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Evaluation and Hydrological Application of CMADS reanalysis precipitation data against Four Satellite precipitation Products in the Upper Huaihe River Basin, China

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Journal of Meteorological Research 2020年第5期34卷 1096-1113页

作者： Shanhu JIANG Ruolan LIU Liliang REN Menghao WANG Junchao SHI Feng ZHONG Zheng DUAN State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai UniversityNanjing 210098China Hydrology and Water Resources Monitoring and Forecasting Center Ministry of Water ResourcesBeijing 100053China Department of Physical Geography and Ecosystem Science L und UniversityLund S-22362Sweden

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 Soil and Water Assessment Tool(SWAT) model(CMADS)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 the Xin’anjiang(XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland 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 rootmean-square error(RMSE) values of CMADS are optimal, although it exhibits certain 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,

关键词： reanalysis precipitation data China Meteorological Assimilation Driving datasets for the Soil and Water Assessment Tool(SWAT)model(CMADS) satellite precipitation hydrological evaluation Xin’anjiang(XAJ)hydrological model

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