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Cities in South America face many of the urban water supply challenges experienced globally including increasingly extreme future hydroclimatic conditions and rapid population growth. These challenges are further exacerbated by historical socioeconomic inequity, informal land occupation, and poor water services management. In recent years, decision support tools that aid in structuring water supply management and infrastructure pathway policies that remain robust under deeply uncertain future scenarios have been developed. However, within the context of developing countries, failing to acknowledge the complex social and institutional dynamics and stark differences in residents' experiences of climate extremes may lead to uneven adaptation capacities across socioeconomic strata. Toward this end, our study extends the deeply uncertain pathways framework by applying multiobjective optimization, disaggregated service area-level assessments of performance and vulnerability across time, and exploratory visual analytics in the Federal District of Brazil (FDB). We highlight the performance and robustness disparities between two water supply service areas in the FDB that differ in socioeconomic standing to reveal the impacts of deeply uncertain future hydroclimatic and socioeconomic scenarios on vulnerable populations. We further show that historical inequity renders poorer residents significantly more vulnerable to deeply uncertain future conditions without urgent and significant infrastructure investments. Overall, the outcomes of our study are largely applicable to urban water utilities in regions with high levels of historical regional inequity seeking to develop water management and infrastructure planning policies that are robust, adaptive, and equitable. [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 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. CE Database subject headings: Groundwater; Monitoring; Design; Water resources management.

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]

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]

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]

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]

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]

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.

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]