Social vulnerability to flooding is shaped by intersectional social marginalization, yet most quantitative assessments employ indicators of single populations. This study applies spatial machine learning to examine how the intersectional social vulnerability indicators of poverty-race, poverty-housing tenure, and race-housing tenure compare with traditional discrete indicators of single populations in predicting flood exposure in California. Using geographically weighted random forests and partial dependence plots, we model spatial heterogeneity and non-linear relationships between social vulnerability and exposure. We quantified flood exposure using a population-adjusted measure derived from building footprints and modeled 500-year fluvial and pluvial flood hazard. The results reveal distinct explanatory power of discrete and intersectional indicators. Variable importance analysis shows that intersectional indicators, such as Poor Renters and Non-white Renters, have stronger predictive importance than their discrete counterparts, particularly in urban regions, with mean local IncMSE values of 15.6-16.9 % compared to 12.3-14.8 %. Partial dependence analysis revealed threshold effects of non-linear indicator influence, with predicted exposure increasing sharply once intersectional populations exceed similar to 60 % of tract-level representation. Our findings highlight limitations of assuming uniform indicator effects, and the need for non-linear, spatially adaptive models that increase conceptual alignment between social vulnerability theory and indicator modeling by integrating intersectional dimensions.

Social vulnerability indices are increasingly employed as policy and planning instruments for disaster risk reduction. Although indices model the magnitude and spatial distribution of vulnerability, they are coarse and often misleading tools for revealing who is most vulnerable, due to uncertainty and information loss during aggregation. The mismatch inhibits the capacity to reveal intersectional vulnerability drivers and tailor risk reduction interventions. This study seeks to identify the major archetypes of compound social vulnerability in the context of flood exposure in the United States. Based on spatial inputs of demographic variables, pluvial and fluvial flood extent, and high-resolution building footprints, we used Hierarchical Clustering on Principal Components to classify, map, and analyze social vulnerability profiles. Six distinct profiles emerged from the analysis, two of which describe the confluence of high levels of both social vulnerability characteristics and flood exposure. The first profile is characterized by linguistic isolation, Hispanic populations, low educational attainment, high population density, and lack of health insurance, while the second is distinguished by a cluster of Black populations, low vehicle access, poverty, and female-headed households. The profile configurations span levels of social vulnerability and flood exposure, revealing intersectional complexity obscured by aggregate index scores. We conclude by discussing how profile typologies and their geographies advance understanding of social vulnerability and can inform strategies for equitable flood adaptation.

The severity of flood impacts is influenced by social vulnerability, which stems from marginalization processes that depress a community’s ability to mitigate and recover from flood events. Understanding how social vulnerability operates in different flood contexts informs who is most susceptible to which types of impacts. This study examines the empirical relationship between social vulnerability and flood risk and how that relationship varies by element at risk and flood magnitude. Using inputs of social vulnerability indicators and flood risk to crops and buildings, we employed spatial clustering and spatial regression to determine which social vulnerability characteristics are most associated with economic risk. Regions with high crop risk are associated with more natural resource-based employment and housing tenure, while low-risk regions are less linguistically isolated. For buildings, high-risk regions have higher proportions of renters and lower proportions of racial minorities, while low-risk areas are associated with mobile homes and vacant housing. Overall, housing tenure and natural resource dependence were consistently correlated with building and crop risk. This study advances scientific knowledge by highlighting how specific social vulnerability dimensions relate to flood risks across sectors and geographies.

