Adhikari Riwaz Kumar, Yilmaz Abdullah Gokhan, Mainali Bandita, Dyson Phil
Department of Engineering, La Trobe University, Melbourne 3086, VIC, Australia.
School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney 2109, Australia.
Sci Total Environ. 2024 Oct 1;945:174015. doi: 10.1016/j.scitotenv.2024.174015. Epub 2024 Jun 18.
Accurate estimation of climate change impacts on catchment hydrology is essential for effective future water management. The efficacy of such estimations is dependent on proper climate model selection. In this study, an attempt was made to formulate a methodology for climate model selection, evaluating eight climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models were assessed for their ability to simulate variables used in hydrological studies and large-scale atmospheric circulation influencing rainfall in Australia. Five statistical indicators Root Mean Square Error (RMSE), Spatial Correlation (SC), Percentage Bias (Pbias), Normalized Root Mean Square Error (NRMSE), and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the performance, and the models were ranked through Compromise Programming (CP), a multiple criteria decision making technique. Results show that HadGEM3-GC31-LL performed well in most of the categories considered and was top top-ranked model overall followed by GFDL-ESM4, CESM2-CAM6-RT, and CanESM5 for Australia. Conversely, MIROC6 consistently ranked lower in most of the categories. In the context of simulating hydrological variables, CESM2-CAM6-RT, HadGEM3-GC31-LL, and GFDL-ESM4 emerged as the top three models. The robustness of the proposed methodology suggests its applicability for model selection, making it a replicable approach for climate change impact assessment studies in diverse regions.
准确估算气候变化对流域水文的影响对于未来有效的水资源管理至关重要。此类估算的有效性取决于气候模型的恰当选择。在本研究中，尝试制定一种气候模型选择方法，对耦合模式比较计划第六阶段（CMIP6）的八个气候模型进行评估。评估这些模型模拟水文研究中使用的变量以及影响澳大利亚降雨的大规模大气环流的能力。使用均方根误差（RMSE）、空间相关性（SC）、偏差百分比（Pbias）、归一化均方根误差（NRMSE）和纳什-萨特克利夫效率（NSE）这五个统计指标来评估性能，并通过多准则决策技术折衷规划（CP）对模型进行排名。结果表明，HadGEM3-GC31-LL在大多数考虑的类别中表现良好，总体上是排名最高的模型，其次是GFDL-ESM4、CESM2-CAM6-RT和澳大利亚的CanESM5。相反，MIROC6在大多数类别中一直排名较低。在模拟水文变量方面，CESM2-CAM6-RT、HadGEM3-GC31-LL和GFDL-ESM4成为排名前三的模型。所提出方法的稳健性表明其适用于模型选择，使其成为不同地区气候变化影响评估研究的可复制方法。
