzocchi et al. 2009, interrelated hazards recognize the These probabilities are assigned 2012), where data on hazards need to incorporate anthropogenic to each of the branches and are tend to be more plentiful than in influences, including environmental determined from historical or developing countries. In developing and climate change, into geological data and often by countries, community knowledge is frameworks (e.g., Marzocchi et expert judgment (see Lacasse et essential since it might be the only al. 2012; Gill and Malamud 2014; al. [2008]). information available to scientists Duncan 2014), the application of regarding the hazard context Each of these methods has these has generally been limited to (Mercer 2012); but it also needs application to the assessment of specific cases, such as coastal risk to be integrated with available evolving risk. Network analysis may (Garcin et al. 2008) rather than scientific insight. Community-based be most useful in identifying past within studies considering the full risk assessments are therefore an occurrences of hazard interrelations spectrum of risk in an area. integral component of reducing risk, as a guide to interrelations that and a number of NGOs conduct risk There are only a few studies that may occur due to evolving spatial assessments at this level. Whether have focused on the capacity of and temporal ranges of hazards these assessments truly account end-users with a nonscientific in the future. The other methods for multiple and interrelated background to implement multi- could account for evolving risk by hazards, however, has received little hazard assessments. Komendantova incorporating predicted patterns of attention until now. et al. (2014) studied the needs future risk. GIS could incorporate and capacity of risk managers and layers of future flood risk based discovered a number of barriers on global river flood models that NGOs, interrelated to the uptake of multi-hazard utilize global climate models to hazards, and evolving risk assessments, including lack of simulate higher precipitation, or NGOs typically work in developing clarity regarding a multi-hazard future coastal flood hazard due countries, acting as key facilitators approach and concern over the level to subsidence of land. In matrix in the implementation of of expertise required to implement approaches, the spatial domain community-based risk assessments. methods (see also Scolobig et al. and frequency of each hazard The application of multi-hazard [2014]). Furthermore, a number can be adjusted to anticipated assessments by NGOs was evaluated or simulated future conditions, of studies of interrelated hazards

138 118 CASE STUDY F / An Interrelated Hazards Approach to Anticipating Evolving Risk by a doctoral research project and analysis is constrained in its that NGO assessments of hazards spatial and temporal scales. on multi-hazard assessments for are typically limited to the disaster risk reduction (DRR) that geographical scale of a community All interviewees expressed concern addressed NGO approaches to multi- and tend to reflect on past events, about an uncertain future, but hazard assessment, particularly in without necessarily anticipating did so largely in the context of 4 the Philippines (Duncan 2014). future change. Historical analysis climate change, regardless of The project studied NGO toolkits is a strong component of the NGO whether they had a DRR or CCA and conducted interviews with claim to a multi-hazard approach background. There was a shared humanitarian/development NGO to community-based assessments preconception that emergent staff from DRR and climate change because different hazards are threats and unknown future risk are adaptation (CCA) backgrounds. identified through the creation of purely driven by climate change, Interviews were conducted between time lines and seasonal calendars. whereas DRR adopts a historical December 2009 and August 2011 Temporally, the process of hazard approach and deals with “known” with 22 NGO staff members in head analysis is constrained by the extent hazards. This preconception offices and 13 staff members in and degree to which communities highlights a shortcoming in the country (11 of the 13 were based can reliably remember disasters— implementation of DRR, since it is in the Philippines). In addition, a especially when specific data conceptualized to adopt a long-term case study of the 2006 Typhoon (e.g., frequency and impact) are perspective (see Mercer [2010]). Durian–triggered lahars at Mayon required—and by their perception Furthermore, the perception that volcano was analyzed. A number of risk. What is apparent from DRR deals with “known” hazards of findings related to perceptions both this study and the literature overlooks instances where hazards and assessments of evolving risk is that in spite of the emphasis on might occur coincidentally or in emerged from this study. a long-term approach, both DRR close succession, resulting in an and CCA are primarily addressing overall impact that far exceeds the In interviews, most head office risk in the short term, partly owing “known” impact of the individual staff emphasized the importance to the overreliance on community hazards. Notably, perceptions of integrating DRR and CCA knowledge for assessment purposes. differed slightly among Philippine approaches and described their own NGOs are struggling to address interviewees; although they also community hazard assessments CCA because they are trying to look emphasized climate change, they as adopting a multi-hazard at time frames 30 to 50 years in tended to better recognize the approach. In reality, however, advance, while ensuring that they interrelations between hazards and these assessments did not always address communities’ immediate to appreciate that all hazards (not fully consider the multiple threats concerns. There is, however, a just those related to climate) are communities face, even less so need to adopt an anticipatory dynamic and need to be reviewed the interrelations between these approach to all hazards: given that over time. This periodic review is hazards. The study identified a conditions for hazard interaction critical when considering evolving number of practical and perceived may not have been met before and risk, especially if approaches are not constraints on the process of multi- that risk evolves (due to changes particularly anticipatory. In reality, hazard assessment, but three are of in environmental conditions, for however, interviewees across the particular interest here: approaches instance), communities may not study stated that review of hazards are designed to look at risk through have experienced certain disasters was unlikely to occur. a DRR or CCA lens; NGOs rely almost in the past. Likewise, even if totally on community knowledge; Hazard interrelations are identified communities have previously through an appropriate spatial experienced specific hazard- 4 Except where otherwise specified, the and temporal extent of analysis. related disasters, they may be at source for all material in this section is Duncan (2014). However, the interviews indicated risk to higher intensity events in

139 Making a riskier future: How our decisions are shaping future disaster risk / 119 the long term; for instance, many science into their multi-hazard communities at Mayon volcano had assessments—an oversight that acts Interrelations between experienced lahars before Typhoon as a major barrier to implementing hazards and their Durian in 2006, but not of the the methods suggested above. influence on vulnerability magnitude of that event. Partnership and collaboration between NGOs and risk scientists are fundamental to While the data resolution and is therefore imperative, but is uncertainty of climate science the understanding and hindered by a series of institutional, arguably hinder their applicability practical, and perceived barriers. assessment of evolving to community-based risk reduction work carried out by NGOs, they risk. Implications for policy have highlighted the need for makers and practitioners agencies to (a) integrate risk science more relevant, so organizations with community knowledge and Interrelations between hazards and implementing community-based (b) consider larger geographical their influence on vulnerability are DRR may consider it essential to and prospective scales in their fundamental to the understanding incorporate the positive as well risk reduction work to better and assessment of evolving risk. as negative interrelations. For anticipate the possible occurrence Risk reduction strategies for one instance, the Philippine Institute of disasters. Addressing both these hazard should take into account of Volcanology and Seismology areas could help build capacity coincidental and chains of hazards observed that the 2006 eruption of to implement assessments that both in the short and long term, Mayon, which occurred prior to the adopt an integrated DRR and CCA to ensure that decisions made Typhoon Reming–triggered lahars, approach to assessing future and to mitigate hazards today do not produced a lava flow that actually evolving risk. An evolving risk increase vulnerability to future protected the provincial capital from approach to risk assessment should events. Furthermore, hazards are the worst effects of the typhoon incorporate the wider natural (and dynamic, and there is also a need to not just socioeconomic) systems lahars that followed two months account for how past hazards might in order to account for hazards later (see Duncan 2014). However, increase the probability or amplify and environmental change that as in the case of the 2006 lahar the location, timing, and severity of might occur at a distance from disaster at Mayon volcano, it may future events (Duncan 2014). communities but still have a be easier to identify the positive notable impact upon them. But While there are a number of hazard influences of hazards after rather there is also evidence to support interrelations, not all processes than before a disaster. a community-focused approach to require consideration within a Methods for assessing interrelated interrelated hazard assessments multi-hazard risk assessment hazards vary depending on their since interrelated hazards can (Marzocchi et al. 2012; Gill and analytical scale, whether they be apparent at the community Malamud 2014). Some hazard adopt a qualitative or quantitative level. Some of the tools discussed interrelations may decrease approach, and whether they earlier (GIS, matrixes, event probability or lower the intensity of anticipate the location, timing, and trees) may be able to assist NGOs the subsequent hazard (see Duncan intensity of subsequent events. For in (at least) the visualization of 2014); but it has been noted that policy makers, the recent attempts interrelated hazards and evolving these positive effects are unlikely at generic and global analyses may risk over different spatial and to be included in risk assessments, be useful for resource allocation; temporal scales. However, Duncan’s which tend to adopt a conservative but practitioners require specific approach (Gill and Malamud (2014) study found that NGOs details about the local level. While 2014). At the local level, however, (with some exceptions in the these positive effects may become methods for assessing interrelated Philippines) tend not to integrate

