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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-382', Anonymous Referee #1, 10 Oct 2021
    • AC1: 'Reply on RC1', Zhihong Song, 12 Oct 2021
  • RC2: 'Comment on hess-2021-382', Anonymous Referee #2, 22 Nov 2021
    • AC2: 'Reply on RC2', Zhihong Song, 24 Nov 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by editor) (12 Dec 2021) by Stacey Archfield
AR by Zhihong Song on behalf of the Authors (13 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (03 Jan 2022) by Stacey Archfield
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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.