Impact Of Hurricane Research Articles

Climate Resilience through AI-Driven Hurricane Damage Assessments

Evolving hurricane patterns intensified by climate change are expected to exacerbate economic hardships on coastal communities. Climate resilience for these communities requires both the capability to recover rapidly from devastating storms, and the ability to develop an accurate and actionable understanding of vulnerabilities to reduce the impact of future storms. Available data from past storms can provide invaluable insight in addressing both these requirements. Post-disaster preliminary damage assessments (PDAs) are a crucial initial step toward a rapid recovery. They also provide the most accurate information on the performance of various types of dwellings after the storm. Traditional door-to-door inspection methods are time-consuming and can hinder efficient resource allocation by governments in the aftermath. To address this, researchers have proposed automated PDA frameworks, often utilizing data from satellites, combined with deep convolutional neural networks. However, before such frameworks can be adopted in practice, the accuracy and fidelity of predictions of damage level at the scale of an entire building must be comparable to human assessments. To bridge this gap, we present an innovative PDA framework that leverages Ultra-High-Resolution Aerial (UHRA) images alongside state-of-the-art transformer models for multi-class damage predictions across entire buildings. Our approach leverages vast amounts of unlabeled data to enhance accuracy of prediction and generalization capabilities. Through a series of experiments, we evaluate the influence of incorporating unlabeled data, and transformer models. By integrating UHRA images and semi-supervised transformer models, our findings indicate that this framework overcomes critical limitations associated with satellite imagery and traditional CNN models, achieving an 88% multiclass accuracy, ultimately leading to more precise, efficient, and reliable damage assessments that are a first step towards building more climate resilient societies.

Component-based estimation of recovery time and time-related expenses after hurricane events

Introduction: Due to hurricane damage, building residents or businesses must be relocated during the recovery time, which leads to time-related expenses (TRE), also known as additional living expenses (ALE) or extra expense coverage (EEC) or business interruption insurance (BIC). TRE are difficult to predict since they depend on the damage and time necessary to repair the building as well as on external factors such as damaged utilities and the availability of labor and materials, among other issues.Methods: In this study, we developed a new TRE hurricane vulnerability model for mid/high-rise buildings. The model combines estimates of repair time (Trepair), delay time (Tdelay), and utilities downtime (Tdown) to predict overall recovery time (Treco).Results: The outputs of the model include 1) TRE vulnerability matrices, which yield probabilities of Trepair, Tdelay, Tdown, Treco, and TRE conditional on either maximum 3-s wind speed or overall building damage ratio; 2) the corresponding vulnerability curves, which yield the mean values as a function wind speed or damage ratio.Discussion: Insurers can use these results to project TRE, and emergency managers and urban planners can use the recovery times to characterize the resilience of coastal communities. This paper summarizes the methodology and illustrates its implementation and results. The selected results of Treco are compared with the recovery times provided using the HAZUS-MH Hurricane Model.

Characteristics and Predictive Modeling of Short-term Impacts of Hurricanes on the US Employment

Characteristics and Predictive Modeling of Short-term Impacts of Hurricanes on the US Employment

Extreme waves in the Caribbean Sea: spatial regionalization and long-term analysis

The extreme wave height distribution in the Caribbean Sea is studied using a new method based on the maximum basin-wide aggregate of significant wave height, Hs, values per month. Besides, by means of the Self-Organizing Maps (SOM) technique, we identify coherent geographical regions with similar extreme wave height variability in the Caribbean Sea. Our findings revealed three primary regions: the eastern side with comparatively lower values, the central region with intermediate values, and the western side with the highest extreme wave heights. The study also examines the wind forcing conditions driving the spatial and temporal variability of the extreme waves, highlighting the influence of the low-pressure belt dynamics as well as the role played by the Caribbean Low-Level Jet (CLLJ) index, and the impact of cold fronts and hurricanes on extreme wave heights. Additionally, we explore the relationship between the extreme wave height distribution and climatic indices, such as the Atlantic Multidecadal Oscillation (AMO), the Atlantic Meridional Mode (AMM), the North Atlantic Oscillation (NAO) and the Oceanic Niño (ONI). The results reveal that the spatial distribution of extreme wave heights in the Caribbean Sea is mostly ruled by the influence of the CLLJ, with correlations close to 80%. In addition, significant correlations were observed between the extreme wave heights and the ENSO in the central Caribbean, as well as positive correlations between the extreme wave heights and NAO in the eastern part of the basin, and significant values of correlation with the negative phases of AMO and AMM in the whole basin. We show that, unlike conventional (or broadly used) methods deployed to identify extreme wave height, such as percentile 99th, Hs99, our methodology allows a further assessment of the wind and climate forcing conditions associated with the extreme wave events. Although, we acknowledge that the method here presented has limitations to capture extreme wave height outliers, it has the advantage of being used concomitant with the wind forcing to develop multivariate wave climate analysis at basin scale, and could be extended to a more local scale when studying coastal processes.

