Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4521-2024
© Author(s) 2024. 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-28-4521-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Oct 2024
Research article |
| 16 Oct 2024
| 16 Oct 2024
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Bu Li
State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Institute for Risk and Disaster Reduction, University College London, London, WC1E 6BT, UK
Fuqiang Tian
State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Mahmut Tudaji
State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Li Qin
Gansu Academy for Water Conservancy, Lanzhou 730030, China
Guangheng Ni
CORRESPONDING AUTHOR
State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
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Cited
18 citations as recorded by crossref.
- Snow phenology-guided regionalization for parameter optimization: enhancing runoff simulation in an alpine basin Y. Shi et al.
- Blueprint conceptualization for a river basin's digital twin D. Pal et al.
- Fusing dynamic physical constraints with PINN-xLSTM to enhance accuracy and physical consistency in runoff prediction under extreme hydrological events Y. Yang et al.
- Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation B. Yin et al.
- A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting L. Jin et al.
- Retrievals and simulations of terrestrial water storage changes and runoff over the Tibetan Plateau: Challenges and opportunities X. Li et al.
- Enhancing hydrological time series forecasting with a hybrid Bayesian-ConvLSTM model optimized by particle swarm optimization H. Kilinc et al.
- Spatial-temporal variability and risk assessment of surface and groundwater resources under climate change and urbanization: A physics-informed analysis K. Joseph Pious & A. Raj
- Two-dimensional differential form of distributed Xinanjiang model J. Zhao et al.
- A hybrid SWAT-LSTM model for streamflow simulation with SHAP-based interpretability: Application in the Wei River Basin, China W. Zhou et al.
- A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al.
- Physics-informed deep learning reveals climate-driven snowpack decline and threatens ecological water availability in a Californian snow-fed catchment S. Maharjan et al.
- Event-based training data thresholds for BiLSTM versus Xinanjiang models: Insights from the applications of 19 Chinese catchments Y. Li et al.
- Combining Physical Hydrological Model with Explainable Machine Learning Methods to Enhance Water Balance Assessment in Glacial River Basins R. Yang et al.
- A distributed hybrid physics–AI framework for learning corrections of internal hydrological fluxes and enhancing high-resolution regionalized flood modeling N. Huynh et al.
- Fast urban flood modeling informing response decisions: Model development and future perspectives T. Duan et al.
- A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl X. Jing et al.
- Differentiable parameter learning of reservoir operation modules Z. Chen & T. Zhao
18 citations as recorded by crossref.
- Snow phenology-guided regionalization for parameter optimization: enhancing runoff simulation in an alpine basin Y. Shi et al.
- Blueprint conceptualization for a river basin's digital twin D. Pal et al.
- Fusing dynamic physical constraints with PINN-xLSTM to enhance accuracy and physical consistency in runoff prediction under extreme hydrological events Y. Yang et al.
- Exploring a process-aware spatiotemporal graph-based surrogate for integrated urban drainage simulation B. Yin et al.
- A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting L. Jin et al.
- Retrievals and simulations of terrestrial water storage changes and runoff over the Tibetan Plateau: Challenges and opportunities X. Li et al.
- Enhancing hydrological time series forecasting with a hybrid Bayesian-ConvLSTM model optimized by particle swarm optimization H. Kilinc et al.
- Spatial-temporal variability and risk assessment of surface and groundwater resources under climate change and urbanization: A physics-informed analysis K. Joseph Pious & A. Raj
- Two-dimensional differential form of distributed Xinanjiang model J. Zhao et al.
- A hybrid SWAT-LSTM model for streamflow simulation with SHAP-based interpretability: Application in the Wei River Basin, China W. Zhou et al.
- A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al.
- Physics-informed deep learning reveals climate-driven snowpack decline and threatens ecological water availability in a Californian snow-fed catchment S. Maharjan et al.
- Event-based training data thresholds for BiLSTM versus Xinanjiang models: Insights from the applications of 19 Chinese catchments Y. Li et al.
- Combining Physical Hydrological Model with Explainable Machine Learning Methods to Enhance Water Balance Assessment in Glacial River Basins R. Yang et al.
- A distributed hybrid physics–AI framework for learning corrections of internal hydrological fluxes and enhancing high-resolution regionalized flood modeling N. Huynh et al.
- Fast urban flood modeling informing response decisions: Model development and future perspectives T. Duan et al.
- A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl X. Jing et al.
- Differentiable parameter learning of reservoir operation modules Z. Chen & T. Zhao
Saved (final revised paper)
Latest update: 11 May 2026
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
This paper developed hybrid semi-distributed hydrological models by employing a process-based...
