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The success of future lunar missions depends on quality positioning, navigation, and timing (PNT) information. Earthbound GNSS signals can be received at lunar distances but suffer from poor geometric dilution of precision (GDOP) and provide no coverage of the lunar far side. This article explores the design space of a dedicated GNSS system in lunar orbit by using a multi-objective evolutionary algorithm framework to optimize GDOP, availability, space segment cost, station-keeping ΔV, and robustness to single-satellite failure. Results show that Pareto approximate solutions that achieve a global GDOP availability (GDOP ≤ 6.0) greater than 98% contain a minimum of 24 satellites in near-circular polar orbits at an altitude of ~2 lunar radii. The impact of single-satellite failure on GDOP outage is analyzed and a no-maneuver scenario is considered. Design rules characterizing optimal solutions are identified and trade-offs between station-keeping maneuver frequency, performance, and design lifetime are discussed. [ABSTRACT FROM AUTHOR]

Worldwide, reservoirs are used to buffer against both extreme floods and extreme droughts. However, operations favoring each of these and other management objectives conflict. Fortunately, tradeoffs in operations can often be mitigated by using hydrologic forecasts to condition release decisions, and significant research has investigated the value of using forecasts for this purpose. However, these studies have struggled with how to quantify forecast value in multi-objective contexts where the benefits may accrue unevenly to different objectives that stakeholders value differently. To address this research gap, we introduce a new metric for quantifying forecast value on multi-objective problems: the average improvement on each objective when moving from a solution that does not use forecast information (a baseline solution) to solutions that do use forecast information and outperform the baseline solution on all objectives. We call this metric "average domination" and use it to investigate whether and how the value of forecast information for multi-objective reservoir operations changes with different flood protection constraints, using the Red River Basin in Vietnam as an example. To assess this, we design multi-objective operations at four reservoirs in the basin both with and without forecast information conditioning release decisions under constraints that the operations ensure protection to downstream water levels with either 100-year or 500-year return levels. Based on the average domination metric, we find that in the Red River Basin, the value of forecast information for all objectives is the same or greater under the more severe 500-year flood design requirement. These findings, identified by our new metric, illustrate that it can be especially beneficial to condition operations on forecast information when stakeholders strongly favor higher levels of risk aversion for flood protection. • We develop a new metric for valuing forecast information on multi-objective problems. • We use this metric to assess forecast value for reservoir operations. • We find forecasts hold more value when constrained to protect to more severe floods. [ABSTRACT FROM AUTHOR]

Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS. [ABSTRACT FROM AUTHOR]

Managing socio-ecological systems is a challenge wrought by competing societal objectives, deep uncertainties, and potentially irreversible tipping points. A classic, didactic example is the shallow lake problem in which a hypothetical town situated on a lake must develop pollution control strategies to maximize its economic benefits while minimizing the probability of the lake crossing a critical phosphorus (P) threshold, above which it irreversibly transitions into a eutrophic state. Here, we explore the use of direct policy search (DPS) to design robust pollution control rules for the town that account for deeply uncertain system characteristics and conflicting objectives. The closed loop control formulation of DPS improves the quality and robustness of key management tradeoffs, while dramatically reducing the computational complexity of solving the multi-objective pollution control problem relative to open loop control strategies. These insights suggest DPS is a promising tool for managing socio-ecological systems with deeply uncertain tipping points. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study demonstrates how Arrow's Impossibility Theorem, a theory of social choice, is of direct concern when formulating water-resources systems planning problems. Traditional strategies for solving multiobjective water resources problems typically aggregate multiple performance measures into single composite objectives (e.g., a priori preference weighting or grouping-like measures by category). Arrow's Impossibility Theorem, commonly referred to as Arrow's Paradox, implies that a subset of performance concerns will inadvertently dictate the properties of the optimized design alternative in unpredictable ways when using aggregated objectives. This study shows how many-objective planning can aid in battling Arrow's Paradox. Many-objective planning explicitly disaggregates measures of performance while supporting the discovery of planning tradeoffs, using tools such as multiobjective evolutionary algorithms (MOEAs). An urban water portfolio planning case study for the Lower Rio Grande Valley, Texas is used to demonstrate how aggregate, lower objective-count formulations can adversely bias risk-based decision support. Additionally, this study employs a comprehensive diagnostic assessment of the Borg MOEA's ability to address Arrow's Paradox by enabling users to explore problem formulations with increasing numbers of objectives and decisions. Counter to conventional assumptions, the diagnostic analysis carefully documents that for modern self-adaptive MOEA searches, increasing objective counts can lead to more effective, efficient, reliable, and controllable searches. The increased objective counts are also shown to directly reduce decision biases that can emerge from problem formulation aggregation and simplification, related to Arrow's Paradox. [ABSTRACT FROM AUTHOR]

