Using Disaster Outcomes to Validate Components of Social Vulnerability to Floods: Flood Deaths and Property Damage across the USA

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.

Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America

“…While every location on Earth is expected to experience increases in near-surface air temperature, ranging from a 1°C increase over oceans to over 5°C in higher latitude areas, the projected changes in precipitation are less certain and less spatially uniform (Hoegh-Guldberg et al, 2018). The projections of hydrologic drought and floods are consequently uncertain, while at the same time, highly costly in terms of their impact on property damage, food shortage, loss of jobs, and loss of lives (Howitt et al, 2015;Achakulwisut et al, 2018;Tellman et al, 2020). More accurate predictions of these extreme events can lead to better mitigation and adaptation procedures, such as green infrastructure, placing restrictions on water consumption, moving water in above-ground reservoirs to below-ground aquifers, or investing in technologies that could improve water use efficiency (Tanaka et al, 2006;Mei et al, 2018;Yang and Liu, 2020).…”

Flood Exposure and Social Vulnerability Analysis in Rural Areas of Developing Countries: An Empirical Study of Charsadda District, Pakistan

“…Most previous index studies have mainly used census data for data input, which does not necessarily reflect the ground truth of local communities. Current empirical validation studies have shown that existing census-based social vulnerability and community resilience indicators cannot sufficiently explain the disaster impacts of local communities [44,45]. By using the household survey data, we can integrate more meaningful exposure, susceptibility, and resilience indicators into social vulnerability assessments.…”

Attending to the social–political dimensions of urban flooding in decision‐support research: A synthesis of contemporary empirical cases

“…The implication is that research on the perspectives and needs of flood exposed urban residents is somehow independent of, or not salient for research focused on decision processes aiming to protect cities from, and adapt to flood waters (Di Baldassarre et al, 2014). A focus on property exposure perhaps can be easily captured in cost–benefit analyses (Watkiss et al, 2015), and can reflect underlying socioeconomic and demographic factors that illuminate social injustice in flood exposure (Tellman et al, 2020). Yet the complexity of social vulnerability to flooding cannot be captured in such metrics (Cutter et al, 2013; Koks et al, 2015; Nobert et al, 2015) and can bias assessments toward property values and landownership to the neglect of less tangible attributes of the city threatened by flooding that may be most valued by a broader diversity of residents.…”