2004 Denver Annual Meeting (November 7–10, 2004)

Paper No. 15
Presentation Time: 5:15 PM

GROUND-WATER FLOW MODELING IN PROSPECT GULCH, SAN JUAN COUNTY, COLORADO, AN ALPINE WATERSHED AFFECTED BY HISTORICAL MINING


JOHNSON, Raymond H., U. S. Geol Survey, P. O. Box 25046, Denver Federal Center, MS 973, Denver, CO 80225-0046, rhjohnso@usgs.gov

Ground-water systems and their influence on surface-water quality within alpine settings such as Prospect Gulch, a subbasin in the upper Animas River watershed, are poorly understood. Existing data include detailed geologic alteration maps, airborne electromagnetic data, surface-water flow rates, surface-water quality, and age dating and geochemistry of water in springs and seeps. All of these data are used to create a preliminary ground-water flow model for the basin. Input parameters include recharge, hydraulic conductivity distribution, and appropriate boundary conditions. These input parameters will be automatically calibrated using the model output parameters of water outflow and water table conditions. Water outflow from Prospect Gulch has been measured using stream tracer tests. A summer 2004 well-drilling program will provide additional calibration data in the form of water table elevation data, along with information on the hydraulic conductivity of the bedrock. These data, along with additional information on the age and geochemistry of the well water, should improve the conceptual model of subsurface geologic conditions for use in numeric simulations. The ground-water modeling approach in Prospect Gulch will occur in three phases: 1) sensitivity analyses on input parameters, which will indicate the most important parameters that control the ground-water flow, 2) detailed model calibration with existing and newly acquired data, and 3) ground-water modeling coupled with geochemical and surface-water modeling. The coupled or holistic modeling approach will provide interpretations on metal production and subsequent loading rates within the basin, with the objectives of determining premining conditions and predicting conditions under possible remedial scenarios.