Water systems planners have long recognized the need for robust solutions capable of withstanding deviations from the conditions for which they were designed. Robustness analyses have shifted from expected utility to exploratory bottom-up approaches which identify vulnerable scenarios prior to assigning likelihoods. Examples include Robust Decision Making (RDM), Decision Scaling, Info-Gap, and Many-Objective Robust Decision Making (MORDM). We propose a taxonomy of robustness frameworks to compare and contrast these approaches based on their methods of (1) alternative generation, (2) sampling of states of the world, (3) quantification of robustness measures, and (4) sensitivity analysis to identify important uncertainties. Building from the proposed taxonomy, we use a regional urban water supply case study in the Research Triangle region of North Carolina to illustrate the decision-relevant consequences that emerge from each of these choices. Results indicate that the methodological choices in the taxonomy lead to the selection of substantially different planning alternatives, underscoring the importance of an informed definition of robustness. Moreover, the results show that some commonly employed methodological choices and definitions of robustness can have undesired consequences when ranking decision alternatives. For the demonstrated test case, recommendations for overcoming these issues include: (1) decision alternatives should be searched rather than prespecified, (2) dominant uncertainties should be discovered through sensitivity analysis rather than assumed, and (3) a carefully elicited multivariate satisficing measure of robustness allows stakeholders to achieve their problem-specific performance requirements. This work emphasizes the importance of an informed problem formulation for systems facing challenging performance tradeoffs and provides a common vocabulary to link the robustness frameworks widely used in the field of water systems planning. [ABSTRACT FROM AUTHOR]

Managing ecosystems with deeply uncertain threshold responses and multiple decision makers poses nontrivial decision analytical challenges. The problem is imbued with deep uncertainties because decision makers do not know or cannot converge on a single probability density function for each key parameter, a perfect model structure, or a single adequate objective. The existing literature on managing multistate ecosystems has generally followed a normative decision-making approach based on expected utility maximization (MEU). This approach has simple and intuitive axiomatic foundations, but faces at least two limitations. First, a prespecified utility function is often unable to capture the preferences of diverse decision makers. Second, decision makers' preferences depart from MEU in the presence of deep uncertainty. Here, we introduce a framework that allows decision makers to pose multiple objectives, explore the trade-offs between potentially conflicting preferences of diverse decision makers, and to identify strategies that are robust to deep uncertainties. The framework, referred to as many-objective robust decision making (MORDM), employs multiobjective evolutionary search to identify trade-offs between strategies, re-evaluates their performance under deep uncertainty, and uses interactive visual analytics to support the selection of robust management strategies. We demonstrate MORDM on a stylized decision problem posed by the management of a lake in which surpassing a pollution threshold causes eutrophication. Our results illustrate how framing the lake problem in terms of MEU can fail to represent key trade-offs between phosphorus levels in the lake and expected economic benefits. Moreover, the MEU strategy deteriorates severely in performance for all objectives under deep uncertainties. Alternatively, the MORDM framework enables the discovery of strategies that balance multiple preferences and perform well under deep uncertainty. This decision analytic framework allows the decision makers to select strategies with a better understanding of their expected trade-offs (traditional uncertainty) as well as their robustness (deep uncertainty). [ABSTRACT FROM AUTHOR]

Integrated assessment models for climate change (IAMs) couple representations of economic and natural systems to identify and evaluate strategies for managing the effects of global climate change. In this study we subject three policy scenarios from the globally-aggregated Dynamic Integrated model of Climate and the Economy IAM to a comprehensive global sensitivity analysis using Sobol' variance decomposition. We focus on cost metrics representing diversions of economic resources from global world production. Our study illustrates how the sensitivity ranking of model parameters differs for alternative cost metrics, over time, and for different emission control strategies. This study contributes a comprehensive illustration of the negative consequences associated with using a priori expert elicitations to reduce the set of parameters analyzed in IAM uncertainty analysis. The results also provide a strong argument for conducting comprehensive model diagnostics for IAMs that explicitly account for the parameter interactions between the coupled natural and economic system components. [ABSTRACT FROM AUTHOR]

Extreme weather variability has long posed difficulties for food, energy, and water systems in the Western US and is only projected to become more variable in a changing climate. The key to understanding weather in the West lies in understanding variability of large-scale weather regimes that dictate regional weather patterns. In this study, we propose a novel, multi-objective optimization and regression-based framework that reconstructs the annual frequency of regional weather regimes based on a gridded, tree-ring based reconstruction of cold season precipitation. The approach optimally smooths and selects the tree-ring based information used in the weather regime reconstruction to enhance out of sample performance and minimize model complexity. Multiple objectives are considered within the optimization to balance the preservation of low and high frequency modes of variability in the multivariate weather regime dynamics. We reconstruct weather regime frequencies back to 1400 CE and show that the reconstructed weather regimes are consistent with previously identified megadroughts and pluvials. Further, the reconstructed weather regimes exhibit significant variability in the 3–15-year frequency band and extend far beyond the bounds of the instrumental period. Overall, the weather regime reconstructions developed here provide important insight into the extent of natural atmospheric variability that can influence Western US weather. [ABSTRACT FROM AUTHOR]

Highlights: [•] Rigorous framework for assessing the scalability of massively parallel many-objective search. [•] Evaluations of scalability for convergence, diversity maintenance, and consistency. [•] Visual analytics clarifies speedup and decision relevant MOEA performance levels. [•] Groundwater monitoring application combines optimization and data assimilation. [•] Parallel many-objective visual analytics has broad applicability in water resources and beyond. [Copyright &y& Elsevier]

