Articles | Volume 26, issue 2
Hydrol. Earth Syst. Sci., 26, 505–524, 2022
https://doi.org/10.5194/hess-26-505-2022
Hydrol. Earth Syst. Sci., 26, 505–524, 2022
https://doi.org/10.5194/hess-26-505-2022

Research article 31 Jan 2022

Research article | 31 Jan 2022

Regionalization of hydrological model parameters using gradient boosting machine

Zhihong Song et al.

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Manuscript not accepted for further review
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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, https://doi.org/10.1016/j.jhydrol.2019.123981, 2019. 
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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, https://doi.org/10.1016/j.scitotenv.2018.06.233, 2018a. 
Bai, P., Liu, X., and Liu, C.: Improving hydrological simulations by incorporating GRACE data for model calibration, J. Hydrol., 557, 291–304, https://doi.org/10.1016/j.jhydrol.2017.12.025, 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, https://doi.org/10.1016/j.jhydrol.2012.07.048, 2012. 
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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.