Shift-and-persist strategies as a potential protective factor against symptoms of psychological distress among young adults in Puerto Rico.

Residents of Puerto Rico are disproportionately exposed to social and environmental stressors (e.g., Hurricane María and the 2020 sequence of tremors) known to be associated with psychological distress. Shift-and-persist (SP), or the ability to adapt the self to stressors while preserving focus on the future, has been linked with lower psychological distress, but no study has evaluated this in Puerto Rico. This study examined the association between SP and psychological distress (including that from natural disasters) in a sample of young adults in Puerto Rico. Data from the Puerto Rico-OUTLOOK study (18-29 y) were used. Participants (n = 1497) completed assessments between September 2020 and September 2022. SP was measured with the Chen scale and categorized into quartiles (SPQ1-SPQ4). Psychological distress included symptoms of depression (CESD-10), anxiety (STAI-10), post-traumatic stress disorder (Civilian Abbreviated Scale PTSD checklist), and ataque de nervios (an idiom of distress used by Latinx groups). Outcomes were dichotomized according to clinical cutoffs when available, otherwise used sample-based cutoffs. Two additional items assessed the perceived mental health impact of Hurricane María and the 2020 sequence of tremors (categorized as no/little impact vs. some/a lot). Adjusted prevalence ratios (PR) and their 95% confidence intervals (CI) were estimated. The most commonly reported psychological distress outcome was PTSD (77%). In adjusted models, compared to SP Q1, persons in SP Q2-Q4 were less likely to have elevated symptoms of depression (PR Q2 = 0.79, 95% CI = 0.72-0.85; PR Q3 = 0.65, 95% CI = 0.58-0.73; and PR Q4 = 0.41, 95% CI = 0.35-0.48), PTSD (PR Q2 = 0.92, 95% CI = 0.87-0.98; PR Q3 = 0.86, 95% CI = 0.80-0.93; and PR Q4 = 0.76, 95% CI = 0.70-0.83), anxiety (PR Q2 = 0.39, 95% CI = 0.31-0.48; PR Q3 = 0.27, 95% CI = 0.20-0.37; and PR Q4 = 0.11, 95% CI = 0.07-0.17) and experiences of ataque de nervios (PR Q2 = 0.85, 95% CI = 0.76-0.94; PR Q3 = 0.79, 95% CI = 0.70-0.90; and PR Q4 = 0.68, 95% CI = 0.60-0.78). Compared to persons in SP Q1, persons in SP Q3-Q4 were less likely to report adverse mental health impacts from Hurricane María (PR Q3 = 0.66, 95% CI = 0.55-0.79; and PR Q4 = 0.53, 95% CI = 0.44-0.65) and the 2020 sequence of tremors (PR Q3 = 0.77, 95% CI = 0.61-0.98; and PR Q4 = 0.74, 95% CI = 0.59-0.94). SP was associated with lower psychological distress. Studies are needed to confirm our findings and evaluate potential mechanisms of action.

The Impact of Projected Changes in Hurricane Frequencies on U.S. Hurricane Wind and Surge Damage

Abstract We use a simple risk model for U.S. hurricane wind and surge economic damage to investigate the impact of projected changes in the frequencies of hurricanes of different intensities due to climate change. For average annual damage, we find that changes in the frequency of category-4 storms dominate. For distributions of annual damage, we find that changes in the frequency of category-4 storms again dominate for all except the shortest return periods. Sensitivity tests show that accounting for landfall, uncertainties, and correlations leads to increases in damage estimates. When we propagate the distributions of uncertain frequency changes to give a best estimate of the changes in damage, the changes are moderate. When we pick individual scenarios from within the distributions of frequency changes, we find a significant probability of much larger changes in damage. The inputs on which our study depends are highly uncertain, and our methods are approximate, leading to high levels of uncertainty in our results. Also, the damage changes we consider are only part of the total possible change in hurricane damage due to climate change. Total damage change estimates would also need to include changes due to other factors, including possible changes in genesis, tracks, size, forward speed, sea level, rainfall, and exposure. Nevertheless, we believe that our results give important new insights into U.S. hurricane risk under climate change. Significance Statement We investigate how changes in the frequencies of hurricanes of different intensities as a result of climate change may contribute to changes in U.S. economic damage due to wind and surge. We find that economic damage will likely increase as a result of projected increases in the frequency of landfalling hurricanes. Analysis of our results shows that increases in the frequency of category-4 storms are the main driver of the changes. Our best estimate results, based on a multimodel ensemble, give modest increases in damage, but within the ensemble there are individual scenarios that give much larger increases in damage. The large range of individual damage estimates is a motivation for continuing efforts to reduce the uncertainty around hurricane projections under climate change.

