Evolutionary Multi-Objective Direct Policy Search (EMODPS) is a prominent framework for designing control policies in multi-purpose environmental systems, combining direct policy search with multi-objective evolutionary algorithms (MOEAs) to identify Pareto approximate control policies. While EMODPS is effective, the choice of functions within its global approximator networks remains underexplored, despite their potential to significantly influence both solution quality and MOEA performance. This study conducts a rigorous assessment of a suite of Radial Basis Functions (RBFs) as candidates for these networks. We critically evaluate their ability to map system states to control actions, and assess their influence on Pareto efficient control policies. We apply this analysis to two contrasting case studies: the Conowingo Reservoir System, which balances competing water demands including hydropower, environmental flows, urban supply, power plant cooling, and recreation; and The Shallow Lake Problem, where a city navigates the trade-off between environmental and economic objectives when releasing anthropogenic phosphorus. Our findings reveal that the choice of RBF functions substantially impacts model outcomes. In complex scenarios like multi-objective reservoir control, this choice is critical, while in simpler contexts, such as the Shallow Lake Problem, the influence is less pronounced, though distinctive differences emerge in the characteristics of the prescribed control strategies.

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Despite progress in multiobjective evolutionary algorithms (MOEAs) research, their efficacy in real-world scenarios remains unclear. This article introduces a diagnostic benchmarking framework to evaluate MOEAs, comprising (1) flexible MOEA construction software, (2) performance evaluation metrics and (3) real-world applications for benchmarking, reflecting diverse mathematical challenges. Utilizing this framework, NSGA-II, NSGA-III, RVEA, MOEA/D and Borg MOEA were evaluated across four applications with three to ten objectives. Collectively, the four applications capture challenges such as stochastic objectives, severe constraints, nonlinearity and complex Pareto frontiers. The study demonstrates how MOEAs that have shown strong performance on standard test problems can struggle on real-world applications. The benchmarking framework and results have value for enhancing the design and use of MOEAs in real-world applications. Further, the results highlight the need to improve the adaptability and ease-of-use of MOEAs given the often ill-defined nature of real-world problem-solving.

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The construction of the Akosombo and Kpong dams in the Lower Volta River basin in Ghana changed the downstream riverine ecosystem and affected the lives of downstream communities, particularly those who lost their traditional livelihoods. In contrast to the costs borne by those in the vicinity of the river, Ghana has enjoyed vast economic benefits from the affordable hydropower, irrigation schemes and lake tourism that developed after construction of the dams. Herein lies the challenge; there exists a trade-off between water for river ecosystems and related services on the one hand and anthropogenic water demands such as hydropower or irrigation on the other. In this study, an Evolutionary Multi-Objective Direct Policy Search (EMODPS) is used to explore the multi-sectoral trade-offs that exist in the Lower Volta River basin. Three environmental flows, previously determined for the Lower Volta, are incorporated separately as environmental objectives. The results highlight the dominance of hydropower production in the Lower Volta but show that there is room for providing environmental flows under current climatic and water use conditions if the firm energy requirement from Akosombo Dam reduces by 12% to 38% depending on the environmental flow regime that is implemented. There is uncertainty in climate change effects on runoff in this region; however multiple scenarios are investigated. It is found that climate change leading to increased annual inflows to the Akosombo Dam reduces the trade-off between hydropower and the environment as this scenario makes more water available for users. Furthermore, climate change resulting in decreased annual inflows provides the opportunity to strategically provide dry-season environmental flows, that is, reduce flows sufficiently to meet low flow requirements for key ecosystem services such as the clam fishery. This study not only highlights the challenges in balancing anthropogenic water demands and environmental considerations in managing existing dams but also identifies opportunities for compromise in the Lower Volta River. @en

We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by MOO algorithms are scattered across fields. We provide an overview of the advances on this topic, including methods for visualization, mining the solution set, and uncertainty exploration as well as emerging research directions, including interactivity, explainability, and ethics. We synthesize these methods drawing from different fields of research to build a unified approach, independent of the application. Our goals are to reduce the entry barrier for researchers and practitioners on using MOO algorithms and to provide novel research directions.@en

