干旱区生态环境知识资源中心(Arid): Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach

Arid

DOI	10.1007/s40333-024-0054-7
	Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach
	Xu, Wenjie; Ding, Jianli; Bao, Qingling; Wang, Jinjie; Xu, Kun
通讯作者	Ding, JL
来源期刊	JOURNAL OF ARID LAND
ISSN	1674-6767
EISSN	2194-7783
出版年	2024
卷号	16 期号:3 页码:331-354
英文摘要	Xinjiang Uygur Autonomous Region is a typical inland arid region in China with a sparse and uneven distribution of meteorological stations, limited access to precipitation data, and significant water scarcity. Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region, which can even improve the performance of hydrological modelling. This study evaluated the applicability of widely used five satellite-based precipitation products (Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA)) and a reanalysis precipitation dataset (ECMWF Reanalysis v5-Land Dataset (ERA5-Land)) in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations. Based on this assessment, we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging (DBMA) approach, the expectation-maximization method, and the ordinary Kriging interpolation method. The daily precipitation data merged using the DBMA approach exhibits distinct spatiotemporal variability, with an outstanding performance, as indicated by low root mean square error (RMSE=1.40 mm/d) and high Person's correlation coefficient (CC=0.67). Compared with the traditional simple model averaging (SMA) and individual product data, although the DBMA-fused precipitation data are slightly lower than the best precipitation product (CMFD), the overall performance of DBMA is more robust. The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final (IMERG-F) precipitation product, as well as hydrological simulations in the Ebinur Lake Basin, further demonstrate the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region. Our results showed that the proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid regions, and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.
英文关键词	precipitation estimates satellite-based and reanalysis precipitation dynamic Bayesian model averaging streamflow simulation Ebinur Lake Basin Xinjiang
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001178044100001
WOS关键词	NEURAL-NETWORK ; GPM IMERG ; PRODUCTS ; TMPA ; 3B42
WOS类目	Environmental Sciences
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/2XILL650/404367
推荐引用方式 GB/T 7714	Xu, Wenjie,Ding, Jianli,Bao, Qingling,et al. Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach[J],2024,16(3):331-354.
APA	Xu, Wenjie,Ding, Jianli,Bao, Qingling,Wang, Jinjie,&Xu, Kun.(2024).Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach.JOURNAL OF ARID LAND,16(3),331-354.
MLA	Xu, Wenjie,et al."Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach".JOURNAL OF ARID LAND 16.3(2024):331-354.