Articles | Volume 26, issue 2
Hydrol. Earth Syst. Sci., 26, 505–524, 2022
Hydrol. Earth Syst. Sci., 26, 505–524, 2022

Research article 31 Jan 2022

Research article | 31 Jan 2022

Regionalization of hydrological model parameters using gradient boosting machine

Zhihong Song et al.

Related authors

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
Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework
Zhengke Pan, Pan Liu, Shida Gao, Jun Xia, Jie Chen, and Lei Cheng
Hydrol. Earth Syst. Sci., 23, 3405–3421,,, 2019
Short summary
Impact of LUCC on streamflow based on the SWAT model over the Wei River basin on the Loess Plateau in China
Hong Wang, Fubao Sun, Jun Xia, and Wenbin Liu
Hydrol. Earth Syst. Sci., 21, 1929–1945,,, 2017

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Aquifer recharge in the Piedmont Alpine zone: historical trends and future scenarios
Elisa Brussolo, Elisa Palazzi, Jost von Hardenberg, Giulio Masetti, Gianna Vivaldo, Maurizio Previati, Davide Canone, Davide Gisolo, Ivan Bevilacqua, Antonello Provenzale, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 26, 407–427,,, 2022
Short summary
Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+
Albert Nkwasa, Celray James Chawanda, Jonas Jägermeyr, and Ann van Griensven
Hydrol. Earth Syst. Sci., 26, 71–89,,, 2022
Short summary
How well are we able to close the water budget at the global scale?
Fanny Lehmann, Bramha Dutt Vishwakarma, and Jonathan Bamber
Hydrol. Earth Syst. Sci., 26, 35–54,,, 2022
Short summary
Bending of the concentration discharge relationship can inform about in-stream nitrate removal
Joni Dehaspe, Fanny Sarrazin, Rohini Kumar, Jan H. Fleckenstein, and Andreas Musolff
Hydrol. Earth Syst. Sci., 25, 6437–6463,,, 2021
Short summary
Quantifying the impacts of land cover change on hydrological responses in the Mahanadi river basin in India
Shaini Naha, Miguel Angel Rico-Ramirez, and Rafael Rosolem
Hydrol. Earth Syst. Sci., 25, 6339–6357,,, 2021
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.