Modeling a Ground Water Circulation Well Alternative

Ground water circulation wells (GCWs) provide an appealing alternative to typical pump-and-treat ground water remediation systems because of the inherent resource-conservative nature of the GCW systems. GCW performance prediction is challenging because the consideration of extraction and recharge in a single well is unusual for most practitioners, the technology is relatively new, and a meaningful body of literature has not been published. A three-part evaluation process using state-of-the-practice numerical ground water flow and mass transport models was developed for application during GCW pilot studies at the former Nebraska Ordnance Plant site. A small-scale ground water flow model was developed during the pilot study planning process to predict the system performance and to locate performance-measuring monitoring wells. Key predictions included the capture zone predicted to develop upgradient of the GCW, the downgradierit GCW recharge zone, and the circulation zone centered on the GCW. The flow model was subsequently verified using ground water elevation data and contaminant concentration data collected during pilot study operation. Aquifer parameters were reestimated as a result of the verification process. Those parameter values were used as input to a larger scale model, which was used to develop a remedial alternative consisting of multiple GCW systems.     

Modeling Ground Water Quality Sampling Decisions

Questions such as what, where, when, and how often to sample play a central role in the development of monitoring strategies. Limited resources will not permit sampling for many contaminants at the same frequency at all well sites. Therefore, a resource allocation strategy is necessary to arrive at answers for the preceding types of questions. Such a strategy for a ground water quality monitoring program is formulated as an integer programming model (an optimization model). The model will be of use in the process of deciding what constituents to sample and where to sample them so as to maximize a given objective, subject to a set of budget, sampling, and regulatory constraints. The maximization objective in the model is defined as a weighted function of population exposure to a scaled measure of observed chemical concentrations. The sampling constraints are based on the observed variability of contaminants in the aquifer, needed precision in estimates, a chosen level of significance, the available budget for implementing the program, and selected regulatory constraints. The model is tested with field data obtained for 10 selected constituents from more than 650 wells in the Cambrian-Ordovician aquifer in Iowa. Results from two alternative formulations of the model are compared, analyzed, and discussed. Further avenues for research are briefly outlined.     

Global Sensitivity Analysis Techniques for Probabilistic Ground Water Modeling

Global sensitivity analysis techniques are better suited for analyzing input-output relationships over the full range of parameter variations and model outcomes, as opposed to local sensitivity analysis carried out around a reference point. This article describes three such techniques: (1) stepwise rank regression analysis for building input-output models to identify key contributors to output variance, (2) mutual information (entropy) analysis for determining the strength of nonmonotonic patterns of input-output association, and (3) classification tree analysis for determining what variables or combinations are responsible for driving model output into extreme categories. These techniques are best applied in conjunction with Monte Carlo simulation-based probabilistic analyses. Two examples are presented to demonstrate the applicability of these methods. The usefulness of global sensitivity techniques is examined vis-a-vis local sensitivity analysis methods, and recommendations are provided for their applications in ground water modeling practice.     

Modeling Management Practice Effects on Pesticide Movement to Ground Water

The assessment of agricultural impacts on water quality are now being redirected to include both ground water and surface water. Mathematical models have enhanced the ability of scientists'to evaluate these impacts. A variety of public domain models are available that can aid in evaluating the effects of managerial activities on pesticide movement to ground water. However, the ideal non-point source (NPS) pollution management model does not exist. Current models fail to adequately describe the transport of chemicals to ground water and, simultaneously, the effect of managerial practices on transport mechanisms. Much more work is necessary to develop a model that can describe water quality impacts of agricultural practices in a holistic framework that includes ground water and surface water concerns.