Assessment of Rainwater Retention Efficiency in Urban Drainage Systems—Model Studies

Abstract

Around the world, there is growing interest in the use of rainwater retention, and in particular channel retention, as part of urban drainage systems. This is made possible by means of intentional damming of rainwater in the existing sewer collectors in order to maximise the use of gravitational spaces in the channels. This approach is particularly favourable for drainage types related to high levels of urban development, where construction of a building is difficult due to the low amount of space available. This article explains the results of a study concerning the efficiency assessment of three retention devices characterised by different hydraulic systems, one of which uses channel retention in its operation. The analysis conducted within the simulation study has demonstrated that the use of standard single-chamber reservoirs is the least efficient solution. A comparison of the functions of different hydraulic systems of retention reservoirs under equal conditions has shown that the necessary retention capacity of a single-chamber reservoir is many times greater in comparison to highly efficient solutions and it may constitute up to 582% of the reservoir’s capacity and works in conjunction with the channel retention system. At the same time, it has been demonstrated that the application of channel retention is not the most efficient solution for all hydraulic conditions for a drainage system or for all hydrological conditions. In addition, the article proposes a set of retention efficiency indices that may be used in the future assessment of individual rainwater storage solutions. Estimation of the necessary capacity of the retention facilities operating in specific hydraulic conditions was made on the basis of model tests—hydrodynamic modelling with the use of SWMM 5.0 software. The course of the research was planned using the theory of experiment planning (DOE) with the use of Statistica software, whereas efficiency indices were developed with the use of artificial neural networks (ANNs). The study results include practical and cognitive aspects. These may constitute guidelines for the designers and potential investors, as well as a tool with the aim of promoting the most efficient rainwater retention solutions in urban drainage systems.