A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty quantifications. On one hand, there are numerous available methods: interpolation, regression, autoregressive, machine learning methods, etc. On the other hand, there are many methods and criteria to estimate efficiencies of these methods, but uncertainties on the interpolated values are rarely calculated. Furthermore, while they are estimated according to standard methods, the prediction uncertainty is not taken into account: a discussion is thus presented on the uncertainty estimation of interpolated/extrapolated data. Finally, some suggestions for further research and a new method are proposed.

Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Because physical urban water infrastructure has life expectancies of up to 100 years or more, contemporary urban drainage systems are strongly influenced by historical decisions and implementations. The current decisions taken in sewer asset management will, therefore, have a long-lasting impact on the functionality and quality of future services provided by these networks. These decisions can be supported by different approaches ranging from various inspection techniques, deterioration models to assess the probability of failure or the technical service life, to sophisticated decision support systems crossing boundaries to other urban infrastructure. This paper presents the state of the art in sewer asset management in its manifold facets spanning a wide field of research and highlights existing research gaps while giving an outlook on future developments and research areas.

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.

This paper introduces the application of fibre-optic distributed temperature sensing (DTS) in combined sewer systems. The DTS-technique uses a fibre-optic cable that is inserted into a combined sewer system in combination with a laser instrument that performs measurements and logs the data. The DTS-technique allows monitoring in-sewer temperatures with dense spatial and temporal resolutions. The installation of a fibre-optic cable in a combined sewer system has proven feasible. The use of a single instrument in an easy accessible and safe location that can simultaneously monitor up to several hundreds of monitoring locations makes the DTS set-up easy in use and nearly free of maintenance. Temperature data from a one-week monitoring campaign in an 1,850 m combined sewer system shows the level of detail with which in-sewer processes that affect wastewater temperatures can be studied. Individual discharges from house-connections can be tracked in time and space. With a dedicated cable configuration the confluence of wastewater flows can be observed with a potential to derive the relative contributions of contributary flows to a total flow. Also, the inflow and in-sewer propagation of stormwater can be monitored.