Infoscience

Thesis

Understanding Small Scale Variability of the Mountain Snow Cover

In mountains the snow cover is heterogeneously distributed in space and time. The spatial and temporal variability of the Alpine snow cover has a major influence on avalanche danger, snow hydrology, mountain ecology and winter tourism. In winter, already deposited snow is redistributed by saltation/suspension or precipitation is deposited preferentially in leeward slopes. The driving mechanism is wind and precipitation interacting with the snow cover surface. Although the mountain snow cover gets patchy in spring, the snow depth patterns typically remain during the ablation period. The patchiness of mountain snow covers is, therefore, mainly caused by end of winter snow depth distribution and the spatially variable energy balance. The local energy balance is driven by net radiation and the turbulent exchange of sensible and latent heat. Once the snow cover is patchy, the heterogeneous temperature field causes the development of thermal internal boundary layers and the advection of warm air from adjacent bare ground to snow covered areas. The main purpose of this work is to enhance the understanding of processes driving the small scale variability of mountain snow covers. The relative importance of the main snow cover processes contributing to observed snow deposition patterns after individual snow storms and during the ablation season are investigated. The different processes are identified and quantified by modelling, measuring and statistically analysing the snowpack, its interaction with the atmosphere and its spatial distribution. Several state of the art techniques of measurements, models and statistical analysis are applied to explore wind induced snow transport processes and the local energy balance. Four studies were conducted at three small Alpine catchments: The Gaudergrat, the Wannengrat and the SLF flat field research site Versuchsfeld. From these three study sites, the Wannengrat features the most complex topography and offers most facilities for high density measurements of snow depths and meteorological variables. All studies are based on atmospheric and snow transport modelling. The meteorological model Advanced Regional Prediction System (ARPS) was used to calculate mean flow fields. The flow fields were then used to drive the snow transport module of Alpine3D, which is a model to calculate snow cover processes. Within the snow transport module, saltation, suspension and preferential deposition of precipitation are calculated. Modelled flow fields were validated against measurements from high density network of permanent and mobile weather stations and indirect estimates from snow surface structures. Modelled snow deposition patterns were validated against snow depth measurements obtained from Terrestrial Laser Scans after ten major storms. In order to explore the spatial characteristics of measured and modelled distribution of snow depths and mean flow fields semivariogram analysis and 2-D autocorrelation functions were applied. Results from snow transport modelling suggest that wind induced snow transport processes are very sensitive to the numerical resolution. Snow transport processes also appear to be highly affected by changing topography, which results from the smoothing effect of the winter snow cover. The numerical analysis further demonstrated that in-slope deposition patterns, particularly two huge cross slope cornice-like drifts, developed only during one specific type of storm. The pronounced drifts were shown to be formed mainly due to redistribution processes (saltation-driven), initiated by strong local wind velocity gradients. On the contrary, patterns of homogeneous snow deposition appeared to be formed due to preferential deposition of precipitation, mainly controlled by up- and downdraft zones. These results could be further supported by autocorrelation analysis. Here, it could be shown that snow deposition patterns caused by the individual snow transport processes strongly differ in their spatial structure. The 2-D autocorrelation analysis also suggests, that the direction of strongest autocorrelation of snow depths was predominantly perpendicular to the local flow direction, even in areas with low wind velocities. The fractal analysis shows that scale breaks are similar for the modelled mean flow field and measured snow depths. This scale break, which was found at about 20 m, was interpreted to define the upper scale of landscape smoothing through snow. For the snow ablation simulations the meteorological model ARPS is combined with the fully distributed energy balance model Alpine3D. In these simulations, the snow depth distribution at the start of the ablation season is considered by driving the model with measured snow depths obtained from Airborne Laser Scanning measurements. Modelled ablation rates were compared to measurements obtained from six Terrestrial Laser Scanning campaigns covering the complete ablation season 2009 and one ablation period in spring 2011. Furthermore, turbulent fluxes above melting snow are investigated by using data from eddy-correlation systems. Snow ablation measurements suggest that local advection of sensible heat from adjacent bare ground to snow covered areas cause significantly higher ablation rates at the leading edges of snow patches than further. This effect was shown to be active on the scale of 5 m for low wind conditions and on the scale of 20 m for high wind conditions. The quantification of the relative contribution of the energy fluxes revealed that radiation dominates snow ablation early in the ablation period. Turbulent fluxes become more important late in the season. Measurements and simulations further suggest that stable internal boundary layers suppress turbulent fluxes of sensible heat close to the snow cover and lead to a significant decrease of snow ablation. This effect, however, is not captured in common energy balance models due to the constant flux layer approach.

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