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Evaluation of multisource precipitation input for hydrological modeling in an alpine basin: a case study from the Yellow River Source Region (China)
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  • Pengfei Gu,
  • Gaoxu WANG,
  • Guodong Liu,
  • Yongxiang Wu,
  • Hongwei Liu,
  • Xi Jiang,
  • Tao Liu
Pengfei Gu
Sichuan University

Corresponding Author:[email protected]

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Gaoxu WANG
Nanjing Hydraulic Research Institute
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Guodong Liu
Sichuan University
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Yongxiang Wu
Nanjing Hydraulic Research Institute
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Hongwei Liu
Nanjing Hydraulic Research Institute
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Xi Jiang
Nanjing Hydraulic Research Institute
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Tao Liu
Nanjing Hydraulic Research Institute
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Abstract

Alpine basins are essential to the conservation of water resources. However, they are typically poorly gauged and inaccessible, owing to the harsh prevailing environment and complex terrain. To investigate the influences of different precipitation inputs on hydrological modeling in alpine basins, two representative satellite precipitation products [Tropical Rainfall Measuring Mission (TRMM) and Integrated Multi-Satellite Retrievals for GPM (IMERG)] 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 indicates that the accuracy of these precipitation products in the warm season is higher than that in the cold season, and IMERG has the best performance, followed by CMADS, CFSR, and TRMM. TRMM seriously overestimates high rainfall of greater than 10 mm/day. CFSR overestimates moderate precipitation events of 1–10 mm/d, while CMADS underestimates the effects of precipitation events of 1–20 mm/d. Models using the GO as input yielded satisfactory performance during 2008–2013, and precipitation products have poor simulation results. Although the model using IMERG as input yielded unsatisfactory performance during 2014–2016, this did not affect the use of IMERG as a potential data source for YRSR. After bias correction, the quality of CFSR improves significantly with R2 and NSE increasing by 0.25 and 0.31 at Tangnaihai station, respectively. Model driven by the combination of GO and CMADS precipitation performed the best in all scenarios (R2 = 0.77, NSE = 0.72 at Tangnaihai station; R2 = 0.53, NSE = 0.48 at Jimai station). These results can provide reference data, and research ideas, for improved hydrological modeling of alpine basins.
07 Aug 2021Submitted to Hydrological Processes
12 Aug 2021Submission Checks Completed
12 Aug 2021Assigned to Editor
12 Aug 2021Reviewer(s) Assigned
12 Aug 2021Review(s) Completed, Editorial Evaluation Pending
01 Feb 2022Published in Hydrology Research volume 53 issue 2 on pages 314-335. 10.2166/nh.2022.105