Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2343-2020
https://doi.org/10.5194/hess-24-2343-2020
Research article
 | 
08 May 2020
Research article |  | 08 May 2020

Multistep-ahead daily inflow forecasting using the ERA-Interim reanalysis data set based on gradient-boosting regression trees

Shengli Liao, Zhanwei Liu, Benxi Liu, Chuntian Cheng, Xinfeng Jin, and Zhipeng Zhao

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (17 Feb 2020) by Giuliano Di Baldassarre
AR by Zhanwei Liu on behalf of the Authors (10 Mar 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (25 Mar 2020) by Giuliano Di Baldassarre
RR by Anonymous Referee #1 (25 Mar 2020)
ED: Publish as is (08 Apr 2020) by Giuliano Di Baldassarre
AR by Zhanwei Liu on behalf of the Authors (13 Apr 2020)  Manuscript 
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Short summary
Inflow forecasting plays an essential role in reservoir management and operation. To improve the accuracy of multistep-ahead daily inflow forecasting, the paper develops a new hybrid inflow forecast framework using ERA-Interim data. We find that the framework significantly enhances the accuracy of inflow forecasting at lead times of 4–10 d compared with widely used and mature methods. This research provides a reference for operational inflow forecasting in remote regions.