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<p>The selection of reliable general circulation models (GCMs) for hydrological modeling is crucial in the context of climate change. In this study, a framework for selecting a GCM for runoff simulations is proposed. The ranking of GCMs considers the different impacts of precipitation and temperature on runoff, which are quantified by the weights assigned to both variables based on the climate elasticity model. In addition to evaluating the historical simulation performance of GCMs for precipitation and temperature using comprehensive rating metrics and the group decision-making method, we also assess their future simulation performances for these variables based on the comprehensive rating metrics and model-as-truth tests. The methodology was applied to select the best GCM for runoff simulations in the You River Basin (YRB) and the Chengbi River Basin (CRB), which differ in spatial scale. The results indicated that the GCM performances differed for different grids and climate variables, and precipitation was assigned a weight greater than 0.6, approximately twice that of temperature. The climate models, NorESM2-MM and CMCC-ESM2, exhibited good historical and future performance in the YRB and CRB, respectively. The GCM ranking results for both basins were reliable, and the best selected GCM performed significantly better than the multimodel ensemble did in the future period, preserving extreme values more effectively. The methodology introduced in this study can provide a scientific basis for hydrologists to select a reliable GCM for runoff modeling under climate change.</p>

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A Methodology for Selecting Climate Models Considering Precipitation and Temperature for Modeling Runoff

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Keke Huang,
  • Chongxun Mo,
  • Na Li,
  • Gang Tang,
  • Yi Huang,
  • Lingling Tang,
  • Yugong Li

摘要

The selection of reliable general circulation models (GCMs) for hydrological modeling is crucial in the context of climate change. In this study, a framework for selecting a GCM for runoff simulations is proposed. The ranking of GCMs considers the different impacts of precipitation and temperature on runoff, which are quantified by the weights assigned to both variables based on the climate elasticity model. In addition to evaluating the historical simulation performance of GCMs for precipitation and temperature using comprehensive rating metrics and the group decision-making method, we also assess their future simulation performances for these variables based on the comprehensive rating metrics and model-as-truth tests. The methodology was applied to select the best GCM for runoff simulations in the You River Basin (YRB) and the Chengbi River Basin (CRB), which differ in spatial scale. The results indicated that the GCM performances differed for different grids and climate variables, and precipitation was assigned a weight greater than 0.6, approximately twice that of temperature. The climate models, NorESM2-MM and CMCC-ESM2, exhibited good historical and future performance in the YRB and CRB, respectively. The GCM ranking results for both basins were reliable, and the best selected GCM performed significantly better than the multimodel ensemble did in the future period, preserving extreme values more effectively. The methodology introduced in this study can provide a scientific basis for hydrologists to select a reliable GCM for runoff modeling under climate change.