Implications of mountain shading on calculating energy for snowmelt using unstructured triangular meshes

In many parts of the world, snowmelt energetics are dominated by solar irradiance. This is particularly the case in the Canadian Rocky Mountains, where clear skies dominate the winter and spring. In mountains, solar irradiance at the snow surface is not only affected by solar angles, atmospheric transmittance, and the slope and aspect of immediate topography but also by shadows from surrounding terrain. Accumulation of errors in estimating solar irradiation can lead to significant errors in calculating the timing and rate of snowmelt due to the seasonal storage of internal energy in the snowpack. Gridded methods, which are often used to estimate solar irradiance in complex terrain, work best with highresolution digital elevation models (DEMs), such as those produced using LiDAR. However, such methods also introduce errors due to the rigid nature of the mesh as well as limiting the ability to represent basin characteristics. Unstructured triangular meshes are more efficient in their use of DEM data than fixed grids when producing solar irradiance information for spatially distributed snowmelt calculations and they do not suffer from the artefact problems of a gridded DEM. This paper demonstrates the increased accuracy of using a horizon-shading algorithm model with an unstructured mesh versus standard self-shading algorithms. A systematic over-prediction in irradiance is observed when only self-shadows are considered. The modelled results are diagnosed by comparison to measurements of mountain shadows by time-lapse digital cameras and solar irradiance by a network of radiometers in Marmot Creek Research Basin, Alberta, Canada. Results show that depending on the depth and aspect of the snowpack of the Mt. Allan cirque, 6.0% to 66.4% of the pre-melt