140 120 An Interrelated Hazards Approach to Anticipating Evolving Risk / CASE STUDY F Development Planning and Practice hazards have application to expert judgment required to identify to Enhance Community Resilience: evolving risk, there need to be more and quantify interrelated hazards Guidelines for NGO Practitioners.” applications that incorporate the (and their uncertainty) emphasize http://www.ukcds.org.uk/resources/ influence of environmental changes, both the need to build the capacity integrating-science-into-humanitarian- such as climate change. of nonscientists to understand and-development-planning. 5 and the need and utilize science Furthermore, although the number of Garcin, M., J. F. Desprats, M. Fontaine, R. to facilitate NGOs’ engagement, Pedreros, N. Attanayake, S. Fernando, methods being explored is growing, communication, and participation C. H. E. R. Siriwardana, U. De Silva, the assessment of interrelations with hazard and risk scientists. and B. Poisson. 2008. “Integrated is largely (a) absent from single or Approach for Coastal Hazards and “multi-hazard” risk assessments; Natural Hazards Risks in Sri Lanka.” References (b) confined to engineering, spatial 8: 577–86. and Earth System Science planning, and hazard science; and Bautista, B. C., M. L. P. Bautista, Gill, J. C., and B. D. Malamud. 2014. (c) not systematically accounted for R. S. Stein, E. S. Barcelona, R. “Reviewing and Visualizing the by NGOs. Punongbayan, E. P. Laguerta, A. Interactions of Natural Hazards.” R. Rasdas, G. Ambubuyog, and E. Reviews of Geophysics 52: 680–722 . The space-time window and Q. Amin. 1996. “Relationship of doi:2013RG000445. design of the assessment should Regional and Local Structures to Kappes, M., M. Keiler, K. Elverfeldt, and T. arguably meet the requirements of Mount Pinatubo Activity.” In Fire Glade. 2012. “Challenges of Analyzing and Mud: Eruptions and Lahars of the end-user (Kappes et al. 2012; Multi-hazard Risk: A Review.” Natural Mount Pinatubo, Philippines , edited Marzocchi et al. 2012). But there Hazards 64: 1925–58. by C. Newhall and R. Punongbayan, are few studies of the capacity of 351–70 . Quezon City and Seattle: Komendantova, N., R. Mrzyglocki, A. end-users to implement multiple Philippine Institute of Volcanology Mignan, B. Khazai, F. Wenzel, A. Patt, and interrelated hazard assessments and Seismology and University of and K. Fleming. 2014. “Multi-hazard (exceptions being the study by Washington Press. http://pubs.usgs. and Multi-risk Decision-Support gov/pinatubo/bbautist/index.html. Komendantova et al. [2014] of risk Tools as a Part of Participatory Risk managers, and Duncan’s [2014] Governance: Feedback from Civil Budimir, M. E. A., P. M. Atkinson, and H. International Protection Stakeholders.” analysis of international and G. Lewis. 2014. “Earthquake-and- Journal of Disaster Risk Reduction 8: Landslide Events Are Associated with Philippines-based NGOs). With 50–67. More Fatalities than Earthquakes regard to NGOs’ risk assessments, 72: 895–914. Alone.” Natural Hazards Lacasse, S., U. Eidsvig, F. Nadim, K. the constrained spatial and Høeg, and L. H. Blikra. 2008. “Event De Pippo, T., C. Donadio, M. Pennetta, C. temporal scales of analysis and Tree Analysis of Åknes Rock Slide Petrosino, F. Terlizzi, and A. Valente. the emphasis on climate change Hazard.” In Comptes rendus de la 2008. “Coastal Hazard Assessment further underline the need for an 4e Conférence canadienne sur les and Mapping in Northern Campania, integrated, evolving risk approach géorisques: des causes à la gestion 97: 451–66. Geomorphology Italy.” across disaster risk assessment. [Proceedings of the 4th Canadian Duncan, M. 2014. “Multi-hazard Conference on Geohazards: From Causes The methods being developed for Assessments for Disaster Risk to Management], edited by J. Locat, Reduction: Lessons from the assessing interrelated hazards D. Perret, D. Turmel, D. Demers, and Philippines and Applications for Non- have very visual outputs (e.g., S. Leroueil, 551–57. Québec: Laval governmental Organisations.” EngD maps, network analysis, matrixes, University Press. diss., University College London. and event trees), which may help Lee, K., and D. Rosowsky. 2006. “Fragility Duncan, M., K. Crowley, R. Cornforth, S. NGOs and other practitioners, as Analysis of Woodframe Buildings Edwards, R. Ewbank, P. Karbassi, C. well as the communities they work Considering Combined Snow and Mclaren, et al. 2014. “Integrating Structural Safety Earthquake Loading.” with, appreciate and capture the Science into Humanitarian and 28: 289–303. dynamic nature of hazards more 5 widely (see Komendantova et al. Martí, J., W. P. Aspinall, R. Sobradelo, A. See the integrating-science guidelines produced by Duncan et al. 2014. [2014]). However, the data and Felpeto, A. Geyer, R. Ortiz, P. Baxter,

141 Making a riskier future: How our decisions are shaping future disaster risk / 121 Reduction and Recovery, 163–67. Mignan, A., S. Wiemer, and D. Giardini. et al. 2008. “Long-Term Volcanic Washington, DC: World Bank. 2014. “The Quantification of Low- Hazard Event Tree for Teide-Pico Viejo Probability-High-Consequences Stratovolcanoes (Tenerife, Canary Tarvainen, T., J. Jarva, and S. Grieving. Events: Part I. A Generic Multi-risk Journal of Volcanology and Islands).” 2006. “Spatial Pattern of Hazards and Approach.” Natural Hazards 73: 178: 543–52. Geothermal Research Hazard Interactions in Europe.” In 1999–2022. Natural and Technological Hazards and Marzocchi, W., A. Garcia-Aristizabal, P. Risks Affecting the Spatial Development Newhall, C., J. W. Hendley II, and P. H. Gasparini, M. L. Mastellone, and A. of European Regions, edited by P. Stauffer. 2005. “The Cataclysmic D. Ruocco. 2012. “Basic Principles of Schmidt-Thomé, 83–92. Geological 1991 Eruption of Mount Pinatubo, Multi-risk Assessment: A Case Study in . Survey of Finland, Special Paper 42 Philippines.” U.S. Geological Survey Natural Hazards Italy.” 62: 551–73. Fact Sheet 113-97. http://pubs.usgs. UNISDR (United Nations International Marzocchi, W., M. Mastellone, A. Di gov/fs/1997/fs113-97/. Strategy for Risk Reduction). 2011. Ruocco, P. Novelli, E. Romeo, and Global Assessment Report on Disaster Newhall, C. and Hoblitt, R. 2002. Principles of P. Gasparini. 2009. Risk Reduction 2011. Geneva: UNIDSR. “Constructing Event Trees for Volcanic Multi-risk Assessment: Interaction http://www.preventionweb.net/ Bulletin of Volcanology 64, 3–20. Crises.” Amongst Natural and Man-induced english/hyogo/gar/2011/en/home/ Brussels: European Commission. Risks. Selva, J. 2013. “Long-Term Multi-Risk index.html . http://ec.europa.eu/research/ Assessment: Statistical Treatment of environment/pdf/multi-risk_ Wolfe, E. W., and R. P. Hoblitt. 1996. Interaction among Risks.” Natural . assessment.pdf “Overview of the Eruptions.” In Fire 67: 701–22. Hazards and Mud: Eruptions and Lahars of Mercer, J. 2010. “Disaster Risk Reduction Scolobig, A., A. Garcia-Aristizabal, N. , edited Mount Pinatubo, Philippines or Climate Change Adaptation: Are Komendantova, A. Patt, A. Di Ruocco, by C. Newhall and R. Punongbayan, We Reinventing the Wheel?” Journal of P. Gasparini, D. Monfort, et al. 2014. 3–20 . Quezon City and Seattle: International Development 22: 247–64. “From Multi-Risk Assessment to Multi- Philippine Institute of Volcanology Risk Governance: Recommendations ———. 2012. “Knowledge and Disaster Risk and Seismology and University of for Future Directions.” In Handbook of Hazards Reduction.” In http://pubs.usgs. Washington Press. Understanding Risk in an Evolving and Disaster Risk Reduction , edited by /. gov/pinatubo/wolfe World: Emerging Best Practices in B. Wisner, J. Gaillard, and I. Kelman, Natural Disaster Risk Assessment, 96–108. Abingdon, UK: Routledge. edited by Global Facility for Disaster

