Probabilistic Models for Analysis of Urban Runoff Control Systems

Abstract

Given the significant urban runoff impacts on many receiving waters and the massive costs of future investments in drainage infrastructure, the design of urban runoff control systems must be cost-effective. Cost-effective design requires that various runoff control system alternatives be investigated at the planning stage so that cost-effective runoff control systems can be identified for design level analysis. To analyze the runoff control performance of various combinations of runoff control systems at the planning stage, efficient screening models are acutely needed. For this purpose, analytical probabilistic models were applied to analyze the runoff quantity/quality control performance of various combinations of storage and treatment systems. These analytical probabilistic models are developed with derived probability distribution theory whereby the input meteorology to the catchment is described by probability density functions (PDFs) of the meteorological characteristics that are transformed by hydrologic/hydraulic functions to PDFs of the system performance variables. The resulting PDFs are then used to determine the average performance conditions. These models provide closed-formed solutions of the performance equations that are highly efficient in both a conceptual and computational sense. As a result, they are particularly useful for the screening analysis of urban runoff control alternatives.