Optimal control of groundwater by the feedback method of control

Groundwater is the world's largest freshwater resource and due to overextraction, levels have declined in many regions causing extensive social and environmental impacts. Groundwater management seeks to balance and mitigate the detrimental impacts of development, with plans commonly used to outline management pathways. Thus, plan efficiency is crucial, but seldom are plans systematically and quantitatively assessed for effectiveness. This study frames groundwater management as a system control problem in order to develop a novel testability assessment rubric to determine if plans meet the requirements of a control loop, and subsequently, whether they can be quantitatively tested. Seven components of a management plan equivalent to basic components of a control loop were determined, and requirements of each component necessary to enable testability were defined. Each component was weighted based upon proposed relative importance, then segmented into rated categories depending on the degree the requirements were met. Component importance varied but, a defined objective or acceptable impact was necessary for plans to be testable. The rubric was developed within the context of the Australian groundwater management industry, and while use of the rubric is not limited to Australia, it was applied to 15 Australian groundwater management plans and approximately 47% were found to be testable. Considering the importance of effective groundwater management, and the central role of plans, our lack of ability to test many plans is concerning.

Section: How Are Management Plans Tested?mentioning

confidence: 99%

“…Considerable effort has been put into using numerical models to evaluate technical aspects of groundwater management. For example, numerical models have been used to predict impacts of groundwater extraction upon aquifers [ Ebraheem et al ., ; Gorelick , ], optimize extraction rates [ Bear and Levin , ; Casola et al ., ; Makinde‐Odusola and Mariño , ; McPhee and Yeh , ; Singh , ; Tankersley and Graham , ; Wagner , ], adjust control based on actual system response [ Jones , ], manage seawater intrusion [ Reichard and Johnson , ; Rejani et al ., ], and investigate implications of economic considerations [ Booker et al ., ; Bredehoeft and Young , ; Bromley , ; Gisser and Sánchez , ; Koundouri , 2004; Mulligan et al ., ], among others. These studies do not, however, investigate if sequential management decisions result in successful resource management or assess the effectiveness of the plan separate from the state of the aquifer.…”

Section: Introductionmentioning

confidence: 99%

Can we manage groundwater? A method to determine the quantitative testability of groundwater management plans

White

Peterson

Costelloe

et al. 2016

“…Many studies have been dedicated to the use of optimization models to solve the increasing complexity of water management problems. Linear programming [ Aguado et al , 1974; Molz and Bell , 1977; El Magnouni and Treichel , 1994], nonlinear programming [ Gorelick et al , 1979, 1984; McKinney and Lin , 1992], dynamic programming [ Makinde‐Odusola and Marino , 1989], goal programming [ Rajabi et al , 1999], and genetic algorithms [ McKinney and Lin , 1994; Cieniawski et al , 1995] have been used as optimization techniques. Linear programming is an efficient technique for problems that can be linearized, but it becomes inaccurate and inappropriate for pronounced nonlinear and interdependent optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multiple‐objective optimization of drinking water production strategies using a genetic algorithm

Vink

Schot

2002

[1] Finding a strategy that allows economically efficient drinking water production at minimal environmental cost is often a complex task. A systematic trade-off among the costs and benefits of possible strategies is required for determining the optimal production configuration. Such a trade-off involves the handling of interdependent and nonlinear relations for drawdown-related objective categories like damage to wetland vegetation, agricultural yield depression, reduction of river base flow rates, and soil subsidence. We developed a method for multiple-objective optimization of drinking water production by combining Busacker and Gowen's [1961] ''minimum cost flow'' procedure for optimal use of the transport network with a genetic algorithm (GA) for optimization of other impacts. The performance of the GA was compared with analytically determined solutions of a series of hypothetical case studies. Pareto-optimality and uniqueness of solutions proved to be effective fitness criteria for identifying trade-off curves with the GA.INDEX TERMS: 1884 Hydrology: Water supply; 1890 Hydrology: Wetlands; 1894 Hydrology: Instruments and techniques; 6304 Policy Sciences: Benefit-cost analysis; KEYWORDS: decision support system, multiple objective optimization, drinking water supply, genetic algorithm, evolutionary programming Citation: Vink K., and P. Schot, Multiple-objective optimization of drinking water production strategies using a genetic algorithm,

“…Failare to implement prompt and prudent management measures is bound to increase future restorative costs and threaten the availahlity and usability of this resource. These ramifica-'tins have sustained interest in groundwater management models [Gorelick, 1983;Feinerman et al , 1985; Willis and Finhey, 1985; Massmann and Freeze, 1987a, b; Makinde-Odusola and Marino, 1989]. Such models use operations research methods with groundwater flow and transport models to assess the relative impacts of various groundwater -'nmagement options.…”

Section: Introductionmentioning

confidence: 99%

“…Optimal control methods have been used to solve deterministic groundwater management problems. Examples of such applications are Noel and Howitt [1982], Willis and Finney [1985], Jones et al [1987], and Makinde-Odusola and Marino [1989]. However, stochastic optimal control applications have not as yet been reported.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Control of Groundwater Systems

Georgakakos¹,

Vlatsa²

1991

In groundwater management, uncertainty mainly stems from imprecise parameters and boundary conditions. This paper first formulates a stochastic groundwater management problem and subsequently proposes an appropriate solution approach. The equations of flow are converted to a dynamical state-space system using finite element and difference techniques. Parameter and boundary condition uncertainty is incorporated using the small perturbation method. Management objectives are expressed as a composite performance index which may be used to minimize pumping costs, maintain hydraulic heads and pumping rates in the vicinity of target sequences, or optimally compromise among various system goals. This problem is solved via a numerical optimal control method which exhibits good computational properties. The approach is applied to the management of a two-layer aquifer system with various boundary conditions and uncertainty levels and sources. The results provide useful insights of the system response under uncertainty and quantify the trade-offs between accomplishing average system goals and minimizing uncertainty.