142 122 CASE STUDY G / Evolution of Risk in Eastern Europe and Central Asia Table G.1. RCP and SSP Scenario Combinations Used to Estimate Future CASE STUDY G Flood Risk SSP scenario Scenario characterization RCP scenario Cautiously optimistic RCP4.5 SSP2 Evolution of Risk in SSP2 RCP8.5 Present trends continue Eastern Europe and SSP3 Worst case RCP8.5 Central Asia Richard J. Murnane (Global Facility Table G.2. Climate Models Used for Flood Risk Estimates for Disaster Reduction and Recovery), James E. Daniell (Karlsruhe Institute Climate model Description of Technology), Hessel C. Winsemius GFDL Earth System Model 2 with medium resolution GFDL ESM2M (Deltares Research Institute), Philip HadGEM2-ES Hadley Global Environment Model 2–Earth System J. Ward (Institute for Environmental Studies, VU University, Annegien Tjissen MIROC-ESM-CHEM MIROC (Model for Interdisciplinary Research on Climate) Earth (Global Facility for Disaster Reduction System CHASER-coupled Model (Atmospheric Chemistry version) and Recovery), Joaquin Toro (Global IPSL Coupled Model 5 IPSL-CM5A Facility for Disaster Reduction and NorESM1-M Norwegian Earth System Model with medium resolution Recovery) ensemble of risk estimates for the in terms of population and gross Introduction 6 2010, 2030, and 2080 time slices. domestic product (GDP) for areas that experience floodwater at any In this study we investigate the The risk assessments provide depth, or ground motion with an evolution of estimated flood and first-order estimates of the intensity consistent with Modified earthquake risk for Turkey. We spatial distribution of flood and Mercalli Intensity (MMI) equal to VI use values in 2010 and a range earthquake risk and how it could or greater. Ground motion at MMI VI of possible values in 2030 and evolve over time. The results will is felt by almost everyone; furniture 2080 that are consistent with be used for opening discussions sometimes moves, and some build- hazard and exposure as specified with governmental institutions ings may experience slight damage. by Representative Concentration in the Europe and Central Asia In addition, for the earthquake mod- Pathways (RCPs) and Shared (ECA) region as defined by the el, vulnerability functions are used to 7 Socioeconomic Pathways (SSPs) World Bank. Due to a number of estimate fatalities and capital loss. created by the Intergovernmental limitations (see below), the results The losses are calculated as average Panel on Climate Change (IPCC) should not be used for making annual loss (AAL) and are for a vari- for the Fifth Assessment Report any decisions regarding specific ety of return periods. (AR5). For flood risk, we use a mitigation and planning measures. combination of two RCPs, two In the following sections, we discuss Flood and earthquake losses esti- SSPs, and five global climate the methodology associated with mated by the project are presented models to create an ensemble of the flood and earthquake models, risk estimates. The combinations provide an overview of the exposure 6 For more information on the RCPs, of RCPs and SSPs used for the data used to estimate the risk at the see Meinshausen et al. (2011). For flood model are listed in table three time slices, and summarize information on the SSP scenarios, see 122, no. 3 (2014), a Climatic Change G.1. Short descriptions of the five the different RCP and SSP scenarios. special issue on new socioeconomic climate models used for estimated We then present the results of the scenarios for climate change research future flood risk are listed in table risk assessment and finally discuss (e.g., Nakicenovic, Lémpert, and Janetos ́ ́ G.2. We assume earthquake risk the relative importance of changes 2014). 7 is independent of climate, and we in climate and exposure for the http://www.worldbank.org/en/ See future evolution of risk. thus use five SSPs to create an region/eca .

143 Making a riskier future: How our decisions are shaping future disaster risk / 123 then used as input to the GLOFRIS combined into risk estimates. The Exposure models are briefly described below. downscaling module to calculate Future exposure data (GDP and flood depths at the 30” x 30” level population) were developed Flood model (Winsemius et al. 2013). using the IMAGE model of PBL The flood modeling results are The GDP and population affected by Netherlands Environmental derived using several modules of floods for each return period were Assessment Agency, forced by the GLOFRIS (Global Flood Risk with based on the population or GDP the socioeconomic conditions IMAGE Scenarios) global flood risk in each grid cell that had nonzero associated with the SSPs in table modeling cascade. The first step is flood depths at the selected return G.1. The population estimates were the simulation of daily discharge periods. The average annual values further modified to be consistent on at a horizontal resolution of 0.5° x a level 1 administrative (province) at each grid point were derived by 0.5° using the PCR-GLOBWB global level using the 2010 round of integrating over the nine return- hydrological model (Van Beek and census data, hindcasted and period loss estimates. The annual Bierkens 2009; Van Beek, Wada, forecasted using census growth average and return period values and Bierkens 2011). For the present- rates to the year 2010 for each of for GDP and population affected by day climate, the model was forced the 863 units included. floods in the level 1 administrative with daily meteorological data at regions were determined by The GDP data were adjusted 0.5° x 0.5° resolution. These data summing the losses within each to match the individual level 1 are derived from reanalysis data area as defined using shape files. administrative GDP per capita data for the years 1960–1999 and are built from provincial and municipal To estimate the GDP and population provided by the EU-WATCH project government and bank estimates affected by flooding in 2030 and (Weedon et al. 2010). The second and forecasted and hindcasted 2080, both flood hazard and step in the hazard modeling is the to 2010 in the CATDAT database exposure for those time periods simulation of daily within-bank and from Daniell, Wenzel, and Khazai were simulated. The future flood overbank flood volumes, again at (2012). Each administrative level a spatial resolution of 0.5° x 0.5°. hazard maps were simulated 1 region had separate values of This is carried out using DynRout using the same GLOFRIS model GDP per capita, distributed via extension (PCR-GLOBWB-DynRout), as described above, but forced by 1 km resolution population data. which simulates flood-wave daily future climate data from the The 2030 and 2080 scenarios propagation within the channel as five climate models (see table G.2) were similarly adjusted using this well as overbank. For a detailed forced by the two RCPs (see table distribution, but remain consistent description of this approach, see G.1). The precipitation estimates with the SSPs and IMAGE model. Winsemius et al. (2013) and Ward et for the climate models are bias al. (2013). corrected using the 1960–1999 Hazard models EU-WATCH data and a methodology From this daily time series of flood developed by the Inter-Sectoral volumes, estimates of flood volumes Risk modeling for large areas such Impact Model Intercomparison per grid cell (0.5 °x 0.5°) were as the ECA region requires global- Project (ISI-MIP). For details on the derived for selected return periods scale data on exposure and hazard. bias correction, see Hempel et al. (2, 5, 10, 25, 50, 100, 250, 500, and Site-specific data are not available (2013). The previously described 1,000 years). The estimates used in most cases, and even if they were, methodology uses estimates of extreme value statistics based on their computational requirements future precipitation generated by the Gumbel distribution and the would be prohibitive. We therefore the five climate models as boundary daily nonzero flood volume time rely here on globally applicable conditions for estimating flood series derived from the hydrological models to estimate hazard and depths. model. These flood volumes were exposure, which are consequently

144 124 Evolution of Risk in Eastern Europe and Central Asia / CASE STUDY G estimates of local site conditions in the combined scenarios remains Earthquake model are used to determine peak ground nearly unchanged. Changes in The stochastic earthquake model acceleration (PGA) at each grid climate associated with RCP4.5 and follows a standard risk modeling point. Local soil conditions are RCP8.5 cause, on average, a slight approach that uses exposure (see based on tectonic regime and decrease in future flooding risk for above), a hazard component that topographic slope following Allen population. This slight decrease is represents earthquake events and Wald (2007). Vulnerability is essentially offset by an increase in as finite and point sources, and quantified using relationships that exposure as specified by the SSPs vulnerability functions to estimate estimate loss as a function of MMI (figure G.2). the loss caused by an earthquake and others that estimate MMI from affecting the exposure. The losses Figures G.3 and G.4 show estimates PGA, as in Daniell (2014). caused by all the events are used of how Turkey’s current annual Earthquake risk is assumed to to estimate risk in the form of average GDP and population at be independent of climate. Thus, return periods and AAL. Like the risk of earthquakes with intensity estimates of return period and flood model, the earthquake model of VI or greater evolve in response annual average GDP and population quantifies exposure in terms of to changes in exposure associated affected by earthquakes for 2030 population and GDP, although it with five different SSPs. There is and 2080 change only in response also includes data on capital stock a monotonic increase in annual to GDP and population exposure. (Daniell 2014). average GDP risk, and the range We provide estimates of earthquake of future possibilities grows The earthquake hazard is quantified risk consistent with all five SSPs significantly from the 2030 using a 10,000-year stochastic associated with IPCC AR5. conditions to the 2080 conditions. catalog of over 15.8 million The annual average population at synthetic earthquake events of Results risk of earthquakes also increases at least magnitude 5 in the ECA with time, but much of the increase region. The earthquake model Figures G.1 and G.2 provide an occurs by 2030, and there is a contains 1,437 source zones and example of how Turkey’s annual 744 faults incorporating various significant variation in the scenarios average GDP and population at regional and local studies over the for 2080. risk of flooding evolve from 2010 past 30 years. The source zones to 2030 and 2080, based on the are used to account for seismicity future scenarios of flood hazard and Discussion and summary of unknown faults and in regions exposure. The seven different panels with low seismicity. The frequency There is a significant increase in each figure show the evolution and magnitude of earthquakes in Turkey’s annual average GDP in flood risk due to variations in within each zone are specified using at risk of earthquakes with MMI flooding associated with climate historical data and a Gutenberg- equal to or greater than VI. The change produced by greenhouse Richter (G-R) relationship that earthquake hazard is assumed gas concentrations consistent with relates earthquake magnitude to to be independent of changes in RCP4.5 and RCP8.5, exposure number of occurrences. Specific climate. This increase is driven by consistent with SSP2 and SSP3, and characteristics (e.g., location or changes in exposure consistent climate and exposure consistent epicenter, fault motion, hypocentral with the five SSPs. The evolution of with three combinations of RCPs depth, fault length) of each Turkey’s annual average flood risk and SSPs. The growth in flood earthquake are defined using known for GDP is much more modest than risk for GDP seen in the combined faults and fault models, previously that for earthquake. The RCP- and scenarios in the top row of figure derived source regions, and SSP-specific model runs show that G.1 is driven primarily by future geophysical knowledge. the changes are largely driven by increases in GDP as specified by Ground motion prediction and the SSPs. Flood risk for population changes in the SSPs.

