Sensitivity Analysis for Computer Model Projections of Hurricane Losses

Projecting losses associated with hurricanes is a complex and difficult undertaking that is fraught with uncertainties. Hurricane Charley, which struck southwest Florida on August 13, 2004, illustrates the uncertainty of forecasting damages from these storms. Due to shifts in the track and the rapid intensification of the storm, real-time estimates grew from 2 billion dollars to 3 billion dollars in losses late on the 12th to a peak of 50 billion dollars for a brief time as the storm appeared to be headed for the Tampa Bay area. The storm struck the resort areas of Charlotte Harbor and moved across the densely populated central part of the state, with early poststorm estimates in the 28 dollars to 31 billion dollars range, and final estimates converging at 15 billion dollars as the actual intensity at landfall became apparent. The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) has a great appreciation for the role of computer models in projecting losses from hurricanes. The FCHLPM contracts with a professional team to perform onsite (confidential) audits of computer models developed by several different companies in the United States that seek to have their models approved for use in insurance rate filings in Florida. The team's members represent the fields of actuarial science, computer science, meteorology, statistics, and wind and structural engineering. An important part of the auditing process requires uncertainty and sensitivity analyses to be performed with the applicant's proprietary model. To influence future such analyses, an uncertainty and sensitivity analysis has been completed for loss projections arising from use of a sophisticated computer model based on the Holland wind field. Sensitivity analyses presented in this article utilize standardized regression coefficients to quantify the contribution of the computer input variables to the magnitude of the wind speed.     

Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk

In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind‐related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.     

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.     

Risk Perceptions,Optimism, and Natural Hazards

This article uses the panel survey developed for the Health and Retirement Study to evaluate whether Hurricane Andrew in 1992 altered longevity expectations of respondents who lived in Dade County, Florida, the location experiencing the majority of about 20 billion dollars of damage. Longevity expectations have been used as a proxy measure for both individual subjective risk assessments and dispositional optimism. The panel structure allows comparison of those respondents' longevity assessments when the timing of their survey responses bracket Andrew with those of individuals where it does not. After controlling for health effects, the results indicate a significant reduction in longevity expectations due to the information respondents appear to have associated with the storm.     

Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out‐of‐sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.     

Low‐Probability Flood Risk Modeling for New York City

The devastating impact by Hurricane Sandy (2012) again showed New York City (NYC) is one of the most vulnerable cities to coastal flooding around the globe. The low‐lying areas in NYC can be flooded by nor'easter storms and North Atlantic hurricanes. The few studies that have estimated potential flood damage for NYC base their damage estimates on only a single, or a few, possible flood events. The objective of this study is to assess the full distribution of hurricane flood risk in NYC. This is done by calculating potential flood damage with a flood damage model that uses many possible storms and surge heights as input. These storms are representative for the low‐probability/high‐impact flood hazard faced by the city. Exceedance probability‐loss curves are constructed under different assumptions about the severity of flood damage. The estimated flood damage to buildings for NYC is between US$59 and 129 millions/year. The damage caused by a 1/100‐year storm surge is within a range of US$2 bn–5 bn, while this is between US$5 bn and 11 bn for a 1/500‐year storm surge. An analysis of flood risk in each of the five boroughs of NYC finds that Brooklyn and Queens are the most vulnerable to flooding. This study examines several uncertainties in the various steps of the risk analysis, which resulted in variations in flood damage estimations. These uncertainties include: the interpolation of flood depths; the use of different flood damage curves; and the influence of the spectra of characteristics of the simulated hurricanes.     

Integrating Models and Data to Estimate the Structural Reliability of Utility Poles During Hurricanes

Utility systems such as power and communication systems regularly experience significant damage and loss of service during hurricanes. A primary damage mode for these systems is failure of wooden utility poles that support conductors and communication lines. In this article, we present an approach for combining structural reliability models for utility poles with observed data on pole performance during past hurricanes. This approach, based on Bayesian updating, starts from an imperfect but informative prior and updates this prior with observed performance data. We consider flexural and foundation failure mechanisms in the prior, acknowledging that these are an incomplete, but still informative, subset of the possible failure mechanisms for utility poles during hurricanes. We show how a model‐based prior can be updated with observed failure data, using pole failure data from Hurricane Katrina as a case study. The results of this integration of model‐based estimates and observed performance data then offer a more informative starting point for power system performance estimation for hurricane conditions.     

