多种特征因子结合GBDT的降水数据降尺度方法研究

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Abstract A spatial downscaling framework based on machine learning algorithms integrated with station data correction is proposed by combining enhanced vegetation index (EVI), precipitable water vapor (PWV), Land Surface Temperature (LST), Slope, Aspect, Altitude, LON, and LAT. Then, the robustness differences among three machine learning algorithms, namely Random Forest (RF), Feedforward Neural Network (FNN), and Gradient Boosting Decision Tree (DBGT), were evaluated for spatially downscaling the GPM IMERG over the Yangtze River basin. The time-lag effect of different seasonal vegetation on the downscaling results of precipitation from 2001~2019 was taken into account to explore how the correction effect of the downscaling framework on GPM is related to the precipitation. The results show that the three downscaling approaches can improve the accuracy of the data to different degrees while obtaining the GPM IMERG data with 1km spatial resolution. Specifically, the simulations of GBDT can express the detailed features of precipitation more obviously in terms of the spatial heterogeneity with the best simulation accuracy and stronger model robustness at annual and seasonal time scales in each study area (annual R²=0.748~0.958, seasonal R²=0.518~0.909). The hysteresis of vegetation in spring and winter were most sensitive to the response of GPM downscaling results, with the best hysteresis of 1and 2months in order. Yet the time lag effect was not significant in summer and autumn, with no lag period essentially. Compared to the original data, the increased R²of the downscaled data was positively correlated with precipitation at R² of 0.630~0.844.

Key words： GPM IMERG spatial downscaling GBDT strong robustness

Received: 02 September 2022

PACS:	X171
	P412.27

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	Articles by authors
	ZHANG Han-bo
	YANG Ji
	JING Wen-long
	DENG Ying-bin

Cite this article:

ZHANG Han-bo,YANG Ji,JING Wen-long等. Downscaling method of precipitation data based on GBDT combined with multiple eigenfactors[J]. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(4): 1867-1882.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2023/V43/I4/1867

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