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This study aims to assess the vulnerability of populations to storm surge flooding in 12 coastal localities of Virginia, USA. Population vulnerability is assessed by way of 3 physical factors (elevation, slope, and storm surge category), 3 built-up components (road availability, access to hospitals, and access to shelters), and 3 household conditions (storm preparedness, financial constraints to recovering from severe weather events, and health fragility). Fuzzy analysis is used to generate maps illustrating variation in several types of population vulnerability across the region. When considering physical factors and household conditions, the most vulnerable neighborhoods to sea level rise and storm surge flooding are largely found in urban areas. However, when considering access to critical infrastructure, we find rural residents to be more vulnerable than nonrural residents. These detailed assessments can inform both local and state governments in catastrophic planning. In addition, the methodology may be generalized to assess vulnerability in other coastal corridors and communities. The originality is highlighted by evaluating socioeconomic conditions at refined scale, incorporating a broader range of human perceptions and predispositions, and employing a geoinformatics approach combining physical, built-up, and socioeconomic conditions for population vulnerability assessment. Integr Environ Assess Manag 2016;12:500–509. © 2015 SETAC Key Points The most vulnerable neighborhoods to storm surge flooding are largely found in urban areas when considering physical factors and household resilience conditions. It is found that rural residents relative to nonrural residents to be more vulnerable when considering access to critical infrastructure. The methodology may be generalized to assess vulnerability in other coastal corridors and communities, where the assessments can inform both local and state governments in catastrophic planning. This population vulnerability assessment presents a unique approach by evaluating socioeconomic conditions at a refined scale, incorporating a broad range of human perceptions and predispositions, and employing a geoinformatics approach combining physical, built-up, and socioeconomic conditions.
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Liu et al. (2015) studied this question.
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