Individual learning as a driver of changes in community vulnerability under repeated hurricanes and changing climate

The risks from singular natural hazards such as a hurricane have been extensively investigated in the literature. However, little is understood about how individual and collective responses to repeated hazards change communities and impact their preparation for future events. Individual mitigation actions may drive how a community's resilience evolves under repeated hazards. In this paper, we investigate the effect that learning by homeowners can have on household mitigation decisions and on how this influences a region's vulnerability to natural hazards over time, using hurricanes along the east coast of the United States as our case study. To do this, we build an agent-based model (ABM) to simulate homeowners’ adaptation to repeated hurricanes and how this affects the vulnerability of the regional housing stock. Through a case study, we explore how different initial beliefs about the hurricane hazard and how the memory of recent hurricanes could change a community's vulnerability both under current and potential future hurricane scenarios under climate change. In some future hurricane environments, different initial beliefs can result in large differences in the region's long-term vulnerability to hurricanes. We find that when some homeowners mitigate soon after a hurricane—when their memory of the event is the strongest—it can help to substantially decrease the vulnerability of a community.     

Explainable deep learning powered building risk assessment model for proactive hurricane response

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities’ preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building-level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine-grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model-agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep-learning models in integrating multi-sourced features and accurately predicting buildings’ risks of weather extremes for property owners and households. The AI-powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long-term climate adaptation.     

The Effects of Revealed Information on Catastrophe Loss Projection Models’ Characterization of Risk: Damage Vulnerability Evidence from Florida

We examine whether the risk characterization estimated by catastrophic loss projection models is sensitive to the revelation of new information regarding risk type. We use commercial loss projection models from two widely employed modeling firms to estimate the expected hurricane losses of Florida Atlantic University's building stock, both including and excluding secondary information regarding hurricane mitigation features that influence damage vulnerability. We then compare the results of the models without and with this revealed information and find that the revelation of additional, secondary information influences modeled losses for the windstorm‐exposed university building stock, primarily evidenced by meaningful percent differences in the loss exceedance output indicated after secondary modifiers are incorporated in the analysis. Secondary risk characteristics for the data set studied appear to have substantially greater impact on probable maximum loss estimates than on average annual loss estimates. While it may be intuitively expected for catastrophe models to indicate that secondary risk characteristics hold value for reducing modeled losses, the finding that the primary value of secondary risk characteristics is in reduction of losses in the “tail” (low probability, high severity) events is less intuitive, and therefore especially interesting. Further, we address the benefit‐cost tradeoffs that commercial entities must consider when deciding whether to undergo the data collection necessary to include secondary information in modeling. Although we assert the long‐term benefit‐cost tradeoff is positive for virtually every entity, we acknowledge short‐term disincentives to such an effort.     

Understanding Household Preferences for Hurricane Risk Mitigation Information: Evidence from Survey Responses

Risk information is critical to adopting mitigation measures, and seeking risk information is influenced by a variety of factors. An essential component of the recently adopted My Safe Florida Home (MSFH) program by the State of Florida is to provide homeowners with pertinent risk information to facilitate hurricane risk mitigation activities. We develop an analytical framework to understand household preferences for hurricane risk mitigation information through allowing an intensive home inspection. An empirical analysis is used to identify major drivers of household preferences to receive personalized information regarding recommended hurricane risk mitigation measures. A variety of empirical specifications show that households with home insurance, prior experience with damages, and with a higher sense of vulnerability to be affected by hurricanes are more likely to allow inspection to seek information. However, households with more members living in the home and households who live in manufactured/mobile homes are less likely to allow inspection. While findings imply MSFH program's ability to link incentives offered by private and public agencies in promoting mitigation, households that face a disproportionately higher level of risk can get priority to make the program more effective.     

Projections of Future Extreme Weather Losses Under Changes in Climate and Exposure

Many attempts are made to assess future changes in extreme weather events due to anthropogenic climate change, but few studies have estimated the potential change in economic losses from such events. Projecting losses is more complex as it requires insight into the change in the weather hazard but also into exposure and vulnerability of assets. This article discusses the issues involved as well as a framework for projecting future losses, and provides an overview of some state‐of‐the‐art projections. Estimates of changes in losses from cyclones and floods are given, and particular attention is paid to the different approaches and assumptions. All projections show increases in extreme weather losses due to climate change. Flood losses are generally projected to increase more rapidly than losses from tropical and extra‐tropical cyclones. However, for the period until the year 2040, the contribution from increasing exposure and value of capital at risk to future losses is likely to be equal or larger than the contribution from anthropogenic climate change. Given the fact that the occurrence of loss events also varies over time due to natural climate variability, the signal from anthropogenic climate change is likely to be lost among the other causes for changes in risk, at least during the period until 2040. More efforts are needed to arrive at a comprehensive approach that includes quantification of changes in hazard, exposure, and vulnerability, as well as adaptation effects.     

