Runo forecasting in the case of seasonally snow covered small catchments with shallow snowpacks requires application of a quasi physical approach wherein the dominant snow accumulation and melting processes are accounted for by an intensive physically based modelling approach and transformation of the snowmelt and the rainfall to streamยฏow is accounted for by a conceptual modelling approach. In the case of shallow snowpacks both high and low water saturation can occur more frequently and therefore the physically based multilayer snowmelt model must account for capillary pressure gradients as well as gravity drainage. One such physically based snowmelt model entitled UCGVDSM which accounts for coupled transport of mass and energy into the snowpack, is ยฎrst validated on point snowmelt data of the Kuร htai station located in Austria. UCGVDSM is then applied to the Tichaร Orlice catchment (96 . 8 km 2 ) located in the Czech Republic. It is shown how the constraints of data availability for application of the physically based snowmelt model can be handled to reproduce accurately, the snow water equivalent (SWE), the snow depth (H) and the melt water ยฏux (qmelt). The snowmelt rates thus obtained for the snowcover periods are then incorporated along with the rainfall and the evapotranspiration data into the Soil Moisture Accounting and Routing model (SMAR), a conceptual rainfall runo model. It is shown that incorporating a number of statistical modelling techniques into the SMAR model has no eect on the model performance while accounting for physical processes improves the model performance. Finally, an updating component is incorporated into the SMAR model to allow its application in a forecasting mode.