Alpine basins are typically poorly gauged and inaccessible owing to the harsh prevailing environment and complex terrain. In this study, two representative satellite precipitation products (Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RTV7 and Integrated Multi-Satellite Retrievals for GPM (IMERG) Final Run Version 06) and two reanalysis precipitation products (China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR)) in the Yellow River Source Region (YRSR) were selected for evaluation and hydrological verification against gauge-observed (GO) data. Results show that the accuracy of these precipitation products in the warm season is higher than that in the cold season, and IMERG exhibits the best performance, followed by the CMADS, CFSR, and 3B42RTV7. Models that use the GO as input yielded satisfactory performance during 2008–2013, and precipitation products have poor simulation results. Although the model using the IMERG as input yielded unsatisfactory performance during 2014–2016, this did not affect the use of the IMERG as a potential data source for the YRSR. The model driven by the combination of GO and CMADS precipitation performed the best in all scenarios (R2=0.77, Nash–Sutcliffe efficiency (NSE)=0.72 at the Tangnaihai station; R2=0.53, NSE=0.48 at the Jimai station).
The number of precipitation products at the global scale has increased rapidly, and the accuracy of these products directly affects the accuracy of hydro-meteorological simulation and forecast. Therefore, the applicability of these precipitation products should be comprehensively evaluated to improve their application in hydrometeorology. This paper evaluated the performances of six widely used precipitation products in southwest China by quantitative assessment and contingency assessment. The precipitation products were Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42 version 7 (TRMM 3B42 V7), Global Satellite Mapping of Precipitation (GSMaP MVK), Integrated Multi-satellitE Retrievals for GPM final run (GPM IMERG Final), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network—Climate Data Record (PERSIANN-CDR), Climate Hazards Infrared Precipitation with Stations version 2.0 (CHIRPS V2.0), and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). From the above six products, the daily-scale precipitation data from 2001 to 2019 were chosen to compare with the measured data of the rain gauge, and the data from the gauges were classified by river basin and elevation. All precipitation products and measured data were evaluated by statistical indicators. Results showed that (1) GPM IMERG Final and CHIRPS V2.0 performed well in the Yarlung Zangbo River (YZ) basin, while GPM IMERG Final and GLDAS V2.0 performed well in the Lantsang River (LS), Nujiang River (NJ), Yangtze River (YT), and Yellow River (YL) basins; (2) in the upper and middle reaches of the YZ basin, GPM IMERG Final and CHIRPS V2.0 were outstanding in all evaluated products; downstream of the YZ basin, all six products performed well; and upstream of the LS and NJ, GPM IMERG Final, TRMM 3B42 V7, CHIRPS V2.0, and GLDAS V2.0 can be recommended as a substitute for measured data; and (3) GPM IMERG Final and GLDAS V2.0 can be seen as substitutes for measured data when elevation is below 4000 m. GPM IMERG Final and CHIRPS V2.0 were recommended when elevation is above 4000 m. This study provides a reference for data selection of hydro-meteorological simulation and forecast in southwest China and also provides a basis for multi-source data assimilation and fusion.
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