Most of the commonly available sensitivity analysis methods cannot reliably compute the interaction effect. Even though the Sobol’ type methods that use Monte Carlo simulation can evaluate the interaction effect, the result is either inaccurate or requires an extraordinary number of model runs to obtain a reasonable estimate. In this study, we evaluate the sparse polynomial chaos (SPC) method as a reasonable way to estimate the interaction effect. This method is evaluated on two mathematical test functions (Ishigami and Sobol’ G) and two hydrologic models (HBV-SASK and SAC-SMA). Our results show the SPC method needs about a sample size of 30 to 70 times the number of dimensions of the parameter space to evaluate the interaction effects of hydrologic models. Our findings are significant for hydrologic simulation and model calibration, as we aim to improve the understanding of complex interactions among model components and to reduce model uncertainty.

大多数常用的灵敏度分析方法无法可靠地计算相互作用效果。尽管使用蒙特卡洛模拟的Sobol型方法可以评估相互作用效果，但结果要么不准确，要么需要大量模型运行才能获得合理的估计。在这项研究中，我们评估稀疏多项式混沌（SPC）方法作为一种合理的方式来评估交互作用。该方法在两个数学测试函数（Ishigami和Sobol'G）和两个水文模型（HBV-SASK和SAC-SMA）上进行了评估。我们的结果表明，SPC方法需要的样本大小约为参数空间维数的30到70倍，才能评估水文模型的相互作用效果。我们的发现对于水文模拟和模型校准具有重要意义，