This study presents a novel modelling approach to address the challenge of simulating snow hydrological regimes in mountainous catchments, which are significantly influenced by snowpack heterogeneity resulting from wind- and gravity-induced redistribution processes. The physics-based snowpack model FSM2oshd was enhanced by integrating the modules SnowTran-3D and SnowSlide to represent wind- and gravity-driven redistribution, respectively. This new framework was further improved by implementing a density-dependent layering scheme to account for erodible snow without resolving microstructural properties. Seasonal simulations were conducted over a 1180 km² mountain range in the Swiss Alps at 25, 50, and 100 m resolution, using appropriate downscaling and snow data assimilation techniques to provide accurate meteorological forcing. The model results were assessed using airborne lidar measurements of snow depths. The study found that the new modelling framework significantly improved the representation of snow accumulation and erosion areas, with major contributions from salitation, suspension, avalanches, and modest contributions from snowdrift sublimation. The aggregated snow depth distribution curve, crucial for snowmelt dynamics, matched the measured distribution better than reference simulations from the peak of winter to the end of the melt season, with improvements at all spatial resolutions. This outcome is promising for better operational representation of snow hydrological processes within an operational framework.This study presents a novel modelling approach to address the challenge of simulating snow hydrological regimes in mountainous catchments, which are significantly influenced by snowpack heterogeneity resulting from wind- and gravity-induced redistribution processes. The physics-based snowpack model FSM2oshd was enhanced by integrating the modules SnowTran-3D and SnowSlide to represent wind- and gravity-driven redistribution, respectively. This new framework was further improved by implementing a density-dependent layering scheme to account for erodible snow without resolving microstructural properties. Seasonal simulations were conducted over a 1180 km² mountain range in the Swiss Alps at 25, 50, and 100 m resolution, using appropriate downscaling and snow data assimilation techniques to provide accurate meteorological forcing. The model results were assessed using airborne lidar measurements of snow depths. The study found that the new modelling framework significantly improved the representation of snow accumulation and erosion areas, with major contributions from salitation, suspension, avalanches, and modest contributions from snowdrift sublimation. The aggregated snow depth distribution curve, crucial for snowmelt dynamics, matched the measured distribution better than reference simulations from the peak of winter to the end of the melt season, with improvements at all spatial resolutions. This outcome is promising for better operational representation of snow hydrological processes within an operational framework.