MSc candidate Chris Marsh will present a seminar on
Implications of mountain shading on calculating energy for snowmelt using unstructured triangular meshes

On Thursday June 28, 2012, at 10am, in 146 Kirk Hall
In many parts of the world, snowmelt energy is dominated by solar irradiance. This is the case in the Canadian Rocky Mountains, where clear skies dominate the winter and spring. In mountainous regions, 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 are often used to estimate solar irradiance in complex terrain. These methods work best with high-resolution gridded digital elevation models (DEMs), such as those produced using LiDAR. However, such methods also introduce errors due to the rigid nature of the mesh, creating artefacts and other artificial problems. Unstructured triangular meshes, such as triangulated irregular networks, 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 project demonstrates the use of 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. This over-prediction can be equivalent to 20% of total pre-melt snow accumulation. 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.