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Application of Stochastic Forest Precipitation Fusion Algorithm With Covariates in Yangtze River Basin
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Zhi XU1, Ya-li MOU2, Li-li LIANG1, He-long WANG3
Water Resources and Power | 2023, 41(8) : 1 - 4
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Water Resources and Power | 2023, 41(8): 1-4
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Application of Stochastic Forest Precipitation Fusion Algorithm With Covariates in Yangtze River Basin
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Zhi XU1, Ya-li MOU2, Li-li LIANG1, He-long WANG3
Affiliations
  • 1.Science and Technology Research Institute, China Three Gorges Corporation, Beijing 101199, China
  • 2.China Institute of Water Resources and Hydropower Research, Beijing 100038, China
  • 3.Zhejiang Institute of Hydraulics & Estuary, Hangzhou 310020, China
Published: 2023-08-25 doi: 10.20040/j.cnki.1000-7709.2023.20222455
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The precipitation data with high quality and high spatial and temporal resolution is of great significance to the research of hydrology, meteorology and other fields. At present, remote sensing and reanalysis of precipitation data are widely used, but there are problems such as low resolution, high uncertainty of accuracy, etc. In this paper, a random forest precipitation fusion algorithm considering covariates is proposed to fuse seven sets of precipitation products, namely CMA, CN05, ERA5, GLDAS, TRMM, IMERG and PERSIANN. Three typical sub-basins of the Yangtze River basin (Jinsha River, Sanxiaqujian and Poyang Lake) are selected to test the effect of random forest fusion data (RFF). The results show that for the accuracy of precipitation products, the accuracy of random forest fusion data is improved compared with the original precipitation products. For the accuracy assessment of different precipitation events, with the increase of rainfall intensity, the TTS score of each precipitation product shows a decreasing trend, and the TTS score of RFF is better than the original precipitation product. Data fusion of precipitation products through random forest model considering covariates can improve the accuracy of precipitation data and the reliability of different precipitation events, which provides support for hydrological simulation.

precipitation fusion  /  random forest  /  covariant  /  Yangtze River Basin  /  accuracy evaluation
Zhi XU, Ya-li MOU, Li-li LIANG, He-long WANG. Application of Stochastic Forest Precipitation Fusion Algorithm With Covariates in Yangtze River Basin[J]. Water Resources and Power, 2023 , 41 (8) : 1 -4 . DOI: 10.20040/j.cnki.1000-7709.2023.20222455
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20222455
  • Receive Date：2022-11-21
  • Online Date：2026-01-28
  • Published：2023-08-25
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History
  • Received：2022-11-21
  • Revised：2023-02-28
Funding
Affiliations
    1.Science and Technology Research Institute, China Three Gorges Corporation, Beijing 101199, China
    2.China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    3.Zhejiang Institute of Hydraulics & Estuary, Hangzhou 310020, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20222455
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光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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