Articles | Volume 29, issue 8
https://doi.org/10.5194/hess-29-2023-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-2023-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2025
Research article |
| 22 Apr 2025
| 22 Apr 2025
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Haoran Hao
CORRESPONDING AUTHOR
State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin, China
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Mingxiang Yang
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Jianhui Wei
Institute of Meteorology and Climate Research (IMKIFU), Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Shiqin Xu
Hydrology, Agriculture and Land Observation (HALO) Laboratory, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Harald Kunstmann
Institute of Meteorology and Climate Research (IMKIFU), Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Institute of Geography, University of Augsburg, Augsburg, Germany
Centre for Climate Resilience, University of Augsburg, Augsburg, Germany
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- Risk-informed flood management under climate variability: A hybrid temporal–ensemble artificial intelligence framework M. Melon et al. https://doi.org/10.1007/s42865-026-00126-4
- Deep learning with spatio-temporal conditioning improves global subseasonal precipitation forecasts G. Noh & K. Ahn https://doi.org/10.1038/s41612-026-01430-8
- Risk-aware AI control framework for resilient distribution grids under extreme events M. Sulaima et al. https://doi.org/10.1016/j.ecmx.2026.102115
- Downscaling of two selected GCM data using a hybrid deep learning method of Wavelet-CNN-LSTM in Iran S. Hosseinpour et al. https://doi.org/10.1007/s00704-025-05685-8
- An integrated method for TCWV prediction using deep learning coupled with physical constraints: a physics-aware spatio-temporal attention network L. Xu et al. https://doi.org/10.1080/20964471.2026.2680809
- Precipitation Prediction and Factor Interpretation at Maqu Station in the Eastern Qinghai-Tibet Plateau Based on XGBoost-SHAP D. Zhao et al. https://doi.org/10.3390/w18111355
- Modeling Daily River Discharge Using Machine Learning Ensembles in the Context of Climate Change: Application To the zhaiyk-caspian basin, Kazakhstan S. Alimkulov et al. https://doi.org/10.1007/s41748-025-00858-x
- A novel hybrid framework for combining process-based models with machine learning for streamflow prediction X. Jiang et al. https://doi.org/10.1016/j.advwatres.2025.105177
- Multi-step ahead streamflow forecasting method using Embedding Multi-Layer Perceptron Y. Li & S. Yang https://doi.org/10.1016/j.ejrh.2026.103349
- Сравнение методов прогнозирования весеннего половодья на примере р. Пур в створе п. Самбург . Волкова https://doi.org/10.34753/HS.2025.7.2.181
- A scalable deep learning framework for daily precipitation downscaling: architecture, accuracy, and adaptability X. Liu et al. https://doi.org/10.2166/wcc.2025.293
- Exploration on Coupling Machine Learning with Hydrological Model to Enhance Runoff Simulation Y. Zhao et al. https://doi.org/10.1007/s11269-026-04624-0
- A novel adaptive time-scale decomposition fused LSTM-transformer framework (ATSD-LT) for TCWV prediction L. Xu et al. https://doi.org/10.1016/j.envsoft.2026.106967
- Zero-shot forecasting of streamflow using time series foundation models: are we there yet? A. Sun & A. Sun https://doi.org/10.1088/3049-4753/ae4982
- Toward Sustainable Water Resource Management Using a DWT-NARX Model for Reservoir Inflow and Discharge Forecasting in the Chao Phraya River Basin, Thailand T. Aribarg et al. https://doi.org/10.3390/su172210091
17 citations as recorded by crossref.
- Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin C. Xu et al. https://doi.org/10.1016/j.envsoft.2025.106592
- Enhancing hydrological hazard early warning: a 60 d streamflow forecasting framework integrating deep learning and process-based modeling Z. Liu et al. https://doi.org/10.5194/nhess-26-2353-2026
- Risk-informed flood management under climate variability: A hybrid temporal–ensemble artificial intelligence framework M. Melon et al. https://doi.org/10.1007/s42865-026-00126-4
- Deep learning with spatio-temporal conditioning improves global subseasonal precipitation forecasts G. Noh & K. Ahn https://doi.org/10.1038/s41612-026-01430-8
- Risk-aware AI control framework for resilient distribution grids under extreme events M. Sulaima et al. https://doi.org/10.1016/j.ecmx.2026.102115
- Downscaling of two selected GCM data using a hybrid deep learning method of Wavelet-CNN-LSTM in Iran S. Hosseinpour et al. https://doi.org/10.1007/s00704-025-05685-8
- An integrated method for TCWV prediction using deep learning coupled with physical constraints: a physics-aware spatio-temporal attention network L. Xu et al. https://doi.org/10.1080/20964471.2026.2680809
- Precipitation Prediction and Factor Interpretation at Maqu Station in the Eastern Qinghai-Tibet Plateau Based on XGBoost-SHAP D. Zhao et al. https://doi.org/10.3390/w18111355
- Modeling Daily River Discharge Using Machine Learning Ensembles in the Context of Climate Change: Application To the zhaiyk-caspian basin, Kazakhstan S. Alimkulov et al. https://doi.org/10.1007/s41748-025-00858-x
- A novel hybrid framework for combining process-based models with machine learning for streamflow prediction X. Jiang et al. https://doi.org/10.1016/j.advwatres.2025.105177
- Multi-step ahead streamflow forecasting method using Embedding Multi-Layer Perceptron Y. Li & S. Yang https://doi.org/10.1016/j.ejrh.2026.103349
- Сравнение методов прогнозирования весеннего половодья на примере р. Пур в створе п. Самбург . Волкова https://doi.org/10.34753/HS.2025.7.2.181
- A scalable deep learning framework for daily precipitation downscaling: architecture, accuracy, and adaptability X. Liu et al. https://doi.org/10.2166/wcc.2025.293
- Exploration on Coupling Machine Learning with Hydrological Model to Enhance Runoff Simulation Y. Zhao et al. https://doi.org/10.1007/s11269-026-04624-0
- A novel adaptive time-scale decomposition fused LSTM-transformer framework (ATSD-LT) for TCWV prediction L. Xu et al. https://doi.org/10.1016/j.envsoft.2026.106967
- Zero-shot forecasting of streamflow using time series foundation models: are we there yet? A. Sun & A. Sun https://doi.org/10.1088/3049-4753/ae4982
- Toward Sustainable Water Resource Management Using a DWT-NARX Model for Reservoir Inflow and Discharge Forecasting in the Chao Phraya River Basin, Thailand T. Aribarg et al. https://doi.org/10.3390/su172210091
Saved (final revised paper)
Latest update: 26 Jul 2026
Short summary
Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.
Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme...