Abstract: This paper proposes and demonstrates a new interactive framework for sensitivity-informed de Novo planning to confront the deep uncertainty within water management problems. The framework couples global sensitivity analysis using Sobol’ variance decomposition with multiobjective evolutionary algorithms (MOEAs) to generate planning alternatives and test their robustness to new modeling assumptions and scenarios. We explore these issues within the context of a risk-based water supply management problem, where a city seeks the most efficient use of a water market. The case study examines a single city’s water supply in the Lower Rio Grande Valley (LRGV) in Texas, using a suite of 6-objective problem formulations that have increasing decision complexity for both a 10-year planning horizon and an extreme single-year drought scenario. The de Novo planning framework demonstrated illustrates how to adaptively improve the value and robustness of our problem formulations by evolving our definition of optimality while discovering key tradeoffs. [Copyright &y& Elsevier]

Abstract: The Mid-Atlantic region in the United States (US) is composed of a diverse range of gradients across its natural and human systems. These gradients encompass heavily urbanized population centers, agriculture dominated valleys, reforested headwaters, and some of the most important estuaries in North America. The Mid-Atlantic Watershed Atlas (MAWA) portal has been developed to encourage public access and use of a wide range of freely available data resources. Moreover, the system has been designed to facilitate emergent geospatial tagging to identify the individuals, groups, and agencies shaping the region''s water resources. The goal of the system is to facilitate improved regional watershed knowledge, provide information to individual stakeholders, and support broad regional initiatives. [Copyright &y& Elsevier]

Plume interpolation consists of estimating contaminant concentrations at unsampled locations using the available contaminant data surrounding those locations. The goal of ground water plume interpolation is to maximize the accuracy in estimating the spatial distribution of the contaminant plume given the data limitations associated with sparse monitoring networks with irregular geometries. Beyond data limitations, contaminant plume interpolation is a difficult task because contaminant concentration fields are highly heterogeneous, anisotropic, and nonstationary phenomena. This study provides a comprehensive performance analysis of six interpolation methods for scatter-point concentration data, ranging in complexity from intrinsic kriging based on intrinsic random function theory to a traditional implementation of inverse-distance weighting. High resolution simulation data of perchloroethylene (PCE) contamination in a highly heterogeneous alluvial aquifer were used to generate three test cases, which vary in the size and complexity of their contaminant plumes as well as the number of data available to support interpolation. Overall, the variability of PCE samples and preferential sampling controlled how well each of the interpolation schemes performed. Quantile kriging was the most robust of the interpolation methods, showing the least bias from both of these factors. This study provides guidance to practitioners balancing opposing theoretical perspectives, ease-of-implementation, and effectiveness when choosing a plume interpolation method. [ABSTRACT FROM AUTHOR]

This study demonstrates the use of high-order Pareto optimization (i.e., optimizing a system for more than two objectives) on a long-term monitoring (LTM) application. The LTM application combines quantile kriging and the nondominated sorted genetic algorithm-II (NSGA-II) to successfully balance four objectives: (1) minimizing sampling costs, (2) maximizing the accuracy of interpolated plume maps, (3) maximizing the relative accuracy of contaminant mass estimates, and (4) minimizing estimation uncertainty. Optimizing the LTM application with respect to these objectives reduced the decision space of the problem from a total of 500 million designs to a set of 1,156 designs identified on the Pareto surface. Visualization of a total of eight designs aided in understanding and balancing the objectives of the application en route to a single compromise solution. This study shows that high-order Pareto optimization holds significant potential as a tool that can be used in the balanced design of water resources systems. [ABSTRACT FROM AUTHOR]

The authors explains various issues concerning water resources management. They highlight a historical criticism of water resources planning and management. They discuss the use of optimization in water systems management and water supply engineering. They argue that the future of water resources management calls for constructive modeling.

The author focuses on the National Science Foundation's recognition of its limitations in managing large-scale environmental systems. The author discusses that the recognition has lead to greater emphasis on investigations and researches in environmental fields. The author highlights the contributions of various environmental observatories. The author also discusses that it will take a while before other initiatives come to advance the field of environmental and water resource systems analysis.

This work forwards time-varying sensitivity analysis of control policies as an approach to identify the value of the alternative timing of forecasts in a complex multipurpose reservoir system. The time-varying forecast diagnostics are demonstrated using the Conowingo Dam in the Lower Susquehanna River Basin. First, we evaluate the utility of multiple lead time forecasts, both in isolation and in combination, to determine the sensitivity of optimal operations to forecast lead time. We then use time-varying sensitivities to determine how the system utilizes a particular forecast through time for two policies that emphasize hydropower and environmental objectives, respectively, in order to determine how the value of inflow information changes with respect to stakeholder preferences and evolving hydrologic conditions. Results show that the value of inflow information varies greatly depending on the objectives being emphasized and can decline or fade entirely when system constraints (e.g., lower bounds on reservoir stage and spillway thresholds) inhibit the use of forecasts to inform decisions, particularly during extreme events. [ABSTRACT FROM AUTHOR]