Integrated Assessment Models (IAMs) vary widely in complexity and underlying assumptions. There have been considerable efforts to increase the complexity of IAMs for improved representation of socioeconomic and environmental outcomes. However, less attention has been given to the foundational assumptions of these models and their distributional consequences. These assumptions are fraught with deep and normative uncertainty and can significantly impact IAM projections. If these assumptions are not explicit, IAMs can perpetuate existing mistakes and exacerbate inequalities due to their black-box nature. This paper introduces a novel IAM called JUSTICE (Justice Universality Spatial Temporal Integrated Climate Economy) to explore the influence on distributive justice outcomes due to underlying modelling assumptions across model components and functions: the economy and climate components, and the damage and social welfare functions. JUSTICE is a simple IAM inspired by the long-established RICE and is designed to be a surrogate for more complex IAMs for eliciting normative insights. As illustrated in Figure 1, JUSTICE contains two distinct economic and climate sub-models, three damage functions, and four social welfare functions (SWFs), each based on fundamentally different assumptions. This modular structure enables JUSTICE to uncover assumptions with nontrivial normative and distributional consequences. Also, the simplicity of JUSTICE makes it suitable for assessing the consequences of these modelling assumptions under deep and normative uncertainty using MS-MORDM and EMODPS frameworks, promoting a more equitable approach to decision-making. Using JUSTICE, we investigate the effects of three SWFs—Utilitarianism, Egalitarianism, and Prioritarianism—on global temperature rise, with two levels of aggregation. We also explore the sensitivity of distributional outcomes for two different climate models. Our findings reveal that different assumptions lead to significantly distinct optimal abatement pathways, underscoring the importance of explicating assumptions and exploring their uncertainties to facilitate deliberation and identify common ground among policymakers with diverse perspectives.

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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

Justice in the climate context has gained more attention in the last decade. One of the main reasons is the increasingly pervasive and aggressive impact of climate change on societies and economies. Existing inequalities and disparities between sectors, regions, and generations are often exacerbated by proposed or applied policies. Hence, protecting different groups’ rights becomes more and more necessary in the climate change adaptation and mitigation policies. It is therefore essential to understand the subjective notions of the ethical principles that underlie the policies, by categorically examining these principles before taking action. For this reason, this study explored different distributive justice principles in integrated assessment models using a descriptive approach. It resulted in a classification of the five most common ethical principles: Utilitarianism, Rawlsianism, Egalitarianism, Prioritarianism, and Sufficientarianism. These principles have been operationalized to find the optimal climate policy for future emissions. The principles have been applied to the Regional Integrated Climate-Economy (RICE) model for a comparative analysis on interregional justice.@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

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

Justice in the allocation and distribution of water is one of the most recent topics in the water resources management literature. This topic, i.e., justice/equity/fairness, is especially noteworthy in integrated water resources management where competing needs, sectors, and societal segments are involved in the utilization of water. Although the concept of justice, such as procedural justice, in general and in water management in particular is not as new, the concept of distributive justice and tools and technics for the allocation and distribution of water resources is very recent. As a result, particularly tools and techniques for the operationalization of such concepts are still lacking. In this study, we operationalized theoretical justice theories in terms of moral principles into functions and parameters for use with traditional water resources optimization models and frameworks. These moral principles include Utilitarianism (which evaluates measures according to their effect on welfare), Sufficientarianism (which makes sure that each individual gets a sufficient threshold), Prioritarianism (which guarantees extra weight to worse-off individuals), and envy-freeness (which requires that each individual prefers his share to the share of others). The result of the study as applied in the case study of the Susquehanna basin, USA, displays undertanding and outlooks of various perspectives of fairness on integrated water resources management among competing stakeholders and needs. Such perspectives are presented together with traditional resource efficiency and/or conservation oriented optimization techniques and methods to highlight synergy and trade-offs in integrated water resources management. We think that the methods and approaches presented here will advance the scientific discussion on the operationalization of justice/equity/fairness in real-world modeling and management of integrated water resources.@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

Efficient multi-purpose reservoir control policies are crucial in the face of frequent and severe floods and droughts, and to balance water allocation across conflicting demands. Evolutionary Multi-Objective Direct Policy Search (EMODPS) is a popular approach to design control policies for multi-purpose reservoir systems. EMODPS, however, relies on experimental choices within the key components of the framework particularly when coupling multi-objective evolutionary optimization with nonlinear approximation networks. This study explores a suite of radial basis functions (RBFs) used to map the system's states to control actions in a flexible manner as time-varying, non-linear relationships. We provide a systematic assessment of different RBF functions to explore their suitability to obtain Pareto efficient control policies. We use the Susquehanna river basin case study in which competing water demands for hydropower, environment, urban water supply, atomic power plant cooling and recreation need to be met. Our findings suggest that the choice of RBF functions have a large impact on the model outcomes and the search behavior of the optimization algorithm.@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