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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Yongyong Zhang, Yongqiang Zhang, Xiaoyan Zhai, Jun Xia, Qiuhong Tang, Wei Wang, Jian Wu, Xiaoyu Niu, and Bing Han
Hydrol. Earth Syst. Sci., 29, 3257–3275, https://doi.org/10.5194/hess-29-3257-2025, https://doi.org/10.5194/hess-29-3257-2025, 2025
Short summary
Short summary
It is challenging to investigate flood variabilities and their formation mechanisms from massive event samples. This study explores spatiotemporal variabilities of 1446 flood events using hierarchical and partitional clustering methods. Control mechanisms of meteorological and physio-geographical factors are explored for individual flood event classes using constrained rank analysis. This gives insights into comprehensive changes in flood events and aids in flood prediction and control.
Song Liu, Dunxian She, Liping Zhang, and Jun Xia
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-414, https://doi.org/10.5194/hess-2022-414, 2023
Publication in HESS not foreseen
Short summary
Short summary
Quantifying the uncertainty in streamflow predictions is a major challenge, with research and operational significance. This study advances the field of catchment-scale hydrological modelling by developing an improved uncertainty analysis technique that provides more reliable and accurate probabilistic streamflow predictions. This finding provides hydrologists with robust modelling tools for handling hydrological modelling uncertainties in engineering practices.
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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...
