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Assessment of four latest long-term satellite-based precipitation products in capturing the extreme precipitation and streamflow across a humid region of southern China

文献类型：期刊论文

作者Wang, Qiang1,2; Xia, Jun1,2,3; She, Dunxian1,2; Zhang, Xiang1,2; Liu, Jie1,2; Zhang, Yin1,2
刊名ATMOSPHERIC RESEARCH
出版日期2021-08-10
卷号257页码:13
关键词Satellite-based precipitation product Extreme precipitation Extreme streamflow Distributed hydrological model
ISSN号0169-8095
DOI10.1016/j.atmosres.2021.105554
通讯作者Xia, Jun(xiajun666@whu.edu.cn) ; She, Dunxian(shedunxian@whu.edu.cn)
英文摘要The lack of ground-based observation data usually limits the analysis of extreme precipitation and streamflow, especially for the ungauged or data-limited regions. Satellite-based precipitation products (SPPs) with high temporal and spatial resolutions can provide potential data sources in the regions without adequate precipitation data, and it has received much attention of regional or global hydro-climatic analysis. This study aims to evaluate the accuracy of four widely used long time series SPPs (CHIRPS, TMPA, CMORPH and PERSIANN) in capturing extreme precipitation and streamflow events in a humid region of southern China. It is found that, for extreme precipitation detection, both CMORPH and TMPA can capture most heavy and extreme precipitation, CMORPH works better on the rainfall distribution characteristics, while TMPA shows the best performance in capturing the extreme precipitation events with relatively low RMSE, ME and BIAS, and high R values of extreme precipitation indices. For the streamflow simulation, the combinations of different inputs and different models present different model performances. TMPA provides the most accurate hydrological model simulation results, while simulated streamflow forced by CMORPH exhibits considerable underestimation of streamflow. However, the combination of CMORPH and VIC model obtains the best efficiency for detecting extreme streamflow, followed by the combination of TMPA and SWAT model, which indicates the selection of a suitable model and input data is essential to obtain reliable and accurate results for the detection of extreme streamflow. This study is expected to provide a valuable reference for the application and comparison of multiple SPPs and models at watershed scales.
WOS关键词HIGH-RESOLUTION SATELLITE ; RIVER-BASIN ; PERSIANN-CDR ; RAINFALL ; MODEL ; CLIMATE ; TRMM ; WATER ; RUNOFF ; SOIL
资助项目National Key Research and Development Program of China[2016YFC0402709] ; National Natural Science Foundation of China[41890823] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23040304]
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000651220900001
出版者ELSEVIER SCIENCE INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/162795]  
专题中国科学院地理科学与资源研究所
通讯作者Xia, Jun; She, Dunxian
作者单位1.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, 8 Donghu South Rd, Wuhan 430072, Peoples R China
2.Wuhan Univ, Hubei Key Lab Water Syst Sci Sponge City Construc, Wuhan 430072, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, 11A Datun Rd, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Qiang,Xia, Jun,She, Dunxian,et al. Assessment of four latest long-term satellite-based precipitation products in capturing the extreme precipitation and streamflow across a humid region of southern China[J]. ATMOSPHERIC RESEARCH,2021,257:13.
APA Wang, Qiang,Xia, Jun,She, Dunxian,Zhang, Xiang,Liu, Jie,&Zhang, Yin.(2021).Assessment of four latest long-term satellite-based precipitation products in capturing the extreme precipitation and streamflow across a humid region of southern China.ATMOSPHERIC RESEARCH,257,13.
MLA Wang, Qiang,et al."Assessment of four latest long-term satellite-based precipitation products in capturing the extreme precipitation and streamflow across a humid region of southern China".ATMOSPHERIC RESEARCH 257(2021):13.

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来源：地理科学与资源研究所

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