Joseph R. Kasprzyk

Water Resources Management: The Myth, the Wicked, and the Future

“The willingness to look ahead ,...a tlow-, medium-, and high-level population densities reveals an awareness of the increasing importance of water in the future; and of the necessity for the present generation to plan for the water needs of the next” Balchin 1960.

Navigating financial and supply reliability tradeoffs in regional drought management portfolios

Rising development costs and growing concerns over environmental impacts have led many communities to explore more diversified water management strategies. These “portfolio”-style approaches integrate existing supply infrastructure with other options such as conservation measures or water transfers. Diversified water supply portfolios have been shown to reduce the capacity and costs required to meet demand, while also providing greater adaptability to changing hydrologic conditions. However, this additional flexibility can also cause unexpected reductions in revenue (from conservation) or increased costs (from transfers). The resulting financial instability can act as a substantial disincentive to utilities seeking to implement more innovative water management techniques. This study seeks to design portfolios that employ financial tools (e.g., contingency funds and index insurance) to reduce fluctuations in revenues and costs, allowing these strategies to achieve improved performance without sacrificing financial stability. This analysis is applied to the development of coordinated regional supply portfolios in the “Research Triangle” region of North Carolina, an area comprising four rapidly growing municipalities. The actions of each independent utility become interconnected when shared infrastructure is utilized to enable interutility transfers, requiring the evaluation of regional tradeoffs in up to five performance and financial objectives. Diversified strategies introduce significant tradeoffs between achieving reliability goals and introducing burdensome variability in annual revenues and/or costs. Financial mitigation tools can mitigate the impacts of this variability, allowing for an alternative suite of improved solutions. This analysis provides a general template for utilities seeking to navigate the tradeoffs associated with more flexible, portfolio-style management approaches.

An iterative approach to multi-objective engineering design

This study contributes an iterative problem reformulation technique for multi-objective evolutionary algorithm (MOEA) decision support. Problem formulations consist of objectives, decision variables, and constraints, and directly influence the results generated by the MOEA. Typically, design problems are optimized based on a single problem formulation established a priori. In this paper, we demonstrate an approach to perform iterative optimization using problem formulations updated from analyses of results from prior rounds of optimization, which often reveal design components not initially considered. To demonstrate the approach, we consider a novel groundwater remediation technique, Engineered Injection and Extraction (EIE), which has never been optimized in the literature. Iterative problem reformulation enabled the MOEA to generate EIE solutions with better performance than the heuristically-developed solution used in prior work. We optimize a groundwater remediation strategy using multi-objective optimization.We demonstrate an iterative approach to adapt the problem formulation.We couple visualizations of objective and decision space to analyze solutions.

Thematic issue on Evolutionary Algorithms in Water Resources

Evolutionary Algorithms (EAs) and other similar optimisation approaches have become very popular in the water resources research literature over the last two decades. One reason for the emergence of EAs in the literature is that they use evolutionary principles found in nature, “evolving” to find better solutions to complex water resources problems. Another reason is that evolutionary optimisation provides a natural extension to the use of simulation models, as EAs simply “bolt onto” existing models. Consequently, the resulting optimisation process is very intuitive, as the way EAs try different solutions and then learn from the outcomes of these trials is similar to the process humans adopt when manually “optimising” or adjusting solutions to problems via a simulation based approach. The only differences when EAs are used are that the decisions as to which options to try are made with the aid of evolutionary operators, rather than human judgement, intuition and experience, and that the number of options considered is much larger. Moreover, outputs of the EA process are equivalent to outputs of trusted simulation models. Therefore, the optimisation results from EAs tend to have more credibility than those obtained using alternative approaches, such as mathematical programming, since the latter generally require gross simplifications of problem representation. Another attractive feature of EAs is that they are not necessarily prescriptive in the sense of suggesting “the” optimal solution. This is because they work with populations of solutions and therefore produce a number of near-optimal solutions, which might be similar in objective function space, but quite different in solution space. This enables consideration of factors other than those captured in the mathematical formulation of the optimisation problemwhen selecting the solution to be implemented. As a result of the loose coupling between the optimisation engine, which decides which parts of the solution space to explore, and the simulation model, which evaluates how well the selected solutions perform in relation to the objectives and/or whether constraints have been violated, EAs can deal with discontinuities and nonlinearities with ease, as long as these have been captured appropriately in the simulationmodel. Another advantage of EAs is that they are well suited to multi-objective problems, as they can evolve optimal trade-offs between objectives (i.e. Pareto fronts) in a single optimisation trial. Given the fascination and intrigue associated with the ability to use evolutionary processes to optimise water resources problems, the practicality and intuitiveness associated with being able to make use of existing simulation models and the advantage of being

Incorporating deeply uncertain factors into the many objective search process

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. A multi-scenario optimization technique for deep uncertainty is proposed.Including severe scenarios within the optimization yields more robust solutions.Parallel coordinates effectively show solution performance in alternate scenarios.

