Hydrology Research (Jun 2021)

An evaluation of CMIP5 precipitation simulations using ground observations over ten river basins in China

  • Xiuqin Yang,
  • Bin Yong,
  • Zhiguo Yu,
  • Yuqing Zhang

DOI
https://doi.org/10.2166/nh.2021.151
Journal volume & issue
Vol. 52, no. 3
pp. 676 – 698

Abstract

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Using the precipitation measurements obtained from 2,419 ground meteorological stations over China from 1960 to 2005 as benchmark, the performance of 21 single-mode precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were evaluated using Taylor diagrams and several statistical metrics. Based on statistical metrics, the models were ranked in terms of their ability to reproduce similar patterns in precipitation relative to the observations. Except in Southeast and Pearl river basins, research results show that all model ensemble means overestimate in the rest of the river basins, especially in Southwest and Northwest. The performance of CMIP5 models is quite different among each river basin; most models show significant overestimation in Northwest and Yellow and significant underestimations in Southeast and Pearl. The simulations are more reliable in Songhua, Liao, Yangtze, and Pearl than in other river basins according to spatial distribution and interannual variability. No individual model performs well in all the river basins both spatially and temporally. In Songhua, Liao, Yangtze, and Pearl, precipitation indices are more consistent with observations, and the spread among models is smaller. The multimodel ensemble selected from the most reasonable models indicates improved performance relative to all model ensembles. HIGHLIGHTS The performance of CMIP5 models were evaluated using the precipitation measurements obtained from meteorological stations.; The performance of CMIP5 models is quite different among each river basin.; No individual model performs well in all the river basins both spatially and temporally.; The multimodel ensemble selected from the most reasonable models indicates improved performance relative to all model ensembles.;

Keywords