Enhancing sediment accumulation monitoring techniques in sewers will enable a better understanding of the build-up processes to develop improved cleaning strategies. Thermal sensors provide a solution to sediment depth estimation by passively monitoring temperature fluctuations in the wastewater and sediment beds, which allows evaluation of the heat-transfer processes in sewer pipes. This study analyses the influence of the flow conditions on heat-transfer processes at the water-sediment interface during dry weather flow conditions. For this purpose, an experimental campaign was performed by establishing different flow, temperature patterns, and sediment depth conditions in an annular flume, which ensured steady flow and room-temperature conditions. Numerical simulations were also performed to assess the impact of flow conditions on the relationships between sediment depth and harmonic parameters derived from wastewater and sediment-bed temperature patterns. Results show that heat transfer between water and sediment occurred instantaneously for velocities greater than 0.1 m/s, and that sediment depth estimations using temperature-based systems were barely sensitive to velocities between 0.1 and 0.4 m/s. A depth estimation accuracy of ±7 mm was achieved. This confirms the ability of using temperature sensors to monitor sediment build-up in sewers under dry weather conditions, without the need for flow monitoring.

Infiltration and inflow (I/I) into sanitary sewers causes numerous negative effects on the whole wastewater management system and ultimately, on the receiving waters. Consequently, a number of methods have been developed to analyse the performance of sewer systems with respect to I/I, including: distributed temperature sensing (DTS), closed-circuit television (CCTV) inspections, flow and conductivity measurements, automatic or grab sampling of ammonium, smoke testing, and visual inspection of manholes. Such methods were compared in an application to sanitary sewers in a small community and assessed with respect to their accuracy and ability to identify locations of sources of I/I, as well as temporal and spatial resolutions of the obtained results. Furthermore, different approaches to ammonium sampling during I/I monitoring campaigns were discussed. It was concluded that among the methods tested in this study, DTS had the highest temporal and spatial resolution, while ammonium grab sampling showed promise for initial screening of large catchments.

Infiltration and inflow (I/I) into sewers cause negative effects on the sewer system, wastewater treatment plant and environment. Identifying the causes and locating the inflows is necessary in order to address the I/I problem. This paper focuses on using distributed temperature sensing (DTS) for identifying, locating and characterising I/I into a sewer system during the end of winter-beginning of summer transition period under dry and wet weather conditions. During snowmelt, several locations with I/I were identified, while these locations did not show I/I during storm events after the snowmelt. In addition, during a very heavy storm after the snowmelt period, I/I was found at other locations. Therefore, DTS was demonstrated to be effective in identifying the type of I/I and in locating I/I. Finally, I/I monitoring campaigns in cold climates should take into account the variety of pathways of I/I during snowmelt and during rainfall.

In the urban water cycle, there are different ways of handling stormwater runoff. Traditional systems mainly rely on underground piped, sometimes named 'gray' infrastructure. New and so-called 'green/blue' ambitions aim for treating and conveying the runoff at the surface. Such concepts are mainly based on ground infiltration and temporal storage. In this work a methodology to create and compare different planning alternatives for stormwater handling on their pathways to a desired system state is presented. Investigations are made to assess the system performance and robustness when facing the deeply uncertain spatial and temporal developments in the future urban fabric, including impacts caused by climate change, urbanization and other disruptive events, like shifts in the network layout and interactions of 'gray' and 'green/blue' structures. With the Info-Gap robustness pathway method, three planning alternatives are evaluated to identify critical performance levels at different stages over time. This novel methodology is applied to a real case study problem where a city relocation process takes place during the upcoming decades. In this case study it is shown that hybrid systems including green infrastructures are more robust with respect to future uncertainties, compared to traditional network design.