Reservoir operations should consider both adaptiveness and robustness to deal with two of the main characteristics of climate change: nonstationarity and deep uncertainty. In particular, robust operational strategies are distinguished from risk-neutral expected value optimization in the sense that they should be satisfactory over a wider range of uncertainty and improve the ability of a reservoir system to adapt to climate change. In this study, a new framework named robust stochastic dynamic programming (RSDP) is proposed that couples robust optimization (RO) with the formulations of objective function or constraints used in stochastic dynamic programming (SDP). Two main approaches of RO, namely feasibility robustness and solution robustness, are both considered in the optimization algorithm. Consequently, this study uses the Boryeong multipurpose dam to evaluate three SDP framings: conventional-SDP (CSDP), RSDP-feasibility robustness (RSDP-F), and RSDP-solution robustness (RSDP-S). These three SDP formulations were used to derive optimal monthly release rules for the Boryeong Dam, and their relative performances were evaluated using simulations of a broader range of inflow scenarios. The simulation-based re-evaluations of the resulting reservoir operational policies were quantified using a wide range of metrics that include reliability, resiliency, and vulnerability, as well as regret-based robustness metrics. The results of this study suggest that the RSDP-S model not only increases the range of possible solutions, but also yields more desirable operation outcomes under extreme climate conditions with respect to both traditional and robustness metrics. [ABSTRACT FROM AUTHOR]

Home to one of the world's most biodiverse and productive freshwater fisheries, the Mekong River basin provides food and economic security to some 60 million people. The extensive hydropower development planned for the river basin has the potential to degrade this ecosystem. The basin's potentially most impactful and downstream-most proposed main stem dam, the 18-km-long Sambor Dam is to be located just upstream of critical nursery and fishery habitats of the Tonle Sap Lake and the Mekong Delta and just downstream of major spawning areas. We explore the potential to modify Sambor's siting, design, and operation (SDO) to balance hydropower and ecological outcomes. Our results demonstrate that there are opportunities to achieve more balanced ecology–energy outcomes. We use probabilistic seasonal measures of fish life cycle processes, energy production, and sediment passage to explore alternative SDO options. We consider both ecological concerns and energy production on equal footing prior to any power purchase agreements or fixed designs for Sambor, which is a first among any of the world's large dams. The Sambor Ecological Alternative (Sambor EA) dam considered in this study includes a combination of design and operational features to improve sediment and fish passage that do not exist at any of the world's large hydropower dams. Yet Sambor EA still poses a significant risk to the Mekong fishery and the millions who depend on it, given the appreciable uncertainty surrounding its potential ecological outcomes. Our results emphasize the need to address ecological concerns in advance of signing power purchase agreements that would otherwise limit the site-specific and integrated SDO modifications needed to achieve them. Our results also emphasize the importance of, and challenges in, carefully defining ecological performance measures and criteria, as well as the importance of carefully sampling reservoir operation possibilities. [ABSTRACT FROM AUTHOR]

Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance. Plain Language Summary: Large‐scale land surface computer models, such as the Community Land Model version 5 (CLM5), play a central role in projecting future changes in water resources and hydrological extreme events such as floods and droughts. These models require representations of land surface processes and properties that vary significantly across space. Consequently, these representations require a large suite of input parameters to distinguish different soils, vegetation, land‐cover, and other features of the landscape that shape flood and drought dynamics. Given the large number of these input parameters required by CLM5 and limits in the information needed to specify them, we hypothesize that they strongly increase uncertainty in our ability to predict floods or droughts. In this study, we develop and apply a diagnostic approach to better understand how CLM5 input parameter uncertainty influences water predictions and the relative importance of different input parameters across the continental United States. We argue and illustrate that addressing model input parameter uncertainty is crucial as the results of uncertainty characterization may have important implications for decision making. This study provides region‐specific insights into CLM5 model input parameter uncertainty and dominant processes for a variety of hydrologic application contexts. Key Points: Community Land Model Version 5 (CLM5) streamflow predictions use default parameters are vulnerable to poor performance especially in the arid Southwest and Central U.SThe effects of CLM5 hydrologic parameter uncertainty are significantly higher for drought versus flood predictions in humid regionsThe dominant hydrologic parameters in CLM5 vary significantly across regions, diagnostic error metrics, and temporal scales [ABSTRACT FROM AUTHOR]

Many coupled human-natural systems have the potential to exhibit a highly nonlinear threshold response to external forcings resulting in fast transitions to undesirable states (such as eutrophication in a lake). Often, there are considerable uncertainties that make identifying the threshold challenging. Thus, rapid learning is critical for guiding management actions to avoid abrupt transitions. Here, we adopt the shallow lake problem as a test case to compare the performance of four common data assimilation schemes to predict an approaching transition. In order to demonstrate the complex interactions between management strategies and the ability of the data assimilation schemes to predict eutrophication, we also analyze our results across two different management strategies governing phosphorus emissions into the shallow lake. The compared data assimilation schemes are: ensemble Kalman filtering (EnKF), particle filtering (PF), pre-calibration (PC), and Markov Chain Monte Carlo (MCMC) estimation. While differing in their core assumptions, each data assimilation scheme is based on Bayes’ theorem and updates prior beliefs about a system based on new information. For large computational investments, EnKF, PF and MCMC show similar skill in capturing the observed phosphorus in the lake (measured as expected root mean squared prediction error). EnKF, followed by PF, displays the highest learning rates at low computational cost, thus providing a more reliable signal of an impending transition. MCMC approaches the true probability of eutrophication only after a strong signal of an impending transition emerges from the observations. Overall, we find that learning rates are greatest near regions of abrupt transitions, posing a challenge to early learning and preemptive management of systems with such abrupt transitions. [ABSTRACT FROM AUTHOR]