145 Making a riskier future: How our decisions are shaping future disaster risk / 125 Figure G.1. Annual average GDP at risk of flooding in 2010, 2030, and 2080. The results are shown for five different climate models forced by RCP4.5 and RCP8.5 and exposures consistent with SSP2 and SSP3. The risk is assessed on the basis of changes in climate only (two bottom left panels), on the basis of changes in exposure only (two bottom right panels), and for three combinations of changes in climate and exposure (top three right panels). Figure G.2. Annual average population at risk of flooding in 2010, 2030, and 2080. The results are shown for five different climate models forced by RCP4.5 and RCP8.5 and exposure consistent with SSP2 and SSP3. The risk is assessed on the basis of changes in climate only (two bottom left panels), on the basis of changes in exposure only (two bottom right panels), and for three combinations of changes in climate and exposure ( top three right panels).

146 126 CASE STUDY G Evolution of Risk in Eastern Europe and Central Asia / Figure G.3. Annual average GDP at risk of earthquakes with intensity greater than or equal to VI in 2010, 2030, and 2080. The results are shown for five different SSPs. Figure G.4. Annual average population at risk of earthquakes with intensity greater than or equal to VI in 2010, 2030, and 2080. The results are shown for five different SSPs.

147 Making a riskier future: How our decisions are shaping future disaster risk / 127 There is very little change in flooding—that is, pluvial and flash meteorological hazards will likely Turkey’s annual average population floods are not considered in this occur in the future, these changes at risk of flooding. While the analysis. need to be considered in context climate-driven changes in with future changes in exposure. In general, risk assessments based population risk are somewhat larger While meteorological hazards might on present-day exposure, hazard, than those for GDP, they are largely increase in the future due to climate and vulnerability estimates can offset by exposure-specific changes, change, if exposure is controlled have significant uncertainties. and as a result the combined RCP or reduced, the impacts can be The uncertainties can be due and SSP results show relatively little moderated. to systematic and/or random increase through time. However, errors that arise from multiple the spread in combined results for References sources, such as flawed and/ 2030, and in particular for 2080, or missing estimates for the Allen, T. I., and D. J. Wald. 2007. are larger than those for RCP- and exposure, inaccurate simulations “Topographic Slope as a Proxy for SSP-specific model runs. of hazard characteristics, the Global Seismic Site Conditions (VS30) and Amplification around the Globe.” In contrast to the results for GDP inherent uncertainty in the Open-File Report 2007-1357. U.S. at risk of flooding, the evolution probability of events given the Geological Survey. http://pubs.usgs. of GDP at risk of earthquakes is limit in sample size, and flawed . gov/of/2007/1357/index.html significant and seen in all 5 SSPs, vulnerability functions based on Daniell, J. E. 2014. “Development of Socio- even though (not surprisingly) the limited knowledge of a structure’s economic Fragility Functions for Use uncertainty in the results grows performance in response to in Worldwide Rapid Earthquake Loss with time. The evolution of the forces generated by a hazard Estimation Procedures.” PhD diss. annual average population at risk event. In addition, it is difficult to Karlsruhe Institute of Technology. of earthquake is less obvious. Most determine what measures, if any, Daniell, J. E., F. Wenzel, and B. Khazai. of the change occurs by 2030, with are taken to lower risk by reducing 2012. “The Normalisation of Socio- some of the SSPs in 2080 showing exposure and/or vulnerability. Risk economic Losses from Historic a decrease in population exposed to assessments for future conditions Worldwide Earthquakes from 1900 to earthquakes. are subject to the same sources of 2012.” Paper no. 2027. Proceedings of the 15th World Conference of error, but the uncertainty for future The earthquake and flood results Earthquake Engineering, Lisbon, conditions is even greater because shown in figures G.1–4 highlight the Portugal. of uncertain future changes in importance for Turkey of changes in Hempel, S., K. Frieler, L. Warszawski, hazard, exposure, and vulnerability. exposure as specified in the SSPs. In J. Schewe, and F. Piontek. 2013. “A addition, both the overall risk and These results for Turkey illustrate Trend-Preserving Bias Correction—The Earth System ISI-MIP Approach.” the relative increase in earthquake two important factors related to the Dynamics 4: 219–36. doi:10.5194/esd- risk tend to be larger than the risk evolution of risk. First, an increase 4-219-2013. for flood. While further analysis is in population and GDP does not Meinshausen, M., S. J. Smith, K. V. Calvin, required to definitively identify the always lead to an increase in risk. J. S. Daniel, M. L. T. Kainuma, J.-F. reason why this is so, we speculate Locating populations and economic Lamarque, K. Matsumoto, et al. that it is due to the limited spatial activity in areas that are not subject 2011. “The RCP Greenhouse Gas area subjected to flooding relative to flood or other hazards will Concentrations and Their Extension to the area subject to earthquake- minimize risk. In cases where this from 1765 to 2300.” Climatic induced ground motion, and to the is not possible—such as in response Change 109 (special issue): 213–41. doi:10.1007/s10584-011-0156-z. distribution of population and GDP to earthquake risk in Turkey—more outside of flood-prone regions. resilient building practices will Nakićenović, N., R. J. Lempert, and A. C. Another consideration is that the help to minimize risk. Second, Janetos. 2014. “A Framework for flood work accounts only for fluvial while changes in climate and the Development of New Socio-

148 128 CASE STUDY G / Evolution of Risk in Eastern Europe and Central Asia Weedon, G. P., S. Gomes, P. Viterbo, H. Van Beek, L. P. H., Y. Wada, and M. F. economic Scenarios for Climate Oesterle, J. C. Adam, N. Bellouin, O. P. Bierkens. 2011. “Global Monthly Change Research: Introductory Essay.” Boucher, and M. Best. 2010. “The Water Stress: 1. Water Balance 122, no. 3 (special Climatic Change WATCH Forcing Data 1958–2001: Water and Water Availability.” issue): 351–61. doi:10.1007/s10584- A Meteorological Forcing Dataset Resources Research 47: W07517. 013-0982-2. for Land Surface- and Hydrological- doi:10.1029/2010WR009791. Van Beek, L. P. H., and M. F. P. Bierkens. Models.” WATCH Technical Report 22. Ward P. J., B. Jongman, F. Sperna Weiland, 2009. “The Global Hydrological Model EU-WATCH. http://www.eu-watch.org/ A. Bouwman, R. Van Beek, M. F. PCR-GLOBWB: Conceptualization, publications/technical-reports . P. Bierkens, W. Ligtvoet, and H. C. Parameterization and Verification.” Winsemius, H. C., L. P. H. Van Beek, B. Winsemius. 2013. “Assessing Flood Department of Physical Geography, Jongman, P. J. Ward, and A. Bouwman. Risk at the Global Scale: Model Faculty of Earth Sciences, Utrecht 2013. “A Framework for Global River Setup, Results, and Sensitivity.” University, Utrecht, Netherlands. Flood Risk Assessments.” Hydrology Environmental Research Letters http://vanbeek.geo.uu.nl/suppinfo/ 17: 1871–92. and Earth System Sciences 8: 044019. doi:10.1088/1748- . vanbeekbierkens2009.pdf doi:10.5194/hess-17-1871-2013. 9326/8/4/044019.

