Abstract. The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner, which is particularly suited for measurements of snow covered surfaces, snow depth was monitored in a high alpine catchment during an ablation period. From these measurements snow water equivalents and ablation rates were calculated. This allowed us for the first time to obtain a high resolution (2.5 m cell size) picture of spatial variability of the snow cover and its temporal development. A very high variability of the snow cover with snow depths between 0-9 m at the end of the accumulation season was observed. This variability decreased during the ablation phase, while the dominant snow deposition features remained intact. The average daily ablation rate was between 15 mm/d snow water equivalent at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of ablation rates increased during the ablation season and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It is qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.

[1] Terrestrial and airborne laser scanning (TLS and ALS) techniques have only recently developed to the point where they allow wide-area measurements of snow distribution in varying terrain. In this paper we present multiple TLS measurements showing the snow depth development for a series of precipitation events. We observe that the pattern of maximum accumulation is similar for the two years presented here (correlation up to r ¼ 0.97). Storms arriving from the northwest show persistent snow depth distributions and contribute most to the final accumulation pattern. Snow depth patterns of maximum accumulation for the two years are more similar than the distribution created by any two pairs of individual storms. Based on the strong link between accumulation patterns and terrain, we investigated the ability of a model based on terrain and wind direction to predict accumulation patterns. This approach of , which describes wind exposure and shelter, was able to predict the general accumulation pattern over scales of slopes but failed to match observed variance. Furthermore, a high sensitivity to the local wind direction was demonstrated. We suggest that Winstral et al.'s model could form a useful tool for application from hydrology and avalanche risk assessment to glaciology.

Abstract. Mountain snow-cover is normally heterogeneously distributed due to wind and precipitation interacting with the snow cover on various scales. The aim of this study was to investigate snow deposition and wind-induced snow-transport processes on different scales and to analyze some major drift events caused by north-west storms during two consecutive accumulation periods. In particular, we distinguish between the individual processes that cause specific drifts using a physically based model approach. Very high resolution wind fields (5 m) were computed with the atmospheric model Advanced Regional Prediction System (ARPS) and used as input for a model of snow-surface processes (Alpine3D) to calculate saltation, suspension and preferential deposition of precipitation. Several flow features during north-west storms were identified with input from a high-density network of permanent and mobile weather stations and indirect estimations of wind directions from snowsurface structures, such as snow dunes and sastrugis. We also used Terrestrial and Airborne Laser Scanning measurements to investigate snow-deposition patterns and to validate the model. The model results suggest that the in-slope deposition patterns, particularly two huge cross-slope cornicelike drifts, developed only when the prevailing wind direction was northwesterly and were formed mainly due to snow redistribution processes (saltation-driven). In contrast, more homogeneous deposition patterns on a ridge scale were formed during the same periods mainly due to preferential deposition of precipitation. The numerical analysis showed that snow-transport processes were sensitive to the changing topography due to the smoothing effect of the snow cover.

Abstract. Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land-atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s −1 , clear skies, high sun angles and surfaces with negligible vegetation cover.

Determination of the spatial snow-depth distribution is important in potential avalanche-starting zones, both for avalanche prediction and for the dimensioning of permanent protection measures. Knowledge of the spatial distribution of snow is needed in order to validate snow depths computed from snowpack and snowdrift models. The inaccessibility of alpine terrain and the acute danger of avalanches complicate snow-depth measurements (e.g. when probes are used), so the possibility of measuring the snowpack using terrestrial laser scanning (TLS) was tested. The results obtained were compared to those of tachymetry and manual snow probing. Laser measurements were taken using the long-range laser profile measuring system Riegl LPM-i800HA. The wavelength used by the laser was 0.9 μm (near-infrared). The accuracy was typically within 30 mm. The highest point resolution was 30 mm when measured from a distance of 100 m. Tachymetry measurements were carried out using Leica TCRP1201 systems. Snowpack depths measured by the tachymeter were also used. The datasets captured by tachymetry were used as reference models to compare the three different methods (TLS, tachymetry and snow probing). This is the first time that the accuracy of TLS systems in snowy and alpine weather conditions has been quantified. The relative accuracy between the three measurement methods is bounded by a maximum offset of ±8 cm. Between TLS and the tachymeter the standard deviation is 1σ = 2 cm, and between manual probing and TLS it is up to 1σ = 10 cm, for maximum distances for the TLS and tachymeter of 300 m.