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We develop a computational framework for the stochastic and dynamic modeling of regional natural catastrophe losses with an insurance industry to support government decision‐making for hurricane risk management. The analysis captures the temporal changes in the building inventory due to the acquisition (buyouts) of high‐risk properties and the vulnerability of the building stock due to retrofit mitigation decisions. The system is comprised of a set of interacting models to (1) simulate hazard events; (2) estimate regional hurricane‐induced losses from each hazard event based on an evolving building inventory; (3) capture acquisition offer acceptance, retrofit implementation, and insurance purchase behaviors of homeowners; and (4) represent an insurance market sensitive to demand with strategically interrelated primary insurers. This framework is linked to a simulation‐optimization model to optimize decision‐making by a government entity whose objective is to minimize region‐wide hurricane losses. We examine the effect of different policies on homeowner mitigation, insurance take‐up rate, insurer profit, and solvency in a case study using data for eastern North Carolina. Our findings indicate that an approach that coordinates insurance, retrofits, and acquisition of high‐risk properties effectively reduces total (uninsured and insured) losses. [ABSTRACT FROM AUTHOR]

A challenge in anion control in periodic solids is to preserve the crystal lattice while substituting for different anions of widely varying size and hardness. Post‐synthetic modification routes that place cations or anions in non‐equilibrium configurations are promising; however, such methods remain relatively unexplored for anion placement. Here, we report the synthesis of LaOI nanocrystals by a non‐hydrolytic sol‐gel condensation reaction and their transformation into LaOBr, LaOCl, and LaOF nanocrystals along hard–soft acid–base principles using post‐synthetic metathesis reactions with ammonium halides. Anion displacement proceeds along halide planes, preserving the tetragonal matlockite structure. Energy‐variant X‐ray excited optical luminesce signatures of alloyed Tb3+‐ions is a sensitive quantum reporter of the preservation of the cation sublattice and hardening of the crystal structure upon anion replacement. [ABSTRACT FROM AUTHOR]

During hurricanes, flooding and wind cause property damage and loss of life. Increased coastal development and recent economic impacts of hurricanes has increased stakeholders' interest in coastal mitigation. Through the use of the Theory of Planned Behaviour, this study focuses on factors that impact the intention of homeowners to mitigate. Methodologically, surveys were sent to a random sample of 2,500 coastal North Carolina homeowners who provided insights about their hurricane and mitigation experiences. The sample was purchased from Genesys, which utilises the postal service's database to randomly select addresses. Binary logistic regression was used to compute homeowners' intention to adopt eight separate mitigation actions for their homes. The findings show limited support on the influence of emotion and risk perception on intention to mitigate. However, the findings clearly support the relationships between intention to mitigate and the influence of others and perceptions of the characteristics of the mitigation actions. The significance of the findings indicates the importance of utilising these factors in future studies to better understand mitigation decision‐making and to inform future mitigation policies and programs. [ABSTRACT FROM AUTHOR]

Hurricane track and intensity can change rapidly in unexpected ways, thus making predictions of hurricanes and related hazards uncertain. This inherent uncertainty often translates into suboptimal decision‐making outcomes, such as unnecessary evacuation. Representing this uncertainty is thus critical in evacuation planning and related activities. We describe a physics‐based hazard modeling approach that (1) dynamically accounts for the physical interactions among hazard components and (2) captures hurricane evolution uncertainty using an ensemble method. This loosely coupled model system provides a framework for probabilistic water inundation and wind speed levels for a new, risk‐based approach to evacuation modeling, described in a companion article in this issue. It combines the Weather Research and Forecasting (WRF) meteorological model, the Coupled Routing and Excess STorage (CREST) hydrologic model, and the ADvanced CIRCulation (ADCIRC) storm surge, tide, and wind‐wave model to compute inundation levels and wind speeds for an ensemble of hurricane predictions. Perturbations to WRF's initial and boundary conditions and different model physics/parameterizations generate an ensemble of storm solutions, which are then used to drive the coupled hydrologic + hydrodynamic models. Hurricane Isabel (2003) is used as a case study to illustrate the ensemble‐based approach. The inundation, river runoff, and wind hazard results are strongly dependent on the accuracy of the mesoscale meteorological simulations, which improves with decreasing lead time to hurricane landfall. The ensemble envelope brackets the observed behavior while providing "best‐case" and "worst‐case" scenarios for the subsequent risk‐based evacuation model. [ABSTRACT FROM AUTHOR]