Water managers must constantly balance investment in infrastructure upgrades to ensure reliable water supply with the affordability of water rates for consumers. As a result, trade-offs between water supply and financial objectives have become central to the long-term sustainability of utility operations. Few studies, however, have directly quantified decision-relevant financial benchmarks of water supply system adaptation. Benchmarks include debt covenants, guidelines required by creditors seeking assurance that debt will be repaid. Violation of covenant thresholds risks a downgrade of a utility's credit rating, higher interest rates on future debt, and higher costs for new infrastructure. This work introduces an exploratory modeling framework that carefully couples adaptive water supply planning with financial modeling to better track how utility budgetary decision making can adapt to infrastructure expansion and future water demand growth. Demonstrated through an evaluation of infrastructure planning for Tampa Bay Water (TBW), results showcase TBW budgetary adaptation in response to water demand growth and supply expansion, quantifying the financial implications of infrastructure planning decisions and demand growth on water rates as well as the bond covenants. This study underscores the importance of integrating dynamic and adaptive financial modeling in providing realistic decision support that addresses both supply reliability and financial stability. [ABSTRACT FROM AUTHOR]

This study contributes a stochastic, multi-objective adaptation of the classic environmental economics Lake Problem as a computationally simple but mathematically challenging benchmarking problem. The Lake Problem considers a hypothetical town by a lake, which hopes to maximize its economic benefit without crossing a nonlinear, and potentially irreversible, pollution threshold. Optimization objectives are maximize economic benefit, minimize phosphorus in the lake, maximize the probability of avoiding the pollution threshold, and minimize the probability of drastic phosphorus loading reductions in a given year. Uncertainty is introduced through a stochastic natural phosphorus inflow. We performed comprehensive diagnostics using six algorithms: the Borg multi-objective evolutionary algorithm (MOEA), MOEA/D, epsilon-MOEA, the Non-dominated Sorting Genetic Algorithm II (NSGAII), epsilon-NSGAII, and Generalized Differential Evolution 3 (GDE3) to evaluate their controllability, reliability, efficiency, and effectiveness. Our results show only the self-adaptive search of the Borg MOEA was capable of performing well on this nontrivial benchmarking problem. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces many objective robust decision making (MORDM). MORDM combines concepts and methods from many objective evolutionary optimization and robust decision making (RDM), along with extensive use of interactive visual analytics, to facilitate the management of complex environmental systems. Many objective evolutionary search is used to generate alternatives for complex planning problems, enabling the discovery of the key tradeoffs among planning objectives. RDM then determines the robustness of planning alternatives to deeply uncertain future conditions and facilitates decision makers'' selection of promising candidate solutions. MORDM tests each solution under the ensemble of future extreme states of the world (SOW). Interactive visual analytics are used to explore whether solutions of interest are robust to a wide range of plausible future conditions (i.e., assessment of their Pareto satisficing behavior in alternative SOW). Scenario discovery methods that use statistical data mining algorithms are then used to identify what assumptions and system conditions strongly influence the cost-effectiveness, efficiency, and reliability of the robust alternatives. The framework is demonstrated using a case study that examines a single city''s water supply in the Lower Rio Grande Valley (LRGV) in Texas, USA. Results suggest that including robustness as a decision criterion can dramatically change the formulation of complex environmental management problems as well as the negotiated selection of candidate alternatives to implement. MORDM also allows decision makers to characterize the most important vulnerabilities for their systems, which should be the focus of ex post monitoring and identification of triggers for adaptive management. [Copyright &y& Elsevier]

The United Nations Framework Convention on Climate Change agreed to “strengthen the global response to the threat of climate change, in the context of sustainable development and efforts to eradicate poverty” (UNFCCC 2015). Designing a global mitigation strategy to support this goal poses formidable challenges. For one, there are trade-offs between the economic costs and the environmental benefits of averting climate impacts. Furthermore, the coupled human-Earth systems are subject to deep and dynamic uncertainties. Previous economic analyses typically addressed either the former, introducing multiple objectives, or the latter, making mitigation actions responsive to new information. This paper aims at bridging these two separate strands of literature. We demonstrate how information feedback from observed global temperature changes can jointly improve the economic and environmental performance of mitigation strategies. We focus on strategies that maximize discounted expected utility while also minimizing warming above 2 °C, damage costs, and mitigation costs. Expanding on the Dynamic Integrated Climate-Economy (DICE) model and previous multi-objective efforts, we implement closed-loop control strategies, map the emerging trade-offs and quantify the value of the temperature information feedback under both well-characterized and deep climate uncertainties. Adaptive strategies strongly reduce high regrets, guarding against mitigation overspending for less sensitive climate futures, and excessive warming for more sensitive ones. [ABSTRACT FROM AUTHOR]

