Parametric sensitivity analysis (SA) aims to select the sensitive parameters that most significantly affect the model output variables, which helps to improve model optimization efficiency by adjusting a small number of sensitive parameters instead of all adjustable parameters. The qualitative and quantitative SA methods have been commonly used to quantify the sensitive parameters of the models. However, the response surface model based quantitative SA method was rarely used. Taking the simulation of a quasi twodimensional (quasi-2D) groundwater model as an example, this study systematically assess eight SA methods divided into three categories (qualitative SA, quantitative SA, and the response surface model-based quantitative SA). The study validates the effectiveness of these methods by comparing the parameter sensitivity results, and also demonstrates the efficiency of these methods by determining the minimum sample size required. Using the minimum samples means the least number of model runs. The results show that P1 and P2 are the most sensitive parameters of the quasi-2D model for simulating groundwater table elevation. Except for local method, four global qualitative SA methods obtain reasonable parameter sensitivity rankings using 200 samples, but the parameter sensitivity scores fail. For obtaining accurate sensitivity scores, at least 2000 samples are required by the quantitative SA methods. However, for the response surface model-based quantitative SA method, 60 samples are sufficient to obtain accurate sensitivity scores, demonstrating that the method is an effective and highly efficient, and should be recommended as the primary parametric SA method, especially for the complex models with large computational demand.

中文翻译：

---

基于准二维地下水模型的参数敏感性分析方法评估

参数敏感性分析（SA）旨在选择对模型输出变量影响最显着的敏感参数，通过调整少量敏感参数而不是所有可调参数，有助于提高模型优化效率。定性和定量 SA 方法已被普遍用于量化模型的敏感参数。然而，很少使用基于响应面模型的定量SA方法。本研究以模拟准二维（quasi-2D）地下水模型为例，系统评估了八种SA方法，分为三类（定性SA、定量SA和基于响应面模型的定量SA）。该研究通过比较参数敏感性结果验证了这些方法的有效性，并且还通过确定所需的最小样本量来证明这些方法的效率。使用最少样本意味着最少的模型运行次数。结果表明，P1和P2是模拟地下水位高程的准二维模型中最敏感的参数。除局部方法外，四种全局定性 SA 方法使用 200 个样本获得了合理的参数灵敏度排名，但参数灵敏度得分失败。为了获得准确的灵敏度分数，定量 SA 方法至少需要 2000 个样本。然而，对于基于响应面模型的定量SA方法，60个样本足以获得准确的灵敏度分数，证明该方法是一种有效且高效的方法，应推荐为主要参数SA方法，