Abstract
Social vulnerability indicators seek to identify populations susceptible to hazards based on aggregated sociodemographic data. Vulnerability indices are rarely validated with disaster outcome data at broad spatial scales, making it difficult to develop effective national scale strategies to mitigate loss for vulnerable populations. This paper validates social vulnerability indicators using two flood outcomes: death and damage. Regression models identify sociodemographic factors associated with variation in outcomes from 11,629 non-coastal flood events in the USA (2008–2012), controlling for flood intensity using stream gauge data. We compare models with (i) socioeconomic variables, (ii) the composite social vulnerability index (SoVI), and (iii) flood intensity variables only. The SoVI explains a larger portion of the variance in death (AIC = 2829) and damage (R2 = 0.125) than flood intensity alone (death—AIC = 2894; damage—R2 = 0.089), and models with individual sociodemographic factors perform best (death—AIC = 2696; damage—R2 = 0.229). Socioeconomic variables correlated with death (rural counties with a high proportion of elderly and young) differ from those related to property damage (rural counties with high percentage of Black, Hispanic and Native American populations below the poverty line). Results confirm that social vulnerability influences death and damage from floods in the USA. Model results indicate that social vulnerability models related to specific hazards and outcomes perform better than generic social vulnerability indices (e.g., SoVI) in predicting non-coastal flood death and damage. Hazard- and outcome-specific indices could be used to better direct efforts to ameliorate flood death and damage towards the people and places that need it most. Future validation studies should examine other flood outcomes, such as evacuation, migration and health, across scales.
Highlights
We focus on riverine flooding and control for flood magnitude, with stream gauge data to examine social factors leading to additional death and damage
Three social variables have significant and positive coefficients across all model formulations: percent rural, percent of the population under 5 years old, and percent of the population over 65 years old. These three characteristics were found in the text mining analysis (Figure 1), providing
These three characteristics were found in the text mining analysis (Figure 1), providing additional validation
Summary
The National Flood Insurance Program in the United States reflects the policy impacts of this research [2] This significance notwithstanding, inadequate attention to the socio-economic conditions that predispose specific populations to greater exposure and consequences has led to various critiques of the risk–hazard approach [3]. New frameworks emerged that focused on societal vulnerability to hazards and captured the root causes of exposure, sensitivity and coping capacity in relation to hazards [4,5]. These frameworks were enlarged to include the vulnerability of the environment or ecosystem in question and its impacts on exposed populations [6]. A simple definition emerging from this research is that vulnerability is the propensity for loss of lives, livelihood or property when exposed to a hazard [6,7]
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