Mass Movement Susceptibility in the Western San Juan Mountains, Colorado: A Preliminary 3-D Mapping Approach

Abstract:

Mass movement is a major activity that impacts lives of humans and their infrastructure. Human activity in steep, mountainous regions is especially at risk to this potential hazard. Thus, the identification and quantification of risk by mapping and determining mass movement susceptibility are fundamental in protecting lives, resources and ensuring proper land use regulation and planning. Specific mass-movement processes including debris flows, rock falls, snow avalanches and landslides continuously modify the landscape of the San Juan Mountains. Historically, large-magnitude slope failures have repeatedly occurred in the region. Common triggers include intense, long-duration precipitation, freeze-thaw processes, human activity and various volcanic lithologies overlying weaker sedimentary formations. Predicting mass movement is challenging because of its episodic and spatially, discontinuous occurrence. Landslides in mountain terrain are characterized as widespread, highly mobile and have a long duration of activity. We developed a 3-D model for landslide susceptibility using Geographic Information Systems Technology (GIST).

The study area encompasses eight USGS quadrangles: Ridgway, Dallas, Mount Sneffels, Ouray, Telluride, Ironton, Ophir and Silverton. Fieldwork consisted of field reconnaissance mapping at 1:5,000 focusing on surficial geomorphology. Field mapping was used to identify potential locations, which then received additional onsite investigation and photographic documentation of features indicative of slope failure. A GIS module was created using seven terrain spatial databases: geology, surficial geomorphology (digitized), slope aspect, slope angle, vegetation, soils and distance to infrastructure to map risk. The GIS database will help determine risk zonation for the study area. Correlations between terrain parameters leading to slope failure were determined through the GIS module. This 3-D model will provide a spatial perspective of the landscape to improve landslide prediction for future occurrences.