Repetitive Flood Loss and Hazard Exposure

This research focuses on understanding and quantifying flood impacts across diverse geographies, with particular attention to communities experiencing repetitive flood exposure. Through the integration of high-resolution flood loss records, machine learning, and hydrodynamic modeling, we investigate spatial and temporal patterns of damage and vulnerability. Our research applies advanced methods—including spatiotemporal clustering algorithms, agent-based models, and probabilistic simulation—to improve flood prediction, assess systemic financial risks, and explore the dynamics of vulnerability over time. Collectively, this work aims to inform land use planning, enhance risk communication, and address limitations in conventional flood mapping that can lead to underestimated and unevenly distributed flood risk.

Flood Impacts

Floods are frequently happening outside the floodplain. Exposure may be leading to inequitable social outcomes and financial burdens.

Data-Driven

Our research integrates National Flood Insurance Program claims and policy data with flood records, satellite data, and modeled extents.

Machine Learning

AI-powered insights identify overlooked flood risk through damage pattern analysis.

Relevant Publications

Garcia, H. M., Sebastian, A., Fitzmaurice, K. P., Hino, M., Collins, E. L., & Characklis, G. W. (2025). Reconstructing repetitive flood exposure across 78 events from 1996 to 2020 in North Carolina, USA. Earth's Future, 13(7), e2025EF006026.

Schlumberger, J., Stolte, T., Garcia, H. M., Sebastian, A., Jäger, W., Ward, P., ... & de Brito, M. M. (2025). Stocktaking of methods for assessing dynamic vulnerability in the context of flood hazard research. EGUsphere, 2025, 1-29.

Kazadi, A., Doss-Gollin, J., Sebastian, A., Silva, A. (2024). FloodGNN-GRU: A Spatio-Temporal Graph Neural Network for Flood Prediction. Environmental Data Science, 3:e21. doi:10.1017/eds.2024.19

Thompson, H., Zeff, H.B., Kleiman, R., Sebastian, A., Characklis, G.W. (2023). Systemic Financial Risk arising from Residential Flood Damages: A Case Study of Hurricane Florence, Earth’s Future, 11, e2022EF003206. doi: 10.1029/2022EF003206.

Collins, E., Sanchez, G.M., Terando, A., Stillwell, C.C., Mitasova, H., Sebastian, A., Meentemeyer, R.K. (2022). Predicting flood damage probability across the conterminous United States, Environmental Research Letters, 17, 034006, doi: 10.1088/1748-9326/ac4f0f.

Wang, C., Pavelsky, T.M., Yao, F., Yang, X., Zhang, S., Chapman, B., Song, C., Sebastian, A., Frizzelle, B., Frankenberg, E., Clinton, N. (2022). Flood Extent Mapping During Hurricane Florence With RepeatPass LBand UAVSAR Images, Water Resources Research, 58(3), e2021WR030606, doi: 10.1029/2021WR030606.