Probabilistic Diffusion Models Advance Extreme Flood Forecasting
Zhonghong Ou, Congyi Nai, Baoxiang Pan, Yi Zheng, Chaopeng Shen, Peishi Jiang, Xingcai Liu, Qiuhong Tang, Wenqing Li, Ming Pan
Abstract
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.