Graz University of Technology, Institute of Urban Water Management and Landscape Water Engineering, Stremayrgasse 10/1, A-8010 Graz, Austria.
Water Res. 2013 Sep 1;47(13):4600-11. doi: 10.1016/j.watres.2013.04.054. Epub 2013 May 10.
While several approaches for global sensitivity analysis (GSA) have been proposed in literature, only few applications exist in urban drainage modelling. This contribution discusses two GSA methods applied to a sewer flow and sewer water quality model: Standardised Regression Coefficients (SRCs) using Monte-Carlo simulation as well as the Morris Screening method. For selected model variables we evaluate how the sensitivities are influenced by the choice of the rainfall event. The aims are to i) compare both methods concerning the similarity of results and their applicability, ii) discuss the implications for factor fixing (identifying non-influential parameters) and factor prioritisation (identifying important parameters) and iii) rank the important parameters for the investigated model. It was shown that both methods lead to similar results for the hydraulic model. Parameter interactions and non-linearity were identified for the water quality model and the parameter ranking differs between the methods. For the investigated model the results allow a sound choice of output variables and rainfall events in view of detailed uncertainty analysis or model calibration. We advocate the simultaneous use of both methods for a first model assessment as they allow answering both factor fixing and factor prioritisation at low computational cost.
尽管文献中已经提出了几种全局敏感性分析（GSA）方法，但在城市排水建模中，仅有少数应用。本研究讨论了两种应用于污水流量和污水水质模型的 GSA 方法：使用蒙特卡罗模拟的标准化回归系数（SRCs）和 Morris 筛选方法。针对选定的模型变量，我们评估了敏感性如何受到降雨事件选择的影响。目的是：i）比较两种方法在结果相似性及其适用性方面的差异，ii）讨论因子固定（确定非影响参数）和因子优先级排序（确定重要参数）的影响，以及 iii）对所研究模型的重要参数进行排序。结果表明，对于水力模型，两种方法得出的结果相似。对于水质模型，确定了参数相互作用和非线性，并且两种方法的参数排序不同。对于所研究的模型，结果允许根据详细的不确定性分析或模型校准，选择输出变量和降雨事件。我们提倡同时使用这两种方法进行初步模型评估，因为它们允许在低计算成本下回答因子固定和因子优先级排序问题。
