In a previous paper we introduced a distribution-based method for Global Sensitivity Analysis (GSA), called PAWN, which uses cumulative distribution functions of model outputs to assess their sensitivity to the model's uncertain input factors. Over the last three years, PAWN has been employed in the environmental modelling field as a useful alternative or complement to more established variance-based methods. However, a major limitation of PAWN up to now was the need for a tailored sampling strategy to approximate the sensitivity indices. Furthermore, this strategy required three tuning parameters whose optimal choice was rather unclear. In this paper, we present an alternative approximation procedure that tackles both issues and makes PAWN applicable to a generic sample of inputs and outputs while requiring only one tuning parameter. The new implementation therefore allows the user to estimate PAWN indices as complementary metrics in multi-method GSA applications without additional computational cost.

在先前的论文中，我们介绍了一种基于分布的全局敏感性分析（GSA）方法，称为PAWN，该方法使用模型输出的累积分布函数来评估其对模型不确定输入因子的敏感性。在过去的三年中，PAWN已在环境建模领域中用作替代更完善的基于方差的方法的有用替代方法或补充方法。但是，到目前为止，PAWN的主要局限性在于需要采用量身定制的采样策略来近似灵敏度指标。此外，该策略需要三个调整参数，其最佳选择尚不清楚。在本文中，我们提出了一种替代的近似程序，可以解决这两个问题，并使PAWN适用于输入和输出的通用样本，而只需要一个调整参数。