Catastrophe Risk Models for Evaluating Disaster Risk Reduction Investments in Developing Countries

Major natural disasters in recent years have had high human and economic costs, and triggered record high postdisaster relief from governments and international donors. Given the current economic situation worldwide, selecting the most effective disaster risk reduction (DRR) measures is critical. This is especially the case for low‐ and middle‐income countries, which have suffered disproportionally more economic and human losses from disasters. This article discusses a methodology that makes use of advanced probabilistic catastrophe models to estimate benefits of DRR measures. We apply such newly developed models to generate estimates for hurricane risk on residential structures on the island of St. Lucia, and earthquake risk on residential structures in Istanbul, Turkey, as two illustrative case studies. The costs and economic benefits for selected risk reduction measures are estimated taking account of hazard, exposure, and vulnerability. We conclude by emphasizing the advantages and challenges of catastrophe model‐based cost‐benefit analyses for DRR in developing countries.     

Implementation of Equity in Resource Allocation for Regional Earthquake Risk Mitigation Using Two‐Stage Stochastic Programming

This article presents a new methodology to implement the concept of equity in regional earthquake risk mitigation programs using an optimization framework. It presents a framework that could be used by decisionmakers (government and authorities) to structure budget allocation strategy toward different seismic risk mitigation measures, i.e., structural retrofitting for different building structural types in different locations and planning horizons. A two‐stage stochastic model is developed here to seek optimal mitigation measures based on minimizing mitigation expenditures, reconstruction expenditures, and especially large losses in highly seismically active countries. To consider fairness in the distribution of financial resources among different groups of people, the equity concept is incorporated using constraints in model formulation. These constraints limit inequity to the user‐defined level to achieve the equity‐efficiency tradeoff in the decision‐making process. To present practical application of the proposed model, it is applied to a pilot area in Tehran, the capital city of Iran. Building stocks, structural vulnerability functions, and regional seismic hazard characteristics are incorporated to compile a probabilistic seismic risk model for the pilot area. Results illustrate the variation of mitigation expenditures by location and structural type for buildings. These expenditures are sensitive to the amount of available budget and equity consideration for the constant risk aversion. Most significantly, equity is more easily achieved if the budget is unlimited. Conversely, increasing equity where the budget is limited decreases the efficiency. The risk‐return tradeoff, equity‐reconstruction expenditures tradeoff, and variation of per‐capita expected earthquake loss in different income classes are also presented.     

Quantifying the Hurricane Catastrophe Risk to Offshore Wind Power

The U.S. Department of Energy has estimated that over 50 GW of offshore wind power will be required for the United States to generate 20% of its electricity from wind. Developers are actively planning offshore wind farms along the U.S. Atlantic and Gulf coasts and several leases have been signed for offshore sites. These planned projects are in areas that are sometimes struck by hurricanes. We present a method to estimate the catastrophe risk to offshore wind power using simulated hurricanes. Using this method, we estimate the fraction of offshore wind power simultaneously offline and the cumulative damage in a region. In Texas, the most vulnerable region we studied, 10% of offshore wind power could be offline simultaneously because of hurricane damage with a 100‐year return period and 6% could be destroyed in any 10‐year period. We also estimate the risks to single wind farms in four representative locations; we find the risks are significant but lower than those estimated in previously published results. Much of the hurricane risk to offshore wind turbines can be mitigated by designing turbines for higher maximum wind speeds, ensuring that turbine nacelles can turn quickly to track the wind direction even when grid power is lost, and building in areas with lower risk.     

Florida Households’ Expected Responses to Hurricane Hazard Mitigation Incentives

This study tested a series of models predicting household expectations of participating in hurricane hazard mitigation incentive programs. Data from 599 households in Florida revealed that mitigation incentive adoption expectations were most strongly and consistently related to hazard intrusiveness and risk perception and, to a lesser extent, worry. Demographic and hazard exposure had indirect effects on mitigation incentive adoption expectations that were mediated by the psychological variables. The results also revealed differences in the factors affecting mitigation incentive adoption expectations for each of five specific incentive programs. Overall, the results suggest that hazard managers are more likely to increase participation in mitigation incentive programs if they provide messages that repeatedly (thus increasing hazard intrusiveness) remind people of the likelihood of severe negative consequences of hurricane impact (thus increasing risk perception).     

