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.

Hydrodynamic models are used to analyse water networks (water distribution, drainage, surface water, district heating, etc.). The non-linear nature of water flows necessitates the use of iterative solution methods in hydraulic modelling. This requires a relatively large computational effort. To reduce this effort, networks, network forcing and/or the flow in networks are often simplified and analysed using the Graph Theory. The simplification options depend on the network characteristics. There are many topological features to describe Graph-based networks. In this paper, these characteristics are summarised, applied on 7 urban drainage networks and discussed. As the topological features do not describe the networks in a uniform manner, a new type of topological characterisation of looped drainage networks (Network Linearisation Parameter, NLP) is proposed based on linearized hydraulics and bottlenecks identified in paths to outfalls.

Drinking water distribution networks (WDNs) are a crucial infrastructure for life in cities. Deterioration of this ageing, and partly hidden from view, infrastructure can result in losses due to leakage and an increased contamination risk. To counteract this, maintenance strategies are required to maintain the service level. Information on the most critical elements of a WDN, with respect to the functioning of the system as a whole, is essential for prioritising maintenance or rehabilitation activities. In this study a Graph theory based method is developed and applied for efficiently identifying the most critical elements. The main advantage of this method is that it avoids the need to perform elaborate hydrodynamic model calculations. Instead, the structure of the network is the main starting point. The results show that the structure of the network is more decisive than the hydraulics with respect to the criticality of the system’s performance as a whole. Results depict that the suggested approach is applicable not only to the main (primary) network, but also to the capillaries which are normally beyond the scope of the traditional methods applied so-far because of the complexity of the networks and the required calculation time.

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.