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Adaptive planning of water infrastructure systems is crucial to bolster urban resilience in the face of climate change while meeting the needs of rapidly changing urban metabolisms. Urban water systems maintain intricate interconnections with other critical infrastructure domains (CIDs). Multi-sector dependencies and joint management of different CIDs have gained interest in recent research to mitigate undesired cascading effects across domains. Yet, combined modeling and joint simulation of multiple CIDs needs to overcome the limitations of tools and software often siloed to individual infrastructure domains. In this paper, we contribute a systematic review of 24 recent peer-reviewed publications on coupled simulation of urban water systems (water supply and drainage networks) and other CIDs, including energy grids, mobility networks, and IT infrastructure systems, extracted from a larger set of 222 publications. First, we identify trends, modeling frameworks, and simulation software enabling the combined simulation of interlinked CIDs. Then, we define an agenda of priorities for future research. Acknowledging the opportunities provided by open-source tools, data, and standardized evaluation schemes, future research fostering coupled simulation across CIDs should prioritize knowledge transfer, address differences in spatial and temporal dependencies, scale up simulations to a network level, and explore multi-sector interconnections beyond bilateral dependencies. • A systematic review on coupled simulation of Critical Infrastructure Domains (CIDs). • Synthesis of interconnection between urban water and energy, IT, and mobility systems. • Overview of models and software for coupled simulation of interlinked CIDs. • Deduction of a future research agenda establishing priorities for simulating CIDs. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) has recently gained increasing interest in the adaptive management of water resources systems due to its capability of incorporating disturbance forecasts into real-time optimal control problems. Yet, related literature is scattered with heterogeneous applications, case-specific problem settings, and results that are hardly generalized and transferable across systems. Here, we systematically review 149 peer-reviewed journal articles published over the last 25 years on MPC applied to water reservoirs, open channels, and urban water networks to identify common trends and open challenges in research and practice. The three water systems we consider are inter-connected, multi-purpose and multi-scale dynamical systems affected by multiple hydro-climatic uncertainties and evolving socioeconomic factors. Our review first identifies four main challenges currently limiting most MPC applications in the water domain: (i) lack of systematic benchmarking of MPC with respect to other control methods; (ii) lack of assessment of the impact of uncertainties on the model-based control; (iii) limited analysis of the impact of diverse forecast types, resolutions, and prediction horizons; (iv) under-consideration of the multi-objective nature of most water resources systems. We then argue that future MPC applications in water resources systems should focus on addressing these four challenges as key priorities for future developments. [ABSTRACT FROM AUTHOR]

Finally, the last two papers hosted in the Special Issue were not fully aligned to the topic of water distribution networks, but they are very interesting in the more general paradigm of smart networks and big data collection with innovative smart sensors. This Editorial presents the paper collection of the Special Issue (SI) on Smart Urban Water Networks. Further, the more advanced challenges delineated in the SI are the topic regarding real-time control and operation of water systems, also with the possibility to activate dynamic changes in the network operation using smart devices controlled in real-time (e.g., regulation valves, on-off valves). In the more general framework of Industry 4.0, the recent development of Internet of Things (IoT) technologies applied to smart grids opens further novel opportunities in the management of water network systems and beyond. [Extracted from the article]

Over the last three decades, the increasing development of smart water meter trials and the rise of demand management has fostered the collection of water demand data at increasingly higher spatial and temporal resolutions. Counting these new datasets and more traditional aggregate water demand data, the literature is rich with heterogeneous urban water demand datasets. They are characterized by heterogeneous spatial scales—from urban districts, to households or individual water fixtures—and temporal sampling frequencies—from seasonal/monthly up to sub-daily (minutes or seconds). Motivated by the need of tracking the existing datasets in this rapidly evolving field of investigation, this manuscript is the first comprehensive review effort of the state-of-the-art urban water demand datasets. This paper contributes a review of 92 water demand datasets and 120 related peer-review publications compiled in the last 45 years. The reviewed datasets are classified and analyzed according to the following criteria: spatial scale, temporal scale, and dataset accessibility. This research effort builds an updated catalog of the existing water demand datasets to facilitate future research efforts end encourage the publication of open-access datasets in water demand modelling and management research. [ABSTRACT FROM AUTHOR]

Water losses are one of the main challenges posed worldwide to an efficient management and maintenance of urban potable water distribution networks. Large effort has been devoted in the literature to design algorithms to detect and localize water losses via physically-based hydraulic modelling, data-driven modelling, and probabilistic approaches. Most of the state-of-the-art research looks at developing such tools to prevent or mitigate both physical and apparent water losses in water networks and help utilities reduce water waste and costs due to non-revenue water. Along with the digital transition of urban water networks, the deployment of intelligent meters, and the acquisition of high-resolution data of water consumption at the household scale, a few data-driven methods have been also developed to detect post meter leakages, i.e., those leakages that occur within a residential property and that generate extra costs for water consumers. Yet, most of these studies consider single-household water use time series as taken individually and not connected within a network model. Thus, potential correlations among different water use time series, due, for instance, to location-based or behavioural similarities among different water consumption patterns are disregarded. Moreover, the limited availability of accessible water use time series observed via intelligent meters limits the scope and the data-driven nature of such methods to small-scale or theoretical experiments.In this work, we propose a demand- and network-based method for post meter leakage generation and detection. Our method integrates (i) the STREaM stochastic simulation model to generate synthetic time series of residential water demand at the scale of single fixtures (i.e., toilet, washing machine, tap, etc.), (ii) a module for pressure-driven simulation of water supply networks and generation of realistic leakages, and (iii) a data-driven learning algorithm for the automatic detection of post meter leakages. We test our method both with synthetic water demand data including post meter leakages, as well as in a lab-controlled setting where leakage values are set at the end-use notes in order to be comparable to real-world fixture leakages. Numerical results show that by integrating both water demand patterns and a network model, our method exploits spatial and temporal correlations among fine-resolution water demand time series to achieve improved accuracy and reduced amount of false alarms as benchmarked against state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• We systematically review 114 studies on the residential end uses of water. • We develop a multi-level analysis to compare the outcomes available in the literature. • The study allows understanding the most investigated aspects of water end uses. • Features from different geographical and cultural regions of the world are explored. A detailed characterization of residential water consumption is essential for ensuring urban water systems' capability to cope with changing water resources availability and water demands induced by growing population, urbanization, and climate change. Several studies have been conducted in the last decades to investigate the characteristics of residential water consumption with data at a sufficiently fine temporal resolution for grasping individual end uses of water. In this paper, we systematically review 114 studies to provide a comprehensive overview of the state-of-the-art research about water consumption at the end-use level. Specifically, we contribute with: (1) an in-depth discussion of the most relevant findings of each study, highlighting which water end-use characteristics were so far prioritized for investigation in different case studies and water demand modelling and management studies from around the world; and (2) a multi-level analysis to qualitatively and quantitatively compare the most common results available in the literature, i.e. daily per capita end-use water consumption, end-use parameter average values and statistical distributions, end-use daily profiles, end-use determinants, and considerations about efficiency and diffusion of water-saving end uses. Our findings can support water utilities, consumers, and researchers (1) in understanding which key aspects of water end uses were primarily investigated in the last decades; and (2) in exploring their main features considering different geographical, cultural, and socio-economic regions of the world. [Display omitted] [ABSTRACT FROM AUTHOR]