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<p>The increasing number of global climate models (GCMs) has intensified the uncertainty of future runoff projection. A multi-model ensemble (MME) approach has emerged to address this issue. However, ambiguities remain regarding whether to include all GCMs or select a subset based on performance, and whether to assign equal or unequal weights to GCMs during MME construction. This study used three MME generation methods which are climate-based, mixed climate-flow-based and flow-based approaches, coupled with two GCM selection methods (all GCMs and five best-performing GCMs), and two weight assignment methods (equal and unequal) to prepare the best MME to assess their performances in simulating historical runoff and reducing uncertainty in future simulations. The GCMs were selected from 20 coupled model intercomparison project phase 6 (CMIP6) models, while Storm Water Management Model (SWMM) was used for long-term runoff simulation based on MMEs for four shared socioeconomic pathway scenarios (SSPs). Four evaluation metrics were used to verify the performance of each method. The uncertainty in future runoff simulations was quantified using the reliability ensemble averaging (REA) method. The flow-based MME approach outperformed the other methods in simulating historical runoff and reducing uncertainty in future runoff simulations. The efficient subset of GCMs combined with unequal weight assignment proved more effective than using all GCMs with equal weights. The results of this study offer valuable insights for researchers conducting future runoff projections using GCMs.</p>

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The Ideal Flow-Based Multi-Model Ensemble Strategy for Projecting Future Runoff with CMIP6 GCMs

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Seung Taek Chae,
  • Mohammed Magdy Hamed,
  • Shamsuddin Shahid,
  • Eun-Sung Chung

摘要

The increasing number of global climate models (GCMs) has intensified the uncertainty of future runoff projection. A multi-model ensemble (MME) approach has emerged to address this issue. However, ambiguities remain regarding whether to include all GCMs or select a subset based on performance, and whether to assign equal or unequal weights to GCMs during MME construction. This study used three MME generation methods which are climate-based, mixed climate-flow-based and flow-based approaches, coupled with two GCM selection methods (all GCMs and five best-performing GCMs), and two weight assignment methods (equal and unequal) to prepare the best MME to assess their performances in simulating historical runoff and reducing uncertainty in future simulations. The GCMs were selected from 20 coupled model intercomparison project phase 6 (CMIP6) models, while Storm Water Management Model (SWMM) was used for long-term runoff simulation based on MMEs for four shared socioeconomic pathway scenarios (SSPs). Four evaluation metrics were used to verify the performance of each method. The uncertainty in future runoff simulations was quantified using the reliability ensemble averaging (REA) method. The flow-based MME approach outperformed the other methods in simulating historical runoff and reducing uncertainty in future runoff simulations. The efficient subset of GCMs combined with unequal weight assignment proved more effective than using all GCMs with equal weights. The results of this study offer valuable insights for researchers conducting future runoff projections using GCMs.