Abstract
The adverse socio-economic effects of natural hazards will likely worsen under climate change. Modeling their risk is essential to developing effective adaptation and mitigation strategies. However, climate models typically do not resolve the detailed information that risk quantification demands. Here, we propose a dynamic data-driven approach to estimate extreme rainfall-induced flood-loss risk. In this approach, coarse-resolution climate model outputs (\(0.25^{\circ } \times 0.25^{\circ }\)) are downscaled to high-resolution (\(0.01^{\circ } \times 0.01^{\circ }\)) rainfall. After that, rainfall, historical insurance loss provided by The Federal Emergency Management Agency (FEMA), and other geographic data train a flood-loss model. Our approach shows promise for quantifying flood-loss risk, showing a weighted average value of \(R^2 = 0.917\) for Cook County, Illinois, USA.
The authors acknowledge support from Liberty Mutual (029024-00020), ONR (N00014-19-1-2273), The MIT Weather Extreme and CREWSNET Climate Grand Challenge projects, and the generosity of Eric and Wendy Schmidt by recommendation of Schmidt Futures as part of its Virtual Earth System Research Institute (VESRI).
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Saha, A., Salas, J., Ravela, S. (2024). Dynamic Data-Driven Downscaling to Quantify Extreme Rainfall and Flood Loss Risk. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_42
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