The occurrence of alpine permafrost in the Front Range of Colorado

Permafrost distribution, or ground that remains frozen for at least 2 years, has been modeled using a combination of Geographic Information System (GIS) techniques, Digital Elevation Model (DEM) variables, and land cover in alpine regions of the world. In the Front Range, however, no such empirical models have been developed, and field data are restricted in spatial extent, but rock glaciers are in abundance. Here, I present a probabilistic logistic regression model that is based on topoclimatic information (elevation and aspect) for rock glaciers derived from U.S. Geological Survey (USGS) 10-m DEMs. Classes of land cover, obtained from an Enhanced Thematic Mapper Plus (ETM+) image classification, were assigned weights and were then multiplied by the regression results to refine estimates. The effectiveness of the model was evaluated by comparing mean probability scores with rock glacier activity categories, Mean Annual Air Temperature (MAAT) from climatic stations on Niwot Ridge, and Bottom Temperature of winter Snow (BTS) measurements, while a Monte Carlo simulation was used to detect uncertainty associated with the original DEM. Permafrost scores >50% covered about 8.9% (242 km2) of the study area (2722 km2) with the highest scores clustered around Longs and Rowe Peaks. Permafrost locations showed a strong correlation with rock glacier activity classes, the −1.0 °C MAAT isotherm, and BTS measurements less than −3.0 °C. The uncertainty analysis revealed that slight global differences exist between the original and error prone DEM; however, local variations in aspect caused the most uncertainty. These results indicate that the model accurately represents regional distribution of permafrost. Therefore, topoclimatic information from rock glaciers and land cover, when combined with an uncertainty analysis, can effectively be used to map the occurrence of Front Range permafrost, providing an imperative tool for cartographers, planners, and geocryologists.