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.

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We considered a common research tool for understanding the mental models behind conservation decisions: cognitive mapping. Developed by cognitive psychologists, the elicitation of mental models with cognitive mapping has been used to understand soil management in Spain, invasive grass management in Australia, community forest management in the Bolivian Amazon, and small-scale fisheries access in Belize, among others. A generalized cognitive mapping process considers specific factors associated with the design, data-collection, data-analyses, and interpretation phases of research. We applied this tool in a study about the integration of social data in shoreline master plans of Washington State. Fourteen policy makers and managers (approximately 85% of the region's potential sample) were asked to identify the factors they considered when making their plans. Researchers coded these factors into mental-model objects and summarized mental-object frequency and cooccurrence trends. Although managers prioritized the perceived needs of social groups in their mental model of shoreline master plans, they focused specifically on tribal and private property rights, even though existing social data identified a diversity of interests around timber harvesting, tourism, and agriculture. Understanding their mental models allowed us to more effectively present this social data so that it could fit within their existing thoughts around planning. Although our case study provides a description of the cognition of a particular policy process, cognitive mapping can be used to understand cognitive processes that influence any conservation planning context.

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 explaining 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 more 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 both Black, Hispanic, and Native American populations in poverty). Future validation studies should examine other flood outcomes, such as evacuation, migration, and health, across scales.

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