Articles | Volume 26, issue 2
https://doi.org/10.5194/hess-26-505-2022
© Author(s) 2022. 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-26-505-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2022
Research article |
| 31 Jan 2022
| 31 Jan 2022
Regionalization of hydrological model parameters using gradient boosting machine
Zhihong Song
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Jun Xia
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Key Laboratory of Water Cycle and Related Land Surface Processes,
Chinese Academy of Sciences, Beijing, 10010, China
Gangsheng Wang
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Institute for Water-Carbon Cycles and Carbon Neutrality, Wuhan
University, Wuhan, 430072, China
Dunxian She
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Chen Hu
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
Si Hong
State Key Laboratory of Water Resources and Hydropower Engineering
Science, Wuhan University, Wuhan, 430072, China
Hubei Key Laboratory of Water System Science for Sponge City
Construction, Wuhan University, Wuhan, 430072, China
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Cited
14 citations as recorded by crossref.
- Climate change rather than vegetation greening dominates runoff change in China Z. Song et al. 10.1016/j.jhydrol.2023.129519
- Surrogate-based multiobjective optimization to rapidly size low impact development practices for outflow capture Y. Yang et al. 10.1016/j.jhydrol.2022.128848
- Future changes in hydrological drought across the Yangtze River Basin: identification, spatial–temporal characteristics, and concurrent probability J. Yu et al. 10.1016/j.jhydrol.2023.130057
- Coupling a Distributed Time Variant Gain Model into a Storm Water Management Model to Simulate Runoffs in a Sponge City Y. Yang et al. 10.3390/su15043804
- Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach C. Hiremath & L. Nandagiri 10.2166/wcc.2023.467
- Machine learning algorithms for merging satellite-based precipitation products and their application on meteorological drought monitoring over Kenya S. Ghosh et al. 10.1007/s00382-023-06893-6
- Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning S. Majnooni et al. 10.1080/02626667.2023.2248112
- Comparative performance of regionalization methods for model parameterization in ungauged Himalayan watersheds N. Karki et al. 10.1016/j.ejrh.2023.101359
- Comparing three machine learning algorithms with existing methods for natural streamflow estimation S. Mehrvand et al. 10.1080/02626667.2023.2273402
- Characterizing future changes in compound flood risk by capturing the dependence between rainfall and river flow: An application to the Yangtze River Basin, China J. Yu et al. 10.1016/j.jhydrol.2024.131175
- Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models V. Kumar et al. 10.3390/w15142572
- Response of blue-green water to climate and vegetation changes in the water source region of China's South-North water Diversion Project X. Li et al. 10.1016/j.jhydrol.2024.131061
- Fuzzy C-Means clustering for physical model calibration and 7-day, 10-year low flow estimation in ungaged basins: comparisons to traditional, statistical estimates A. DelSanto et al. 10.3389/frwa.2024.1332888
- Suspended sediment load modeling using Hydro-Climate variables and Machine learning S. Aldin Shojaeezadeh et al. 10.1016/j.jhydrol.2024.130948
14 citations as recorded by crossref.
- Climate change rather than vegetation greening dominates runoff change in China Z. Song et al. 10.1016/j.jhydrol.2023.129519
- Surrogate-based multiobjective optimization to rapidly size low impact development practices for outflow capture Y. Yang et al. 10.1016/j.jhydrol.2022.128848
- Future changes in hydrological drought across the Yangtze River Basin: identification, spatial–temporal characteristics, and concurrent probability J. Yu et al. 10.1016/j.jhydrol.2023.130057
- Coupling a Distributed Time Variant Gain Model into a Storm Water Management Model to Simulate Runoffs in a Sponge City Y. Yang et al. 10.3390/su15043804
- Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach C. Hiremath & L. Nandagiri 10.2166/wcc.2023.467
- Machine learning algorithms for merging satellite-based precipitation products and their application on meteorological drought monitoring over Kenya S. Ghosh et al. 10.1007/s00382-023-06893-6
- Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning S. Majnooni et al. 10.1080/02626667.2023.2248112
- Comparative performance of regionalization methods for model parameterization in ungauged Himalayan watersheds N. Karki et al. 10.1016/j.ejrh.2023.101359
- Comparing three machine learning algorithms with existing methods for natural streamflow estimation S. Mehrvand et al. 10.1080/02626667.2023.2273402
- Characterizing future changes in compound flood risk by capturing the dependence between rainfall and river flow: An application to the Yangtze River Basin, China J. Yu et al. 10.1016/j.jhydrol.2024.131175
- Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models V. Kumar et al. 10.3390/w15142572
- Response of blue-green water to climate and vegetation changes in the water source region of China's South-North water Diversion Project X. Li et al. 10.1016/j.jhydrol.2024.131061
- Fuzzy C-Means clustering for physical model calibration and 7-day, 10-year low flow estimation in ungaged basins: comparisons to traditional, statistical estimates A. DelSanto et al. 10.3389/frwa.2024.1332888
- Suspended sediment load modeling using Hydro-Climate variables and Machine learning S. Aldin Shojaeezadeh et al. 10.1016/j.jhydrol.2024.130948
Latest update: 23 Apr 2024
Short summary
We performed a machine learning approach to regionalize the parameters of a China-wide hydrological model by linking six model parameters with 10 physical attributes (terrain and soil properties). The results show the superiority of machine-learning-based regionalization approach compared with the traditional linear regression method in ungauged regions. We also obtained the relative importance of attributes against model parameters.
We performed a machine learning approach to regionalize the parameters of a China-wide...
