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.

Model code and software

Songzh101/GBM_HydroModel_Regionalization: The regionalization of hydrological model Z. H. Song https://doi.org/10.5281/zenodo.5914086

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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.