A GIS AND STATISTICAL APPROACH TO AID IN WATER QUALITY PREDICTION IN MINERALIZED TERRAIN

Empirical modeling methods are needed to assess physical watershed variables that affect water quality. This information could aid in identifying areas posing the greatest geoenvironmental challenges where new mining is proposed. A comprehensive USGS abandoned mine land (AML) study conducted in Silverton, Colorado, between 1996 and 2002 was the source of data used for this study and includes: digital terrain, GIS layers for geology, alteration, vegetation, and an extensive water chemistry database. Digital terrain analysis involved extracting geomorphometric data for elevation, slope, aspect, flow direction, flow accumulation, drainage network, Strahler stream order, and watersheds. Statistics for watershed area and embedded 30-meter GIS stream buffered area were determined for vegetation cover, bedrock geology and geochemical alteration types. Geochemical analyses for surface water samples collected at watershed outlets were used to test statistical correlations between physical watershed variables and water quality. Results of these analyses revealed the importance of the propylitic alteration assemblage in mitigating acid rock drainage. For example, 88 % of the Silverton area is overprinted by propylitic rocks containing an acid neutralizing assemblage (calcite-chlorite-epidote). Neutral to slightly acidic catchments average 78 % propylitic rocks that occur high in the watershed– greater than the median elevation. In contrast, acidic catchments average only 48% propylitic rocks that occur low in the watershed– below the median elevation. Linear regression analysis identified correlations of quartz-sericite-pyrite and acid sulfate alteration with metal concentrations (iron and aluminum) and pH. These correlations were more accurately predicted using total catchment area rather than analysis that focused on the intersection of altered areas with buffered streams and non-vegetated terrain. While this study utilized AVIRIS and field alteration mapping data, future analyses will evaluate whether coarse resolution remote sensing datasets (TM, ASTER) can be similarly applied to larger regions. These GIS and statistical analyses provide a useful tool to help qualitatively predict the effect of alteration on watershed water quality.