Groundwater well optimization to minimize contaminant movement from a surficial shallow aquifer to a lower water supply aquifer using stochastic simulation-optimization modeling techniques: Strategy formulation

Paul, Sondipon; Waldron, Brian; Jazaei, Farhad; Larsen, Daniel; Schoefernacker, Scott

doi:10.1016/j.mex.2022.101765

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…The strategies aim to protect wells from contamination, extend the existing wells' life, and identify suitable locations for future well development. Each strategy of the decision‐making framework is briefly summarized below, while a detailed description of the strategies' assumptions, development, and workflow can be found in Paul et al (2022).…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, Strategy-I evaluates a maximum of 12,000 permutations of well configurations to find the best solution for a wellfield. The mathematical framework of the genetic algorithm-based optimization formulation for Strategy-I can be written as (modified from Paul et al, 2022):…”

Section: Strategy-i: Well Depth Optimizationmentioning

confidence: 99%

“…Although a decision-optimization modeling tool would be invaluable to Memphis Light, Gas and Water (MLGW), a major water utility in Memphis that operates between 150 and 170 wells. Paul et al (2022) presents several potential wellfield optimization strategies and corresponding modeling frameworks incorporating flow, advective transport, and optimization tools to evaluate the strategies. This paper presents the specific characteristics of these potential management strategies and modeling frameworks and evaluates the strategies considering the potential contamination risk posed by UCCU breaches to provide practical solutions to the wellfield management problems in Memphis, Tennessee.…”

mentioning

confidence: 99%

“…The number of times each month's pumping rate falls below the 25th percentile pumping rate at Lichterman wellfield.F I G U R E 6Workflow of Strategy-II (modified fromPaul et al, 2022).…”

mentioning

confidence: 99%

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Wellfield optimization to minimize contaminant migration from a surficial to a semi‐confined aquifer using numerical modeling

Paul

Waldron

Jazaei

et al. 2023

J American Water Resour Assoc

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The shallow, Memphis, and Fort Pillow aquifers are the three major water‐bearing strata beneath Memphis, Tennessee, where the Memphis aquifer serves as the primary groundwater source. The upper Claiborne confining unit (UCCU) separates shallow and Memphis aquifers across the majority of Shelby County, acting as an upper protective layer for the Memphis aquifer. However, hydrogeologic breaches within the UCCU create a hydraulic connection and provide an avenue for potential contaminant migration from the shallow to the Memphis aquifer. This research aims to minimize contaminant migration, mitigate risks, extend existing wells' life that may face water contamination, and find suitable locations for future well construction. Several strategies are developed addressing well depth, seasonal well operation, and mapping no‐drilling or red zones to provide practical solutions. A numerical groundwater modeling technique is developed for each strategy that includes stochastic simulation–optimization and customized simulation models depending on the strategy. The models result in thousands of numerical simulations for each scenario to identify recurring patterns of contaminant movement to and through the Memphis aquifer. The results indicate that optimum well positions (spatially and vertically) and modification to pumping can increase the life expectancy of wellfields, offer sustainable management of the Memphis aquifer, and reduce contaminant migration through 2050.

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Section: Methodsmentioning

confidence: 99%

Section: Strategy-i: Well Depth Optimizationmentioning

confidence: 99%

mentioning

confidence: 99%

“…The number of times each month's pumping rate falls below the 25th percentile pumping rate at Lichterman wellfield.F I G U R E 6Workflow of Strategy-II (modified fromPaul et al, 2022).…”

mentioning

confidence: 99%

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Wellfield optimization to minimize contaminant migration from a surficial to a semi‐confined aquifer using numerical modeling

Paul

Waldron

Jazaei

et al. 2023

J American Water Resour Assoc

Self Cite

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“…Regarding development of optimal groundwater strategies, research carried out over the last couple of decades has proposed a broad spectrum of approaches based on coupling groundwater simulation models with optimization algorithms [20]. Different methods have been used, that can be broadly classified in two categories: (1) response matrix approaches, in which simulation models are combined with linear, non-linear, and mixedinteger optimization algorithms [21,22], and (2) direct coupling of simulation models with global optimization algorithms (e.g., genetic algorithms), which has gained popularity in recent years [23][24][25][26][27][28]. For the case of the Eocene aquifer, only one study has been identified that used this approach to propose optimal abstraction strategies, considering sustainability concerns [29], in which a steady-state, single-layer MODFLOW groundwater model was coupled with the GWM tool of USGS [30].…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Sustainable Exploitation of the Eocene Aquifer in the West Bank, Palestine

