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Most traditional regional natural disaster risk assessments use either engineering-based loss estimation modeling that focuses on probabilistic assessment of damage to the physical infrastructure, or a social science–based index approach that focuses on assessing vulnerability or resilience of the people exposed. Both have important strengths, but to date, the two have remained largely separate. In this paper, we introduce a new framework for comprehensive assessment of a city's natural disaster risk that aims to integrate the strengths of the two approaches into a single method that can be used to assess a city's risk. The framework essentially extends the probabilistic engineering loss estimation framework to include social resilience in a way that treats social resilience and physical infrastructure damage equally with a parallel structure. We illustrate application of the framework through a case study application to the seismically active city of Padang, Indonesia. [ABSTRACT FROM AUTHOR]

Community resilience to natural disasters is a complex social problem that can benefit greatly from truly interdisciplinary research (IDR). While funders and universities frequently endorse interdisciplinarity, much of what actually happens can best be described as multidisciplinary, that is, parallel and additive rather than integrated and synergistic. Moving beyond that is harder than it might initially seem. Doing so requires understanding both the benefits of and barriers to its successful implementation. Benefits include better addressing questions at disciplinary intersections; providing more cohesive, useful recommendations to practitioners; improving assumptions in disciplinary studies based on input from other disciplines; and facilitating idea sharing across analogous parts of the problem, such as different hazards. Barriers include institutional systems that discourage researchers from engaging in IDR, and disciplinary differences that make it difficult to do. The paper concludes with thoughts on how community resilience research can more fully realise the potential of IDR. [ABSTRACT FROM PUBLISHER]

This paper introduces a new modeling framework to understand and improve regional natural disaster risk management in the USA, including the interactions among key stakeholders and between the two important risk management mechanisms of insurance and retrofit. The framework includes a stochastic programming optimization to represent insurer decisions, which interacts with a utility-based model of individual homeowners' decisions to insure and/or retrofit. Reinsurer and government roles are represented as inputs, and the decision models are integrated with a detailed regional catastrophe loss estimation model. This modeling framework is applied to a full-scale, realistic case study for hurricane risk to residential buildings in Eastern North Carolina. Several alternative system configurations are considered that affect the incentives for adoption of alternative risk management methods. They include providing a government subsidy for insured homeowners to encourage retrofit, providing both a government subsidy and insurance rebate to reduce retrofit costs, and mandating insurance purchase with a cap on insurance premiums. For each configuration, outcomes are presented from the perspectives of all key stakeholders-primary insurer, homeowners (insured and uninsured, in high- and low-risk areas), reinsurers, and the government. Results suggest that it is possible to design policies in which all stakeholders can be better off simultaneously. Retrofit incentives for insured homeowners can be effective in linking and strengthening the benefits of retrofit and insurance. Mandatory insurance coupled with capped profit loading factors and possibly retrofit rebates from the insurer to the homeowner can also reduce overall system risk. [ABSTRACT FROM AUTHOR]

The current system for managing natural disaster risk in the United States is problematic for both homeowners and insurers. Homeowners are often uninsured or underinsured against natural disaster losses, and typically do not invest in retrofits that can reduce losses. Insurers often do not want to insure against these losses, which are some of their biggest exposures and can cause an undesirably high chance of insolvency. There is a need to design an improved system that acknowledges the different perspectives of the stakeholders. In this article, we introduce a new modeling framework to help understand and manage the insurer's role in catastrophe risk management. The framework includes a new game-theoretic optimization model of insurer decisions that interacts with a utility-based homeowner decision model and is integrated with a regional catastrophe loss estimation model. Reinsurer and government roles are represented as bounds on the insurer-insured interactions. We demonstrate the model for a full-scale case study for hurricane risk to residential buildings in eastern North Carolina; present the results from the perspectives of all stakeholders-primary insurers, homeowners (insured and uninsured), and reinsurers; and examine the effect of key parameters on the results. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a new method to estimate the long-term regional hurricane wind and storm surge hazard. The output is a relatively small set of hurricane scenarios that together represent the regional hazard. For each scenario, the method produces a hazard-consistent annual occurrence probability, and wind speeds and surge levels throughout the study area. These scenarios can be used for subsequent evacuation or loss estimation modeling. This optimization-based probabilistic scenario (OPS) method involves first simulating tens of thousands of candidate hurricane scenarios with wind speeds and approximate surge depths. A mixed-integer linear optimization is then used to select a subset of scenarios and assign hazard-consistent annual occurrence probabilities to each. Finally, a surge model is used to estimate accurate surge depths for the reduced set of events. The method considers the correlation between winds and surge depths and the spatial correlations of each; it is computationally efficient; and it makes explicit the tradeoff between the number of scenarios selected and the errors introduced by using a reduced set of events. A case study for Eastern North Carolina is presented in which a final set of 97 hurricanes provides unbiased results with errors small enough for many practical uses. [Copyright &y& Elsevier]