This paper introduces PySedSim, an open-source, object-oriented, Python-based, daily time step river basin simulation screening model for flow, sediment, and hydropower in networks of reservoirs and river channels. The model enables users to explore representations of four key concerns relevant to the selection and evaluation of alternative reservoir configurations: 1) management approaches to improve the passage of sediment through and around reservoirs to avoid storage capacity loss and downstream ecological impacts; 2) search for flexible and adaptive reservoir operating policies designed to achieve multiple objectives; 3) alternative design features such as dam gates, which are necessary to enable ecologically-focused reservoir features; and 4) uncertainties associated with hydroclimatic drivers and sediment processes. PySedSim is intended to support deliberative decision-making and design processes. We highlight PySedSim's functionality by demonstrating its use in a real decision context focused on identifying siting, design, and operation alternatives for the proposed Sambor mega Dam in Cambodia. Image 1 • Reservoir design and operation alternatives improve sediment passage. • Flexible problem formulation improves understanding of tradeoffs and alternatives. • Evaluating sediment, ecology, and hydropower together yields synergistic insights. • PySedSim supported iterative deliberation of alternatives to Sambor Dam (Cambodia). [ABSTRACT FROM AUTHOR]

Increasing irrigation demand has heavily relied on groundwater use, especially in places with highly variable water supplies that are vulnerable to drought. Groundwater management in agriculture is becoming increasingly challenging given the growing effects from overdraft and groundwater depletion worldwide. However, multiple challenges emerge when seeking to develop sustainable groundwater management in irrigated systems, such as trade-offs between the economic revenues from food production and groundwater resources, as well as the broad array of uncertainties in food–water systems. In this study we explore the applicability of Evolutionary Multi-Objective Direct Policy Search (EMODPS) to identify adaptive irrigation policies that water agencies and farmers can implement including operational decisions related to land use and groundwater use controls as well as groundwater pumping fees. The EMODPS framework yields state-aware, adaptive policies that respond dynamically as system state conditions change, for example with variable surface water (e.g., shifting management strategies across wet versus dry years). For this study, we focus on the Semitropic Water Storage district located in the San Joaquin Valley, California to provide broader insights relevant to ongoing efforts to improve groundwater sustainability in the state. Our findings demonstrate that adaptive irrigation policies can achieve sufficiently flexible groundwater management to acceptably balance revenue and sustainability goals across a wide range of uncertain future scenarios. Among the evaluated policy decisions, pumping restrictions and reductions in inflexible irrigation demands from tree crops are actions that can support dry-year pumping while maximizing groundwater storage recovery during wet years. Policies suggest that an adaptive pumping fee is the most flexible decision to control groundwater pumping and land use. • Bi-level hydro-economic model and evolutionary multi-objective direct policy search. • Optimize economic and groundwater sustainability trade-offs for irrigation policies. • Land and groundwater controls, with a pumping fee, are dynamically implemented. • State-aware policies balance trade-offs across uncertain scenarios. • Robustness analysis and scenario discovery clarify drivers of policy success. [ABSTRACT FROM AUTHOR]

• Benchmarking multi-objective financial risk portfolios for snow-driven hydropower. • Self-adaptive search can more effectively capture complex financial risk tradeoffs. • Decomposition and reference point algorithms deteriorate and misrepresent tradeoffs. Hydropower generation in the Hetch Hetchy Power System is strongly tied to snowmelt dynamics in the central Sierra Nevada and consequently is particularly financially vulnerable to changes in snowpack availability and timing. This study explores the Hetchy Hetchy Power System as a representative example from the broader class of financial risk management problems that hold promise in helping utilities such as SFPUC to understand the tradeoffs across portfolios of risk mitigation instruments given uncertainties in snowmelt dynamics. An evolutionary multi-objective direct policy search (EMODPS) framework is implemented to identify time adaptive stochastic rules that map utility state information and exogenous inputs to optimal annual financial decisions. The resulting financial risk mitigation portfolio planning problem is mathematically difficult due to its high dimensionality and mixture of nonlinear, nonconvex, and discrete objectives. These features add to the difficulty of the problem by yielding a Pareto front of solutions that has a highly disjoint and complex geometry. In this study, we contribute a diagnostic assessment of state-of-the-art multi-objective evolutionary algorithms' (MOEAs') abilities to support a DPS framework for managing financial risk. We perform comprehensive diagnostics on five algorithms: the Borg multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Reference Vector Guided Evolutionary Algorithm (RVEA), and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). The MOEAs are evaluated to characterize their controllability (ease-of-use), reliability (probability of success), efficiency (minimizing model evaluations), and effectiveness (high quality tradeoff representations). Our results show that newer decomposition, reference point, and reference vector algorithms are highly sensitive to their parameterizations (difficult to use), suffer from search deterioration (losing solutions), and have a strong likelihood of misrepresenting key tradeoffs. The results emphasize the importance of using MOEAs with archiving and adaptive search capabilities in order to solve complex financial risk portfolio problems in snow-dependent water-energy systems. [ABSTRACT FROM AUTHOR]

