Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 24, issue 5
Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2343-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-2343-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 5
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

Data sets

ERA Interim European Centre for Medium Range Weather Forecasts https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/

Model code and software

Multi-step ahead daily inflow forecasting using ERA-Interim reanalysis dataset based on gradient boosting regression trees S. Liao, Z. Liu, B. Liu, C. Cheng, X. Jin, and Z. Zhao https://doi.org/10.5281/zenodo.3701182

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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.
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