149 Making a riskier future: How our decisions are shaping future disaster risk / 129 thousands of people and causes projects conducted elsewhere, CASE STUDY H millions of dollars of damage, partnerships with local amounting to an estimated 0.7 communities can produce up-to- percent of annual gross domestic date and accurate information Open Data product per year (GFDRR 2014). about societal assets to inform and Dynamic The 2015 flood season has been risk assessment. When a local exceptionally severe, with over Understandings community is involved in 600,000 people affected and creating and curating data, it of Risk 170,000 displaced in January provides a foundation for ongoing Robert Soden (Global Facility for and February alone (Hallegatte, maintenance of risk information Disaster Reduction and Recovery) Bangalore, and Nkoka 2015). The and supports an evolving poor are particularly vulnerable to understanding of hazard and risk. The experience of the Open flooding and possess the least ability Data for Resilience Initiative ■ ■ Tools that communicate risk in to recover from natural disasters (see (OpenDRI) project in Malawi different ways can broaden the figure H.1). Floods are not the only provides an important example of range of stakeholders involved in hazard that Malawi faces; the country how emerging approaches to risk understanding risk. InaSafe and is also exposed to drought, landslide, information—open data, community similar tools that help nonexperts and seismic hazard. mapping, and new tools for risk make sense of complex risk communication—can provide a In order to effectively build information can engage new more dynamic understanding resilience to natural disasters and communities and actors in of disaster risk, and a better the impacts of climate change, the challenge of disaster risk understanding of the evolving policy makers and the public in management. nature of risk. Two years on, the Malawi need access to accurate Time and sustained investment ■ ■ project has demonstrated a number and timely information on hazards, are needed to make meaningful of important lessons in this regard: vulnerability, and exposure. In the changes to risk information past, however, these data have too ■ Lack of access to information ■ systems. The partnership often been inaccessible. The results contributes to static between OpenDRI and the Malawi of disaster risk assessments have understanding of risk. In many Spatial Data Working Group typically been delivered in the form countries, risk data remain has developed in valuable and of PDF reports, with the valuable fragmented and inaccessible, unexpected ways since it began data collected or produced during even between government in 2012, and it will continue the assessment locked away on ministries. This can result to evolve. Most technical someone’s hard drive. In other in disaster risk assessments assistance programs have short cases, data have been fragmented that incorporate outdated or life spans that don’t allow for across various government inaccurate data. Open data such evolution, whereas ongoing ministries, which were unable helps to address this issue partnerships can promote or unwilling to freely share them by making data available to continued data generation as because of government mandates all risk modelers, and allows disaster is evolving into the that data be sold in the name of countries to fully leverage the future. cost recovery. These barriers to investment made in creating risk information access are common in information. many parts of the world, and they Case Study: OpenDRI ■ Community engagement can ■ severely limit countries’ ability Malawi support efforts to understand both to understand and manage Malawi experiences severe annual risk. As shown in Malawi, risk and to respond in the case of flooding that affects tens of but also in multiple similar disasters.

150 130 / Open Data and Dynamic Understandings of Risk CASE STUDY H Poverty map of Malawi (based on World Bank estimates) overlaid with data on flooding. Figure H.1. The poorest parts of Malawi are among the most flood-prone. Legend Poverty headcount ■ 0.80–0.92 0.60–0.80 ■ ■ 0.40–0.60 ■ 0.20–0.40 ■ no poverty estimate Observed flood extent ■ Flooded area Sources: German Space Agency, UNOSAT, World Bank Poverty Estimates 10 0 40 km 30 20 Hallegatte, Bangalore, and Nkoka 2015. Source: would be open and accessible to participating government ministries The Malawi Spatial the public. This gave birth to the were able to share at that time. Data Portal (MASDAP): Malawi Spatial Data Working Group, However, thanks to continued work Improving access to a new partnership between the and negotiation by the Malawi information Department of Surveys, the National Spatial Data Working Group, In 2012 the World Bank’s Open Statistics Office, the Department of new data sets have been made Data for Resilience Initiative Disaster Management and Affairs, available and added to the platform. launched a project to help support and other key producers and users Today, MASDAP contains over 140 disaster risk management in of data across government. individual data sets describing Malawi by improving access to risk everything from Malawi’s road With the support of OpenDRI, the information. With the support of network to land cover, elevation, working group launched the Malawi the World Bank, the government and administrative units. In the Spatial Data Platform, or MASDAP of Malawi was developing new words of World Bank Disaster Risk (figure H.2), in November 2012. The flood risk maps for the Lower Shire Management Specialist Francis initial offerings of the platform were River basin, one of the most at- Nkoka, “Instead of being dispersed limited to the results of the Shire risk catchments in the country. and hard to access, disaster risk River basin flood risk assessment The team wanted to ensure that the results of the mapping work as well as a few other data sets that and climate-relevant data are

151 Making a riskier future: How our decisions are shaping future disaster risk / 131 http://masdap.mw Data listing on the Malawi Spatial Data Portal (MASDAP), . Figure H.2. now consolidated in one open of the region; but similarly detailed 2 million registered members and data describing the location and local chapters in over 100 countries. and accessible platform, which is characteristics of roads, houses, After providing vital data to the particularly useful for pre-event and other aspects of the built international response following planning” (World Bank 2014). environment did not exist. With this the 2010 Haiti earthquake, OSM has in mind, the Malawi Spatial Data since been used in Indonesia, Nepal, Community mapping Working Group, in partnership with and numerous other countries of the Lower Shire River the Humanitarian OpenStreetMap around the world to support disaster Basin Team (HOT), launched a community- risk management efforts. mapping project in target districts One of the benefits of the MASDAP From July through September of the Lower Shire basin. platform was that it allowed, for 2014, working with local partners the first time, a comprehensive and The project made use of the from the Department of Surveys accessible picture of the availability OpenStreetMap (OSM) platform. and Department of Disaster of spatial data in the country. Recent OSM, often called “the Wikipedia of Management, HOT conducted a investments in flood modeling in the maps,” was founded in the United series of outreach and training Shire River basin had created high- Kingdom in 2004. It has since events with university students resolution and accurate flood maps grown to a global project with nearly and community groups in the

152 132 CASE STUDY H Open Data and Dynamic Understandings of Risk / Data to insight with InaSafe Thanks to the efforts of the community mapping team, detailed information on the built infrastructure in the Lower Shire River basin is now available in OpenStreetMap and on the Malawi Spatial Data Portal. When combined with updated flood hazard layers created in 2012, these data allow for a more complete understanding of the potential impacts of floods in the region. In September 2014, in order to support flood preparedness and mitigation efforts, the OpenDRI team organized a training session for officials from Malawi’s Department of Disaster Management Affairs and other ministries on the use of InaSafe software. InaSafe (figure H.4) is a free and OpenStreetMap activities in Malawi. open source impact-analysis tool https:// Source: Humanitarian OSM Team. Licensed under Creative Commons Attribution 3.0 IGO, initially developed in Indonesia in . creativecommons.org/licenses/by/3.0/us/ partnership between the Indonesian government, Australian AID, and to support data collection and Chikwawa and Nsanje Districts. the World Bank. Designed for ease outreach with the goal of expanding Over this period, 55 people were of use by disaster managers and the OSM community in Malawi. trained in the use of OpenStreetMap policy makers, InaSafe allows users This mechanism for ongoing data during three- to four-day sessions. to combine data from a variety of collection and curation will help to Participants also engaged in hands- sources to produce insights about ensure that exposure information on data collection in key parts of various hazard scenarios. Following in these districts is kept up-to-date. the flood-prone districts, mapping its initial development, it has This, in turn, will enable future risk numerous towns and villages. The been deployed in Sri Lanka, the assessments to quantify risk based group collected exposure data Philippines, and elsewhere as part on current exposure rather than a for 21,000 residential buildings of disaster risk management efforts. snapshot from the past, and thus and improved overall coverage of In Malawi, the tool is being provide a more accurate view of road infrastructure and other key used in support of flood impact risk. Current data are particularly features in the Shire River basin projections that can both inform ex important in areas where population (figure H.3). All data collected ante mitigation and preparedness growth, development, or new through the project are available on construction is occurring rapidly. the Malawi Spatial Data Portal. At work and support rapid ex post Ongoing data collection can also be the conclusion of this stage of the disaster needs assessments. These valuable for understanding growth project, a team of six interns from analyses are possible because of trends through time. the local university is continuing the increased information available

153 Making a riskier future: How our decisions are shaping future disaster risk / 133 Figure H.3. Nchalo District and other parts of the Lower Shire River basin, before and after volunteer mappers added detailed information about transportation infrastructure and other elements of the built environment to OpenStreetMap. These data are now openly available to be used for risk assessments and other purposes. The two images show the improvement in data coverage for the area as a result of the OpenDRI Malawi project. Source: OpenStreetMap. © OpenStreetMap contributors. Licensed under Open Database License, http://opendatacommons.org/licenses/odbl/1.0/ . Figure H.4. The InaSafe Tool. More information can be found at http://inasafe.org .

