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.

Combined sewage overflows (CSOs) are a common consequence of heavy rainfall events and can have significant implications for water quality in receiving waterbodies. With climate change, these events are becoming more frequent and intense, placing greater pressure on aquatic ecosystems. To prevent water pollution, it is essential to utilize numerical tools to investigate, forecast, and establish control measures for CSOs. Typically, these tools involve a dynamic model for flow simulation combined with either a detailed model for pollutants or a simplified event mean concentration (EMC) calculation. However, both approaches have drawbacks: a detailed model requires extensive calibration time, while the EMC does not account for system dynamics. To overcome these issues, a novel system was developed that integrates the dynamic nature of the detailed model with the rapid calibration of the EMC. This model employs two distinct concepts for pollution modeling: one for soluble compounds and one for suspended solids. The resulting model was evaluated at multiple locations with varying hydraulic dynamics, demonstrating its potential utility at any location where a dynamic model of the sewer system is available.

The performance of most urban drainage systems is adversely affected by unintended connections of groundwater and surface water, often denoted as inflow and infiltration (I&I). Various methods exist to locate and characterise these effects. Yet, it remains difficult to quantify them accurately, especially in terms of spatial distribution over a larger drainage area. One of the reasons for this is the lack of sufficient high-quality sewer flow measurements at a high temporal resolution, which would enable the calibration of detailed spatio-temporal relationships between rainfall and I&I flows. In this paper, a methodology is presented for deriving sewer flow time series from operational measurements at pumping stations, and the results from four pilot locations are discussed. It shows the potential of the methodology to be implemented at a large scale and to contribute to a better understanding and remediation of I&I in urban drainage management planning.