Predicting hurricane evacuation for local neighborhoods across a metropolitan region

To mitigate the devastating impacts of hurricanes on people's lives, communities, and societal infrastructures, disaster management would benefit considerably from a detailed understanding of evacuation, including the socio-demographics of the populations that evacuate, or remain, down to disaggregated geographic levels such as local neighborhoods. A detailed household evacuation prediction model for local neighborhoods requires both a robust household evacuation decision model and individual household data for small geographic units. This paper utilizes a recently published statistical meta-analysis for the first requirement and then conducts a rigorous population synthesis procedure for the second. Our model produces predicted non-evacuation rates for all US Census block groups for the Tampa-St. Petersburg-Clearwater Metropolitan Statistical Area for a Hurricane Irma-like storm along with their socio-demographic and hurricane impact risk profiles. Our model predictions indicate that non-evacuation rates are likely to vary considerably, even across neighboring block groups, driven by the variability in evacuation risk profiles. Our results also demonstrate how different predictors may come to the fore in influencing non-evacuation in different block groups, and that predictors which may have an outsize impact on individual household evacuation decisions, such as Race, are not necessarily associated with the greatest differentials in non-evacuation rates when we aggregate households to block group level and above. Our research is intended to provide a framework for the design of hurricane evacuation prediction tools that could be used in disaster management.

Inception-VR70: An Advanced Inception-Net Artificial Intelligence based Novel Hybrid Architecture

In the present day, Convolutional Neural Network (CNN) architectures are undergoing a great deal of development, which has resulted in the creation of models like VGG16, ResNet50, InceptionV3 etc., that significantly increase accuracy. Yet, a network that can deal with overfitting, a significant challenge in deep learning besides having greater accuracy and extracting useful features is required. In this paper, we propose a Deep hybrid model which is an inception of pretrained models with a different input image size, significantly leading to improved accuracy which has been tested on various datasets of different domains including health care, agriculture, and the remote sensing. The performance of this hybrid model is superior to the standalone pretrained models. It is observed that the hybrid model proposed in this paper merely has overfitting despite of having very deep layers compared to all other deep architectures. Best accuracy achieved for this model is 99.64% being train accuracy and 96.33% being test accuracy for the Satellite Images of Hurricane Damage dataset with minimum overfitting.

The effect of hurricanes on mental health over the long term

Existing causal studies examining the impact of hurricanes on health and health-related outcomes typically focus on short-run impacts and specific outcomes associated with physical health. In this paper, I explore the long-term effects of Hurricanes Katrina and Rita on the mental health of adults using two individual-level datasets from the Behavioral Risk Factor Surveillance System and the Panel Survey of Income Dynamics. Difference-in-differences models are used to estimate the long-run causal impact of hurricanes. I compare the mental health of adults living in Katrina and Rita affected counties to those in other counties before and after the hurricanes. My findings suggest that the hurricanes increased the number of poor mental health days by 0.49 days per 30 days (14.5 %) during a seven-year post period (2006–2012) and psychological distress by 0.46 K-6 points (15.2 %) during a six-year post period. I also find that the estimated effects were notably larger among specific sub-groups, such as single mothers and black respondents. These results are robust to different sample and functional form specifications. From a policy perspective, these findings suggest that long-lasting effects need to be included in any analysis of the impact of hurricanes in order to capture their full effect.

The impact of hurricanes on a property portfolio: an empirical study based on loss data in Portugal

PurposeThe simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.Design/methodology/approachA complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.FindingsSeveral scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.Originality/valueA statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.

Natural disasters resulting from climate change: The impact of hurricanes and flooding on perinatal outcomes

Although the earth's climate has been continuously changing over billions of years, human influence has accelerated that rate of change. While high latitudes suffer the greatest increase in incremental temperature, moderate latitudes are highly vulnerable due to their temperate/tropical rain storms and hurricanes that bring about extreme flooding events. We and others have shown that there is a link between the occurrence and severity of these climate events and risk of adverse perinatal outcomes. In this review, we will discuss the data and consider interacting near and intermediate sequelae of worsening natural disasters–including food scarcity, disrupted or compromised built environments and infrastructure, and loss of communities with human migration. While certainly tackling these and other proximal mediators of adverse perinatal outcomes will benefit maternal and child health, a failure to meaningfully address the root causes of climate change and resultant environmental chemical exposures will be of little long-term benefit.

