A Regional Groundwater Flow Model of Ft. Belvoir Military Reservation
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Abstract
Contaminant fate and transport are two of the most important issues project officers have to deal with when developing a sound remediation strategy for a subsurface contamination site. To accurately assess these issues, knowledge of possible pathways, travel times and groundwater receptors are required. A groundwater flow model of a site facilitates the assessment process by determining flow paths, discharge areas and travel time from a contaminant source to a potential receptor. The resulting model can also be used to show potential impacts on drinking water sources and surface habitats.
This project is Phase II of the proposed three phase project, Groundwater Flow Modeling of the Aquifer System at Ft. Belvoir (Widdowson, 1998). Phase I consisted of developing a conceptual model of the aquifer system, recommending a modeling strategy and developing a data collection strategy. The objectives of this phase are to design and construct a computer simulation of the groundwater flow system in the aquifers below Ft. Belvoir and to develop a strategy for improved data collection using the results of the model. Steps in this phase included creation of the numerical model, calibration to known water surface elevations, and a sensitivity analysis of the boundary conditions. The numerical model was created in the Department of Defense Groundwater Modeling System environment using MODFLOW.
The model was calibrated to pre-1970 wells for the deep aquifer and recent site characterization wells for the upper aquifer. The head distribution was influenced the greatest by topography and the major creeks. Accuracy of the well surface elevations played a major role in the calibration process, as well as tidal influences. A sensitivity analysis showed that adjusting the recharge and the seepage face parameters affected the model results (head and groundwater flow rates) the greatest. While adjusting the constant head and general head boundaries affected the model results the least.