Indicators of social vulnerability are frequently analyzed using methods that assume spatial stationarity, meaning that the relationships between these indicators and the outcome of interest are presumed to remain consistent across space. However, this assumption can obscure important variations if spatial heterogeneity is present, that is if the relationships between social vulnerability indicators and the outcome vary across different geographic locations. Failing to account for spatial heterogeneity may lead to mischaracterizations of where socially vulnerable populations are most at risk when assessing vulnerability to specific hazards like flood exposure. This study investigates the spatial dynamics of the relationships between social vulnerability indicators and flood exposure. First, a systematic literature review assesses whether spatial heterogeneity is evident in existing studies. Next, using Texas as a case study, we apply Multiscale Geographically Weighted Regression (MGWR) to directly assess spatial heterogeneity and then compare these results to those from Ordinary Least Squares (OLS) regression, which assumes spatial stationarity. The systematic literature review highlights significant variability across study findings, with no indicator consistently demonstrating the same relationship with flood exposure. In the MGWR, we find that only six of twenty indicators exhibit stationary relationships to flood exposure, while the majority demonstrate spatial heterogeneity, with localized variations in strength, direction, and significance. Only four indicators show complete consistency between OLS and MGWR, underscoring how accounting for spatial heterogeneity unveils critical localized patterns masked by the assumption of spatial stationarity. These findings highlight the importance of spatially nuanced approaches for assessing social vulnerability.

Rural distributed storage systems are green infrastructure that decentrally store flood water across the landscape to reduce downstream flood peaks. Despite growing understanding of their flow reduction potential, the evidence base for their economic risk reduction is thin. This study quantifies the economic benefits of distributed storage constructed in an agricultural region of Iowa. The system was financed through the US National Disaster Resilience Competition, and adopted a voluntary conservation approach that provided cost-share assistance to landowners. Our sequential analysis employed high-resolution modeling of watershed hydrology, flood exposure of buildings and crops, and economic risk. Construction and maintenance costs were included in scenario analyses of constructed storage structures and maximum potential structure buildout. The maximum benefit-cost ratio was 0.34, falling below the traditional threshold of 1.0 used for project selection in hazard mitigation. The results are highly sensitive to the geospatial accuracy of exposed buildings, and the flood-reduction benefits diminished at larger spatial scales. To improve risk reduction benefits, project siting should consider the location along the stream network and proximity to high-value properties. The inclusion of nonmonetary co-benefits such as improved water quality, landowner amenities, ecosystem services, and community collaboration would also strengthen the case for resilience cost-effectiveness.

The interacting effects of multiple hazards pose a substantial challenge to poverty reduction and national development. Yet, social vulnerability to multiple hazards is a relatively understudied, though growing concern. The impacts of climate hazards in particular, leave increasingly large populations becoming more exposed and susceptible to the devastating effects of repeat, chronic and sequential natural hazards. Multi-hazard research has focused on the physical aspects of natural hazards, giving less attention to the social facets of human-hazard interaction. Further, there is no single conceptualization of ‘multi-hazard’. This systematic review utilizes correlations and hierarchical clustering to determine how social vulnerability is assessed in the context of the three most common classifications of ‘multi-hazard’: aggregate, cascading and compound. Results reveal these classifications of ‘multi-hazard’ each focus on different aspects of social vulnerability. Studies in the aggregate classification of multi-hazard were more likely to represent social vulnerability as an outcome of hazard events, while those in the cascading and compound classifications more often addressed social vulnerability as a preexisting condition. Further, knowledge of social vulnerability to multi-hazards comes mainly from the aggregate classification and the mitigation phase of the disaster cycle. The difference in perspectives of social vulnerability covered, and limited context in which multi-hazard studies of social vulnerability have been applied, mean a full understanding of social vulnerability remains elusive. We argue that research should focus on the cascading and compound classifications of multi-hazards, which are more suited to interrogating how human-(multi)hazard interactions shape social vulnerability.

