Articles | Volume 26, issue 2
Research article
31 Jan 2022
Research article |  | 31 Jan 2022

Regionalization of hydrological model parameters using gradient boosting machine

Zhihong Song, Jun Xia, Gangsheng Wang, Dunxian She, Chen Hu, and Si Hong

Related authors

Understanding meteorological and physio-geographical controls of variability of flood event classes in China
Yongyong Zhang, Yongqiang Zhang, Xiaoyan Zhai, Jun Xia, Qiuhong Tang, Wei Wang, Jian Wu, Xiaoyu Niu, and Bing Han
Hydrol. Earth Syst. Sci. Discuss.,,, 2024
Preprint under review for HESS
Short summary
An improved Approximate Bayesian Computation approach for high-dimensional posterior exploration of hydrological models
Song Liu, Dunxian She, Liping Zhang, and Jun Xia
Hydrol. Earth Syst. Sci. Discuss.,,, 2023
Revised manuscript under review for HESS
Short summary
Drought-induced non-stationarity in the rainfall-runoff relationship invalidates the role of control catchment at the Red Hill paired-catchment experimental site
Yunfan Zhang, Lei Cheng, Lu Zhang, Shujing Qin, Liu Liu, Pan Liu, Yanghe Liu, and Jun Xia
Hydrol. Earth Syst. Sci. Discuss.,,, 2021
Manuscript not accepted for further review
Short summary
Multi-coverage optimal location model for emergency medical service (EMS) facilities under various disaster scenarios: a case study of urban fluvial floods in the Minhang district of Shanghai, China
Yuhan Yang, Jie Yin, Mingwu Ye, Dunxian She, and Jia Yu
Nat. Hazards Earth Syst. Sci., 20, 181–195,,, 2020
Short summary
Assessing the impacts of reservoirs on downstream flood frequency by coupling the effect of scheduling-related multivariate rainfall with an indicator of reservoir effects
Bin Xiong, Lihua Xiong, Jun Xia, Chong-Yu Xu, Cong Jiang, and Tao Du
Hydrol. Earth Syst. Sci., 23, 4453–4470,,, 2019
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869,,, 2024
Short summary
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829,,, 2024
Short summary
Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807,,, 2024
Short summary
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719,,, 2024
Short summary
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650,,, 2024
Short summary

Cited articles

Adnan, R. M., Liang, Z., Trajkovic, S., Zounemat-Kermani, M., Li, B., and Kisi, O.: Daily streamflow prediction using optimally pruned extreme learning machine, J. Hydrol., 577, 123981,, 2019. 
Akbarimehr, M. and Naghdi, R.: Assessing the relationship of slope and runoff volume on skid trails (Case study: Nav 3 district), J. Forest Sci., 58, 357–362,, 2012. 
Bai, P., Liu, X., Zhang, Y., and Liu, C.: Incorporating vegetation dynamics noticeably improved performance of hydrological model under vegetation greening, Sci. Total Environ., 643, 610–622,, 2018a. 
Bai, P., Liu, X., and Liu, C.: Improving hydrological simulations by incorporating GRACE data for model calibration, J. Hydrol., 557, 291–304,, 2018b. 
Bao, Z., Zhang, J., Liu, J., Fu, G., Wang, G., He, R., Yan, X., Jin, J., and Liu, H.: Comparison of regionalization approaches based on regression and similarity for predictions in ungauged catchments under multiple hydro-climatic conditions, J. Hydrol., 466–467, 37–46,, 2012. 
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.