ABSTRACT

Assessing uncertainties of urban drainage models is important for their applications. While most attention in the literature was paid to large comprehensive models, little has been published about Low-Complexity Conceptual Models (LCCMs). This paper explores the uncertainties inherent to a conceptual, data-based proprietary model StormTac Web, simulating annual urban runoff quantity and quality, and serving here as an example of a LCCM. The analyses were demonstrated for a small urban catchment, Sätra in Stockholm, Sweden, using the Law of Propagation of Uncertainties and Morris screening methods. The results indicate that the uncertainty of the modelled annual runoff quality (about 30%) is greater than that of annual runoff volumes (about 24%), and the latter uncertainties can significantly contribute to the uncertainty in runoff quality. In computations of pollutant loads, the most sensitive inputs were land-use specific parameters, including the annual volumetric runoff coefficients and default pollutant concentrations for various land uses.

摘要

评估城市排水模型的不确定性对其应用很重要。虽然文献中的大部分注意力都集中在大型综合模型上，但关于低复杂度概念模型 (LCCM) 的文章却很少。本文探讨了基于概念的、基于数据的专有模型 StormTac Web 所固有的不确定性，模拟年度城市径流数量和质量，并在此作为 LCCM 的示例。使用不确定性传播定律和莫里斯筛选方法对瑞典斯德哥尔摩的 Sätra 小城市集水区进行了分析。结果表明，模拟的年径流质量的不确定性（约30%）大于年径流量的不确定性（约24%），后者的不确定性可以显着增加径流质量的不确定性。