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New open-access publication in Earth's Future

In our new open access paper in Earth's Future, led by Nadja Veigel from our Smart Water Networks team, with Heidi Kreibich (GFZ German Research Centre for Geosciences) and Andrea Cominola, we we investigate the relationship between socio-economic backgrounds, household flood insurance purchase (bottom-up), and the community-scale CRS (top-down) in the whole continental US based on interpretable machine learning and large insurance and socio-economic open data sets.

Flood resilience of individuals and communities can be improved by bottom-up strategies, such as insurance purchase, or top-down measures like the US National Flood Insurance Program's Community Rating System (CRS). Our interpretable machine learning approach shows that flood insurances are mostly purchased reactively, after the occurrence of a flood event. Yet, reactive behaviors are ill-suited as more extreme events are expected under future climate, also in areas that were not previously flooded. The CRS counteracts this behavior by fostering proactive adaptation across a widespread range of socio-economic backgrounds. Future risk management including the CRS should support and motivate individuals' proactive adaptation with a particular focus on highly vulnerable social groups to overcome existing inequalities in flood risk.

Read the full paper: Veigel, N.,  Kreibich, H., &  Cominola, A. (2023).  Interpretable machine learning reveals potential to overcome reactive flood adaptation in the continental US. Earth's Future, 11, e2023EF003571. https://doi.org/10.1029/2023EF003571

Feature values and related SHapley Additive exPlanations (SHAP) values for the number of people affected per flood (a–d), the average annual number of floods (b–e), and Community Rating System (CRS) average class (c–f). SHAP values are computed from modeling insurance coverage in each census tract. The maximum number of people affected by a flood (a) is skewed toward the hurricane-affected coastal communities. This historical flood severity directly translates to higher insurance coverage values (d). Flood Frequency (b) shows similar patterns with a reduced magnitude and high SHAP values centered around fluvial flood zones, such as the Lower Mississippi and the Minnesota Red River (e). The features related to flood history (flood severity and flood frequency) are the most important predictors of insurance coverage, followed by the CRS class (c) that leads to increased insurance coverage in participating communities (f).