Urban drainage systems are composed of subsystems. The ratio of the storage and discharge capacities of the subsystems determines the performance. The performance of the urban water system may deteriorate as a result of the change in the ratio of storage to discharge capacity due to aging, urbanisation and climate change. We developed the graph-based weakest link method (GBWLM) to analyse urban drainage systems. Flow path analysis from graph theory is applied instead of hydrodynamic model simulations to reduce the computational effort. This makes it practically feasible to analyse urban drainage systems with multi-decade rainfall series. We used the GBWLM to analyse the effect of urban water system aging and/or climate scenarios on flood extent and frequency. The case study shows that the results of the hydrodynamic models and the GBWLM are similar. The rainfall intensities of storm events are expected to increase by approximately 20% in the Netherlands due to climate change. For the case study, such an increase in load has little impact on the flood frequency and extent caused by gully pots and surface water. However, it could lead to a 50% increase in the storm sewer flood frequency and an increase in the extent of flooding.

A question arising when considering the changing climate is whether real time control (RTC) can be considered as a ‘No Regret’ measure, i.e. can RTC maintain its proven current added-value to reduce emissions from sewage systems in the future under altered rainfall patterns and often higher extreme rainfall intensities. This study explored four climate scenarios relevant for the lowland area of North-western Europe under two time horizons and proved that RTC’s performance only marginally decreased for a representative Flemish catchment under study. Based on this case study, it was found that effects of climate change will lead to, on average, 30–40% more overflow volume in 2050 and 35–65% more overflow volume in 2085. To restore the current situation, additional measures need to be taken, but RTC preserves its contribution to the reduction of overflows. The elaborated methodology is transposable to other locations provided that the necessary information is available.

Underground water infrastructure is essential for life in cities. The aging of these infrastructures requires maintenance strategies to maintain a minimum service level. Not all elements are equally important for the functioning of the infrastructure as a whole. Identifying the most critical elements in a network is crucial for formulating asset management strategies. The graph theory is presented as a means to identify the most critical elements in a network with respect to malfunctioning of the system as a whole. As opposed to conventional methods, the proposed method does not rely on iterative hydraulic calculations; instead, the structure of the network is taken as a starting point. In contrast to methods applied in practise, the results are independent on the chosen test-load. Because of the limited calculation effort, the method allows the analysis of large networks that are now, for practical reasons, beyond the scope of methods applied so-far.

Due to a variety of contaminants in floodwater, exposure to urban pluvial flooding may pose a health risk to humans. In-sewer defects may cause increased pluvial flooding, possibly increasing health risks. This paper addresses the impact of in-sewer defects on urban pluvial flooding and, subsequently, on infection probabilities for humans. As such, it provides a necessary input for risk-informed sewer maintenance strategies in order to preserve the hydraulic performance of a sewer system. Critical locations in sewer networks can be safeguarded through detecting changes in hydraulic properties of the sewer system, by using monitoring equipment or alternative inspection methods. Two combined sewer systems in The Netherlands with different characteristics are studied. The catchment-wide average infection probability was calculated using Quantitative Microbial Risk Assessment (QMRA) and flooding frequencies from Monte Carlo simulations with a hydrodynamic model. For the studied catchments, it is concluded that the occurrence of flooding is significantly affected by sediment deposits and, consequently, the infection probability as well. The impact of sediment deposits on infection probabilities depends on sewer systems characteristics and varies within the catchment. The results in this paper also demonstrate that further research on the relationship between flood duration and infection probabilities is required.