This study presents a novel intermediate-complexity snow hydrology model, FSM2trans, which integrates wind- and gravity-driven snow redistribution processes to improve the representation of snowpack dynamics. The model incorporates 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 resolutions, using downscaled meteorological data and snow data assimilation. The model was evaluated against airborne lidar measurements, showing significant improvements in representing snow accumulation and erosion areas, with major contributions from saltation, suspension, and avalanches. The aggregated snow depth distribution curve matched measured data better than reference simulations, indicating the model's effectiveness in capturing snowmelt dynamics. The model's ability to simulate snow redistribution at hectometre scales is promising for operational snow hydrology applications. The study highlights the importance of considering redistribution processes in snowpack models to accurately represent spatial variability and improve snow hydrological predictions. The results demonstrate that incorporating snow redistribution enhances the model's ability to capture snow depth patterns and snowmelt dynamics, particularly in areas with complex topography. The model's performance was validated using structural similarity indices, showing improved agreement with lidar data compared to reference simulations. The study underscores the value of integrating snow redistribution processes in intermediate-complexity models for operational snow hydrology applications.This study presents a novel intermediate-complexity snow hydrology model, FSM2trans, which integrates wind- and gravity-driven snow redistribution processes to improve the representation of snowpack dynamics. The model incorporates 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 resolutions, using downscaled meteorological data and snow data assimilation. The model was evaluated against airborne lidar measurements, showing significant improvements in representing snow accumulation and erosion areas, with major contributions from saltation, suspension, and avalanches. The aggregated snow depth distribution curve matched measured data better than reference simulations, indicating the model's effectiveness in capturing snowmelt dynamics. The model's ability to simulate snow redistribution at hectometre scales is promising for operational snow hydrology applications. The study highlights the importance of considering redistribution processes in snowpack models to accurately represent spatial variability and improve snow hydrological predictions. The results demonstrate that incorporating snow redistribution enhances the model's ability to capture snow depth patterns and snowmelt dynamics, particularly in areas with complex topography. The model's performance was validated using structural similarity indices, showing improved agreement with lidar data compared to reference simulations. The study underscores the value of integrating snow redistribution processes in intermediate-complexity models for operational snow hydrology applications.