154 134 Open Data and Dynamic Understandings of Risk / CASE STUDY H Hallegatte, Stéphane, Mook Bangalore, River basin and other at-risk areas from community mapping exercises and Francis Samson Nkoka. 2015. in the country; aided by student and the work of the Malawi Spatial “Recent Floods in Malawi Hit the volunteers, the Survey Department Data Working Group. The working Poorest Areas: What This Implies.” will continue to work full time on group and local OSM community Voices: Perspectives on Development OSM data collection and community are continuing to collect and create http:// (World Bank blog). February 6. building. Finally, plans are under new information, and this will also blogs.worldbank.org/voices/recent- floods-malawi-hit-poorest-areas-what- way to expand upon the initial be available for use with the InaSafe . implies InaSafe trainings in Malawi and platform. customize the software and training Humanitarian OpenStreetMap Team. In 2015, the OpenDRI program has 2014. “OSM Community Mapping for program for the country’s particular built on the foundation established http:// Flood Preparedness in Malawi.” requirements for contingency during the first two years of work hot.openstreetmap.org/projects/ planning and post-disaster needs osm_community_mapping_for_flood_ in Malawi. The project continues to assessment. Together these . preparedness_in_malawi focus on and support the Malawi activities will contribute to a more Spatial Data Working Group. A World Bank. 2104. “In Malawi, Citizens detailed and dynamic understanding Get Involved as Innovative technical committee, comprising of risk across new sectors of society Technologies Help Them Understand a subset of this group, was formed in Malawi. and Manage Disaster Risks.” in 2013 to meet the development http://www.worldbank. December 4. and maintenance needs of the org/en/news/feature/2014/12/04/ platform. During a recent meeting, References in-malawi-citizens-get-involved-as- the committee prioritized a number innovative-technologies-help-them- GFDRR (Global Facility for Disaster better-understand-and-manage- of user-interface customizations as Reduction and Recovery). 2014. disaster-risks . well as further collaboration from “Malawi Country Program Update.” the working group related to data https://www.gfdrr.org/sites/gfdrr/ May. . files/region/MW.pdf curation. The community mapping work will also continue in the Shire

155 Making a riskier future: How our decisions are shaping future disaster risk / 135 result of the submission. The new all promote sustainability CASE STUDY I management or development, provisions (objectives, policies, and are intended to be integrated and rules) included in the final plan in their purposes. The RMA is change strengthened the requirement Science Influencing New Zealand’s primary planning that new development within the Land-Use Policy: legislation. It seeks to promote southwestern portion of Petone take the sustainable management of A Story from New into account the various natural natural and physical resources. hazards that may affect the area. Zealand Toward that end, it calls for an This paper describes the plan Wendy S. A. Saunders and James Beban effects-based approach (involving change process and the revisions (GNS Science) environmental assessments) rather made to the plan change when GNS than an activities-based approach; In 2012 the Hutt City Council Science brought relevant scientific it devolves responsibilities through (part of the Wellington Region, and technical information to the regional and territorial (i.e., city or and located at the northern end council’s attention. It also details district) authorities; and it supports of Wellington Harbor), notified the hazards to which the area in public participation in decision a plan change (known as Plan question is prone. making (May et al. 1996). Change 29) that allowed for increased development within the More specifically, the RMA requires southwestern portion of Petone, a Summary of land-use (a) that planning take health suburb of Hutt City. The proposed planning in New Zealand and safety into account—i.e., not plan-change area is subject to consider them as just a building In New Zealand, no one agency a number of natural hazards, or emergency management is responsible for natural hazard including fault rupture, subsidence, responsibility; and (b) that local management. Rather, a number sea-level rise, liquefaction, flooding, authorities avoid or mitigate the of organizations, including and tsunami. The previous district effects, not the occurrence, of the Ministry of Civil Defence plan had very limited rules to natural hazards. However, the RMA Emergency Management (MCDEM), address the risks from natural does not explicitly require that regional councils, territorial hazards, and no new rules were natural hazard risk be planned for. authorities, civil defense proposed as part of this plan emergency management (CDEM) change. groups, and engineering lifeline Proposed development As a corporate citizen of Hutt City, groups hold these responsibilities Proposed Plan Change 29 sought GNS Science lodged a submission (Saunders and Beban 2012). to expand the existing zone known opposing the plan change. Much of Cooperation between these as Petone Commercial Activity– the submission was informed by agencies is essential to ensure a Area 2. This expansion included natural hazard information gathered streamlined and holistic national some rezoning of a portion of from the “It’s Our Fault” research approach to planning for disasters. 8 the General Business Activity project. While the plan change still There are four key pieces of Area to bring it within the Petone proceeded, it was amended as a legislation that have a primary Commercial Activity Area–Area 2. 8 influence on natural hazard The plan change area is bordered The goal of the It’s Our Fault research program is to see Wellington positioned management in New Zealand: the by two main arterial roads that to become a more resilient city through Resource Management Act 1991 link the main state highway a comprehensive study of the likelihood (RMA), Building Act 2004, Civil to Wellington City, and by the and effects of large Wellington Defence Emergency Management Wellington Harbor to the south. earthquakes. See GNS Science, “It’s Our Act 2002, and Local Government Figure I.1 shows the area covered Fault,” http://www.gns.cri.nz/Home/ IOF/It-s-Our-Fault . by the plan change. Act 2002. These four statutes

156 136 CASE STUDY I Science Influencing Land-Use Policy: A Story from New Zealand / Figure I.1. Area covered by Plan Change 29, Petone West. Source: Hutt City Council 2012, 101. 12 m requiring a wind Residential Residential. Plan Change 29 proposed a single ■ ■ set of objectives, policies, and rules development permitted, subject assessment; maximum permitted to compliance with the permitted to encompass the area subject building height of 15 m along to the plan change. These new activity conditions. the three main roads, with a 45° objectives, policies, and rules would degree recession plane sloping Commercial. Commercial ■ ■ replace the existing provisions for inward from this 15 m height, development permitted both the Petone Commercial Activity up to the maximum permitted everywhere, subject to Area–Area 2 and the portion of height of 30 m. compliance with the permitted the General Business Activity Area activity conditions, along with subject to the plan change. New ■ ■ Design guidelines. some light industrial uses. and more specific design As notified, Plan Change 29 ■ Wellington Fault. Current ■ guidelines indicated for buildings proposed a number of changes, requirements retained for along the three main roads. including the following (Hutt City addressing the extra risk of Council 2012): Retail developments Retail. ■ ■ building within the Wellington permitted up to a maximum Fault area. Building heights and ■ Building height. ■ of 10,000 m2 of floor space, density provisions within the Maximum building height of subject to compliance with the fault area would be the same as 30 m permitted throughout the elsewhere in the area. area, with any building over permitted activity conditions.

157 Making a riskier future: How our decisions are shaping future disaster risk / 137 Essentially, Plan Change 29 sought The Wellington Fault is located Liquefaction to introduce more types of activities along the western edge of the Figure I.2 presents the liquefaction valley floor of Hutt City, as shown and more intense development to potential for Lower Hutt. While in figure I.2. In a single Wellington the area by establishing a mixed- there are no areas of very high Fault event, Hutt City would likely use area within the southwestern susceptibility, the Petone West experience subsidence of up to portion of Petone. The rules of the area is classified as having ~1.2 m at Petone West. district plan prior to Plan Change high susceptibility. In order for 29 allowed for development that liquefaction to occur in the most significantly increased the risk to Ground shaking susceptible soils, ground shaking people and property. Proposed Plan would be required of peak ground Change 29 was notified with no The amount of ground shaking a acceleration of 0.1 g or more new or additional rules to address location experiences is dependent (Saunders and Berryman 2012). the risks associated with natural on the ground materials. As a This threshold would certainly be hazards. general rule, the weaker the exceeded if the Wellington Fault materials are, the longer and ruptures. The expected return time stronger the ground shaking is. Petone hazardscape of 0.1–0.2 g shaking in Petone West To assess soil types, five ground- is approximately 100 years (based Petone West is susceptible to shaking amplification classes on Stirling et al. 2012 and applying a range of hazards, including have been formulated (Standards “deep or soft soil” site conditions). fault rupture, ground shaking, Australia/New Zealand 2004): liquefaction, tsunami, flooding, Since the Canterbury earthquakes ■ ■ Class A: strong rock landslides, sea-level rise, and of 2010–2011, both the public tectonic subsidence. Each of these is ■ ■ Class B: weak rock and councils have better discussed in further detail below. understanding of liquefaction and ■ ■ Class C: shallow soil its consequences. They also better Class D: deep or soft soil ■ ■ understand related zoning issues Fault rupture (e.g., the “red zoning,” or retirement ■ ■ Class E: very soft soil The Wellington region lies within from use, of residential properties the deforming boundary zone These soil classes have implications in Christchurch that are highly between the Pacific and Australian for the foundations and subsequent vulnerable to liquefaction) and plates, and is located within one performance of buildings. For options to mitigate the hazard (i.e., of the most seismically active example, ground classified as Class engineered remediation). areas of the country. The region D can require far more extensive is cut by a number of earthquake- engineering—and hence be more Tsunami producing active faults, both costly to build on—than Class C Wellington is susceptible to onshore and offshore. Since 1840, ground. tsunami from both distant and the region has been violently The Petone Plan Change 29 area is regional sources. In 2013 a review shaken by earthquakes three times, within the Class D sites, overlain of tsunami hazard was undertaken in 1848, 1855, and 1942 (Downes with a zone that may contain Class to summarize the current state of 1995; Robinson, Van Dissen, and E sites. The presence of deep or soft knowledge and to produce revised Litchfield 2011; Stirling et al. 2012). soil, along with very soft soil, has probabilistic hazard models. Petone The likelihood of a Wellington implications for building foundation West is located directly opposite Fault earthquake (approximately design, liquefaction potential, and the Wellington Harbor, within magnitude 7.5) occurring within the nonstructural building damage. the “red” and “orange” tsunami next 100 years is approximately 10– evacuation zones (figure I.3), based 15 percent (Rhoades et al. 2011).

