Evaluation and Hydrological Application of CMADS Reanalysis Precipitation Data against Four Satellite Precipitation Products in the Upper Huaihe River Basin, China

• 作者: Feng ZHONG Junchao SHI Liliang REN Menghao WANG Ruolan LIU Shanhu JIANG Zheng DUAN
• 来源期刊: 气象学报（英文版）
• 年，卷（期）: 2020,（5）
• 所属期刊栏目: ORIGINAL PAPER
• 页码范围: 1096-1113
• 页数: 18页
• 语种: 英文

摘要

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 Predic-tion 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 for-cing 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 as-sessed. The results show that: (1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, fol-lowed 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 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 cal-ibration 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 hydro-logical evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in data-scarce regions with similar climates and topography in the Global Precipitation Measurement (GPM) era.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	Shanhu JIANG		1	0	0.0	0.0
2	Ruolan LIU		1	0	0.0	0.0
3	Liliang REN		1	0	0.0	0.0
4	Menghao WANG		1	0	0.0	0.0
5	Junchao SHI		1	0	0.0	0.0
6	Feng ZHONG		1	0	0.0	0.0
7	Zheng DUAN		1	0	0.0	0.0