Snow cover characteristics in a glacierized catchment in the Tyrolean Alps - Improved spatially distributed modelling by usage of Lidar data

- Basin-wide SWE is derived from Lidar data.
- Mean SWE in the catchment has an uncertainty in the order of 15%.
- SES accurately simulates the variability of SWE on the watershed scale.
- SWE is superior to snow covered area for the reduction of parameter uncertainties.

SummaryIn the present paper multi-temporal Lidar (Light detection and ranging) data and Landsat images are used to assess the spatial variability of snow at the end of the accumulation season (April-May) in a glacierized catchment (167 km2) in Tyrol, Austria. Snow cover characteristics in the Tyrolean Alps have been analysed using regular snow measurements and snow course data. Results are used for the conversion of basin-wide Lidar snow depth into snow water equivalent (SWE). When considering different possible error sources, uncertainties of the mean basin-wide SWE obtained from Lidar are between 12% and 16%. Available distributions of SWE and snow covered area (SCA) in the catchment are used for the calibration and validation of the fully distributed hydrological model SES. The study focuses especially on the simulation of snow accumulation and the corresponding variability of snow. Observed accumulation patterns are related to the topography (elevation, slope and curvature), and according parameter settings of the hydrological model are derived by means of Monte Carlo simulations. The majority of the model runs simulates SCA for various datasets with an accuracy of 85-95%. The paper demonstrates that using SWE data is superior to SCA for constraining model parameter ranges. Results at the watershed scale are in agreement with respect to the total water volume of the snow cover with deviations lower than 5% between SWE from Lidar or from the hydrological model.