Articles | Volume 26, issue 5
https://doi.org/10.5194/nhess-26-2353-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-2353-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 May 2026
Research article |
| 26 May 2026
| 26 May 2026
Enhancing hydrological hazard early warning: a 60 d streamflow forecasting framework integrating deep learning and process-based modeling
Zhijie Liu
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Dawen Yang
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
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Short summary
Reliable medium- and long-term streamflow forecasts are essential for hazard early warning. We develop a 60-day forecasting framework that corrects precipitation from numerical weather prediction models, utilizes a physical hydrologic model and mitigates systematic simulation errors. Applied to the Upper Yangtze River Basin, it yields practical 60-day forecasts with good accuracy, providing a robust tool for proactive decision making in hazard mitigation to ensure regional water security.
Reliable medium- and long-term streamflow forecasts are essential for hazard early warning. We...
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