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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Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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Cited articles

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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.