Stochastic Control of Groundwater Systems

Georgakakos, Aris P.; Vlatsa, Dimitra A.

doi:10.1029/91wr00763

Cited by 13 publications

(3 citation statements)

References 31 publications

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“…Deterministic management models aim to obtain the optimal operation policy by utilizing simulation models without uncertainty (Minsker and Shoemaker 1998;Park and Aral 2004;Reichard and Johnson 2005;Abarca et al 2006;Guan et al 2008). On the contrary, stochastic management models account for uncertain predictions of flow and transport owing to imprecise model parameters (mainly hydraulic conductivity) (Tung 1986;Wagner and Gorelick 1987;Wagner and Gorelick 1989;Wagner et al 1992;Ranjithan et al 1993 Morgan et al 1993;Chan 1993;Watkins and McKinney 1997;Aly and Peralta 1999;Smalley et al 2000;Feyen and Gorelick 2004;Singh and Minsker 2008;Ko and Lee 2009), initial condition (Baú and Mayer 2008), and boundary condition (Georgakakos and Vlatsa 1991;Oliver and Christakos 1996;Feyen and Gorelick 2004). Feyen and Gorelick (2005) employed a multiple-realization groundwater management model to assess the economic worth of data collection to reduce management uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model averaging assessment on groundwater management under model structure uncertainty

Tsai

2010

Stoch Environ Res Risk Assess

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This study introduces Bayesian model averaging (BMA) to deal with model structure uncertainty in groundwater management decisions. A robust optimized policy should take into account model parameter uncertainty as well as uncertainty in imprecise model structure. Due to a limited amount of groundwater head data and hydraulic conductivity data, multiple simulation models are developed based on different head boundary condition values and semivariogram models of hydraulic conductivity. Instead of selecting the best simulation model, a variance-window-based BMA method is introduced to the management model to utilize all simulation models to predict chloride concentration. Given different semivariogram models, the spatially correlated hydraulic conductivity distributions are estimated by the generalized parameterization (GP) method that combines the Voronoi zones and the ordinary kriging (OK) estimates. The model weights of BMA are estimated by the Bayesian information criterion (BIC) and the variance window in the maximum likelihood estimation. The simulation models are then weighted to predict chloride concentrations within the constraints of the management model. The methodology is implemented to manage saltwater intrusion in the ''1,500-foot'' sand aquifer in the Baton Rouge area, Louisiana. The management model aims to obtain optimal joint operations of the hydraulic barrier system and the saltwater extraction system to mitigate saltwater intrusion. A genetic algorithm (GA) is used to obtain the optimal injection and extraction policies. Using the BMA predictions, higher injection rates and pumping rates are needed to cover more constraint violations, which do not occur if a single best model is used.

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Section: Introductionmentioning

confidence: 99%

Bayesian model averaging assessment on groundwater management under model structure uncertainty

Tsai

2010

Stoch Environ Res Risk Assess

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“…Gorelick and Voss (1984) combined a solute-transport simulation model (SUTRA), with a nonlinear optimization solver (MINOS) to produce a methodology for aquifer rehabilitation. Georgakakos and Vlatsa (1991) used a gradient search method (ELQG) to solve a stochastic groundwater management problem.…”

Section: Introductionmentioning

confidence: 99%

A surface water management model for the Integrated Southern Ghor Project, Jordan

Rumman

Hiyassat²,

Alsmadi³

et al. 2009

Construction Innovation

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Purpose -The purpose of this paper is to assess the long-term ability of the Integrated Southern Ghor Project (ISGP) to meet the required water demands, assess the resulting energy requirements, pumping costs, water transfers, benefits of the current system with respect to predevelopment conditions and effect of projected water demands increase on the resulting water deficits. Design/methodology/approach -A surface water resources management model is developed using dynamic programming. The model inputs are the hydrological inflows from the different wadis in the project area, reservoirs characteristics and evaporation rates, system water demands. The model outputs are water deficits at the different demand areas, reservoirs storage and release sequences, water transfers and energy requirements and the associated costs. The average annual values of different performance criteria with the annual frequency curves are used to evaluate the implications of different water scenarios on the ISGP. Findings -The results show the efficiency of the ISGP model in reducing the water deficits in the demand areas as compared to predevelopment conditions. Increased demand scenario showed the importance of finding new water projects to supplement the Southern Ghor Area in the future in order to meet the increasing water demands. The proposed water transfer will reduce the resulting deficits at the agricultural areas without the expenses of increasing the water deficits at other demand areas. The application of this model is expected to enhance decision making regarding water policies in Jordan. Originality/value -This paper provides critical quantitative information to decision makers in Jordan about the potential of the different storage facilities and proposed transfers in meeting the required water demands in the Southern Ghor Project and assesses the required energy for that. This can help decision makers to have a holistic view about the expected water deficits in the area and therefore assist them in determining the areas impacted most and what alternative solution to use. The paper also shows the importance of using optimal control/management models to support water resources decision making in Jordan.

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“…Additional state variables may be included to represent the distributed characteristics of a well field or the persistence of local water level changes from earlier pumping or recharge. However, studies that incorporate complex models [Andricevic and Kitanidis, 1990;Basagaoglu and Yazicigil, 1995;Burt, 1976;Georgakakos and Vlatsa, 1991;Lee and Kitanidis, 1991;Provencher and Burt, 1994;Reichard, 1995] have only been solved by optimization methods that are approximate.…”

Section: Value Modelmentioning

confidence: 99%

Optimal conjunctive‐use operations and plans

Philbrick¹,

Kitanidis²

1998

Water Resources Research

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Abstract. Heuristic or intuitive rules based on experience may not be efficient when applied to the management of water supply systems that contain both surface and subsurface storage. In particular, rules that assign subsurface storage the role of a backup to surface storage do not recognize the different capabilities of surface and subsurface storage. We demonstrate how to incorporate the different capabilities of surface and subsurface storage in appropriately cautious long-term control of conjunctive-use systems. We also demonstrate how these results may be used to evaluate the benefit of adding groundwater supplies to an existing surface water supply system.

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Stochastic Control of Groundwater Systems

Cited by 13 publications

References 31 publications

Bayesian model averaging assessment on groundwater management under model structure uncertainty

Bayesian model averaging assessment on groundwater management under model structure uncertainty

A surface water management model for the Integrated Southern Ghor Project, Jordan

Optimal conjunctive‐use operations and plans

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