Evaluation and Hydrological Application of CMADS against TMPA 3B42V7, CMORPH-BLD, CHIRPS, and PERSIANN-CDR in the Upper Huaihe River Basin, China

Satellite- and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) (SWAT无全称)reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs; i.e., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (TMPA 3B42V7), Climate Prediction Center (CPC) morphing technique satellite–gauge blended product (CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by Xin’anjiang (XAJ) hydrological model separately under two scenarios with different calibration procedures. The performance of CMADS precipitation product for mainland China was also assessed. The results show that: (1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient (CC) and root-mean-square error (RMSE) values of CMADS are optimal, although it exhibits significant negative relative bias (BIAS; −22.72%); (2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation; (3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash–Sutcliffe efficiency (NSE) values (0.85 and 0.75 for calibration period and validation period, respectively); and (4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluation show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary and the research community should focus on the improvements and assure its quality for eastern Asia. This study can serve as a reference for selecting precipitation products in data-scarce regions with similar climates and topography in GPM-era(GPM无全称).