IMPORTANCE Hurricanes and flooding can interrupt health care utilization. Understanding the magnitude and duration of interruptions, as well as how they vary according to hazard exposure, race, and income, are important for identifying populations in need of greater retention in care. OBJECTIVE To determine how the differential exposure to Hurricane Harvey in August 2017 is associated with changes in utilization of Veterans Health Administration health care. DESIGN, SETTING, AND PARTICIPANTS This is a retrospective cohort analysis of primary care practitioner (PCP) visits, emergency department visits, and inpatient admissions in the Veterans Health Administration among Texas veterans residing in counties impacted by Hurricane Harvey from 2016 to 2018. Data analysis was performed from September 2020 to May 2021. EXPOSURES Residential flooding after Hurricane Harvey. MAIN OUTCOMES AND MEASURES Interrupted time series analysis measured changes in health care utilization over time, stratified by residential flood exposure, race, and income. RESULTS Of the 99 858 patients in the cohort, 89 931 (90.06%) were male, and their median (range) age was 58 (21 to 102) years. Compared with veterans in nonflooded areas, veterans living in flooded areas were more likely to be Black (24 715 veterans [33.80%] vs 4237 veterans [15.85%]) and low-income (14 895 veterans [20.37%] vs 4853 veterans [18.15%]). Rates of PCP visits decreased by 49.78% (95% CI, -64.52% to -35.15%) for veterans in flooded areas and by 45.89% (95% CI, -61.93% to -29.91%) for veterans in nonflooded areas and did not rebound until more than 8 weeks after the hurricane. Rates of PCP visits in flooded areas remained lower than expected for 11 weeks among White veterans (-6.99%; 95% CI, -14.36% to 0.81%) and for 13 weeks among racial minority veterans (-7.22%; 95% CI, -14.11% to 0.30%). Low-income veterans, regardless of flood status, experienced greater suppression of PCP visits in the 8 weeks following the hurricane (-13.72%; 95% CI, -20.51% to -6.68%) compared with their wealthier counterparts (-9.63%; 95% CI, -16.74% to -2.26%). CONCLUSIONS AND RELEVANCE These findings suggest that flood disasters such as Hurricane Harvey may be associated with declines in health care utilization that differ according to flood status, race, and income strata. Patients most exposed to the disaster also had the greatest delay or nonreceipt of care.

Distributed attenuation in flood management relies on small and low-impact runoff attenuating features variously distributed within a catchment. Distributed systems of reservoirs, natural flood management, and green infrastructure are practical examples of distributed attenuation. The effectiveness of attenuating features lies in their ability to work in concert, by reducing and slowing runoff in strategic parts of the catchment, and desynchronizing flows. The spatial distribution of attenuating features plays an essential role in the process. This article proposes a framework to place features in a hydrologic network, group them into spatially distributed systems, and analyze their flood attenuation effects. The framework is applied to study distributed systems of reservoirs in a rural watershed in Iowa, USA. The results show that distributed attenuation can be an effective alternative to a single centralized flood mitigation approach. The different flow peak attenuation of considered distributed systems suggest that the spatial distribution of features significantly influences flood magnitude at the catchment scale. The proposed framework can be applied to examine the effectiveness of distributed attenuation, and its viability as a widespread flood attenuation strategy in different landscapes and at multiple scales.

Human exposure to floods continues to increase, driven by changes in hydrology and land use. Adverse impacts amplify for socially vulnerable populations, who disproportionately inhabit flood-prone areas. This study explores the geography of flood exposure and social vulnerability in the conterminous United States based on spatial analysis of fluvial and pluvial flood extent, land cover, and social vulnerability. Using bivariate Local Indicators of Spatial Association, we map hotspots where high flood exposure and high social vulnerability converge and identify dominant indicators of social vulnerability within these places. The hotspots, home to approximately 19 million people, occur predominantly in rural areas and across the US South. Mobile homes and racial minorities are most overrepresented in hotspots compared to elsewhere. The results identify priority locations where interventions can mitigate both physical and social aspects of flood vulnerability. The variables that most distinguish the clusters are used to develop an indicator set of social vulnerability to flood exposure. Understanding who is most exposed to floods and where, can be used to tailor mitigation strategies to target those most in need.

Natural hazard impacts and resources allocated for risk reduction and disaster recovery are often inequitably distributed. New research is developing and applying methods to measure these inequities.