• Hydrometeorological variables drive prices and emissions on California's grid. • Time scale controls which combination of variables exert the greatest influence. • Market prices appear most sensitive to periods of extreme abundance (over supply) • Historical data severely underrepresents the occurrence of these low price events. Hydrometeorological conditions influence the operations of bulk electric power systems and wholesale markets for electricity. Streamflow is the "fuel" for hydropower generation, wind speeds and solar irradiance dictate the availability of wind and solar power production, and air temperatures strongly affect heating and cooling demands. Despite growing concern about the vulnerability of power systems to hydrometeorological uncertainty, including "compound" extremes (multiple extremes occurring simultaneously), quantifying baseline probabilistic risks remains difficult even without factoring in climate change. Here, we use newly developed power system simulation software to show how uncertainties in spatially and temporally correlated hydrometeorological processes affect market prices and greenhouse gas emissions in California's wholesale electricity market. Results highlight the need for large synthetic datasets to access rare, yet plausible system states that have not occurred in the recent historical record. We find that time scale strongly controls which combinations of hydrometeorological variables cause extreme outcomes. Although scarcity caused by low streamflows and high air temperatures has long been considered a primary concern in Western power markets, market prices are more profoundly impacted by weather and streamflow conditions that lead to an overabundance of energy on the grid. [ABSTRACT FROM AUTHOR]

In the eastern United States, intermittent transfers of treated water are being used more often as a drought management tool; however, untreated raw water transfers, similar to those observed in the western US, are rare. Nonetheless, raw water transfers, free of most physical and financial constraints associated with treating and piping water, are possible within an eastern US regulatory context and could aid in meeting future demands while delaying or avoiding expensive infrastructure. This study demonstrates how raw water transfer schemes along a common river course can influence trade-offs between reliability and financial objectives in the Research Triangle in North Carolina. Regional utility actions are simulated under a wide range of future hydrologic conditions with and without raw water transfer schemes available. Raw water transfers are found to maintain regional supply reliability while reducing demand management interventions. Raw water transfers also significantly reduce politically contentious interbasin water transfers, as well as lowering regional financial risk and long-term debt through decreased dependence on infrastructure and increased planning flexibility. Relaxing US Army Corps of Engineers rules related to the accounting of reservoir inflows as applied to raw water transfers was also observed to improve regional objectives. [ABSTRACT FROM AUTHOR]

Exploratory simulation allows analysts to discover scenarios in which existing or planned water supplies may fail to meet stakeholder objectives. These robustness assessments rely heavily on the choice of plausible future scenarios, which, in the case of drought management, requires sampling or generating a streamflow ensemble that extends beyond the historical record. This study develops a method to modify synthetic streamflow generators by increasing the frequency and severity of droughts for the purpose of exploratory modeling. To support management decisions, these synthetic droughts can be related to recent observed droughts of consequence for regional stakeholders. The method approximately preserves the spatial and temporal correlation of historical streamflow in drought-adjusted scenarios. The approach is demonstrated in a bottom-up planning context using an urban water portfolio design problem in North Carolina, a region whose water supply faces both climate and population pressures. Synthetic scenarios are used to simulate the implications for reliability and cost if events with similar severity to the recent 2007–2008 drought become more frequent under climate change, and in general, the system-level consequences of increasingly frequent and/or severe droughts. Finally, synthetically generated drought extremes are compared with runoff projections derived from downscaled climate model output, serving to support bottom-up robustness methods in water systems planning. [ABSTRACT FROM AUTHOR]

Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP's practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa Binh water reservoir in Vietnam, where water operators are seeking to balance the conflicting objectives of maximizing hydropower production and minimizing flood risks. A key choice in the EMODPS approach is the selection of alternative formulations for flexibly representing reservoir operating policies. This study distinguishes between the relative performance of two widely-used nonlinear approximating networks, namely artificial neural networks (ANNs) and radial basis functions (RBFs). The results show that RBF solutions are more effective than ANN ones in designing Pareto approximate policies for the Hoa Binh reservoir. Given the approximate nature of EMODPS, the diagnostic benchmarking uses SDP to evaluate the overall quality of the attained Pareto-approximate results. Although the Hoa Binh test case's relative simplicity should maximize the potential value of SDP, the results demonstrate that EMODPS successfully dominates the solutions derived via SDP. [ABSTRACT FROM AUTHOR]

