Evaluating a prediction system for snow management

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.

Modelling Drifting Snow Sublimation and Investigating Small-Scale Snow Transport

Christine Dorothea Groot Zwaaftink

Drifting snow has a large influence on the mountain snow cover and the overlying atmospheric boundary layer. Consequently, drifting snow also affects avalanches, snow hydrology, vegetation and climate. Considerable effort is devoted to understanding the process in order to model the effects on the mountain snow cover or the overlying atmosphere. This thesis work aims to I) provide estimates of drifting snow sublimation within a small catchment in the Swiss Alps and II) test an alternative method to describe snow transport on short time and small spatial scales. Drifting snow sublimation moistens and cools the surrounding air and causes a loss of snow mass. Based on the feedback mechanisms integrating air temperature, humidity and snow concentration, it should be seen as a self-limiting process. Drifting snow sublimation (including these feedback mechanisms) has been implemented in Alpine3D, which is a high-resolution snow transport model for alpine terrain. This model has been applied to a small area in the Swiss Alps for a case study of approximately two days. Results show that, in general, the negative feedbacks of sublimation on the snow mass concentration, air temperature, and humidity are small. They are, however, relevant on the slope scale, as could be retrieved from analyzing the deposition on a leeward slope. Compared to a reference simulation without sublimation, drifting snow sublimation reduced the deposition by approximately 12% on this leeward slope when omitting all feedback mechanisms. In a simulation where all feedbacks were included, drifting snow sublimation reduced the snow deposition on this slope by 10%. Drifting snow sublimation thus appears to be a significant process for a leeward slope influenced by drifting snow. On the contrary, the spatially averaged reduction of deposition due to drifting snow sublimation in the complete modelled domain was 2.3%. During this case study, this value was comparable to surface sublimation. At the same field site a simulation of a complete season was performed and in this case, sublimation of drifting snow was accounted for only in the absence of concurrent precipitation. As for the case study, the slope affected the most by drifting snow sublimation was shown to be a leeward slope for SE wind. This could be explained by generally warmer and dryer conditions during periods with SE wind. In this particular slope, the snow amount was reduced by about 1.8% due to drifting snow sublimation. For the complete domain however, the model results indicate that drifting snow sublimation is much smaller than surface sublimation and negligible for the total water equivalent. Thus, at the studied field site drifting snow sublimation seems only significant locally or on short time scales. Limitations of drifting snow simulations based on mean wind fields are seen as the variability of the snow cover is underestimated on scales of few meters. Moreover, these models are also not able to represent snow transport on very short time scales. High-frequency observations of drifting snow and sand saltation have shown that turbulent structures have an influence on the snow or sand mass flux. Improvements in modelling drifting snow on such time scales can thus be reached by considering turbulence. Here, a combination of wind fields from large eddy simulations and a Lagrangian stochastic model was used to describe snow particle trajectories over flat terrain. This model reproduced a time series of the snow mass flux that qualitatively corresponds to observations from the Weissfluhjoch Versuchsfeld. Furthermore, results show that the use of the surface shear stress rather than the friction velocity, as in conventional models, resulted in a spatial variation of drifting snow despite homogeneous snow properties and the flatness of the considered terrain. The distribution of the surface shear stress in space and time was found reasonable yet narrow based on the ratio between the standard deviation and the mean shear stress, which was 0.17 compared to a value of 0.4 reported for measurements. Nonetheless, this variable surface shear stress played a key role in the modelled spatial variation of drifting snow. This also points at a relevant influence of the particle aerodynamic entrainment process for drifting snow. The results thus show that this model seems to overcome limitations of conventional three-dimensional snow transport models as it gives a more realistic description of drifting snow on short time scales and it may be used to gain further insight in drifting snow.

EPFL2013

Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution

Mathias Thierry Pierre Bavay, Michael Lehning, Nander Wever

Accurate knowledge on snow distribution in alpine terrain is crucial for various applications such as flood risk assessment, avalanche warning or managing water supply and hydro-power. To simulate the seasonal snow cover development in alpine terrain, the spatially distributed, physics-based model Alpine3D is suitable. The model is typically driven by spatial interpolations of observations from automatic weather stations (AWS), leading to errors in the spatial distribution of atmospheric forcing. With recent advances in remote sensing techniques, maps of snow depth can be acquired with high spatial resolution and accuracy. In this work, maps of the snow depth distribution, calculated from summer and winter digital surface models based on Airborne Digital Sensors (ADS), are used to scale precipitation input data, with the aim to improve the accuracy of simulation of the spatial distribution of snow with Alpine3D. A simple method to scale and redistribute precipitation is presented and the performance is analyzed. The scaling method is only applied if it is snowing. For rainfall the precipitation is distributed by interpolation, with a simple air temperature threshold used for the determination of the precipitation phase. It was found that the accuracy of spatial snow distribution could be improved significantly for the simulated domain. The standard deviation of absolute snow depth error is reduced up to a factor 3.4 to less than 20 cm. The mean absolute error in snow distribution was reduced when using representative input sources for the simulation domain. For inter-annual scaling, the model performance could also be improved, even when using a remote sensing dataset from a different winter. In conclusion, using remote sensing data to process precipitation input, complex processes such as preferential snow deposition and snow relocation due to wind or avalanches, can be substituted and modeling performance of spatial snow distribution is improved.

Frontiers Media Sa2016

A review in snow saltation dynamics and its implications for the surface mass balance

Daniela Brito Melo, Francesco Comola, Franziska Gerber, Michael Lehning, Varun Sharma, Armin Sigmund

Snow covered regions are frequently subjected to strong winds. This leads to the erosion of the snow surface and the occurrence of drifting and blowing snow events. To correctly predict and model these phenomena is of the utmost importance to assess snowpack stability and avalanche formation, as well as airborne snow sublimation and the resultant surface mass balance. Nonetheless, snow transport is frequently neglected or misrepresented in regional and mesoscale models. One of the main challenges is the accurate representation of snow transport close to the ground, where snow particles are transported by a process called saltation. This shallow layer comprises most of the horizontal mass flux and sets the lower boundary condition to model snow suspension clouds. A detailed study of snow saltation dynamics has been conducted using a Large-Eddy-Simulation flow solver coupled with a Lagrangian model for particle trajectories. The effect of particle size distribution and interparticle cohesion on particle speed, mass flux and surface friction velocity has systematically been investigated. The results show that snow cohesion and grain size heterogeneity can significantly increase saltation mass flux, specially at high friction velocities. Moreover, the agreement between simulation results and the saltation models typically used in large scale atmospheric models is highly dependent on the assumed bed characteristics. These findings can support the development of comprehensive saltation models that specifically take into account snow bed properties. These new saltation models can be implemented in mesoscale atmospheric models and have the potential to significantly improve surface mass balance predictions if grain-scale snow surface properties are available. This is possible in the newly developed CRYOWRF model which expands WRF’s surface modeling suite by including the advanced complexity, grain-scale snow model, SNOWPACK, along with a blowing snow scheme.

2021