. Light-absorbing impurities (LAIs) decrease snow albedo, increasing the amount of solar energy absorbed by the snowpack. Its most intuitive and direct impact is to accelerate snowmelt. Enhanced energy absorption in snow also modifies snow metamorphism, which can indirectly drive further variations of snow albedo in the near-infrared part of the solar spectrum because of the evolution of the near-surface snow microstructure. New capabilities have been implemented in the detailed snowpack model SURFEX/ISBA-Crocus (referred to as Crocus) to account for impurities' deposition and evolution within the snowpack and their direct and indirect impacts. Once deposited, the model computes impurities' mass evolution until snow melts out, accounting for scavenging by meltwater. Taking advantage of the recent inclusion of the spectral radiative transfer model TARTES (Two-stream Analytical Radiative TransfEr in Snow model) in Crocus, the model explicitly represents the radiative impacts of light-absorbing impurities in snow. The model was evaluated at the Col de Porte experimental site (French Alps) during the 2013–2014 snow season against in situ standard snow measurements and spectral albedo measurements. In situ meteorological measurements were used to drive the snowpack model, except for aerosol deposition fluxes. Black carbon (BC) and dust deposition fluxes used to drive the model were extracted from simulations of the atmospheric model ALADIN-Climate. The model simulates snowpack evolution reasonably, providing similar performances to our reference Crocus version in terms of snow depth, snow water equivalent (SWE), near-surface specific surface area (SSA) and shortwave albedo. Since the reference empirical albedo scheme was calibrated at the Col de Porte, improvements were not expected to be significant in this study. We show that the deposition fluxes from the ALADIN-Climate model provide a reasonable estimate of the amount of light-absorbing impurities deposited on the snowpack except for extreme deposition events which are greatly underestimated. For this particular season, the simulated melt-out date advances by 6 to 9 days due to the presence of light-absorbing impurities. The model makes it possible to apportion the relative importance of direct and indirect impacts of light-absorbing impurities on energy absorption in snow. For the snow season considered, the direct impact in the visible part of the solar spectrum accounts for 85 % of the total impact, while the indirect impact related to accelerated snow metamorphism decreasing near-surface specific surface area and thus decreasing near-infrared albedo accounts for 15 % of the total impact. Our model results demonstrate that these relative proportions vary with time during the season, with potentially significant impacts for snowmelt and avalanche prediction.

/pdf/a-multilayer-physically-based-snowpack-model-simulating-2tgkyski1l.pdf

A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow

The physical risk from snow avalanches poses a serious threat to mountain backcountry travelers. Avalanche risk is primarily managed by (1) assessing avalanche hazard through analysis of the local weather, snowpack, and recent avalanche activity and (2) selecting terrain that limits exposure to the identified hazard. Professional ski guides have a tremendous wealth of knowledge about using terrain to manage avalanche risk, but their expertise is tacit, which makes it difficult for them to explicitly articulate the underlying decision rules. To make this existing expertise more broadly accessible, this study examines whether it is possible to derive quantitative measures for avalanche terrain severity and condition-dependent terrain guidance directly from observed terrain selection of professional guides. We equipped lead guides at Mike Wiegele Helicopter Skiing with GPS tracking units during the 2014/2015 and 2015/2016 winters creating a dataset of 10,592 high-resolution tracked ski runs. We used four characteristics—incline, vegetation, down-slope curvature (convexities/concavities), and cross-slope curvature (gullies/ridges)—to describe the skied terrain and employed a mixed-effects ordered logistic regression model to examine the relationship between the character of most severe avalanche terrain skied on a day and the associated field-validated avalanche hazard ratings. Patterns in the regression parameter estimates reflected the existing understanding of how terrain is selected to manage avalanche risk well: the guides skied steeper, less dense vegetation, and more convoluted slopes during times of lower avalanche hazard. Avalanche terrain severity scores derived from the parameter estimates compared well to terrain previously zoned according to the Avalanche Terrain Exposure Scale. Using a GIS implementation of the regression analysis, we created avalanche condition-dependent maps that provide insights into what type of terrain guides deemed acceptable for skiing under different avalanche hazard conditions. These promising results highlight the potential of tracking guides’ terrain selection decisions as they manage avalanche hazard for the development of evidence-based avalanche terrain ratings and decision aids for professional and recreational backcountry travelers.

