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B.J. Wagner

Publications and source records attributed to B.J. Wagner.

6 recordsLinked to original sources
USGS2012

Recent advances in simulation-optimization groundwater management modeling

In recent years, the aquifer simulation model has been combined with techniques of optimization to address important groundwater management problems. The combined simulation and optimization model accounts for the complex behavior of the groundwater system and identifies the best management strategy under consideration of the management objectives and constraints. Simulation-optimization groundwater management models have been developed for a variety of applications, such as restoration of contaminated groundwater, control of aquifer hydraulics, allocation of ground and surface water resources, and evaluation of groundwater policies [see reviews by Gorelick , 1983, 1990; Yeh , 1992; Ahlfeld and Heidari , 1994; Bredehoeft et al , 1994]. Gorelick [1983] divides groundwater management models into three categories: groundwater hydraulic management, groundwater quality management, and groundwater policy evaluation and allocation. This review focuses in detail on the recent advances made in groundwater hydraulic and groundwater quality management modeling, which encompasses most of the aquifer management work done in the past four years. For a review of recent research in groundwater policy evaluation and allocation, see Bredehoeft et al . [1994].

Reviews of Geophysics↗
USGS2005

Pumping strategies for management of a shallow water table: The value of the simulation-optimization approach

The simulation-optimization approach is used to identify ground-water pumping strategies for control of the shallow water table in the western San Joaquin Valley, California, where shallow ground water threatens continued agricultural productivity. The approach combines the use of ground-water flow simulation with optimization techniques to build on and refine pumping strategies identified in previous research that used flow simulation alone. Use of the combined simulation-optimization model resulted in a 20 percent reduction in the area subject to a shallow water table over that identified by use of the simulation model alone. The simulation-optimization model identifies increasingly more effective pumping strategies for control of the water table as the complexity of the problem increases; that is, as the number of subareas in which pumping is to be managed increases, the simulation-optimization model is better able to discriminate areally among subareas to determine optimal pumping locations. The simulation-optimization approach provides an improved understanding of controls on the ground-water flow system and management alternatives that can be implemented in the valley. In particular, results of the simulation-optimization model indicate that optimal pumping strategies are constrained by the existing distribution of wells between the semiconfined and confined zones of the aquifer, by the distribution of sediment types (and associated hydraulic conductivities) in the western valley, and by the historical distribution of pumping throughout the western valley.

Ground Water↗
USGS2001

Analysing the capabilities and limitations of tracer tests in stream-aquifer systems

The goal of this study was to identify the limitations that apply when we couple conservative-tracer injection with reactive solute sampling to identify the transport and reaction processes active in a stream. Our methodology applies Monte Carlo uncertainty analysis to assess the ability of the tracer approach to identify the governing transport and reaction processes for a wide range of stream-solute transport and reaction scenarios likely to be encountered in high-gradient streams. Our analyses identified dimensionless factors that define the capabilities and limitations of the tracer approach. These factors provide a framework for comparing and contrasting alternative tracer test designs.

IAHS-AISH Publication↗
USGS1999

Evaluating data worth for ground-water management under uncertainty

A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models-a chance-constrained ground-water management model and an integer-programing sampling network design model-to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information-i.e., the projected reduction in management costs-with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology's ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management.A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models - a chance-constrained ground-water management model and an integer-programming sampling network design model - to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information - i.e., the projected reduction in management costs - with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology's ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management.

Journal of Water Resources Planning and Management↗
USGS1992

Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling

Parameter estimation and contaminant source characterization are key steps in the development of a coupled groundwater flow and contaminant transport simulation model. Here a methodologyfor simultaneous model parameter estimation and source characterization is presented. The parameter estimation/source characterization inverse model combines groundwater flow and contaminant transport simulation with non-linear maximum likelihood estimation to determine optimal estimates of the unknown model parameters and source characteristics based on measurements of hydraulic head and contaminant concentration. First-order uncertainty analysis provides a means for assessing the reliability of the maximum likelihood estimates and evaluating the accuracy and reliability of the flow and transport model predictions. A series of hypothetical examples is presented to demonstrate the ability of the inverse model to solve the combined parameter estimation/source characterization inverse problem. Hydraulic conductivities, effective porosity, longitudinal and transverse dispersivities, boundary flux, and contaminant flux at the source are estimated for a two-dimensional groundwater system. In addition, characterization of the history of contaminant disposal or location of the contaminant source is demonstrated. Finally, the problem of estimating the statistical parameters that describe the errors associated with the head and concentration data is addressed. A stage-wise estimation procedure is used to jointly estimate these statistical parameters along with the unknown model parameters and source characteristics.

Journal of Hydrology↗