Optimal design of active spreading systems to remediate sorbing groundwater contaminants in situ

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.

Estimating the Impacts of Climate Change and Population Growth on Flood Discharges in the United States

AbstractThis study reflects a portion of the riverine analysis for a Federal Emergency Management Agency initiative to estimate the economic risks associated with climate and land use change to the U.S. National Flood Insurance Program. Specifically, this paper investigates how the 1% annual chance flood discharge, Q1% (equivalent to a 100-year return period flood), may change based on climate change and population projections through the year 2100. Watershed characteristics and observations of climate indicators at 2,357 U.S. Geological Survey gauging stations were used to develop regression relationships to estimate Q1%. Projections of the climate indicators that measure extremes in temperature and precipitation from a suite of global climate models were then used within a Monte Carlo sampling framework to estimate future changes to Q1% throughout the United States, while also translating the uncertainty resulting from multiple climate model projections into uncertainty in estimating the future Q1%. Pop...

Exploring snow model parameter sensitivity using Sobol' variance decomposition

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. Historically-fixed parameters in the VIC snow model can greatly affect SWE simulations.Calibrating the VIC snow model, although not common practice, improves simulated SWE.Greater model complexity was not necessarily associated with higher model performance.

Emerging investigators series: a critical review of decision support systems for water treatment: making the case for incorporating climate change and climate extremes

Water treatment plants (WTPs) are tasked with providing safe potable water to consumers. However, WTPs face numerous potential obstacles, including changes in source water quality and quantity, financial burdens related to operations and upgrades, and stringent water quality regulations. Moreover, these challenges may be exacerbated by climate change in the form of long-term climatic perturbations and the increasing frequency and intensity of extreme weather events. To help WTPs overcome these issues, decision support systems (DSSs), which are used to aid and enhance the quality and consistency of decision-making, have been developed. This paper reviews the scientific literature on the development and application of DSSs for water treatment, including physically-based models, statistical models, and artificial intelligence techniques, and suggests future directions in the field. We first set the context of how water quality is impacted by climate change and extreme weather events. We then provide a comprehensive review of DSSs and conclude by offering a series of recommendations for future DSS efforts for WTPs, suggesting that these tools should (1) more accurately reflect the practical needs of WTPs, (2) represent the tradeoffs between the multiple competing objectives inherent to water treatment, (3) explicitly handle uncertainty to better inform decision makers, (4) incorporate nonstationarity, especially with regard to extreme weather events and climate change for long-term planning, and (5) use standardized terminology to accelerate the dissemination of knowledge in the field.

Many-Objective Design of Engineered Injection and Extraction Sequences for In Situ Remediation of Contaminated Groundwater

Although in situ treatment methods exist for groundwater contamination remediation, it is often difficult to increase the efficiency of the reactions under time constraints. During in-situ remediation, a treatment solution is injected into the contaminated region of the aquifer to react with and degrade the groundwater contaminant. Ideally, the treatment solution should be spread throughout the contaminated region to increase its contact with the contaminant, leading to more opportunities for degradation reactions. Engineered injection and extraction (EIE) is a promising technique for generating spreading, in which transient flow fields are induced using a sequence of injections and extractions of clean water at wells surrounding the contaminant plumes. While simulations of in situ remediation using a single unique sequence of EIE have shown that spreading due to EIE can enhance reaction, a new approach is needed to design high-quality EIE designs that consider multiple engineering performance objectives. This study uses a multiobjective evolutionary algorithm (MOEA) to determine a set of EIE designs that balance multiple objectives, such as maximizing the amount of reaction while minimizing the amounts of required treatment solution and extracted groundwater contaminant. Such tradeoff surfaces allow stakeholders to determine how EIE designs perform under various degrees of aquifer heterogeneity, types of contaminant (aqueous or sorbed), number of wells, and location of wells.