This article introduces a new integrated scenario‐based evacuation (ISE) framework to support hurricane evacuation decision making. It explicitly captures the dynamics, uncertainty, and human–natural system interactions that are fundamental to the challenge of hurricane evacuation, but have not been fully captured in previous formal evacuation models. The hazard is represented with an ensemble of probabilistic scenarios, population behavior with a dynamic decision model, and traffic with a dynamic user equilibrium model. The components are integrated in a multistage stochastic programming model that minimizes risk and travel times to provide a tree of evacuation order recommendations and an evaluation of the risk and travel time performance for that solution. The ISE framework recommendations offer an advance in the state of the art because they: (1) are based on an integrated hazard assessment (designed to ultimately include inland flooding), (2) explicitly balance the sometimes competing objectives of minimizing risk and minimizing travel time, (3) offer a well‐hedged solution that is robust under the range of ways the hurricane might evolve, and (4) leverage the substantial value of increasing information (or decreasing degree of uncertainty) over the course of a hurricane event. A case study for Hurricane Isabel (2003) in eastern North Carolina is presented to demonstrate how the framework is applied, the type of results it can provide, and how it compares to available methods of a single scenario deterministic analysis and a two‐stage stochastic program. [ABSTRACT FROM AUTHOR]

Any entity offering flood insurance, whether it is private or government‐administered such as the National Flood Insurance Program (NFIP), faces the challenge of solvency. This is especially true for the NFIP, where homeowner affordability criteria limit the opportunity to charge fully risk‐based premiums. One solution is to remove the highest flood risk properties from the insurer's book of business. Acquisition (buyout) of flood‐prone structures is a potentially permanent solution that eliminates the highest risk properties while providing homeowners with financial assistance to relocate in a less risky location. To encourage participation, homeowners are offered a preflood fair market value of their damaged (or at risk of damage) structures. Although many factors have been shown to affect a homeowner's decision to accept an acquisition offer, very little research has been devoted to the influence of price or monetary incentive offered on homeowners' willingness to participate in acquisition programs. We estimate a pooled probit model and employ a bootstrap methodology to determine the effects of hypothetical home price offers on homeowners' acquisition decisions. We do so while controlling for environmental factors, property characteristics, and homeowner sociodemographic characteristics. Results show that price indeed has a positive effect on likelihood of accepting an acquisition contract. Furthermore, estimated homeowner supply curves differ significantly based on the damage status of the acquisition offer, as well as homeowner and property characteristics. [ABSTRACT FROM AUTHOR]

In this article, we develop statistical models to predict the number and geographic distribution of fires caused by earthquake ground motion and tsunami inundation in Japan. Using new, uniquely large, and consistent data sets from the 2011 Tōhoku earthquake and tsunami, we fitted three types of models-generalized linear models (GLMs), generalized additive models (GAMs), and boosted regression trees (BRTs). This is the first time the latter two have been used in this application. A simple conceptual framework guided identification of candidate covariates. Models were then compared based on their out-of-sample predictive power, goodness of fit to the data, ease of implementation, and relative importance of the framework concepts. For the ground motion data set, we recommend a Poisson GAM; for the tsunami data set, a negative binomial (NB) GLM or NB GAM. The best models generate out-of-sample predictions of the total number of ignitions in the region within one or two. Prefecture-level prediction errors average approximately three. All models demonstrate predictive power far superior to four from the literature that were also tested. A nonlinear relationship is apparent between ignitions and ground motion, so for GLMs, which assume a linear response-covariate relationship, instrumental intensity was the preferred ground motion covariate because it captures part of that nonlinearity. Measures of commercial exposure were preferred over measures of residential exposure for both ground motion and tsunami ignition models. This may vary in other regions, but nevertheless highlights the value of testing alternative measures for each concept. Models with the best predictive power included two or three covariates. [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]

