Quantifying snowfall and avalanche release synchronization: a case study

We quantify the synchronization between snowfall and natural avalanches in relation to terrain properties at the detachment zone. We analyze field statistics of 549 avalanche events in terms of slope, aspect, timing, coordinate, and release area, identified by a georeferencing procedure applied on terrestrial photography. The information from the digital pictures, together with associated meteorological data, provides us with the input needed for model calibration, namely, the magnitude of snowfall, the snow compaction rate, and the timing of precipitation and of avalanche events. Synchronization between snowfall and avalanches is established for different slope categories. We obtain an average probability of release after a snow event of 30% and 16% for the high- and low-slope categories (average slope 44 degrees and 36 degrees, respectively). Using the notion of information entropy, we quantify the uncertainty in predicting avalanche occurrence from a snow event. The steeper slopes correspond to a larger entropy in avalanche prediction. Further, the presented method allows us to establish the return period of avalanches without requiring a long series of data. When considering events regardless of their release depth, the avalanches had a return period of 48 days (higher slopes) and 88 days (lower slopes). Finally, we determine the average daily detachment rate as a function of snow depth and the return period of avalanches as a function of the release depth.

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

Operational snowcover simulations with avalanche dynamics calculations coupling to assess avalanche danger in high altitude mining operations

Nander Wever

The Codelco Andina copper mine is operating in high alpine terrain where avalanches pose a threat to the operations and the infrastructure. A dedicated avalanche warning service is responsible for opening and closing the heavily used access road. To support their decision making process, a system is developed in which numerical snowpack modelling is coupled to avalanche dynamics simulations to assess avalanche risks. The primary system output is an assessment of snow cover stability as well as avalanche size and runout. Based on measurements from automatic weather stations, the temporal evolution of the snowpack is simulated using the SNOWPACK model. This model is run within the spatially explicit Alpine3D tool, taking into account the radiation budget in complex terrain. The SNOWPACK model provides snowpack stability estimates and, based on the weak layer depth, potential fracture depths and snowpack properties of the slab. For example, for wet snow avalanches, which is the major threat in the mine, the simulations indicate if water is accumulating at layer boundaries inside the snowpack. The model accumulation depth defines the potential fracture height. This information is displayed on maps, but is also directly used to provide the initial conditions for the avalanche dynamics model RAMMS for predetermined avalanche paths. The properties of the entrained snow, which we show is also an important factor to determine avalanche runout, are also provided by the Alpine3D simulations. The system in which real-time snow cover simulations are combined with avalanche dynamics simulations is a novel approach to provide avalanche forecasters with a new source of objective information to aid the avalanche risk assessment

2016

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.