Sensitivity testing for the effects of sub-grid scale topography on snow evolution in the Canadian land surface scheme

Seasonal snow is an essential component of hydrological systems and climate feedbacks, particularly in western North America, providing cool water to downstream river systems and regulating the global climate. Spatial and temporal variability in snow is high in complex terrain such as mountains, and on spatial scales smaller than regional climate model (RCM) grid sizes. This thesis tests the importance of slope, aspect and elevation effects in the Canadian Land Surface Scheme (CLASS) using three years of data from a climate station near Likely, BC, and extrapolated to a digital elevation model (DEM) domain. Results show that, as expected, snow depth and snow water equivalent (SWE) are sensitive to elevation, and this sensitivity is enhanced when combined with slope and aspect angles. Including sub-grid snow variability increases simulated SWE by up to 45% during snowmelt. Finally, this study supports the use of standardised comparison datasets to validate disparities between simulated and observed data.