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
Viewed
Total article views: 3,768 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 Aug 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,744 | 949 | 75 | 3,768 | 207 | 54 | 59 |
- HTML: 2,744
- PDF: 949
- XML: 75
- Total: 3,768
- Supplement: 207
- BibTeX: 54
- EndNote: 59
Total article views: 2,903 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 31 Jan 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,193 | 656 | 54 | 2,903 | 114 | 48 | 50 |
- HTML: 2,193
- PDF: 656
- XML: 54
- Total: 2,903
- Supplement: 114
- BibTeX: 48
- EndNote: 50
Total article views: 865 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 09 Aug 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 551 | 293 | 21 | 865 | 93 | 6 | 9 |
- HTML: 551
- PDF: 293
- XML: 21
- Total: 865
- Supplement: 93
- BibTeX: 6
- EndNote: 9
Viewed (geographical distribution)
Total article views: 3,768 (including HTML, PDF, and XML)
Thereof 3,521 with geography defined
and 247 with unknown origin.
Total article views: 2,903 (including HTML, PDF, and XML)
Thereof 2,742 with geography defined
and 161 with unknown origin.
Total article views: 865 (including HTML, PDF, and XML)
Thereof 779 with geography defined
and 86 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
25 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
- Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach 10.1007/s12145-024-01390-8
- Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach C. Hiremath & L. Nandagiri 10.2166/wcc.2023.467
- 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
- 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
- Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal U. Marasini & M. Pokhrel 10.1007/s44290-024-00084-w
- 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
- An explainable hybrid framework for estimating daily reference evapotranspiration: Combining extreme gradient boosting with Nelder-Mead method B. Mohammadi et al. 10.1016/j.jhydrol.2024.132130
- 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
- Analytical Survey on the Sustainable Advancements in Water and Hydrology Resources with AI Implications for a Resilient Future A. Bhadauria et al. 10.1051/e3sconf/202455201074
- Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
- 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
- Multi-Model Comparison in the Attribution of Runoff Variation across a Humid Region of Southern China Q. Wang et al. 10.3390/w16121729
- 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
- Enhancing streamflow simulation accuracy in ungauged catchments via parameter calibration with triple collocation-based merged evapotranspiration and streamflow features Z. Xu et al. 10.1016/j.jhydrol.2024.131627
- 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
- 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
- A Proposal for a New Python Library Implementing Stepwise Procedure L. Fávero et al. 10.3390/a17110502
- Regional Flood Frequency Analysis of Northern Iran M. Adhami 10.21205/deufmd.2024267711
- Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context M. El Garnaoui et al. 10.3390/rs16203756
- Climate and vegetation change impacts on future conterminous United States water yield H. Duarte et al. 10.1016/j.jhydrol.2024.131472
25 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
- Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach 10.1007/s12145-024-01390-8
- Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach C. Hiremath & L. Nandagiri 10.2166/wcc.2023.467
- 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
- 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
- Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal U. Marasini & M. Pokhrel 10.1007/s44290-024-00084-w
- 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
- An explainable hybrid framework for estimating daily reference evapotranspiration: Combining extreme gradient boosting with Nelder-Mead method B. Mohammadi et al. 10.1016/j.jhydrol.2024.132130
- 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
- Analytical Survey on the Sustainable Advancements in Water and Hydrology Resources with AI Implications for a Resilient Future A. Bhadauria et al. 10.1051/e3sconf/202455201074
- Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
- 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
- Multi-Model Comparison in the Attribution of Runoff Variation across a Humid Region of Southern China Q. Wang et al. 10.3390/w16121729
- 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
- Enhancing streamflow simulation accuracy in ungauged catchments via parameter calibration with triple collocation-based merged evapotranspiration and streamflow features Z. Xu et al. 10.1016/j.jhydrol.2024.131627
- 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
- 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
- A Proposal for a New Python Library Implementing Stepwise Procedure L. Fávero et al. 10.3390/a17110502
- Regional Flood Frequency Analysis of Northern Iran M. Adhami 10.21205/deufmd.2024267711
- Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context M. El Garnaoui et al. 10.3390/rs16203756
- Climate and vegetation change impacts on future conterminous United States water yield H. Duarte et al. 10.1016/j.jhydrol.2024.131472
Latest update: 20 Nov 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...
