Real Time Control (RTC) has become an accepted technique for improving the performance of Urban Drainage Systems (UDS) due to its flexibility and sustainability. Numerous implementations of RTC have been reported during the last decades. At the same time, guideline documents and state-of-the-art reports have been published. Whereas the general aspects and challenges of planning and installation of RTC systems are well covered, there is a lack of information about the adequate equipment for RTC of UDS. After identifying and briefly discussing the basic components of RTC systems for UDS, this paper describes the specific components in detail. This comprises the introduction of available technologies for sensors, actuators, controllers and telemetry systems in the context of RTC and the discussion of their potential and limitations. Lessons learned from the field operational experiences and future trends and challenges are identified.

Dry detention ponds are commonly implemented to mitigate the impacts of urban runoff on receiving water bodies. They currently rely on static control through a fixed limitation of their maximum outflow rate. Real-Time Control (RTC) allows optimizing their performance by manipulation of an outlet valve. This study developed several enhanced RTC scenarios of a dry detention pond located at the outlet of a small urban catchment near Québec City, Canada. The catchment's runoff quantity and TSS concentration were simulated by a SWMM5 model with an improved wash-off formulation. The control procedures rely on rainfall detection, on measures of the pond's water height, and in some of the RTC scenarios on rainfall forecasts. The implemented RTC strategies allow a substantial improvement of the pond's performance - the TSS removal efficiency increases from 46% (current state) to about 90% - while remaining safe and taking a mosquito-breeding risk constraint into account.

Rainfall runoff models are frequently used for design processes for urban infrastructure. The most sensitive input for these models is precipitation data. Therefore, it is crucial to account for temporal and spatial variability of rainfall events as accurately as possible to avoid misleading simulation results. This paper aims to show the significant errors that can occur by using rainfall measurement resolutions in urban environments that are too coarse. We analyzed the spatial variability of rainfall events from two years with the validated data of 22 rain gauges spread out over an urban catchment of 125 km². By looking at the interstation correlation of the rain gauges for different classes of rainfall intensities, we found that rainfall events with low and intermediate intensities show a good interstation correlation. However, the correlation drops significantly for heavy rainfall events suggesting higher spatial variability for more intense rainstorms. Further, we analyzed the possible deviation from the spatial rainfall interpolation that uses all available rain gauges when reducing the number of rain gauges to interpolate the spatial rainfall for 24 chosen events. With these analyses we found that reducing the available information by half results in deviations of up to 25% for events with return periods shorter than one year and 45% for events with longer return periods. Assuming uniformly distributed rainfall over the entire catchment resulted in deviations of up to 75% and 125%, respectively. These findings are supported by the work of past research projects and underline the necessity of a high spatial measurement density in order to account for spatial variability of intense rainstorms.