158 138 CASE STUDY I Science Influencing Land-Use Policy: A Story from New Zealand / Liquefaction potential for Lower Hutt. Figure I.2. Source: Adapted from Beetham et al. 2012. warnings that extend beyond the red on distant and regional source a concrete median barrier, then zone, for tsunami from sources more tsunami modeling (Leonard et al. proceed up a very steep, scrub-clad than one hour of travel time away 2008). The red zone is intended as hill, and wait for hours as the many from the mapped location (MCDEM a shore-exclusion zone that can be waves swept in. Given the hurdles 2008). designated off-limits in the event of and the steepness of the hills, any expected tsunami. It represents this option is not very realistic. As For the red and orange zones, the highest level of risk and is the yet, there are no certified tsunami evacuation is limited to vertical first place that should be evacuated evacuation buildings located in structures because of the area’s in case of any sort of tsunami Petone West. topography and infrastructure. For warning. People could expect example, to evacuate on foot up activation of this zone several times the nearest hill, one would need Flooding during their lifetime. The orange to scale a two-meter-high fence Flooding from the Hutt River is one zone is to be evacuated following to cross the electrified railway of the biggest environmental and most if not all distant and regional line, scale another two-meter-high source official warnings—i.e., emergency management issues fence to State Highway 2, hop over

159 Making a riskier future: How our decisions are shaping future disaster risk / 139 Tsunami evacuation zones for Lower Hutt. Figure I.3. of the flood protection system would affect parts of Petone West. Also relevant is the impact of a high tide and the need for water to drain across the road adjacent to Wellington Harbor (which could be impeded by an existing seawall). The Hutt River Floodplain Management Plan includes both structural and nonstructural measures to reduce risks. Structural measures are physical works, such as embankments, rock linings, and vegetation buffers, while nonstructural measures include land-use planning regulations that keep people, possessions, and development out of or away from flood-prone areas. According to the Hutt River Floodplain Management Plan, “non-structural measures enable a community to be more resilient to flooding through flood awareness, preparation, and sensible land use ” (Wellington Regional Council, 2001 13; emphasis added). However, the Hutt River is not the only source of flooding for Petone. The nearby Korokoro Stream also has a history of flooding, with the last major event occurring in 1976. The consequences of that flood are shown in figure I.5: State Highway 2, the railway, and access to the Leonard et al. 2008; Wellington Region Emergency Management Office 2013. Source: overpass from Petone West were all affected by the floodwater, making planning is keeping floodwaters facing residents of the Hutt Valley. evacuation options limited. away from people and development The Hutt Valley is the second-most (Wellington Regional Council densely populated and asset-rich Landslides 9 2001). This means continued floodplain in New Zealand. The key While not a direct hazard for reliance on physical protection (i.e., focus of floodplain management Petone West, landslides do have embankments) against flooding. 9 the potential to make access to The population is approximately Figure I.4 shows that any breach 130,000. Petone difficult. For example, after

160 140 CASE STUDY I Science Influencing Land-Use Policy: A Story from New Zealand / Flooding of the Hutt Valley with breaches for a 2,300 cumec flood extent (440-year event) Figure I.4. under the upgraded flood protection system. Source: Wellington Regional Council 2001, 8. the 1855 Wairarapa earthquake, a including Petone West, which is the Sea-level rise area subject to the Hutt City Council large landslide occurred south of One of the main outcomes of climate plan change. Petone on State Highway 2, between change for Petone is sea-level rise. Hutt City and Wellington City. If a A recent report (Bell and Hannah Role of GNS Science similar event happened today, it is 2012) that assessed sea-level rise likely that State Highway 2 and the and coastal flooding from storm In response to the notified plan events in the Wellington region railway (servicing the Hutt Valley change, GNS Science decided to found that Wellington has the lodge a submission in opposition and Wairarapa) could be blocked for highest rate of sea-level rise in New to the suggested changes. This many days or more (Brabhaharan Zealand. All low-lying areas around submission was prepared with the 2010). This would have major the coast are subject to storm-tide input of several GNS staff members, implications for evacuation and flooding, but this vulnerability will including an engineering geologist, would also affect those needing increase due to sea-level rise. Areas an earthquake geologist, a natural to travel to the Hutt Valley from at risk include the mouth of the Hutt hazards planner, and a PhD student Wellington. investigating vertical evacuation River and low-lying parts of Petone,

161 Making a riskier future: How our decisions are shaping future disaster risk / 141 Flooding from Korokoro Stream in 1976. The Petone Plan Change 29 area extends approximately from the Odlins Figure I.5. Timber Yard corner between The Esplanade (located underwater on the far right) and the railway line. Evening Post. ©Fletcher Trust Archives. Reproduced with permission; further permission required for reuse. Source: structures for tsunami. Science, a new section has been with them, the submission also commented on specific portions of inserted on natural hazards, which The submission outlined the hazard the plan change. specifically includes ground rupture environment of the plan change area as well as subsidence, liquefaction, and, where appropriate, identified tsunami, and sea-level rise. Without Outcome of GNS Science measures to avoid or reduce the GNS Science input, the result response risk associated with these hazards. might have been different. Table I.1 The hazards identified in the Prior to the submission process, summarizes the provisions before submission included fault rupture, the plan change did not include and after GNS Science’s submission earthquake-induced subsidence, any specific natural hazard–related and shows the direct changes as a tsunami hazard, liquefaction, objectives, policies, or additional result of the submission process. and sea-level rise due to climate restrictions. What was included change. In addition to providing Ideally, these provisions should focused on the Wellington Fault information on the specific hazards be incorporated into the entire Special Study Area; no other and the measures required to district plan. Currently, the district hazards were specified. Based on the information provided by GNS avoid or reduce the risk associated plan addresses only the Wellington

162 142 / Science Influencing Land-Use Policy: A Story from New Zealand CASE STUDY I Natural Hazard Provisions in Plan Change 29 before and after Submission Process Table I.1. Before submission process: After submission process: Proposed Plan Change 29 Decision for plan change Addition of a natural hazard–specific objective: To avoid or mitigate to an acceptable level the Wellington Fault line: Retain current vulnerability and risk of people and development to natural hazards. requirements to cope with the extra risk of building within the Wellington All new buildings require a case-by-case assessment of the natural hazard risks and Fault area. Building heights and consequences. There are specific references to the ground rupture, subsidence, liquefaction, and density provisions within the fault tsunami risks as well as the requirement for sea-level rise to be considered. area would be the same as elsewhere In response to the risk from natural hazards, emergency facilities were made a noncomplying in the area. activity for the entire Petone Mixed Use Area. In response to the natural hazard risk, places of assembly, child-care facilities, education and training facilities, commercial activities (accommodating more than 300 people), community activities/facilities, housing for the elderly, and residential facilities were made a discretionary activity. Any development that includes these activities must consider the natural hazard risk and measures to avoid or reduce this risk. Special Fault Study Area and can be used in appropriate forums References only one hazard, flooding, even to help educate planners and to Beetham, R. D., J. Cousins, M. Craig, G. D. though other hazards (subsidence, inform policy debate regarding Hutt Dellow, and R. J. van Dissen. 2012. liquefaction, tsunami, and sea-level development and the mitigation Valley Trunk Wastewater Earthquake rise) have the potential to affect of risks due to natural hazards. Vulnerability Study . Lower Hutt, New areas outside of the Petone West Zealand: GNS Science. It is often assumed that councils plan change area. and decision makers are aware of Sea-Level Bell, R. G., and J. Hannah. 2012. Variability and Trends: Wellington the natural hazards in their area. GNS Science presented the Region . Hamilton, New Zealand: community and council with the However, there may be only a basic National Institute of Water and latest scientific understanding understanding of what the natural Atmospheric Research Ltd. of the geological hazards in this hazards are, while the scale of the Brabhaharan, P. 2010. “Initiatives area, and reminded the council hazards and the potential risks they towards Integrated Resilience of of its legislative responsibilities pose are often poorly understood. Road Transportation Lifelines in the for hazard management. The Wellington Region.” Paper presented While Plan Change 29 still went mayor and council staff indicated at the New Zealand Society of ahead in a highly hazardous area, afterward that the presentation of Earthquake Engineers, Wellington, GNS Science research was used March 26–28. this scientific information to the council planners, the community with positive effect at a local scale. Downes, G. L. 1995. Atlas of Isoseismal (via the pre-hearing meeting), and This was a successful instance of Maps of New Zealand Earthquakes . the commissioners played a key Lower Hutt, New Zealand: Institute of scientific information being used to Geological and Nuclear Sciences. role in ensuring the objectives, educate decision makers and inform policies, and rules pertaining to Hutt City Council. 2012. “District Plan policy in order to reduce future risks natural hazards were included in Change 29.” http://www.huttcity.govt. from development in areas subject nz/district-plan-change-29. the plan change. This experience to natural hazards. demonstrates that information Leonard, G. S., W. Power, B. Lukovic, provided by scientific and technical W. Smith, D. Johnston, and G. organizations like GNS Science Downes. 2008. Tsunami Evacuation