Summary In this study, we link a water resource management simulator to multi-objective search to reveal the key trade-offs inherent in planning a real-world water resource system. We consider new supplies and demand management (conservation) options while seeking to elucidate the trade-offs between the best portfolios of schemes to satisfy projected water demands. Alternative system designs are evaluated using performance measures that minimize capital and operating costs and energy use while maximizing resilience, engineering and environmental metrics, subject to supply reliability constraints. Our analysis shows many-objective evolutionary optimization coupled with state-of-the art visual analytics can help planners discover more diverse water supply system designs and better understand their inherent trade-offs. The approach is used to explore future water supply options for the Thames water resource system (including London’s water supply). New supply options include a new reservoir, water transfers, artificial recharge, wastewater reuse and brackish groundwater desalination. Demand management options include leakage reduction, compulsory metering and seasonal tariffs. The Thames system’s Pareto approximate portfolios cluster into distinct groups of water supply options; for example implementing a pipe refurbishment program leads to higher capital costs but greater reliability. This study highlights that traditional least-cost reliability constrained design of water supply systems masks asset combinations whose benefits only become apparent when more planning objectives are considered. [ABSTRACT FROM AUTHOR]

This study introduces the California Food-Energy-Water System (CALFEWS) simulation model to describe the integrated, multi-sector dynamics that emerge from the coordinated management of surface and groundwater supplies throughout California's Central Valley. The CALFEWS simulation framework links the operation of state-wide, interbasin transfer projects (i.e., State Water Project, Central Valley Project) with coordinated water management strategies abstracted to the scale of irrigation/water districts. This study contributes a historic baseline (October 1996–September 2016) evaluation of the model's performance against observations, including reservoir storage, inter-basin transfers, environmental endpoints, and groundwater banking accounts. State-aware, rules-based representations of critical component systems enable CALFEWS to simulate adaptive management responses to alternative climate, infrastructure, and regulatory scenarios. Moreover, CALFEWS has been designed to maintain interoperability with electric power dispatch and agricultural production models. As such, CALFEWS provides a platform to evaluate internally consistent scenarios for the integrated management of water supply, energy generation, and food production. • Rules-based simulation model of water storage and conveyance in California's Central Valley. • State-aware adaptation enables representation of key institutional decision-making. • Supports Monte Carlo exploratory modelling results. • Interoperable with power dispatch and agricultural production models. [ABSTRACT FROM AUTHOR]

Estimating the source term in the case of multiple leaks using a sparse sensor array is a challenging task. Here, the effect of sensor array/leak configurations on the reliability of the source term estimation is studied using two new measures. The first describes the overall change in the sensor array response to different source terms. The second represents the effect of the source term on the readout of each sensor in the array. These measures are subjected to several model cases differing in sensor array/leak configurations. Then, the source term is estimated using a self-adaptive multiobjective evolutionary (MOEA) search algorithm combined with a gas dispersion model. The method searches for a set of leaks, each one of which has a typical emission rate and location that results in a minimal difference between the sensors' actual and computed pollution concentration. This objective, which is often used for source term estimation, is traded off against the second objective of maintaining a minimum number of active sources, which follows Occam's razor principle of parsimony. Analysis of the results obtained for these model cases suggests that the measures can be implemented as a design tool using a combination of computer simulation and field experiments before operational deployment. • New metrics to assess the effect of air pollution sources/sensor array configurations on the accuracy of the source term estimation. • The estimation is based on sensors' response to changes in the source term. • The source term is estimated for several cases using a multiobjective search algorithm combined with an atmospheric dispersion model. [ABSTRACT FROM AUTHOR]

Globally, many countries are actively seeking to maximize the hydropower potential of major river basins, yielding proposals for constructing approximately 3,700 major dams in the near future. At present, cost-benefit analyses are the dominant approach for evaluating candidate dam designs, where the typical problem framing has the following features: (i) a single objective, in which potentially conflicting and heterogeneous objectives are monetized and aggregated into a single Net Present Value (NPV) or Levelized Cost of Energy (LCE) metric; (ii) dam sizing using a pre-defined operating rule; and (iii) a limited accounting for potentially non-stationary external uncertainties. These features in standard cost-benefit analyses strongly bias dam design methods as they fail to explore the interdependent nature of reservoir sizing and operations. In particular, by neglecting a careful consideration of alternative reservoir operating schemes, standard design frameworks are likely to result in over-sized, under-performing dams. Moreover, deeply uncertain changes in hydro-climatic variability and human demands motivate the need for highly flexible assessment frameworks that capture the key tradeoffs between users, how dependencies in dam sizing and operations influence these tradeoffs, and the ultimate robustness of the infrastructure systems. In this work, we combine MORDM (Multi-Objective Robust Decision Making) and EMODPS (Evolutionary Multi-Objective Direct Policy Search) with the specific focus on advancing coupled dam sizing (i.e., discrete reservoir volumes) and operation design (i.e., continuous release policy parameters) problems. We demonstrate the potential of this integrated, highly flexible assessment framework through an ex post analysis of the Kariba dam in the Zambezi river basin. The Kariba dam was built in 1960 and has struggled to follow its specified operational rule curve that originally claimed to maximize hydropower production, independent of irrigation users' demands. Our results show that careful exploration of the coupling of dam sizing and operations yields designs that strongly outperforms the performance achieved under current Kariba system. Moreover, we demonstrate that our integrated sizing and operations framework leads to a significant reduction in capital cost requirements (i.e., smaller reservoir sizes) while simultaneously improving system robustness with respect to changing hydro-climatology and human irrigation demands. [ABSTRACT FROM AUTHOR]