In DWC, different digital solutions will be tested and assessed regarding their potential to improve the performance and return on investment of water infrastructures. The present report (D2.4) describes the individual solutions with their technical specifications, their addressed challenges and their added value in the form of fact sheets. The document aims to help cities and water utilities in finding appropriate solutions for their operational, environmental or public health deficits

The present report summarizes the benefits of the eleven digital solutions demonstrated within DWC-WP2 in the form of fact sheets. The document aims to help cities and water utilities in finding appropriate solutions for their operational, environmental or public health deficits. The report is the final version which was submitted in Nov. 2022 after incorporating the recommendations and amendments by the EC.

Water utilities are challenged to reduce their water losses through detecting, localizing, and repairing leaks as quickly as possible in their aging distribution systems. In this work, we solve this challenging problem by detecting multiple leaks simultaneously in a water distribution network for the Battle of the Leak Detection and Isolation Methods. The performance of leak detection and localization depends on how well the system roughness and demand are calibrated. In addition, existing leaks affect the diagnosis performance unless they are identified and explicitly represented in the model. To circumvent this chicken-and-egg dilemma, we decompose the problem into multiple levels of decision-making (a hierarchical approach) where we iteratively improve the water distribution network model and so are able to solve the multileak diagnosis problem. First, a combination of time series and cluster analysis is used on smart meter data to build patterns for demand models. Second, point and interval estimates of pipe roughnesses are retrieved using least squares to calibrate the hydraulic model, utilizing the demand models from the first step. Finally, the calibrated primal model is transformed into a dual model that intrinsically combines sensor data and network hydraulics. This dual model automatically converts small pressure deviations caused by leaks into sharp and localized signals in the form of virtual leak flows. Analytical derivations of sensitivities with respect to these virtual leak flows are calculated and used to estimate the leakage impulse responses at candidate nodes. Subsequently, we use the dual network to (1) detect the start time of the leaks, and (2) compute the Pearson correlation of pressure residuals, which allows further localization of leaks. This novel dual modeling approach resulted in the highest true-positive rates for leak isolation among all participating teams in the competition.

https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29WR.1943-5452.0001515

 IWA WaterLoss2022 Conference

Global mean sea-level rise (SLR) has accelerated since 1900 from less than 2 mm yr−1 during most of the century to more than 3 mm yr−1 since 1993. Decision-makers in coastal countries, however, require information on SLR at the regional scale, where detection of an acceleration in SLR is difficult, because the long-term sea-level signal is obscured by large inter-annual variations with multi-year trends that are easily one order of magnitude larger than global mean values. Here, we developed a time series approach to determine whether regional SLR is accelerating based on tide gauge data. We applied the approach to eight 100-year records in the southern North Sea and detected, for the first time, a common breakpoint in the early 1990s. The mean SLR rate at the eight stations increases from 1.7 ± 0.3 mm yr−1 before the breakpoint to 2.7 ± 0.4 mm yr−1 after the breakpoint (95% confidence interval), which is unprecedented in the regional instrumental record. These findings are robust provided that the record starts before 1970 and ends after 2015. Our method may be applied to any coastal region with tidal records spanning at least 40 years, which means that vulnerable coastal communities still have time to accumulate the required time series as a basis for adaptation decisions in the second half of this century.

Water utilities worldwide are under constant stress to reduce water loss due to urbanization, population growth, and climate change. Globally, Water Distribution Networks (WDNs) lose about 30% of the treated water on an average during supply. In addition to the amount of water lost, leaky WDNs consume additional energy and increase the risk of contamination. Deteriorating pipes and pipe network elements such as valves and joints, as well as improper pressure management are the main contributing factors for water loss in WDNs. Due to the increasing concern about water loss, leakage detection and localization have been widely researched in recent decades, both in continuously pumped and intermittently pumped systems.The techniques used for leakage detection and repair range from conventional methods with direct inspection on-site to model-based optimization methods. In the present era of low-cost sensors and the availability of high computing power, the transformation of WDNs into smart water systems is higher than ever. This has led to the research and development of data-driven and hybrid methods for solving leakage detection and localization methods. Irrespective of the class of methods used, their ultimate goal can be distilled primarily into two questions - a) How quickly and reliably can the presence of leak(s) be detected, and b) How accurate and precise can the location and size of the leak(s) be estimated?Answers to these questions include uncertainties inherent to the methods and models used, their underlying assumptions and necessary abstractions. Although much research has been done for many years to reduce uncertainties in leakage detection and localization, a comprehensive study using a consistent terminology of their types, sources, and effects on the outcome are missing. The main contribution of this work is to discuss (i) why there are uncertainties in the formulation of leakage detection and localization problem, (ii) identify the sources and types of uncertainties for different classes of modeling approaches (i.e., data-driven vs. model-based), and (iii) provide a brief review of their influence concerning error bounds from existing literature.