Articles | Volume 24, issue 9
https://doi.org/10.5194/nhess-24-3155-2024
© 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/nhess-24-3155-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China
Sheik Umar Jam-Jalloh
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Jia Liu
CORRESPONDING AUTHOR
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Yicheng Wang
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Yuchen Liu
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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Cited
15 citations as recorded by crossref.
- Modeling Surface Runoff in Surakarta Using HEC-HMS and DEM S. Handoyo et al. https://doi.org/10.18400/tjce.1793654
- Technical note: A flexible framework for precision reduction of WRF inputs and outputs to balance storage efficiency and scientific fidelity S. Wu et al. https://doi.org/10.5194/acp-26-7261-2026
- A multi-source data-driven framework for probabilistic flood risk assessment using cascade machine learning models: case study in the Sichuan Basin Y. Lu et al. https://doi.org/10.1038/s41598-025-12391-y
- Coupling WRF with HEC-HMS for streamflow forecasting in typical mountainous catchments of northwestern Iran S. Akbari Moghaddam Sani et al. https://doi.org/10.1007/s11600-026-02016-x
- Elevating predictive reliability: time-varying parameter bayesian deep learning techniques for flood probability forecasting H. Xu et al. https://doi.org/10.1016/j.jhydrol.2025.134597
- Simulation of flood processes under extreme precipitation scenarios based on an improved HEC-HMS model in a typical karst mountainous basin, southwest China C. Mo et al. https://doi.org/10.1016/j.jhydrol.2026.135154
- Novel bias correction framework for CMIP6 climate projections over Sri Lanka and streamflow impacts in the Malwathu River Basin S. Vasanthakumar et al. https://doi.org/10.1007/s40808-026-02858-w
- Evaluation of the Hydrological Response to Land Use Change Scenarios in Urban and Non-Urban Mountain Basins in Ecuador D. Mejía-Veintimilla et al. https://doi.org/10.3390/land13111907
- Synergistic flood forecasting: combining physics-based and spatio-temporal deep learning models M. Masoudimoghaddam et al. https://doi.org/10.1080/02626667.2025.2604257
- Scale-dependent flood hazard amplification under warming climates: a tropical cyclone Hinnamnor storyline in South Korea Y. Lee et al. https://doi.org/10.1186/s40562-026-00507-9
- Reconstructing flash flood in a data-poor mountainous catchment using radar-derived storm characteristics and hydrological simulation Y. Wang & X. Wang https://doi.org/10.1080/19942060.2026.2678115
- Flood simulation using HEC-HMS model based on antecedent soil moisture changes in the Fenshui River Basin S. Xiang et al. https://doi.org/10.2166/nh.2026.153
- Assessing the role of precipitation inputs and overbank flow in hydrological modeling: a case study of the Irrawaddy River Basin in Myanmar using WRF-Hydro Q. Sun et al. https://doi.org/10.3389/fclim.2025.1644481
- Enhancing resilience with operational hydrometeorological-based flood early warning system in northeast India D. Barman et al. https://doi.org/10.1007/s12040-025-02674-3
- From Flood Vulnerability Mapping Using Coupled Hydrodynamic Models to Optimizing Disaster Prevention Funding Allocation: A Case Study of Wenzhou A. Zhu et al. https://doi.org/10.3390/w17233369
15 citations as recorded by crossref.
- Modeling Surface Runoff in Surakarta Using HEC-HMS and DEM S. Handoyo et al. https://doi.org/10.18400/tjce.1793654
- Technical note: A flexible framework for precision reduction of WRF inputs and outputs to balance storage efficiency and scientific fidelity S. Wu et al. https://doi.org/10.5194/acp-26-7261-2026
- A multi-source data-driven framework for probabilistic flood risk assessment using cascade machine learning models: case study in the Sichuan Basin Y. Lu et al. https://doi.org/10.1038/s41598-025-12391-y
- Coupling WRF with HEC-HMS for streamflow forecasting in typical mountainous catchments of northwestern Iran S. Akbari Moghaddam Sani et al. https://doi.org/10.1007/s11600-026-02016-x
- Elevating predictive reliability: time-varying parameter bayesian deep learning techniques for flood probability forecasting H. Xu et al. https://doi.org/10.1016/j.jhydrol.2025.134597
- Simulation of flood processes under extreme precipitation scenarios based on an improved HEC-HMS model in a typical karst mountainous basin, southwest China C. Mo et al. https://doi.org/10.1016/j.jhydrol.2026.135154
- Novel bias correction framework for CMIP6 climate projections over Sri Lanka and streamflow impacts in the Malwathu River Basin S. Vasanthakumar et al. https://doi.org/10.1007/s40808-026-02858-w
- Evaluation of the Hydrological Response to Land Use Change Scenarios in Urban and Non-Urban Mountain Basins in Ecuador D. Mejía-Veintimilla et al. https://doi.org/10.3390/land13111907
- Synergistic flood forecasting: combining physics-based and spatio-temporal deep learning models M. Masoudimoghaddam et al. https://doi.org/10.1080/02626667.2025.2604257
- Scale-dependent flood hazard amplification under warming climates: a tropical cyclone Hinnamnor storyline in South Korea Y. Lee et al. https://doi.org/10.1186/s40562-026-00507-9
- Reconstructing flash flood in a data-poor mountainous catchment using radar-derived storm characteristics and hydrological simulation Y. Wang & X. Wang https://doi.org/10.1080/19942060.2026.2678115
- Flood simulation using HEC-HMS model based on antecedent soil moisture changes in the Fenshui River Basin S. Xiang et al. https://doi.org/10.2166/nh.2026.153
- Assessing the role of precipitation inputs and overbank flow in hydrological modeling: a case study of the Irrawaddy River Basin in Myanmar using WRF-Hydro Q. Sun et al. https://doi.org/10.3389/fclim.2025.1644481
- Enhancing resilience with operational hydrometeorological-based flood early warning system in northeast India D. Barman et al. https://doi.org/10.1007/s12040-025-02674-3
- From Flood Vulnerability Mapping Using Coupled Hydrodynamic Models to Optimizing Disaster Prevention Funding Allocation: A Case Study of Wenzhou A. Zhu et al. https://doi.org/10.3390/w17233369
Saved (final revised paper)
Latest update: 17 Sep 2026
Short summary
Our paper explores improving flood forecasting using advanced weather and hydrological models. By coupling the WRF model with WRF-Hydro and HEC-HMS, we achieved more accurate forecasts. WRF–WRF-Hydro excels for short, intense storms, while WRF–HEC-HMS is better for longer, evenly distributed storms. Our research shows how these models provide insights for adaptive atmospheric–hydrologic systems and aims to boost flood preparedness and response with more reliable, timely predictions.
Our paper explores improving flood forecasting using advanced weather and hydrological models....
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