Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 11
Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2969-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2969-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 11
Research article
 | 
15 Jun 2022
Research article |  | 15 Jun 2022

A two-step merging strategy for incorporating multi-source precipitation products and gauge observations using machine learning classification and regression over China

Huajin Lei, Hongyu Zhao, and Tianqi Ao

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Short summary
How to combine multi-source precipitation data effectively is one of the hot topics in hydrometeorological research. This study presents a two-step merging strategy based on machine learning for multi-source precipitation merging over China. The results demonstrate that the proposed method effectively distinguishes the occurrence of precipitation events and reduces the error in precipitation estimation. This method is robust and may be successfully applied to other areas even with scarce data.
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