Jonoski

Ahmed

Almasri

et al. 2023

Water

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Groundwater is a crucial resource for water supply and irrigation in many parts of the world, especially in the Middle East. The Eocene aquifer, located in the northern part of the West Bank, Palestine, is threatened by unsustainable groundwater abstractions and on-ground pollution. Analysis and management of this aquifer are challenging because of limited data availability. This research contributes to the long-term sustainability of the aquifer by model-based design of future abstraction strategies considered within an uncertainty analysis framework. The methodology employed started with development of a single-layer steady-state MODFLOW groundwater model of the area, followed by uncertainty analysis of model parameters using Monte Carlo simulations. The same model was afterwards coupled with a Successive Linear Programming (SLP) optimization algorithm, implemented in the Groundwater Management tool (GWM) of the United States Geological Survey (USGS). The purpose of optimization was deriving five optimal abstraction strategies, each aiming to maximize groundwater abstraction, subject to different constraints regarding groundwater depletion. Given the uncertainty of model parameters, the sensitivity and reliability of these optimal strategies were then tested. Sensitivity was checked for two optimal strategies by performing re-optimization with different values of uncertain model parameters (one at a time). Reliability of the five strategies was tested by analyzing the extent of constraints’ violation for each strategy when varying the uncertain parameters using Monte Carlo simulations. Finally, the model was used for determining capture zones of wells for the five optimal abstraction strategies, land-use in these capture zones, and the associated estimates of on-ground nitrogen loading. The developed strategies were then deployed in a web-based decision support application (named Groundwater Decision Support System—GDSS), together with other relevant information. Users can analyze results of different optimal strategies in terms of groundwater level variations and total water balance results, and test consequences of uncertain parameters. Capture zones of wells for different abstraction strategies, together with land-use and on-ground nitrogen loading in these capture zones, are also presented. Results show that critical uncertain parameters are recharge, hydraulic conductivity, and conductance at key boundary condition locations. Optimal abstraction strategies results indicate that an increase in total abstractions could be between 5% and 20% from the current level (estimated at about 56 × 106 m3/year, which is about 74% of estimated annual recharge). The uncertain parameters, however, are impacting the sensitivity and the reliability of the optimal strategies to variable degrees. Recharge and hydraulic conductivity are the most critical uncertain parameters regarding sensitivity of the optimal strategies, while reliability is also impacted by the level of abstraction proposed in a given strategy (number, locations, and abstraction rates of new wells). The main novelty and contribution of this research is in combining modelling, uncertainty analysis, and optimization techniques in a comprehensive decision support system for the area of the Eocene aquifer, characterized with limited data availability.

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Exploring Simulation–Optimization for Sustainable Groundwater Management: A Critical Review

Mishra,

Gaur,

Kacem

et al. 2024

CLEAN Soil Air Water

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Simulation–optimization (S–O) is a well‐regarded method for solving groundwater (GW) management problems. Although S–O has significantly improved the decision support system for GW management, it still lacks practical applicability. As a result, many researchers have been improving its components, leading to slightly or significantly better performance. To understand these challenges efficiently, this article delves into principal components of S–O that offer in‐depth critical insights into GW's sustainability. The discussed segments are divided into simulation models, optimization methods, categories and conceptualization of management problems, and the formulation of real‐world objective functions. This review also examines surrogate‐assisted simulation models to reduce computational challenges. Methods to address model uncertainty and decision‐making in applying S–O for sustained yield problems are addressed. The review outlays critical steps in S–O methodology and recommends potential research directions to aid researchers in further enhancing the practicality of S–O.

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Groundwater well optimization to minimize contaminant movement from a surficial shallow aquifer to a lower water supply aquifer using stochastic simulation-optimization modeling techniques: Strategy formulation

Cited by 7 publications

References 22 publications

Wellfield optimization to minimize contaminant migration from a surficial to a semi‐confined aquifer using numerical modeling

Wellfield optimization to minimize contaminant migration from a surficial to a semi‐confined aquifer using numerical modeling

Decision Support System for Sustainable Exploitation of the Eocene Aquifer in the West Bank, Palestine

Exploring Simulation–Optimization for Sustainable Groundwater Management: A Critical Review

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