Understanding the rate of snowmelt helps inform how water stored as snow will transform into streamflow. Data from 87 snow telemetry (SNOTEL) stations across the Southern Rocky Mountains were used to estimate spatio-temporal melt factors. Decreases in snow water equivalent were correlated to temperature at these monitoring stations for eight half-month periods from early March through late June. Time explained 70% of the variance in the computed snow melt factors. A residual linear correlation model was used to explain subsequent spatial variability. Longitude, slope, and land cover type explained further variance. For evergreen trees, canopy density was relevant to find enhanced melt rates; while for all other land cover types, denoted as non-evergreen, lower melt rates were found at high elevation, high latitude and north facing slopes, denoting that in cold environments melting is less effective than in milder sites. A change in the temperature sensor about mid-way through the time series (1990 to 2013) created a discontinuity in the temperature dataset. An adjustment to the time series yield larger computed melt factors.