Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

Remote Sensing

Hao Guo¹  Baofu Li¹  Lishu Lian¹  Xiangzhen Wang¹  Yunqian Wang¹  Yaning Chen² 

[1] School of Geography and Tourism, Qufu Normal University, Rizhao 276826, China;State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;

关键词: CHIRPS;    GSMaP;    TRMM 3B42 V7;    SM2RAIN;    Qaraqash River;    applicability evaluation;   

DOI  :  10.3390/rs12182886

来源: DOAJ

【 摘 要 】

Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R²). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R² > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas.

【 授权许可】

Unknown