Profiles of social vulnerability for flood risk reduction
Eric Tate, Samuel Rufat, Md Asif Rahman, Shelley Hoover
Abstract
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. • Developed profiles of social vulnerability and flood exposure in the United States. • We employed Hierarchical Clustering on Principal Components. • The profiles revealed six major archetypes of compound vulnerabilities. • We contrasted the results with a social vulnerability index. • Informs intersectional social vulnerability modeling and adaptation planning.