Combined sewer overflows (CSOs) may represent a significant source of pollution, but they are difficult to quantify at a large scale (e.g. regional or national), due to a lack of accessible data. In the present study, we use a large scale, 6-parameter, lumped hydrological model to perform a screening level assessment of different CSO management scenarios for the European Union and United Kingdom, considering prevention and treatment strategies. For each scenario we quantify the potential reduction of CSO volumes and duration, and estimate costs and benefits. A comparison of scenarios shows that treating CSOs before discharge in the receiving water body (e.g. by constructed wetlands) is more cost-effective than preventing CSOs. Among prevention strategies, urban greening has a benefit/cost ratio one order of magnitude higher than grey solutions, due to the several additional benefits it entails. We also estimate that real time control may bring on average a CSO volume reduction of just above 20%. In general, the design of appropriate CSO management strategies requires consideration of context-specific conditions, and is best made in the context of an integrated urban water management plan taking into account factors such as other ongoing initiatives in urban greening, the possibility to disconnect impervious surfaces from combined drainage systems, and the availability of space for grey or nature-based solutions.

Environmental regulators frequently stipulate the modeling approaches required for water utilities managing sewer networks to demonstrate regulatory compliance. The performance of drainage systems with regard to combined sewer overflow (CSO) discharges is required to be assessed using urban drainage models to prove compliance before large investments can be authorized. However, as far as the authors are aware, the modeling approaches to demonstrate regulatory compliance currently provide no opportunity for considering model uncertainty. This paper therefore addresses a knowledge gap in the role of model uncertainty in environmental compliance studies by describing an objective uncertainty quantification process that enables the water utilities to evaluate and report the uncertainty in their modeling predictions and that is also transparent enough to satisfy regulators. The sewer network was modeled in InfoWorks CS software using a design storm defined by the regulator to test the performance of CSOs. Uncertainty in the model and input parameters was propagated using Monte Carlo simulations with Latin hypercube sampling, and the results were presented to show the trade-off between the infrastructure investment and the risk of spilling.

Regulations require U.K. water companies to reduce the number of properties at risk of sewer flooding. One of the potential causes of sewer flooding is the presence of persistent sediment deposits in sewers, such deposits are a common problem in many combined sewers. Although the regulations are risk based, there is a gap in the current knowledge on how the risk assessment is affected by the uncertainty in sewer sediment transport prediction. This paper describes the development of a methodology for estimating uncertainty in sewer sediment deposit depth predictions using existing empirically calibrated sediment load equations and Monte Carlo simulations combined with a response database. This methodology has been used to estimate the range of uncertainty of in-pipe deposit build-up predictions for a U.K. combined sewer system that suffered persistent deposition problems.

Real-time control (RTC) and sustainable drainage systems (SuDS) can be implemented simultaneously to enhance the performance of existing urban drainage systems (UDS). However, significant challenges arise when choosing the optimal locations due to hydraulic interactions between the different interventions and the high number of possible configurations. This paper presents a novel optimization–simulation framework to optimize the spatial allocation of flow control devices (FCDs) combined with SuDS for combined sewer overflow (CSO) spill mitigation within UDS. Optimal intervention schemes were identified by a genetic algorithm (GA), combining different numbers of FCDs installed in existing manholes with simplified SuDS implemented in different portions of the catchment. The methodology was tested on two case-study catchments with different characteristics to mitigate CSO spills during synthetic storm events. FCD-SuDS configurations were then validated during continuous rainfall series, resulting in CSO spill volumes reduction ranging between 11% and 45% compared to the baseline networks. The results demonstrate how the GA-based method can efficiently identify optimal placement schemes within UDS characterized by different distributions of in-pipe storage potential as well as hydrological responses to rainfall-runoff events, enhancing the combined benefits of the two decentralized solutions for mitigating CSO spills.

The selection of flow control device (FCD) location is an essential step for designing real-time control (RTC) systems in sewer networks. In this paper, existing storage volume-based approaches for location selection are compared with hydraulic optimisation-based methods using genetic algorithm (GA). A new site pre-screening methodology is introduced, enabling the deployment of optimisation-based techniques in large systems using standard computational resources. Methods are evaluated for combined sewer overflow (CSO) volume reduction using the CENTAUR autonomous local RTC system in a case study catchment, considering overflows under both design and selected historic rainfall events as well as a continuous 3-year rainfall time series. The performance of the RTC system was sensitive to the placement methodology, with CSO volume reductions ranging between −6 and 100% for design and lower intensity storm events, and between 15 and 36% under continuous time series. The new methodology provides considerable improvement relative to storage-based design methods, with hydraulic optimisation proving essential in relatively flat systems. In the case study, deploying additional FCDs did not change the optimum locations of earlier FCDs, suggesting that FCDs can be added in stages. Thus, this new method may be useful for the design of adaptive solutions to mitigate consequences of climate change and/or urbanisation.