Extreme Value Analysis of Ocean Still Water Levels along the USA East Coast—Case Study (Key West, Florida)

This paper provides an Extreme Value Analysis (EVA) of the hourly still water level (SWL) record at Key West, Florida dating back to 1913 to understand the statistical likelihood of the combination of high predicted tides and the more dynamic influences (predominantly hurricane induced storm surges) that can drive ocean water levels higher at the coast. The impact of hurricane ‘Wilma’ in 2005 dominates the records, producing a super-elevation of the SWL above Mean Sea Level (MSL) of 1155 mm with an estimated return period of around 147 years. This paper explores the duality of increasing risks of oceanic inundation due to extreme events and increasing mean sea level over time, whilst also providing a range of recommendations for ensuring improved predictive model fitting and robustness of EVA for SWLs measured at tide gauges. When integrated with future IPCC AR6 sea level projections, the return level plots from the EVA provide decision makers with key guidance for design, strategic planning and climate change adaptation purposes at Key West, Florida.

A Decadal Climate Service for Insurance: Skillful Multiyear Predictions of North Atlantic Hurricane Activity and U.S. Hurricane Damage

Abstract North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful. Significance Statement The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.

EXPERIMENTAL EVALUATION OF THE PERFORMANCE OF A HYBRID ARTIFICIAL CORAL REEF WITH BRAIN AND STAGHORN CORALS

Considering that more than 40 percent of the world's population resides within 100 km of coastal areas [WRI, 2007], the protection of coastal communities is critical. Coral reefs provide a large variety of ecosystem services from fishing, recreation and tourism to coastal flood risk reduction to nearby populations. They act as a green, self-building and self-repairing breakwaters dissipating wave energy through wave breaking and bed friction [Beck, 2018]. However, coral reefs are not typically accounted for as a coastal infrastructure as their effects are not easy to quantify [Ferrario, 2014] as their interaction with waves depends on multiple parameters including the variable morphologies of the organisms that form the reef. In this study, the wave energy dissipation of a scaled reef model with two coral species (staghorn and brain corals) of variable cover was evaluated through a series of laboratory experiments at the University of Miami SUrge STructure Atmosphere INteraction (SUSTAIN) Facility, a wind/wave tank with hurricane capabilities. A scaled coral reef model was tested under the direct impact of swell and hurricane generated waves considering Froude similarity with a prototype reef in South Florida.

Black Marble Nighttime Light Data for Disaster Damage Assessment

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation.

Revisiting the model system for forest succession: Eighty years of resampling Piedmont forests reveals need for an improved suite of indicators of successional change

Understanding of secondary succession, long-used as a unifying theme in ecology, continues to be informed by early-20th century chronosequence studies of old fields in the southeastern United States. However, growing evidence suggests classical, site-based indicators of successional change alone are not robust enough to capture realized compositional variation in eastern North American forests a century later. We illustrate how long-term data can provide deeper insight into forest dynamics and help identify additional indicators for predicting successional change.Using 80 years of permanent plot data from 36 forest stands in the Piedmont of North Carolina (USA) as a case study within the model system examined by foundational authors, we examine long-term trends in tree species abundance in both old-field pine forests transitioning to hardwood dominance and long-standing hardwood stands representing a range of historical, topographic, and edaphic conditions. We use a suite of descriptive and multivariate analyses to examine these long-term data and to assess them within the context of site conditions and novel drivers of change (e.g., removal of chronic fire, hurricane damage, increase in herbivore populations, and introduction of non-native plants and pathogens).Results indicate that these southeastern forests have undergone various perturbations that have collectively resulted in forests that are developing differently than predicted by classical models. Of particular note is the low recruitment of putative climax species such as oaks (Quercus spp.) and hickories (Carya spp.) and their replacement by novel understory communities, dramatic loss of dominant species (e.g., Cornus florida) due to nonnative pathogens, overcrowding by invasive exotic species, shifts in stem size distributions due to deer herbivory, and overall accelerated shifts in successional trajectory due to hurricane damage.We propose that potential shifts in predator abundance, nonnative species dispersal risk, pest and pathogen potential, changes in disturbance regimes, and frequency and timing of high-intensity disturbance can interact in various ways leading to variable and sometimes stochastic successional outcomes, and so these variables should be considered repeatedly through time as indicators of successional change in addition to classically-utilized indicators such as underlying site and abundance conditions. Using such a broad (and even dynamic) suite of indicators of forest change can more comprehensively enable accurate, long-term successional floristics modeling and resulting ecological and silvicultural management of temperate forests.