We tested whether the crustacean zooplankton species composition and size structure (which are important determinants of energy flow in plankton food webs) during late summer in reservoirs would be related to ecoregional designations, catchment land use, temperature and latitude. We also examined whether the predictions of decreasing cladoceran body size with decreasing latitude (as observed for natural lakes) would be valid for reservoirs. Zooplankton samples were collected in late summer 2010 from 102 western U.S. reservoirs distributed over three major ecoregions from 32.5 to 48.6°N latitude., Large-bodied cladocerans ( Daphnia pulex complex) and cyclopoid copepods ( Diacyclops thomasi) were found in deeper, cooler reservoirs with forested catchments (Northwestern Forested Mountains). Small-bodied cladocerans ( Daphnia retrocurva, Diaphanosoma spp. and Chydorus sphaericus) and cyclopoid copepods ( Tropocyclops prasinus) were more common in productive reservoirs in catchments influenced by agriculture (Great Plains). Small-bodied cladocerans ( Bosmina longirostris) dominated the otherwise modest crustacean zooplankton in reservoirs located in catchments dominated by evergreen shrub vegetation with limited forestation (Western Xeric). Calanoid copepods also displayed clear distribution by ecoregion., Increasing water temperature and decreasing latitude were associated with smaller mean Daphnia and cladoceran body lengths and lower absolute biomass of daphniids. Daphniids were rare or absent in warm waters (i.e. below 36o N latitude and/or from sites located <500 m altitude)., Species composition and mean body size of the cladoceran zooplankton were consistent with the feeding habits of the common planktivorous fish - salmon ( Oncorhynchus spp.) and lake trout ( Salvelinus spp.) in the Northwestern Forested Mountains, gizzard shad ( Dorosoma cepedianum) in the Great Plains and threadfin shad ( Dorosoma petenense) in the Western Xeric ecoregion., Our study indicates that the taxonomic composition and size structure of the cladoceran zooplankton in reservoirs are highly connected to ecoregional attributes such as catchment land use, temperature and latitude as has been observed for natural lakes. [ABSTRACT FROM AUTHOR]

Quantitative assessment of post-disaster housing recovery is critical to enhancing understanding of the process and improving the decisions that shape it. Nevertheless, few comprehensive empirical evaluations of post-disaster housing recovery have been conducted, and no standard measurement methods exist. This paper presents a quantitative assessment of housing recovery in Punta Gorda, Florida, United States, following Hurricane Charley of August 2004, including an overview of the phases of housing recovery, progression of recovery over time, alternative trajectories of recovery, differential recovery, incorporation of mitigation, and effect on property sales. The assessment is grounded in a conceptual framework that considers the recovery of both people and place, and that emphasises recovery as a process, not as an endpoint. Several data sources are integrated into the assessment-including building permits, remotely sensed imagery, and property appraiser data-and their strengths and limitations are discussed with a view to developing a standardised method for measuring and monitoring housing recovery. [ABSTRACT FROM AUTHOR]