Short-term disaster assistance is an important component of federal disaster response in the United States, providing over $63 billion from 2007 to 2016. Though assistance programs are designed to facilitate the return to basic living conditions, ambiguity surrounds their relationship with socially vulnerable populations who are most likely to require external aid. This study explores the spatial and statistical association between short-term disaster assistance and social vulnerability across the contiguous US. Analysis using bivariate Local Indicators of Spatial Association revealed places with high levels of both assistance and social vulnerability to be clustered in the southeastern United States, as were those with low assistance and high social vulnerability. Overall, places with high social vulnerability were predominantly rural. Based on multivariate regression analysis, dollar damage was the major determinant of allocated assistance for homeowners, but was not explanatory for renters. Indicators of race were associated with lower levels of assistance to homeowners in places where assistance was otherwise high. Among renters, indicators associated with increased coping capacities were associated with greater levels of assistance in places with low allocations of assistance disbursement. Our findings indicate disaster assistance may be underserving some places with more socially vulnerable populations. We recommend that social vulnerability should be explicitly considered in the allocation of assistance to improve social equity in short-term assistance programs.

We would like to thank the CDC colleagues for their Commentary in what constitutes the first comment on a paper published in the Annals in decades.We view critique as a strength of scientific exploration.The Commentary concludes with as statement agreeing with the findings and conclusions of our paper:"we recognize CDC SVI may not identify the most vulnerable populations in all applications and has not done so in the Rufat et al. study (…) we agree with the authors.

Disaster recovery spending for major flood events in the United States is at an all-time high. Yet research examining equity in disaster assistance increasingly shows that recovery funding underserves vulnerable populations. Based on a review of academic and grey literature, this article synthesizes empirical knowledge of population disparities in access to flood disaster assistance and outcomes during disaster recovery. The results identify renters, low-income households, and racial and ethnic minorities as populations that most face barriers accessing federal assistance and experience adverse recovery outcomes. The analysis explores the drivers of these inequities and concludes with a focus on the performance of disaster programs in addressing unmet needs, recognition of intersectional social vulnerabilities in recovery analysis, and gaps in data availability and transparency.

Leading flood loss estimation models include Federal Emergency Management Agency’s (FEMA’s) Hazus, FEMA’s Flood Assessment Structure Tool (FAST), and (U.S.) Hydrologic Engineering Center’s Flood Impact Analysis (HEC-FIA), with each requiring different data input. No research to date has compared the resulting outcomes from such models at a neighborhood scale. This research examines the building and content loss estimates by Hazus Level 2, FAST, and HEC-FIA, over a levee-protected census block in Metairie, in Jefferson Parish, Louisiana. Building attribute data in National Structure Inventory (NSI) 2.0 are compared against “best available data” (BAD) collected at the individual building scale from Google Street View, Jefferson Parish building inventory, and 2019 National Building Cost Manual, to assess the sensitivity of input building inventory selection. Results suggest that use of BAD likely enhances flood loss estimation accuracy over existing reliance on default data in the software or from a national data set that generalizes over a broad scale. Although the three models give similar mean (median) building and content loss, Hazus Level 2 results diverge from those produced by FAST and HEC-FIA at the individual building level. A statistically significant difference in mean (median) building loss exists, but no significant difference is found in mean (median) content loss, between building inventory input (i.e., NSI 2.0 vs BAD), but both the building and content loss vary at the individual building scale due to difference in building-inventory-reported foundation height, foundation type, number of stories, replacement cost, and content cost. Moreover, building loss estimation also differs significantly by depth-damage function (DDF), for flood depths corresponding with the longest return periods, with content loss differing significantly by DDF at all return periods tested, from 10 to 500 years. Knowledge of the extent of estimated differences aids in understanding the degree of uncertainty in flood loss estimation. Much like the real estate industry uses comparable home values to appraise a home, flood loss planners should use multiple models to estimate flood-related losses. Moreover, results from this study can be used as a baseline for assessing losses from other hazards, thereby enhancing protection of human life and property.

Mid-to-large size commercial buildings are not significant participants in demand-response programs because of the risk and effort associated with predicting the actual amount of load they can shed. This paper describes a tool designed to rapidly predict the amount of demand reduction these buildings can provide. The tool uses commercial building reference models published by the Department of Energy. Users can adjust certain key parameters so that peak demands given by the reference model match actual billed demand. The paper describes the basic tool and provides field test results from several schools in El Paso, Texas.

