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Probabilistic Diffusion Models Advance Extreme Flood Forecasting

文献类型：期刊论文

作者Ou, Zhigang7,8; Nai, Congyi8; Pan, Baoxiang8; Zheng, Yi2,7; Shen, Chaopeng3; Jiang, Peishi4; Liu, Xingcai6; Tang, Qiuhong6; Li, Wenqing1; Pan, Ming5
刊名GEOPHYSICAL RESEARCH LETTERS
出版日期2025-07-31
卷号52期号:15页码:e2025GL115705
关键词flood hazard flood forecasting early warning generative AI diffusion model deep learning
ISSN号0094-8276
DOI10.1029/2025GL115705
产权排序6
文献子类Article
英文摘要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 parts per thousand 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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WOS关键词DATA SET ; RAINFALL ; UNCERTAINTY
WOS研究方向Geology
语种英语
WOS记录号WOS:001542381200001
出版者AMER GEOPHYSICAL UNION
源URL[http://ir.igsnrr.ac.cn/handle/311030/215557]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Pan, Baoxiang; Zheng, Yi
作者单位1.China Inst Water Resources & Hydropower Res, State Key Lab Stimulat & Regulat Water Cycles Rive, Beijing, Peoples R China;
2.Southern Univ Sci & Technol, State Environm Protect Key Lab Integrated Surface, Shenzhen, Peoples R China;
3.Penn State Univ, Civil & Environm Engn, University Pk, PA USA;
4.Pacific Northwest Natl Lab, Atmospher Climate & Earth Sci Div, Richland, WA USA;
5.Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA USA
6.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China;
7.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen, Peoples R China;
8.Chinese Acad Sci, Inst Atmospher Phys, Key Lab Earth Syst Numer Modeling & Applicat, Beijing, Peoples R China;
推荐引用方式
GB/T 7714		 Ou, Zhigang,Nai, Congyi,Pan, Baoxiang,et al. Probabilistic Diffusion Models Advance Extreme Flood Forecasting[J]. GEOPHYSICAL RESEARCH LETTERS,2025,52(15):e2025GL115705.
APA Ou, Zhigang.,Nai, Congyi.,Pan, Baoxiang.,Zheng, Yi.,Shen, Chaopeng.,...&Pan, Ming.(2025).Probabilistic Diffusion Models Advance Extreme Flood Forecasting.GEOPHYSICAL RESEARCH LETTERS,52(15),e2025GL115705.
MLA Ou, Zhigang,et al."Probabilistic Diffusion Models Advance Extreme Flood Forecasting".GEOPHYSICAL RESEARCH LETTERS 52.15(2025):e2025GL115705.

入库方式： OAI收割

来源：地理科学与资源研究所

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