干旱区生态环境知识资源中心(Arid): Spatial Downscaling of Precipitation Data in Arid Regions Based on the XGBoost-MGWR Model: A Case Study of the Turpan-Hami Region

Arid

DOI	10.3390/land13040448
	Spatial Downscaling of Precipitation Data in Arid Regions Based on the XGBoost-MGWR Model: A Case Study of the Turpan-Hami Region
	He, Huanhuan; Wang, Jinjie; Ding, Jianli; Wang, Lei
通讯作者	Ding, JL
来源期刊	LAND
EISSN	2073-445X
出版年	2024
卷号	13 期号:4
英文摘要	Accurate and reliable precipitation data are important for analyzing regional precipitation distribution, water resource management, and ecological environment construction. Due to the scarcity of meteorological stations in the Turpan-Hami region, precipitation observation conditions are limited, and it is difficult to obtain precipitation data. Firstly, the applicability of TRMM 3B43v7, GPM_3IMERGM 06, and CMORPH CDR satellite precipitation data for the Turpan-Hami Region was evaluated, and the products with better applicability were selected. Next, the Extreme Gradient Boosting Algorithm (XGBoost) and the Shapley Additive Explanations for Machine Learning (SHAP) model were combined to carry out a feature importance analysis on the climate factors affecting precipitation (mean temperature, actual evapotranspiration, wind speed, cloud cover), from which climate factors with a greater influence on precipitation were selected. Combined with climate factors, normalized difference vegetation index (NDVI), slope, aspect, and elevation as explanatory variables, a Multi-Scale Geographically Weighted Regression (MGWR) model was constructed to obtain the monthly precipitation data of 1 km spatial resolution in the Turpan-Hami area from 2001 to 2020. Finally, the spatiotemporal distribution characteristics and changing trend of precipitation in the Turpan-Hami region from 2001 to 2020 were analyzed. The results show that (1) GPM_3IMERGM 06 satellite precipitation data exhibits good applicability in the Turpan-Hami region. (2) The precision verification of the downscaling results from a monthly scale and an annual scale shows that the accuracy and spatial resolution of the data are improved after downscaling. (3) From 2001 to 2020, the precipitation in the Turpan-Hami region showed an insignificantly increasing trend.
英文关键词	precipitation MGWR GPM downscaling arid regions
类型	Article
语种	英语
开放获取类型	gold
收录类别	SSCI
WOS记录号	WOS:001220257900001
WOS关键词	CHINA ; TMPA ; ELEVATION ; ALGORITHM ; PRODUCTS ; RAINFALL ; CMORPH ; RADAR ; IMERG
WOS类目	Environmental Studies
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404791
推荐引用方式 GB/T 7714	He, Huanhuan,Wang, Jinjie,Ding, Jianli,et al. Spatial Downscaling of Precipitation Data in Arid Regions Based on the XGBoost-MGWR Model: A Case Study of the Turpan-Hami Region[J],2024,13(4).
APA	He, Huanhuan,Wang, Jinjie,Ding, Jianli,&Wang, Lei.(2024).Spatial Downscaling of Precipitation Data in Arid Regions Based on the XGBoost-MGWR Model: A Case Study of the Turpan-Hami Region.LAND,13(4).
MLA	He, Huanhuan,et al."Spatial Downscaling of Precipitation Data in Arid Regions Based on the XGBoost-MGWR Model: A Case Study of the Turpan-Hami Region".LAND 13.4(2024).