Snow is a key initial condition for hydrological forecasting in cold regions. Snow distribution and cover are key variables to estimate the snow water equivalent and simulate surface energy and mass balances. Over complex mountainous terrain, these variables are challenging to measure and model accurately due to complex physical processes and high temporal and spatial variability. Fractional snow cover area (fSCA) is the percentage of the land surface covered by snow. The fSCA is generally tightly linked to snow depth and spatial distribution, and exerts strong control over grid-scale snow balances and calculation. Inclusion of fSCA is therefore important for a range snow modelling applications, though has been limited by both to data availability and considerations of computational cost. High resolution measurements of fSCA and snow depth are now increasingly being exploited to understand snow distribution and cover. This study focuses on the simulation of fSCA within a semi-distributed, physically based hydrological modelling framework applied over the Tuolumne catchment in California. The model is discretized to match the dominant controls of snow over the landscape. We leverage new observational data products, both airborne lidar and advanced remote sensing algorithms to investigate fSCA representation in complex mountainous terrain. This will improve snow simulation and provide a basis for future assimilation of fSCA measurements for real-time runoff forecasting.