Quantifying Social Inequalities in Flood Risk

The distribution and inequalities of flood risks across space and social groups are important considerations for infrastructure design and adaptation. However, neither civil infrastructure practitioners nor researchers routinely assess inequalities quantitatively, which limits opportunities to address them. The Lorenz curve method has been used in many fields to study inequalities in both resource and risk distributions across populations, and it supports a quantitative measure of inequality, the Gini index. Here we present the formulation of Lorenz curves and a data analysis workflow to measure inequalities in both flood damages and flood exposure using flood hazard, flood damage, and social data. We define flood exposure as flood depth multiplied by population density, whereas flood damages are taken strictly as a measure of economic losses. We then show that Lorenz curves for flood damages have the potential to differ markedly from Lorenz curves for flood exposure since the latter is dependent on the population density. For example, higher population density reduces per person damages but increases total flood exposure. Furthermore, we illustrate the Lorenz curve method at scale with an application to Los Angeles County, home to nearly 10 million people. These results demonstrate both similarities and differences between flood damage and flood exposure inequalities for different racial and ethnic groups, and they also show that regional inequalities are sensitive to changes in flood severity and the capacity of flood infrastructure. These sensitivities illuminate the complexity of achieving social equality in flood adaptation, and they emphasize both the utility of detailed modeling and the need for community-engaged decision-making to evaluate and implement response measures. A review of state and federal flood assistance programs reveals heightened opportunities to shape public investments with quantitative measures of flood risk inequalities.