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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.11942 |
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| _version_ | 1866914154771644416 |
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| author | Ou, Zhigang Nai, Congyi Pan, Baoxiang Zheng, Yi Shen, Chaopeng Jiang, Peishi Liu, Xingcai Tang, Qiuhong Li, Wenqing Pan, Ming |
| author_facet | Ou, Zhigang Nai, Congyi Pan, Baoxiang Zheng, Yi Shen, Chaopeng Jiang, Peishi Liu, Xingcai Tang, Qiuhong Li, Wenqing Pan, Ming |
| contents | Extreme floods pose escalating risks in a changing climate, yet forecasting remains challenging due to peak flow underestimation and high uncertainty. We introduce DRUM, a diffusion-based probabilistic deep learning 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 0.1% 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 U.S. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11942 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DRUM: Diffusion-based runoff model for probabilistic flood forecasting Ou, Zhigang Nai, Congyi Pan, Baoxiang Zheng, Yi Shen, Chaopeng Jiang, Peishi Liu, Xingcai Tang, Qiuhong Li, Wenqing Pan, Ming Geophysics Extreme floods pose escalating risks in a changing climate, yet forecasting remains challenging due to peak flow underestimation and high uncertainty. We introduce DRUM, a diffusion-based probabilistic deep learning 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 0.1% 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 U.S. 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. |
| title | DRUM: Diffusion-based runoff model for probabilistic flood forecasting |
| topic | Geophysics |
| url | https://arxiv.org/abs/2412.11942 |