After the rain falls: A panel survey of disaster‐related attitudes and behaviors

In this study, we take advantage of a temporarily unique panel survey to examine how Hurricane Harvey shifted individual perceptions of coastal resilience in two coastal shoreline counties (Galveston and Brazoria) directly affected by the storm. We find that, in the aftermath of the hurricane, individuals recognized they were less prepared and that recovery would take longer than expected. At the same time, social trust increased as did confidence in local government and support for increasing building standards and restricting new building structures and housing in high risk areas. We find no change in support for increased taxes or financial incentives to relocate. These shifts in attitudes were largely offset by reported hurricane damage, meaning individuals who reported experiencing the most damage became less trusting, expressed less confidence in local organizations, and were less supportive of resilience building policies. As the scope and scale of disaster-associated damages increase along with the frequency and severity of disasters, these findings can provide important insight to emergency preparedness planners and policy makers as they develop strategies to increase resilience, particularly in repetitive loss areas like the Texas Gulf Coast.

Hurricane Impacts on Land in the Central and Eastern Caribbean Since 1494 CE From Written Records

AbstractWritten accounts of hurricanes and their impacts in the Caribbean region date back to 1494 CE We report a new compilation of hurricanes in the longest settled regions of the central and eastern Caribbean region (CECR) that is the most complete yet produced and present basic statistics. We assess likely undercounts of hurricanes due to incomplete documentary records in the earliest part of the record by using the most complete reporting from Puerto Rico relative to the remainder of the CECR. We compare our results with other documentary and proxy data from the region and demonstrate using wavelet analysis significant activity related to the El Niño‐Southern Oscillation (ENSO) at inter‐annual time scales and lower frequency activity from decadal to multi‐decadal scales. Our results offer evidence that, along with ENSO, fluctuations in sulfate aerosols are a possible contributor to the hurricane history. The frequency of hurricane landfalls and impacts in the CECR was lower in the 20th century than any time since 1494 CE.

Forest Disturbances and Nonindustrial Forest Landowners: Management of Invasive Plants, Fire Hazards and Wildlife Habitats After a Hurricane

AbstractThere is limited research about the interacting impacts of hurricane damage and forest disturbances like invasive species and wildfires on nonindustrial forest land management. We administered a mail survey to 1,000 randomly selected nonindustrial private forest landowners in the Panhandle of Florida who owned forestlands in the counties most severely affected by Hurricane Michael. Results show that most landowners did little to no forest management to prepare for the hurricane. Following Hurricane Michael, 34% of respondents reported increased invasive plants whereas 82% experienced timber loss, 62% had damaged wildlife habitats, and 53% had increased vulnerability to wildfire hazards. Landowners who experienced timber damage from Hurricane Michael were more likely to reduce fire hazard, eliminate or reduce invasive plants, perform road and trail maintenance, improve wildlife habitat, reforest both salvage harvested and areas that were not salvage harvested, search for information on the internet about forest management, and pass their forestland to family.Study Implications: Forest disturbances such as hurricanes, invasive species, and wildfires are predicted to increase in severity and frequency, consequently affecting forest productivity, sustainability, and management. Despite significant impacts on forest lands, there is limited understanding of how these disturbances may affect nonindustrial private forest landowner management behaviors. This article highlights the attitudes, experiences, and perceptions of nonindustrial private forest landowners, including their awareness and overall forest management plans regarding invasive species control, wildlife habitat improvement, and reducing wildfire hazards after a hurricane. We assessed the important factors that contribute to landowners’ willingness to take action to address these interacting forest disturbances.

The changing demography of hurricane at‐risk areas in the United States (1970–2018)

AbstractHurricanes have proven to be one of the most deadly and costly natural hazards in the Gulf and Atlantic coast regions of the United States. Looming climate change and increasing population in coastal areas means these hazards could become even more devastating in the future. This paper first develops estimates of areas most impacted by hurricane flooding and wind damage and then assesses the generalised patterns of demographic change in those at‐risk locations, disaggregating by both race and age to account for the complex shifts in demographic composition that have occurred over the past five decades. We find evidence of racial, ethnic, and age disparities in exposure to hurricane flooding and wind impacts. Our results highlight not only the uneven burden of risk placed on those unable or unwilling to move, but also the structural privilege that enables the white population to remain in place, even in the face of increased hurricane damage risk.