SUMMARY Two key issues distinguish probabilistic seismic risk analysis of a lifeline or portfolio of structures from that of a single structure. Regional analysis must consider the correlation among lifeline components or structures in the portfolio, and the larger scope makes it much more computationally demanding. In this paper, we systematically identify and compare alternative methods for regional hazard analysis that can be used as the first part of a computationally efficient regional probabilistic seismic risk analysis that properly considers spatial correlation. Specifically, each method results in a set of probabilistic ground motion maps with associated hazard-consistent annual occurrence probabilities that together represent the regional hazard. The methods are compared according to how replicable and computationally tractable they are and the extent to which the resulting maps are physically realistic, consistent with the regional hazard and regional spatial correlation, and few in number. On the basis of a conceptual comparison and an empirical comparison for Los Angeles, we recommend a combination of simulation and optimization approaches: (i) Monte Carlo simulation with importance sampling of the earthquake magnitudes to generate a set of probabilistic earthquake scenarios (defined by source and magnitude); (ii) the optimization-based probabilistic scenario method, a mixed-integer linear program, to reduce the size of that set; (iii) Monte Carlo simulation to generate a set of probabilistic ground motion maps, varying the number of maps sampled from each earthquake scenario so as to minimize the sampling variance; and (iv) the optimization-based probabilistic scenario again to reduce the set of probabilistic ground motion maps. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic integer program developed to determine how to schedule inspection, damage assessment, and repair tasks so as to optimize the post-earthquake restoration of the electric power system. The objective of the optimization is to minimize the average time each customer is without power, and a genetic algorithm is used to solve it. The effectiveness of the schedules recommended by the optimization are evaluated by running a detailed discrete event simulation model of the restoration process with both the optimization-generated schedules and the power company's original schedules, and comparing the resulting restorations according to three measures-average time each customer is without power, time required to restore 90 of customers, and time required to restore 98 of customers. The optimization and simulation models both consider all the earthquakes that could affect the power system and represent the uncertainty surrounding expected restoration times. he models were developed through an application to the Los Angeles Department of Water and Power (LADWP) electric power system, but the general approach is extendable to other electric power systems, other lifelines, and other hazards. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Hurricane wind risk in a region changes over time due to changes in the number, type, locations, vulnerability, and value of buildings. A model was developed to quantitatively estimate changes over time in hurricane wind risk to wood-frame houses (defined in terms of potential for direct economic loss), and to estimate how different factors, such as building code changes and population growth, contribute to that change. The model, which is implemented in a simulation, produces a probability distribution of direct economic losses for each census tract in the study region at each time step in the specified time horizon. By changing parameter values and rerunning the analysis, the effects of different changes in the built environment on the hurricane risk trends can be estimated and the relative effectiveness of hypothetical mitigation strategies can be evaluated. Using a case study application for wood-frame houses in selected counties in North Carolina from 2000 to 2020, this article demonstrates how the hurricane wind risk forecasting model can be used: (1) to provide insight into the dynamics of regional hurricane wind risk—the total change in risk over time and the relative contribution of different factors to that change, and (2) to support mitigation planning. Insights from the case study include, for example, that the many factors contributing to hurricane wind risk for wood-frame houses interact in a way that is difficult to predict a priori, and that in the case study, the reduction in hurricane losses due to vulnerability changes (e.g., building code changes) is approximately equal to the increase in losses due to building inventory growth. The potential for the model to support risk communication is also discussed. [ABSTRACT FROM AUTHOR]

In light of the increased emphasis on lightweighting of vehicular components and continued use of high‐performance aluminum alloys in the aerospace industry, designing alternatives to carcinogenic chromium‐based corrosion control systems has emerged as an urgent imperative. The high activity of aluminum and the heterogeneous surface structure of Al alloys renders effective corrosion inhibition a formidable challenge. Here, the effective corrosion protection of AA7075 alloys by Mg/polyetherimide nanocomposite coatings prepared by dispersing solution‐grown Mg nanocrystals within a polyamic acid matrix followed by imidization on the substrate are demonstrated. The active nanocrystal filler and nanocomposite are characterized using powder X‐ray diffraction (XRD), Fourier transform infrared spectroscopy, and cross‐sectional electron microscopy. Electrochemical impedance spectroscopy (EIS) and open circuit potential (OCP) responses of coatings are evaluated over the course of 100 days of exposure to a 3.5 wt% aqueous solution of NaCl. These results suggest that the nanocomposite coatings endow efficacious cathodic and barrier protection to the underlying alloy substrate. The Mg/PEI nanocomposite coatings endow immediate cathodic protection to AA7075 substrates upon salt water immersion with rapid mobilization of the active filler. The nanocomposites represent a vital addition to the sparse set of chrome‐free options for corrosion protection of lightweight alloys. [ABSTRACT FROM AUTHOR]