163 Making a riskier future: How our decisions are shaping future disaster risk / 143 Stirling, M. W., G. H. McVerry, M. C. Robinson, R., R. J. Van Dissen, and N. J. Zones for Wellington and Horizons Gerstenberger, N. J. Litchfield, R. J. Van Litchfield. 2011. “Using Synthetic Regions Defined by a GNS-Calculated Dissen, K. R. Berryman, et al. 2012. Seismicity to Evaluate Seismic . Lower Hutt, New Attenuation Rule “National Seismic Hazard Model for Hazard in the Wellington Region, Zealand: GNS Science. New Zealand: 2010 Update.” Bulletin Geophysical Journal New Zealand.” May, P. J., R. J. Burby, N. J. Ericksen, J. W. of the Seismological Society of America 187 (1): 510–28. International Handmer, J. E. Dixon, S. Michaels, et 102 (4): 1514–42. Saunders, W. S. A., and J. G. Beban. 2012. al. 1996. Environmental Management Wellington Region Emergency “Putting R(isk) in the RMA: Technical and Governance: Intergovernmental Management Office. 2013. “Wellington Advisory Group Recommendations on Approaches to Hazards and Region Tsunami Evacuation Zones: the Resource Management Act 1991 Sustainability . London: Routledge. Lower Hutt.” http://www.getprepared. and Implications for Natural Hazards MCDEM (Ministry of Civil Defence org.nz/sites/default/files/uploads/ Planning.” GNS Science Miscellaneous Emergency Management). 2008. lower-hutt-petone.pdf. Series 48, GNS Science, Lower Hutt, Tsunami Evacuation Zones: Director’s New Zealand. Hutt Wellington Regional Council. 2001. Guideline for Civil Defence Emergency River Floodplain Management Plan Saunders, W. S. A., and K. R. Berryman. Management Groups [DGL08/08] . for the Hutt River and Its Environment . 2012. “ Just Add Water: When Should Wellington, New Zealand: Ministry Wellington, New Zealand: Wellington Liquefaction Be Considered in Land of Civil Defence and Emergency Regional Council. Use Planning? ” Miscellaneous Series Management. 47, GNS Science, Lower Hutt, New Rhoades, D. A., R. J. Van Dissen, R. M. Zealand. Langridge, T. A. Little, D. Ninis, E. G. Standards Australia/New Zealand. 2004. C. Smith, et al. 2011. “Re-evaluation NZS 1170.5 Sturctural Design Actions— of Conditional Probability of Rupture . Wellington, Part 5: Earthquake Actions of the Wellington-Hutt Valley Segment New Zealand: Standards New Zealand. Bulletin of of the Wellington Fault.” the New Zealand Society for Earthquake 44 (2): 9. Engineering

164

165

166 The Global Facility for Disaster Reduction and Recovery (GFDRR) is a global partnership that helps developing countries better understand and reduce their vulnerabilities to natural hazards and adapt to climate change. Working with over 400 local, national, regional, and international partners, GFDRR provides grant financing, technical assistance, training and knowledge sharing activities to mainstream disaster and climate www.gfdrr.org risk management in policies and strategies. Managed by the World Bank, GFDRR is supported by 34 countries and 9 international organizations.

Related documents

NCDOT Current STIP

NCDOT Current STIP

May 2019 NCDOT Current STIP

More info »
Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation

Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation

MANAGING THE RISKS OF EXTREME EVENTS AND DISASTERS TO ADVANCE CLIMATE CHANGE ADAPTATION SPECIAL REPORT OF THE INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE

More info »
AndersBehringBreivikManifesto

AndersBehringBreivikManifesto

2011 , London – By Andrew Berwick

More info »
Microsoft Word   Table of Contents Full Report with ES.doc

Microsoft Word Table of Contents Full Report with ES.doc

Stern Review: The Economics of Climate Change PAGE TABLE OF CONTENTS i-xxvii Executive Summary i Preface & Acknowledgements iv Introduction to Review vi Summary of Conclusions Part I Climate change: o...

More info »
Microsoft Word   A) Division 245.docx

Microsoft Word A) Division 245.docx

tables Attachment Division 245, including A: Nov. 15-16, 2018, EQC meeting 1 of 121 Page Division 245 CLEANER AIR OREGON 340-245-0005 Purpose and Overview (1) This statement of purpose and overview is...

More info »
wcms 546256

wcms 546256

Towards a better future for women and work: Voices of women and men

More info »
Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space

Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space

TIONAL ACADEMIES PRESS THE NA This PDF is available at http://nap.edu/24938 SHARE     Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space DET AILS 700 pages | 8.5 ...

More info »
DoD7045.7H

DoD7045.7H

DoD 7045.7-H EPARTMENT OF D EFENSE D F UTURE Y EARS D EFENSE P ROGRAM (FYDP) S TRUCTURE Codes and Definitions for All DoD Components Office of the Director, Program Analysis and Evaluation A pril 2004

More info »
ASAALT Weapon Systems Handbook 2018

ASAALT Weapon Systems Handbook 2018

AMERICA’S ARMY: TODAY’S MODERNIZATION IS TOMORROW’S READINESS

More info »
WEF Future of Jobs

WEF Future of Jobs

The Future of Jobs Global Challenge Insight Report The Future of Jobs Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution January 2016

More info »
PPIC232 Hanak.indb

PPIC232 Hanak.indb

Managing California’s Water From Conict to Reconciliation  Ariel Dinar Ellen Hanak Jay Lund  Richard Howitt Jerey Mount  Brian Gray  Peter Moyle  Barton “Buzz” Thompson

More info »
Handbook: Revenue for software and SaaS

Handbook: Revenue for software and SaaS

Revenue for software and SaaS Handbook US GAAP December 201 8 _____ kpmg.com/us/ frv

More info »
meinkampf

meinkampf

Mein Kampf Adolf Hitler Translated into English by James Murphy

More info »
Microsoft Word   Excessive Speculation in the Natural Gas Market.doc

Microsoft Word Excessive Speculation in the Natural Gas Market.doc

U n d S ta te s S e n a te ite UBCOM NVESTIG ATIONS PERMANENT S MITTEE ON I om m i t t ee on H om el an d S ecuri t y an d G overnm ent al A f C ai rs f C L e v in, C hai rm an arl g M No n, Ra nk in ...

More info »
Government Finance Statistics Manual 2014

Government Finance Statistics Manual 2014

MANUAL GOVERNMENT FINANCE STATISTICS MANUAL 2014 2014 INTERNATIONAL MONETARY FUND

More info »
a i4787e

a i4787e

2015 ISSN 2412-5474 nimal genetic resource diversity underpins the supply livestock products and A services across a wide range of production environments. It promotes resilience and serves as a basis...

More info »
The Independent Living Movement: History and Philosophy to Implementation and Practice

The Independent Living Movement: History and Philosophy to Implementation and Practice

The Independent Living Movement: History and Philosophy to Implementation and Practice by Maggie Shreve I have the right to believe freely, to be a slave to no man’s authority. If this be heresy so be...

More info »
Climate Adaptation: The State of Practice in U.S. Communities

Climate Adaptation: The State of Practice in U.S. Communities

CLIMATE ADAPTATION THE STATE OF PRACTICE IN U.S. COMMUNITIES NOVEMBER 2016 AUTHORS Jason Vogel Karen M. Carney Joel B. Smith Charles Herrick Missy Stults Megan O’Grady Alexis St. Juliana Heather Hoste...

More info »