Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs.

动态地球和环境系统模型通常是计算密集型的，并且具有许多不确定参数，需要高度参数化。总之，这些特征严重限制了全局灵敏度分析（GSA）在高维模型中的适用性，因为通常需要大量的模型运行才能实现收敛并提供可靠的评估。矛盾的是，在环境建模文献中，只有30％的GSA应用程序研究了具有20多个参数的模型，这表明GSA在应被证明最有用的问题上未得到充分利用。我们基于基于引导程序的群集开发了一种新颖的分组策略，该策略可将GSA有效地应用于高维模型。我们还提供了一种用于评估GSA稳定性和收敛性的鲁棒性新指标。对于两个参数分别为50和111的模型，我们证明了启用分组功能的GSA提供的结果对采样变异性具有很高的鲁棒性，同时可以与数量较少的模型运行进行收敛。