Environmental models involve inherent uncertainties, the understanding of which is required for use by practitioners. One method of uncertainty quantification is global sensitivity analysis (GSA), which has been extensively used in environmental modeling. The suitability of GSA methods depends on the model, implementation, and computational complexity. Thus, we present a comparative analysis of different GSA methods (Morris, Sobol, FAST, and PAWN) applied to empirical fire spread models (Dry Eucalypt and Rothermel) and explain their implications. GSA methods such as PAWN, may not be able to explain all the interactions whereas methods such as Sobol can result in high computational costs for models with several parameters. We found that the Morris or the PAWN method should be prioritized over the Sobol and the FAST methods for a balanced trade-off between convergence and robustness under computational constraints. Additionally, the Sobol method should be chosen for more detailed sensitivity information.

环境模型涉及固有的不确定性，从业者需要对其进行理解。不确定性量化的一种方法是全局敏感性分析 (GSA)，它已广泛用于环境建模。GSA 方法的适用性取决于模型、实现和计算复杂性。因此，我们对应用于经验火灾蔓延模型（Dry Eucalypt 和 Rothermel）的不同 GSA 方法（Morris、Sobol、FAST 和 PAWN）进行了比较分析，并解释了它们的含义。诸如 PAWN 之类的 GSA 方法可能无法解释所有的相互作用，而诸如 Sobol 之类的方法可能会导致具有多个参数的模型的计算成本很高。我们发现 Morris 或 PAWN 方法应优先于 Sobol 和 FAST 方法，以便在计算约束下在收敛性和鲁棒性之间取得平衡。此外，应选择 Sobol 方法以获得更详细的灵敏度信息。