The Complementary Roles of Mitigation and Insurance in Managing Catastrophic Risks

This paper examines the impact that insurance coupled with specific risk mitigation measures (RMMs) could have on reducing losses from hurricanes and earthquakes as well as improving the solvency position of insurers who provide coverage against these hazards. We first explore why relatively few individuals adopt cost-effective RMMs by reporting on the results of empirical studies and controlled laboratory studies. We then investigate the impact that an RMM has on both the expected losses and those from a worst case scenario in two model cities—Oakland (an earthquake-prone area) and Miami/Dade County (a hurricane-prone area) which were constructed respectively with the assistance of two modeling firms. The paper then explores three programs for forging a meaningful public-private sector partnership: well-enforced building codes, insurance premium reductions linked with long-term loans, and lower deductibles on insurance policies tied to mitigation. We conclude by briefly examining four issues for future research on linking mitigation with insurance.     

An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 2—Hazard Modeling

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.     

An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 1—Decision Support System

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.     

An Assessment of Change in Risk Perception and Optimistic Bias for Hurricanes Among Gulf Coast Residents

This study focuses on levels of concern for hurricanes among individuals living along the Gulf Coast during the quiescent two‐year period following the exceptionally destructive 2005 hurricane season. A small study of risk perception and optimistic bias was conducted immediately following Hurricanes Katrina and Rita. Two years later, a follow‐up was done in which respondents were recontacted. This provided an opportunity to examine changes, and potential causal ordering, in risk perception and optimistic bias. The analysis uses 201 panel respondents who were matched across the two mail surveys. Measures included hurricane risk perception, optimistic bias for hurricane evacuation, past hurricane experience, and a small set of demographic variables (age, sex, income, and education). Paired t‐tests were used to compare scores across time. Hurricane risk perception declined and optimistic bias increased. Cross‐lagged correlations were used to test the potential causal ordering between risk perception and optimistic bias, with a weak effect suggesting the former affects the latter. Additional cross‐lagged analysis using structural equation modeling was used to look more closely at the components of optimistic bias (risk to self vs. risk to others). A significant and stronger potentially causal effect from risk perception to optimistic bias was found. Analysis of the experience and demographic variables’ effects on risk perception and optimistic bias, and their change, provided mixed results. The lessening of risk perception and increase in optimistic bias over the period of quiescence suggest that risk communicators and emergency managers should direct attention toward reversing these trends to increase disaster preparedness.     

A Bayesian Benefit-Risk Model Applied to the South Florida Building Code

A Bayesian compound Poisson benefit-risk model is described in this paper, and used to evaluate recent revisions to the South Florida Building Code (SFBC). The model accounts for natural variability in hurricane frequency and severity, and uncertainty in the effectiveness of the revised code. Ranges of residential growth rate, code effectiveness, construction cost increase, and planning period length are assumed, to show the ranges of cost-to-performance ratio within which the code will make sense economically. The expected cost of residential hurricane damage over 50 years for ten South Florida counties assuming continuation of previous building practices was $93 billion, equivalent to the residential damage of 5.2 Andrews. Assuming a reduction in the growth of damageable housing in South Florida from 5.5% to 2% as a result of code revision, estimated damages under the new code were $45 billion. At a per-house construction cost increase of 5%, the probability of at least recovering the estimated $40 billion cost of the specified wind-resistant construction was estimated to be 47%. Expected return on investment was estimated at $7 billion over 50 years. The expected return lies between a $44 billion loss and a $47 billion gain, when growth in damageable housing is allowed to range from 1% to 4% and construction cost increases are assumed to lie between 3% and 8%. Actual monetary return for a 5% cost increase and 2% growth in damageable housing ranges from a $20 billion loss to a $100 billion gain with 95% probability, as a result of weather variability alone. Results support SFBC revisions on solely economic grounds, a conclusion strengthened considerably in light of potentially avoided deaths and hurricane traumas. The model represents one approach to evaluating economic aspects of the sustainability of new technological measures on the basis of available information.     

Sizing Up a Superstorm: Exploring the Role of Recalled Experience and Attribution of Responsibility in Judgments of Future Hurricane Risk

Research suggests that hurricane‐related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals’ distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York (n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual‐level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences.     

Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models

Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.     