As a concept, social vulnerability describes combinations of social, cultural, economic, political, and institutional processes that shape socioeconomic differentials in the experience of and recovery from hazards. Quantitative measures of social vulnerability are widely used in research and practice. In this paper, we establish criteria for the evaluation of social vulnerability indicators and apply those criteria to the most widely used measure of social vulnerability, the Social Vulnerability Index (SoVI). SoVI is a single quantitative indicator that purports to measure a place’s social vulnerability. We show that SoVI has some critical shortcomings regarding theoretical and internal consistency. Specifically, multiple SoVI-based measurements of the vulnerability of the same place, using the same data, can yield strikingly different results. We also show that the SoVI is often misaligned with theory; increases in variables that contribute to vulnerability, like the unemployment rate, often decrease vulnerability as measured by the SoVI. We caution against the use of the index in policy making or other risk-reduction efforts, and we suggest ways to more reliably assess social vulnerability in practice.

Deconstructing causal linkages between place attributes and disaster outcomes at coarse scales like zip codes and counties is difficult because heterogeneous socio-economic characteristics operating at finer scales are masked. However, capturing detailed disaster outcomes about individuals and households for large areas can be equally complicated. This dichotomy highlights the need for a more nuanced and empirically-driven approach to understanding financial disaster recovery support. This study assessed how social characteristics influenced federal disaster recovery support following the 2015 South Carolina floods. Ordinary linear and spatial regression models provided a mechanism for pinpointing statistically significant links between individual/compound vulnerabilities and resource distribution from four federal disaster response and recovery programmes. The study makes two unique contributions. First, exploration of how social characteristics influence recovery support is a critical, yet understudied path toward fair and equitable disaster recovery. Second, finer scale inquiry across a large impact area is rare in quantitative case studies of US disasters. While we found flood recovery assistance to be strongly associated with physical damage overall the relationship was more tenuous in places with higher social vulnerability. Results indicate that future disaster recovery programs focusing on both physical damage and social vulnerable would lead to a more equitable disaster recoveries. Findings provide new understanding of equity at the intersection of social vulnerability, impacts, and disaster recovery and showcase both best-practices and areas for programme improvements for future disasters.

Best management practices (BMPs) play an important role in improving impaired water quality from conventional row crop agriculture. In addition to reducing nutrient and sediment loads, BMPs such as fertilizer management, reduced tillage, and cover crops could alter the hydrology of agricultural systems and reduce surface water runoff. While attention is devoted to the water quality benefits of BMPs, the potential co-benefits of flood loss reduction are often overlooked. This study quantifies the effects of selected commonly applied BMPs on expected flood loss to agricultural and urban areas in four Iowa watersheds. The analysis combines a watershed hydrologic model, hydraulic model outputs, and a loss estimation model to determine relationships between hydrologic changes from BMP implementations and annual economic flood loss. The results indicate a modest reduction in peak discharge and economic loss, although loss reduction is substantial when urban centers or other high-value assets are located downstream in the watershed. Among the BMPs, wetlands, and cover crops reduce losses the most. The research demonstrates that watershed-scale implementation of agricultural BMPs could provide benefits of flood loss reduction in addition to water quality improvements.

Social vulnerability models are becoming increasingly important for hazard mitigation and recovery planning, but it remains unclear how well they explain disaster outcomes. Most studies using indicators and indexes employ them to either describe vulnerability patterns or compare newly devised measures to existing ones. The focus of this article is construct validation, in which we investigate the empirical validity of a range of models of social vulnerability using outcomes from Hurricane Sandy. Using spatial regression, relative measures of assistance applicants, affected renters, housing damage, and property loss were regressed on four social vulnerability models and their constituent pillars while controlling for flood exposure. The indexes best explained housing assistance applicants, whereas they poorly explained property loss. At the pillar level, themes related to access and functional needs, age, transportation, and housing were the most explanatory. Overall, social vulnerability models with weighted and profile configurations demonstrated higher construct validity than the prevailing social vulnerability indexes. The findings highlight the need to expand the number and breadth of empirical validation studies to better understand relationships among social vulnerability models and disaster outcomes.