Spatial Snow Surface Roughness across Multiple Resolutions

Accurate assessments of snowmelt timing are critical in mountainous regions. Most hydrological and climate models ignore the variability of the snow surface roughness, although this variability affects the sensible and latent heat fluxes in snow-dominated systems. This work applied existing methods, specifically the random roughness and a fractal analysis, to estimate snow surface roughness across two scales. We used data collected during the NASA Cold Land Processes Experiment of 2002 and 2003: manual measurements from photographs of snow roughness boards at resolution of about 1 mm over a length of 1 m, and airborne LiDAR measurements of the snow surface at a 1.5 m resolution. We examined temporal variability with the finer resolution snow board data, and spatial variability and scaling using both datasets. Results illustrated that snow surface roughness did scale and were moderately consistent over time when land cover was considered. Ongoing work is investigating directionality of roughness characteristics based on modeled wind characteristics.