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This paper proposes an approach for including deeply uncertain factors directly into a multi-objective search procedure, to aid in incorporating divergent quantitative scenarios within the model-based decision support process. Specifically, we extend Many Objective Robust Decision Making (MORDM), a framework for finding and evaluating planning solutions under multiple objectives, to include techniques from robust optimization. Traditional MORDM first optimized a problem under a baseline scenario, then evaluated candidate solutions under an ensemble of uncertain conditions, and finally discovered scenarios under which solutions are vulnerable. In this analysis, we perform multiple multi-objective search trials that directly incorporate these discovered scenarios within the search. Through the analysis, we have created multiple problem formulations to show how methodological choices of severe scenarios affect the resulting candidate planning solutions. We demonstrate the approach through a water planning portfolio example in the Lower Rio Grande Valley of Texas. [ABSTRACT FROM AUTHOR]

We propose an approach for screening future infrastructure and demand management investments for large water supply systems subject to uncertain future conditions. The approach is demonstrated using the London water supply system. Promising portfolios of interventions (e.g., new supplies, water conservation schemes, etc.) that meet London’s estimated water supply demands in 2035 are shown to face significant trade-offs between financial, engineering and environmental measures of performance. Robust portfolios are identified by contrasting the multi-objective results attained for (1) historically observed baseline conditions versus (2) future global change scenarios. An ensemble of global change scenarios is computed using climate change impacted hydrological flows, plausible water demands, environmentally motivated abstraction reductions, and future energy prices. The proposed multi-scenario trade-off analysis screens for robust investments that provide benefits over a wide range of futures, including those with little change. Our results suggest that 60 percent of intervention portfolios identified as Pareto optimal under historical conditions would fail under future scenarios considered relevant by stakeholders. Those that are able to maintain good performance under historical conditions can no longer be considered to perform optimally under future scenarios. The individual investment options differ significantly in their ability to cope with varying conditions. Visualizing the individual infrastructure and demand management interventions implemented in the Pareto optimal portfolios in multi-dimensional space aids the exploration of how the interventions affect the robustness and performance of the system. [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]

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 This paper introduces Parasol—an open source, interactive visualization library to support the development of web applications for multi-objective decision making. Multi-objective optimization is a popular way to explore competing objectives in environmental management problems. Interactive visualizations allow stakeholders to explore and gain insights about the large, high-dimensional datasets produced by multi-objective optimization. Among visualization methods, parallel coordinates are well-suited for this task. However, current software and open source libraries have limited support for these plots. The Parasol library described in this work provides developers with the building blocks to create sharable, interactive parallel coordinates web applications. Moreover, by incorporating state of the art clutter reduction techniques—such as clustering, linking, brushing, marking, and bundling—Parasol improves upon traditional parallel coordinates visualizations. We demonstrate the benefit of such features through simple examples and by exploring a real-world water resources problem commonly used in multi-objective optimization literature. Highlights • We introduce Parasol, an open source visualization library. • Parallel coordinates (PC) are well-suited for environmental decision making. • Parasol provides building blocks for constructing PC-based web apps. • Web apps are easily shared and promote interactive data visualization. [ABSTRACT FROM AUTHOR]

Abstract Decision-support tools (DSTs) are often produced from collaborations between technical experts and stakeholders to address environmental problems and inform decision making. Studies in the past two decades have provided key insights on the use of DSTs and the importance of bidirectional information flows among technical experts and stakeholders – a process that is variously referred to as co-production, participatory modeling, structured decision making, or simply stakeholder participation. Many of these studies have elicited foundational insights for the broad field of water resources management; however, questions remain on approaches for balancing co-production with uncertainty specifically for watershed modeling decision support tools. In this paper, we outline a simple conceptual model that focuses on the DST development process. Then, using watershed modeling case studies found in the literature, we discuss successful outcomes and challenges associated with embedding various forms of co-production into each stage of the conceptual model. We also emphasize the "3 Cs" (i.e., characterization, calculation, communication) of uncertainty and provide evidence-based suggestions for their incorporation in the watershed modeling DST development process. We conclude by presenting a list of best practices derived from current literature for achieving effective and robust watershed modeling decision-support tools. Highlights • Technical experts and stakeholders oftentimes co-produce decision-support tools. • We discuss co-production and uncertainty in watershed modeling DSTs. • We present a list of best practices derived from watershed modeling case studies. [ABSTRACT FROM AUTHOR]

This study advances model diagnostics for snowmelt-based hydrological systems using Sobol’ sensitivity analysis, illuminating parameter sensitivities and contrasting model structural differences. We consider several distinct snow-dominated locations in the western United States, running both SNOW-17, a conceptual degree-day model, and the Variable Infiltration Capacity (VIC) snow model, a physically-based model. Model performance is rigorously evaluated through global sensitivity analysis and a temperature warming analysis is conducted to explore how model parameterizations affect portrayals of climate change. Both VIC and SNOW-17 produce comparable results with SNOW-17 performing slightly better for shallower snowpacks and VIC performing better for deeper snowpacks. However, the lack of sensitivity of SNOW-17 to climate warming suggests that it may not be as reliable as a more sensitive model like VIC. Inter-model differences presented here offer insights into physical features with greatest uncertainty and may inform future model development and planning activities. [ABSTRACT FROM AUTHOR]

The effectiveness of in situ remediation to treat contaminated aquifers is limited by the degree of contact between the injected treatment chemical and the groundwater contaminant. In this study, candidate designs that actively spread the treatment chemical into the contaminant are generated using a multi-objective evolutionary algorithm. Design parameters pertaining to the amount of treatment chemical and the duration and rate of its injection are optimized according to objectives established for the remediation – maximizing contaminant degradation while minimizing energy and material requirements. Because groundwater contaminants have different reaction and sorption properties that influence their ability to be degraded with in situ remediation, optimization was conducted for six different combinations of reaction rate coefficients and sorption rates constants to represent remediation of the common groundwater contaminants, trichloroethene, tetrachloroethene, and toluene, using the treatment chemical, permanganate. Results indicate that active spreading for contaminants with low reaction rate coefficients should be conducted by using greater amounts of treatment chemical mass and longer injection durations relative to contaminants with high reaction rate coefficients. For contaminants with slow sorption or contaminants in heterogeneous aquifers, two different design strategies are acceptable — one that injects high concentrations of treatment chemical mass over a short duration or one that injects lower concentrations of treatment chemical mass over a long duration. Thus, decision-makers can select a strategy according to their preference for material or energy use. Finally, for scenarios with high ambient groundwater velocities, the injection rate used for active spreading should be high enough for the groundwater divide to encompass the entire contaminant plume. [ABSTRACT FROM AUTHOR]