Abstract. Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CV_ds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets from mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CV_ds exhibited a wide range of variability across the 321 km² study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was an important factor of CV_ds variability in both alpine and subalpine areas, as CV_ds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influences seasonal snow variability. Subgrid CV_ds was also correlated with topography and forest variables; important drivers of CV_ds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two simple statistical models were developed (alpine and subalpine) for predicting subgrid CV_ds that show reasonable performance statistics. The methodology presented here can be used for parameterizing CV_ds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.