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.@en

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.@en

A resurgence of dam planning and construction is under way in river basins where untapped hydropower potential could meet growing energy demands. Despite calls for more comprehensive evaluation of dam projects, most dams continue to be planned with traditional methods that neglect interdependencies between planning and management and cumulative impacts of multiple new dams. Using the transboundary Zambezi Watercourse as a case study where competing demands for water, energy, and food are increasing, we contribute to a novel dam planning approach that integrates sequencing of planned reservoirs with adaptive operations. While additional hydropower capacity reduces structural energy deficits, operating polices emerge as the main driver of human-environmental tradeoffs, so much so that single-objective operating policy selection may lead to erroneous perceptions of tradeoffs across infrastructure options. Furthermore, compared to an operation and sequencing strategy that singularly maximizes hydropower, seeking compromise through operations while constructing dams early improves environmental and irrigation objectives by 50% and 80% with an 8% loss in hydropower. Alternatively, seeking compromise only through delayed dam construction yields modest environmental and irrigation improvements of 6% and 9%, respectively, with a 22% loss in hydropower. The robustness of this result is tested under an ensemble of stochastic streamflow where environmental flow and irrigation deficits are found more sensitive to operations than shifts in water availability. The predominance of operating policies is relevant for improving multi-objective dam planning in other river basins already fragmented by dams built in the twentieth century.@en

Integrated management of water reuse technologies and coordinated operations with other water system components is fundamental to fully exploiting reuse potential. Yet, these technologies are primarily designed considering their individual efficiency more than possible synergies with traditional water management practices. In this paper, we introduce a general-purpose framework that couples physical and surrogate modelling with optimal control methods to support policy-makers in selecting robust and efficient water planning portfolios, integrating traditional water management strategies and water loops. The framework is developed for the case study of the Apulia Region, Southern Italy, characterised by the presence of a complex water distribution network and multiple conflicting users across irrigation districts, industry, and urban water supply. In addition, the Apulia system shares strategic reservoirs in a drought-prone area. Numerical computations, here performed for the historical period 2010-2019, can be directly applied to consider future climatic scenarios (i.e., modification in precipitation and temperature patterns), socio-economic changes (i.e., variation in the water demand), and technological innovation (i.e., different water reuse strategies). This work represents a first step towards enabling a circular water economy by integrating water management and treatment-reuse technologies.@en

Shared water resource systems spark a number of conflicts related to their multi sectorial, regional, and intergenerational use. They are also vulnerable to a myriad of uncertainties stemming from changes in the hydrology, population demands, and climate change. Planning and management under these conditions are extremely challenging. Fortunately, our capability to approach these problems has evolved dramatically over the last few decades. Increased computational power enables the testing of multiple hypotheses and expedites the results across a range of planning alternatives. Advances in flexible multi-objective optimization tools facilitate the analyses of many competing interests. Further, major shifts in the way uncertainties are treated allow analysts to characterize candidate planning alternatives by their ability to fail or succeed instead of relying on fallible predictions. Embracing the fact that there are indeterminate uncertainties whose probabilistic descriptions are unknown, and acknowledging relationships whose actions and outcomes are not well-characterized in planning problems, have improved our ability to perform diligent analysis. Multi-objective robust planning of water systems emerged in response to the need to support planning and management decisions that are better prepared for unforeseen future conditions and that can be adapted to changes in assumptions. A suite of robustness frameworks has emerged to address planning and management problems in conditions of deep uncertainty. That is, events not readily identified or that we know so little about that their likelihood of occurrence cannot be described. Lingering differences remain within existing frameworks. These differences are manifested in the way in which alternative plans are specified, the views about how the future will unfold, and how the fitness of candidate planning strategies is assessed. Differences in the experimental design can yield diverging conclusions about the robustness and vulnerabilities of a system. Nonetheless, the means to ask a suite of questions and perform a more ambitious analysis is available in the early 21st century. Future challenges will entail untangling different conceptions about uncertainty, defining what aspects of the system are important and to whom, and how these values and assumptions will change over time.@en

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs’ candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs’ abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ϵ-MOEA, the auto-adaptive Borg MOEA, and ϵ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity.@en

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs’ candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs’ abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ϵ-MOEA, the auto-adaptive Borg MOEA, and ϵ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity.@en