Analyzing Risk Response Dynamics on the Web: The Case of Hurricane Katrina

Using a unique data set that documented the hourly web-surfing behavior of over 140,000 Internet users in five southeastern states in August 2005, we explore the dynamics of information gathering as Hurricane Katrina developed and then hit South Florida and the Northern Gulf Coast. Using both elementary statistical methods and advanced techniques from functional data analysis,^{( 1 )} we examine both how storm events (such as the posting of warnings) affected traffic to weather-related websites, and how this traffic varied across locations and by characteristics of the web user. A general finding is that spatial-temporal variation in weather-site web traffic generally tracked the timing and scale of the storm threat experienced by a given area. There was, however, considerable variation in this responsiveness. Residents in Florida counties that had been most directly affected by Hurricane Dennis just a month earlier, for example, displayed more active visitation rates than those who had been less affected. We also find evidence of a gender effect where male users displayed a disproportionately larger rate of visitation to weather sites given the onset of storm warnings than females. The implications of this work for the broader study of behavioral risk response dynamics during hazards are explored.     

Retrospective Cost-Effectiveness Analyses for Polio Vaccination in the United States

The history of polio vaccination in the United States spans 50 years and includes different phases of the disease, multiple vaccines, and a sustained significant commitment of resources. We estimated cost-effectiveness ratios and assessed the net benefits of polio vaccination applicable at various points in time from the societal perspective and we discounted these back to appropriate points in time. We reconstructed vaccine price data from available sources and used these to retrospectively estimate the total costs of the U.S. historical polio vaccination strategies (all costs reported in year 2002 dollars). We estimate that the United States invested approximately US dollars 35 billion (1955 net present value, discount rate of 3%) in polio vaccines between 1955 and 2005 and will invest approximately US dollars 1.4 billion (1955 net present value, or US dollars 6.3 billion in 2006 net present value) between 2006 and 2015 assuming a policy of continued use of inactivated poliovirus vaccine (IPV) for routine vaccination. The historical and future investments translate into over 1.7 billion vaccinations that prevent approximately 1.1 million cases of paralytic polio and over 160,000 deaths (1955 net present values of approximately 480,000 cases and 73,000 deaths). Due to treatment cost savings, the investment implies net benefits of approximately US dollars 180 billion (1955 net present value), even without incorporating the intangible costs of suffering and death and of averted fear. Retrospectively, the U.S. investment in polio vaccination represents a highly valuable, cost-saving public health program. Observed changes in the cost-effectiveness ratio estimates over time suggest the need for living economic models for interventions that appropriately change with time. This article also demonstrates that estimates of cost-effectiveness ratios at any single time point may fail to adequately consider the context of the investment made to date and the importance of population and other dynamics, and shows the importance of dynamic modeling.     

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.     

The Dynamics of Disaster Recovery: Resilience and Entropy in Hurricane Response Systems 2005–2008

The challenge for policy makers and disaster managers is to achieve a balance between two dynamics — resilience and entropy — in order to develop sustainable risk reduction. Achieving an appropriate balance between resilience and entropy in any given community requires a systematic exploration of both dynamics. The recent hurricanes that struck Louisiana, Hurricane Katrina on August 29, 2005 and Hurricane Gustav, on September 1, 2008, offer an unusual opportunity to assess the degree to which both dynamics operated following Hurricane Katrina.     

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.     

Nonparametric Tree‐Based Predictive Modeling of Storm Outages on an Electric Distribution Network

This article compares two nonparametric tree‐based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high‐resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2‐km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree‐leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources.     

Probabilistic,Multivariable Flood Loss Modeling on the Mesoscale with BT‐FLEMO

Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth‐damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable B agging decision T ree F lood L oss E stimation MO del (BT‐FLEMO) for residential buildings was developed. The application of BT‐FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT‐FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth‐damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT‐FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT‐FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT‐FLEMO well represents the variation range of loss estimates of the other models in the case study.     

