GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 349-11
Presentation Time: 9:00 AM-6:30 PM

GEOSPATIAL CHARACTERIZATION OF SURFACE WATER QUALITY FOR WATERSHEDS WITH ABANDONED MINE LANDS: A CASE STUDY FROM THE SUGARLOAF MINING DISTRICT, LEADVILLE, COLORADO, USA


HALLNAN, Rachel, Bureau of Land Management, Colorado, 3028 E Main St, CaƱon City, CO 81212; Bureau of Land Management, 3028 East Main Street, Canon City, CO 81212, RASMUSSEN, Dirk, Colorado Mountain College, Natural Resource Management, 901 South US-24, Leadville, CO 80461; Natural Resource Management Program, Colorado Mountain College, 901 US-24, Leadville, CO 80461 and SMEINS, Melissa, Bureau of Land Management, 3028 East Main Street, Canon City, CO 81212, rmhallnan@gmail.com

Abandoned mine lands span across the western United States and are often located in mountainous areas forming watershed headwaters. Resultant acid mine drainage from these areas impairs downstream water quality and habitat and poses significant health risks to humans and animals. With estimates of over 500,000 abandoned mines in the U.S. alone, it is important to address water quality degradation associated with these sites. The Sugarloaf mining district near Leadville Colorado underwent high impact historic metals mining during the late 1800s and early 1900s. Throughout the district there are numerous test pits, mine shafts and adits, and abandoned tailings and waste rock piles which have since impacted local water quality in the Lake Fork Creek and upper Arkansas River. High metal concentrations and low pH are common in the watersheds throughout the district. We characterize spatial and temporal trends of water quality within the Lake Fork watershed using Python, ArcGIS, and statistical analysis tools with a long-term water quality dataset. The workflow employed may be used at similar sites elsewhere to examine the relationships between legacy mining, water quality, and reclamation efforts. The integration of multiple analytical methods for long-term spatial and temporal water quality datasets presented here greatly enhances data driven remedial design planning and implementation.