Infiltration and inflow (I/I) of extraneous water in separate sewer systems are serious concerns in urban water management for their environmental, social and economic consequences. Effective reduction of I/I requires knowing where excess water ingress and illicit connections are located. The present study focuses on I/I detection in the foul sewer network of a catchment in Trondheim, Norway, during a period without snowmelt or groundwater infiltration. Fiber-optic distributed temperature sensing (DTS) was used for the first time in Norway to detect I/I sources in tandem with closed-circuit television inspection (CCTV) and smoke testing. DTS was an accurate and feasible method for I/I detection, though it cannot identify exact types of failure and sources of I/I. Therefore, other complementary methods must be used, e.g., CCTV or smoke testing. CCTV was not completely useful, however, in confirming the DTS results. This study provides practical insights for the rehabilitation and repair of sewer networks that suffer from the undesirable I/I of extraneous water.

Infiltration and inflow (I/I) of unwanted water in separate urban sewer networks are critical issues for sustainable urban water management. Accurate quantification of unwanted water I/I from individual sources into a sewer system is an essential task for assessing the status of the sewer network and conducting rehabilitation measures. The study aim was to quantify extraneous water I/I into a sanitary sewer network by a temperature-based method, i.e., fiber-optic distributed temperature sensing (DTS), which was applied for the first time in a separate sewer network of a catchment in Trondheim, Norway. The DTS technology is a relatively new technology for sewer monitoring, developed over the past decade. It is based on continual temperature measurement along a fiber-optic cable installed in the sewer network. The feasibility of this method has been tested in both experimental discharges and for the rainfall-derived I/I. The results achieved from the monitoring campaign established the promising applicability of the DTS technique in the quantification analysis. Furthermore, the application of this method in quantifying real-life, rainfall-derived I/I into the sewer system was demonstrated and verified during wet weather conditions.

Climate change can cause serious problems for future hydropower plant projects and make them less economically justified. Changing precipitation patterns, rising temperatures, and abrupt snow melting affect river stream patterns and hydropower generation. Thus, study of climate change impacts during the useful life of a hydropower dam is essential and its outcome should be considered in assessing long-term dam feasibility. The aim of this research is to evaluate the impacts of climate change on future hydropower generation in the Karun-III dam located in the southwest region of Iran in two future tri-decadal periods: near (2020–2049) and far (2070–2099). Had-CM3 general circulation model predictions under A2 and B2 SRES scenarios were applied, and downscaled by a statistical downscaling model (SDSM). An artificial neural network (ANN) and HEC-ResSim reservoir model respectively simulated the rainfall–runoff process and hydropower generation. The projections showed that the Karun-III dam catchment under the two scenarios will generally become warmer and wetter with a slightly larger increase in annual precipitation in the near than the far future. Runoff followed the precipitation trend by increasing in both periods. The runoff peak also switched from April to March in both scenarios, due to higher winter precipitation, and earlier snowmelt, which was caused by temperature rise. According to both scenarios, hydropower generation increased more in the near future than in the far future. Annual average power generation increased gradually by 26.7–40.5% under A2 and by 17.4–29.3% under B2 in 2020–2049. In the far period, average power generation increased by 1.8–8.7% in A2 and by 10.5–22% under B2. In the near future, A2 showed energy deduction in the months of June and July, while B2 revealed a decrease in the months of April and June. Additionally, projections in the 2070–2099 under A2 exhibited energy reduction in the months of March through July, while B2 revealed a decrease in April through July. The framework utilized in this study can be exploited to analyze the susceptibility of hydropower production in the long term.

Sewer networks represent high value in water infrastructure assets and it is important to develop and operate them by specified sustainable management. This paper presents the results of a sustainability analysis on the wastewater transport system of Trondheim, Norway, for future planning (2014–2040) from a metabolism-based performance analysis by the Dynamic Metabolism Model (DMM). The aim of this work is to demonstrate a methodology for comparing different pathways toward a sustainable management of wastewater systems. For this purpose, four intervention strategies ‘infiltration and inflow reduction’, ‘increasing rehabilitation rate’, ‘extension of system regarding population growth’, ‘energy management’ along with different combinations of them have been analysed. The results of this study may give some support to decision-makers in wastewater departments. In practice, to achieve strategic level planning of sustainable sewer asset management, it is vital to assess different aspects of sustainability and manage them in a comprehensive system.