DOE OSTI · 2575724
Probabilistic Diffusion Models Advance Extreme Flood Forecasting
Abstract
Extreme floods pose escalating risks in a changing climate, yet forecasting remains challenging due to peak flow underestimation and high uncertainty. We introduce diffusion-based runoff model (DRUM), a probabilistic deep learning (DL) approach that advances extreme flood forecasting across representative basins in the contiguous United States. DRUM outperforms state-of-the-art benchmarks, enhancing nowcasting skill for the top 1‰ of flows in 72.3% of studied basins. Under operational scenarios, DRUM extends reliable lead times by nearly a full day for 20- and 50-year floods. When evaluated with measured precipitation, an ideal condition, recall improves by 0.3–0.4 and the early warning window extends by 2.3 days for 50-year floods. The enhancement potential varies regionally, with precipitation-driven flood zones in the eastern and northwestern US benefiting most, gaining 3–7 days in lead time. These findings highlight the transformative potential of diffusion models as a cutting-edge generative AI technique for advancing hydrology and broader Earth system sciences.
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Ou, Zhigang [Southern University of Science and Technology (SUSTech), Shenzhen (China); Chinese Academy of Science, Beijing (China)], Nai, Congyi [Chinese Academy of Science, Beijing (China)], Pan, Baoxiang [Chinese Academy of Science, Beijing (China)] (ORCID:0000000234095568), Zheng, Yi [Southern University of Science and Technology (SUSTech), Shenzhen (China)] (ORCID:000000018442182X), Shen, Chaopeng [Pennsylvania State Univ., University Park, PA (United States)] (ORCID:0000000206851901), Jiang, Peishi [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000349684258), Liu, Xingcai [Chinese Academy of Sciences, Beijing (China)] (ORCID:0000000157267353), Tang, Qiuhong [Chinese Academy of Sciences, Beijing (China)] (ORCID:0000000208866699), Li, Wenqing [China Institute of Water Resources and Hydropower Research, Beijing (China)] (ORCID:000000026086958X), Pan, Ming [Univ. of California, San Diego, La Jolla, CA (United States)] (ORCID:0000000333508719). 2025-07-31. Probabilistic Diffusion Models Advance Extreme Flood Forecasting. https://doi.org/10.1029/2025gl115705
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