Evaluation of multisource precipitation input for hydrological modeling in an Alpine basin: a case study from the Yellow River Source Region (China)
Author(s) -
Pengfei Gu,
Gaoxu Wang,
Guodong Liu,
Yongxiang Wu,
Hongwei Liu,
Xi Jiang,
Tao Liu
Publication year - 2022
Publication title -
hydrology research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 48
eISSN - 1996-9694
pISSN - 0029-1277
DOI - 10.2166/nh.2022.105
Subject(s) - environmental science , precipitation , climatology , terrain , global precipitation measurement , data assimilation , satellite , drainage basin , soil and water assessment tool , climate forecast system , meteorology , china , geology , geography , cartography , aerospace engineering , streamflow , engineering , archaeology
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 data (GO). 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).
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