Lazin Rehenuma, Pallotta Giuliana, Bonfils Céline
Lawrence Livermore National Laboratory Livermore, Livermore, CA, USA.
Sci Rep. 2025 Apr 13;15(1):12754. doi: 10.1038/s41598-025-97610-2.
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.
气候变化通过改变降水模式和水循环，对易发生洪水的地区构成重大威胁。在此，我们分析了气候变化对未来洪水趋势的影响。我们训练了一个长短期记忆（LSTM）模型，根据gridMET气象数据集的输入以及降尺度和偏差校正后的耦合模式比较计划第5阶段（CMIP5）的预测，来估算美国本土（CONUS）638个河流水位站的长期流量。我们的结果表明，LSTM模型能够以可靠的精度复制观测到的流量。对于10年和100年一遇洪水重现期的洪水规模预测变化，揭示了跨气候模型一致的地理模式，东部和西部沿海地区呈上升趋势，约为+10%至+40%，而西南部地区呈下降趋势，约为-10%至-30%。洪水呈上升趋势的地区可能是由于季节性极端降水总量增加以及洪峰流量的时间和数量变化所致。相比之下，洪水呈下降趋势是由于积雪显著减少。为支持适应规划，我们开发了一个交互式地图，提供了美国本土638个选定流域10年和100年洪水的历史和预测洪水变化情况。