Benefits of and strategies to update premium rates in the US National Flood Insurance Program under climate change

The United States’ National Flood Insurance Program (NFIP) has accumulated over $20 billion in debt to the US Treasury since 2005, partly due to discounted premiums on homes in flood-prone areas. To address this issue, FEMA introduced Risk Rating 2.0 in October 2021, which is able to assess and charge more accurate and equitable rates to homeowners. However, rates must be continually updated to account for increasing flood damage caused by sea level rise and more intense hurricanes due to climate change. This study proposes a strategy to adopt updated premium rates that account for climate change effects and address affordability and risk mitigation issues with a means-tested voucher program. The strategy is tested in a coastal community, Ortley Beach, NJ, by projecting its future flood risk under sea level rise and storm intensification. Compared with using static rates for all the properties in Ortley Beach, the proposed strategy is shown to reduce the NFIP's potential losses to the community from 2020 to 2050 by half (from $4.6 million to $2.3 million), improve the community's flood resistance, and address affordability concerns. Sensitivity analysis of varying incomes, loan interest rates, and conditions for a voucher indicates that the strategy is feasible and effective under a wide range of scenarios. Thus, the proposed strategy can be applied to various communities along the US coastline as an effective way of updating risk-based premiums while addressing affordability and resilience concerns.     

Policy Learning for Flood Mitigation: A Longitudinal Assessment of the Community Rating System in Florida

Floods continue to inflict the most damage upon human communities among all natural hazards in the United States. Because localized flooding tends to be spatially repetitive over time, local decisionmakers often have an opportunity to learn from previous events and make proactive policy adjustments to reduce the adverse effects of a subsequent storm. Despite the importance of understanding the degree to which local jurisdictions learn from flood risks and under what circumstances, little if any empirical, longitudinal research has been conducted along these lines. This article addresses the research gap by examining the change in local flood mitigation policies in Florida from 1999 to 2005. We track 18 different mitigation activities organized into four series of activities under the Federal Emergency Management Agency's (FEMA) Community Rating System (CRS) for every local jurisdiction in Florida participating in the FEMA program on a yearly time step. We then identify the major factors contributing to policy changes based on CRS scores over the seven-year study period. Using multivariate statistical models to analyze both natural and social science data, we isolate the effects of several variables categorized into the following groups: hydrologic conditions, flood disaster history, socioeconomic and human capital controls. Results indicate that local jurisdictions do in fact learn from histories of flood risk and this process is expedited under specific conditions.     

Maternal Hurricane Exposure and Fetal Distress Risk

Logistic regression and spatial analytic techniques are used to model fetal distress risk as a function of maternal exposure to Hurricane Andrew. First, monthly time series compare the proportion of infants born distressed in hurricane affected and unaffected areas. Second, resident births are analyzed in Miami‐Dade and Broward counties, before, during, and after Hurricane Andrew. Third, resident births are analyzed in all Florida locales with 100,000 or more persons, comparing exposed and unexposed gravid females. Fourth, resident births are analyzed along Hurricane Andrew's path from southern Florida to northeast Mississippi. Results show that fetal distress risk increases significantly with maternal exposure to Hurricane Andrew in second and third trimesters, adjusting for known risk factors. Distress risk also correlates with the destructive path of Hurricane Andrew, with higher incidences of fetal distress found in areas of highest exposure intensity. Hurricane exposed African‐American mothers were more likely to birth distressed infants. The policy implications of in utero costs of natural disaster exposure are discussed.     

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.     

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.     

Total Economic Consequences of an Influenza Outbreak in the United States

Pandemic influenza represents a serious threat not only to the population of the United States, but also to its economy. In this study, we analyze the total economic consequences of potential influenza outbreaks in the United States for four cases based on the distinctions between disease severity and the presence/absence of vaccinations. The analysis is based on data and parameters on influenza obtained from the Centers for Disease Control and the general literature. A state‐of‐the‐art economic impact modeling approach, computable general equilibrium, is applied to analyze a wide range of potential impacts stemming from the outbreaks. This study examines the economic impacts from changes in medical expenditures and workforce participation, and also takes into consideration different types of avoidance behavior and resilience actions not previously fully studied. Our results indicate that, in the absence of avoidance and resilience effects, a pandemic influenza outbreak could result in a loss in U.S. GDP of $25.4 billion, but that vaccination could reduce the losses to $19.9 billion. When behavioral and resilience factors are taken into account, a pandemic influenza outbreak could result in GDP losses of $45.3 billion without vaccination and $34.4 billion with vaccination. These results indicate the importance of including a broader set of causal factors to achieve more accurate estimates of the total economic impacts of not just pandemic influenza but biothreats in general. The results also highlight a number of actionable items that government policymakers and public health officials can use to help reduce potential economic losses from the outbreaks.