Earthquakes are low probability-high consequence events, regional earthquake mitigation is therefore a risky investment. Despite its importance, the risk-return tradeoff is often not examined explicitly in regional earthquake risk management resource allocation decisions. This paper introduces a stochastic optimization model developed to help decision-makers understand the risk-return tradeoff in regional earthquake risk mitigation, and to help state and local governments comply with the Disaster Mitigation Act of 2000 requirement that they develop a mitigation plan. Taking advantage of the special structure of the optimization, Dantzig-Wolfe decomposition is used as the solution method. A case study for Central and Eastern Los Angeles illustrates an application of the model. Results include a graph of the tradeoff between risk and return, quantification of the relative contributions of each possible earthquake scenario, and discussion of the effect of risk aversion on the selection of mitigation alternatives. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for estimating changes in hurricane risk (annual expected economic losses) over time. A case study illustrates application of the methodology in Dare and New Hanover counties in North Carolina. Most current available loss estimation models use the present-day building inventory as input to estimate future hurricane losses. However, the number, locations, types, and vulnerability of buildings in a region vary with time, thus compromising the accuracy of loss estimates and the effectiveness of mitigation plans based on those estimates. The methodology presented here integrates four models—wind hazard, building inventory change, building vulnerability change, and economic change—in a simulation framework to compute changes in expected annual hurricane losses over time. Case study results indicate the estimated rate of change of expected annual hurricane losses and the relative importance of different factors affecting that change. [ABSTRACT FROM AUTHOR]

This paper introduces a quantitative methodology to model the way that the number, locations, and types of buildings in a region change over time, specifically in order to understand how those changes affect hurricane risk. The methodology is intended ultimately to be incorporated into regional risk-assessment models so they can more accurately estimate expected future hurricane losses. Current regional risk-assessment models assume that a region’s building inventory remains static over time. The methodology begins with population projections, transforms them into household projections, then housing-unit projections, and finally projections of the number of buildings with different structural characteristics. The final output is the number of buildings of a certain type in each census tract and each future year, where the building type is based on units in structure, year built, value, type of roof covering, predominant roof shape, and number of stories. A case study is presented for residential buildings in 15 counties in North and South Carolina. An uncertainty analysis examines the effect of modeling assumptions and data uncertainty on the results. [ABSTRACT FROM AUTHOR]

This paper describes the Hurricane Disaster Risk Index (HDRI), a composite index developed to compare the risk of hurricane disaster in U.S. coastal counties. Analogous to a quality of life index, the HDRI was developed to be an easily understandable tool that can be used to compare the relative risk of economic and life loss in different coastal counties in the United States, and to compare the different relative contributions of various factors, e.g., frequency of hurricanes and quality of emergency evacuation plan. The HDRI is specifically intended to support local, state, and national government agencies as they (1) make resource allocation decisions; (2) make high-level planning decisions; and (3) raise public awareness of hurricane risk, its causes, and ways to manage it. This paper discusses the intended uses and background of the HDRI, the procedure used to develop it, and an analysis of 15 sample counties. [ABSTRACT FROM AUTHOR]

The article focuses on remote sensing serves as a valuable tool in many aspects of regional risk modeling for earthquakes and other natural hazards. Independent assessments of the recovery process using remote sensing imagery and building permits yielded remarkably results following Hurricane Charley (2004) in Punta Gorda, Florida.