Identifying Promising Messages to Increase Hurricane Mitigation Among Coastal Homeowners in the United States

Hurricanes threaten the physical and financial well-being of coastal residents throughout the United States. Though hurricane-related losses are largely avoidable through property mitigation (e.g., structural modifications to existing homes), few homeowners invest in mitigation. Communication campaigns, which have influenced risk-related behaviors in other domains, hold promise for persuading coastal residents to engage in hurricane mitigation. The development of successful campaign messages relies, in part, on formative research to assess the potential influence of candidate message strategies. We present results from mixed-methods, theory-driven research to identify promising beliefs for persuading homeowners in coastal/coastal-adjacent regions of Alabama and Florida to install a high wind–resistant (HWR) roof. In Study 1, we elicited homeowners’ (n = 74) salient behavioral, normative, and control beliefs about installing an HWR roof. Using established procedures, we content analyzed open-ended responses and categorized them by thematic content. In Study 2, we surveyed another sample of homeowners (n = 533) to examine the extent to which salient beliefs/themes about installing an HWR roof (elicited in Study 1) are promising targets for a communication campaign, given their associations with homeowners’ intentions to retrofit. Results demonstrate that across elicited beliefs, common themes include the protection and property resilience reroofing affords, and anticipated expenses and financial barriers associated with reroofing. The most promising beliefs include behavioral beliefs that installing an HWR roof will protect oneself and one's family, and normative beliefs about the likelihood that one's family and community will install an HWR roof. We discuss the implications of findings for the development of hurricane mitigation messaging.     

Insurability and Mitigation of Flood Losses in Private Households in Germany

In Germany, flood insurance is provided by private insurers as a supplement to building or contents insurance. This article presents the results of a survey of insurance companies with regard to eligibility conditions for flood insurance changes after August 2002, when a severe flood caused 1.8 billion euro of insured losses in the Elbe and the Danube catchment areas, and the general role of insurance in flood risk management in Germany. Besides insurance coverage, governmental funding and public donations played an important role in loss compensation after the August 2002 flood. Therefore, this article also analyzes flood loss compensation, risk awareness, and mitigation in insured and uninsured private households. Insured households received loss compensation earlier. They also showed slightly better risk awareness and mitigation strategies. Appropriate incentives should be combined with flood insurance in order to strengthen future private flood loss mitigation. However, there is some evidence that the surveyed insurance companies do little to encourage precautionary measures. To overcome this problem, flood hazards and mitigation strategies should be better communicated to both insurance companies and property owners.     

Simulating Behavioral Influences on Community Flood Risk under Future Climate Scenarios

Flood risk is a function of both climate and human behavior, including individual and societal actions. For this reason, there is a need to incorporate both human and climatic components in models of flood risk. This study simulates behavioral influences on the evolution of community flood risk under different future climate scenarios using an agent-based model (ABM). The objective is to understand better the ways, sometimes unexpected, that human behavior, stochastic floods, and community interventions interact to influence the evolution of flood risk. One historic climate scenario and three future climate scenarios are simulated using a case study location in Fargo, North Dakota. Individual agents can mitigate flood risk via household mitigation or by moving, based on decision rules that consider risk perception and coping perception. The community can mitigate or disseminate information to reduce flood risk. Results show that agent behavior and community action have a significant impact on the evolution of flood risk under different climate scenarios. In all scenarios, individual and community action generally result in a decline in damages over time. In a lower flood risk scenario, the decline is primarily due to agent mitigation, while in a high flood risk scenario, community mitigation and agent relocation are primary drivers of the decline. Adaptive behaviors offset some of the increase in flood risk associated with climate change, and under an extreme climate scenario, our model indicates that many agents relocate.     

Rural Nevada and Climate Change: Vulnerability,Beliefs, and Risk Perception

In this article, we present the results of a study investigating the influence of vulnerability to climate change as a function of physical vulnerability, sensitivity, and adaptive capacity on climate change risk perception. In 2008/2009, we surveyed Nevada ranchers and farmers to assess their climate change‐related beliefs, and risk perceptions, political orientations, and socioeconomic characteristics. Ranchers’ and farmers’ sensitivity to climate change was measured through estimating the proportion of their household income originating from highly scarce water‐dependent agriculture to the total income. Adaptive capacity was measured as a combination of the Social Status Index and the Poverty Index. Utilizing water availability and use, and population distribution GIS databases; we assessed water resource vulnerability in Nevada by zip code as an indicator of physical vulnerability to climate change. We performed correlation tests and multiple regression analyses to examine the impact of vulnerability and its three distinct components on risk perception. We find that vulnerability is not a significant determinant of risk perception. Physical vulnerability alone also does not impact risk perception. Both sensitivity and adaptive capacity increase risk perception. While age is not a significant determinant of it, gender plays an important role in shaping risk perception. Yet, general beliefs such as political orientations and climate change‐specific beliefs such as believing in the anthropogenic causes of climate change and connecting the locally observed impacts (in this case drought) to climate change are the most prominent determinants of risk perception.