Basin-informed flood frequency analysis using deep learning exhibits consistent projected regional patterns over CONUS

Climate change poses a significant threat to flood-prone areas by altering precipitation patterns and the water cycle. Here, we analyzed the impact of climate change on future flood trends. We trained a Long Short-Term Memory (LSTM) model to estimate long term discharge at 638 river sites over contiguous United States (CONUS) based on inputs from the gridMET meteorological datasets, and downscaled and bias-corrected Coupled Model Intercomparison Project 5 (CMIP5) projections. Our results indicate that the LSTM model can replicate observed discharge with reliable accuracy. The projected changes in flood magnitude for the 10-year and 100-year return periods reveal consistent geographical patterns robust across climate models, with increasing trends of approximately + 10 to + 40% in the East and West coastal regions and decreasing trends of about - 10 to - 30% in the Southwestern areas. The regions exhibiting an increasing flood trend are likely driven by an increase in total seasonal extreme precipitation and changes in the timing and amount of peak flow. In contrast, the decreasing flood trends result from a significant reduction in snowpack. To support adaptation planning, we developed an interactive map providing the historical and projected flood changes for 10- and 100-year floods across the 638 selected basins over CONUS.