ALBERT

Description: We investigate the problem of balancing model complexity and input data requirements in snow hydrology. For this purpose, we analyze the performance of two models of different complexity in estimating variables of interest in snow hydrology applications. These are snow depth, bulk snow density, snow water equivalent and snowmelt run-off. We quantify the differences between data and model prediction using 18 years of measurements from an experimental site in the French Alps (Col de Porte, 1325 m AMSL). The models involved in this comparison are a one-layer temperature-index model (HyS) and a multilayer model (Crocus). Results show that the expected loss in performance in the one-layer temperature-index model with respect to the multilayer model is low when considering snow depth, snow water equivalent and bulk snow density. As for run-off, the comparison returns less clear indications for identification of a balance. In particular, differences between the models' prediction and data with an hourly resolution are higher when considering the Crocus model than the HyS model. However, Crocus is better at reproducing sub-daily cycles in this variable. In terms of daily run-off, the multilayer physically based model seems to be a better choice, while results in terms of cumulative run-off are comparable. The better reproduction of daily and sub-daily variability of run-off suggests that use of the multilayer model may be preferable for this purpose. Variation in performance is discussed as a function of both the calibration solution chosen and the time of year. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Description: The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

Description: The evaluation of snowpack models capable of accounting for snow management in ski resorts is a major step towards acceptance of such models in supporting the daily decision-making process of snow production managers. In the framework of the EU Horizon 2020 (H2020) project PROSNOW, a service to enable real-time optimization of grooming and snow-making in ski resorts was developed. We applied snow management strategies integrated in the snowpack simulations of AMUNDSEN, Crocus, and SNOWPACK–Alpine3D for nine PROSNOW ski resorts located in the European Alps. We assessed the performance of the snow simulations for five winter seasons (2015–2020) using both ground-based data (GNSS-measured snow depth) and spaceborne snow maps (Copernicus Sentinel-2). Particular attention has been devoted to characterizing the spatial performance of the simulated piste snow management at a resolution of 10 m. The simulated results showed a high overall accuracy of more than 80 % for snow-covered areas compared to the Sentinel-2 data. Moreover, the correlation to the ground observation data was high. Potential sources for local differences in the snow depth between the simulations and the measurements are mainly the impact of snow redistribution by skiers; compensation of uneven terrain when grooming; or spontaneous local adaptions of the snow management, which were not reflected in the simulations. Subdividing each individual ski resort into differently sized ski resort reference units (SRUs) based on topography showed a slight decrease in mean deviation. Although this work shows plausible and robust results on the ski slope scale by all three snowpack models, the accuracy of the results is mainly dependent on the detailed representation of the real-world snow management practices in the models. As snow management assessment and prediction systems get integrated into the workflow of resort managers, the formulation of snow management can be refined in the future.