Describing the severity of avalanche terrain numerically using the observed terrain selection practices of professional guides

Snow avalanches are a significant natural hazard in Norway. One method to manage the backcountry avalanche hazard is through detailed mapping of avalanche terrain. Avalanche terrain can be mapped using a variety of methods, including using the Avalanche Terrain Exposure Scale (ATES); however, manual classification of terrain using ATES is time consuming. This study has developed and compared a fully automated algorithm to provide ATES mapping for all of Norway. Our new algorithm is based on the technical model for ATES mapping. This model has specific terrain-based thresholds that can be applied for automated terrain-based modeling. Our algorithm expands on prior work by including the potential release area (PRA) model to identify and calculate the likelihood of an avalanche releasing from a start zone. We also use the raster-based TauDEM-model to determine the avalanche runout length. The final product is a 10-m resolution ATES map. We compared this nationwide ATES map with areas that have been manually mapped by avalanche experts, and find that the automated approach yields similar and reliable results. In addition to comparing mapped areas, we also examine manually mapped linear routes and compare these with the automated mapped ATES areas. Our results suggest that for open terrain, the vast majority of the manually classified tracks are predominantly in the same ATES class as our algorithm. For forested areas, we get mixed results, which can be attributed to a lack of suitable vegetation data at an appropriate scale. Despite this limitation, the current ATES algorithm and resulting spatial data are already valuable as a large portion (~ 70%) of the Norwegian backcountry terrain is above tree line. The automated algorithm is also useful to ensure consistent manual classification across different regions in Norway, or globally, and will permit greater reproducibility and easier updating of mapping for the future.

/pdf/developing-nationwide-avalanche-terrain-maps-for-norway-5egau45568.pdf

Developing nationwide avalanche terrain maps for Norway

. Consistency in assigning an avalanche danger level when forecasting or locally assessing avalanche hazard is essential but challenging to achieve, as relevant information is often scarce and must be interpreted in light of uncertainties.
Furthermore, the definitions of the danger levels, an ordinal variable, are vague and leave room for interpretation.
Decision tools developed to assist in assigning a danger level are primarily experience-based due to a lack of data.
Here, we address this lack of quantitative evidence by exploring a large data set of stability tests ( N=9310 ) and avalanche observations ( N=39  017) from two countries related to the three key factors that characterize avalanche danger:
snowpack stability, the frequency distribution of snowpack stability, and avalanche size.
We show that the frequency of the most unstable locations increases with increasing danger level.
However, a similarly clear relation between avalanche size and danger level was not found.
Only for the higher danger levels did the size of the largest avalanche per day and warning region increase.
Furthermore, we derive stability distributions typical for the danger levels 1-Low to 4-High using four stability classes (very poor, poor, fair, and good) and define frequency classes describing the frequency of the most unstable locations (none or nearly none, a few, several, and many).
Combining snowpack stability, the frequency of stability classes and avalanche size in a simulation experiment, typical descriptions for the four danger levels are obtained.
Finally, using the simulated stability distributions together with the largest avalanche size in a stepwise approach, we present a data-driven look-up table for avalanche danger assessment.
Our findings may aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.

/pdf/on-the-importance-of-snowpack-stability-the-frequency-yugtjudt6a.pdf

On the importance of snowpack stability, the frequency distribution of snowpack stability, and avalanche size in assessing the avalanche danger level

Terrain characteristics are one of the main factors contributing to avalanche formation. Hence, terrain assessment is crucial for planning and decision making when travelling in the backcountry. So far, terrain is mainly interpreted manually from topographic maps and by observations in the field. Recent support for interpreting avalanche terrain is given by slope angle layers derived from digital elevation models or the Avalanche Terrain Exposure Scale (ATES) for classifying avalanche terrain manually. We developed avalanche terrain maps by combining terrain characteristics of avalanches with the avalanche simulation model RAMMS::EXTENDED and with fall simulations, all based on a high resolution digital elevation model. The focus was on mapping terrain of size class 3 avalanches, which typically threaten backcountry recreationists. We propose a Geographic Information System (GIS) based methodology for a fully automatic classification of the avalanche terrain taking into account: a) potential avalanche release areas, b) remote triggering of avalanches, c) possible runout zones, and d) the potential of being seriously injured or deeply buried by small or medium-sized avalanches. To considered all these aspects several simulations were performed where from we created two different avalanche terrain maps for the entire Swiss Alps and the Jura. One map classifies the avalanche terrain thematically into: i) potential release areas, ii) areas with remote triggering potential, and iii) the runout zones of size 3 avalanches. The second map provides continuous values illustrating how serious or dangerous the terrain is in terms of avalanche release and the consequences of being caught. These maps assist the interpretation of avalanche terrain for travelling in the backcountry. Although they focus on Switzerland, the methods can also be applied to other mountain areas worldwide.

/pdf/avalanche-terrain-maps-for-backcountry-skiing-in-switzerland-796mizduut.pdf

Avalanche terrain maps for backcountry skiing in Switzerland

/pdf/integrating-the-ates-into-the-avalanche-information-in-aran-7v2gddinsy.pdf

Integrating the ATES into the avalanche information in Aran Valley

During the 2011-12 winter season, the Aran Avalanche Center established an Ava- lanche Terrain Exposure Scale (ATES) pilot area in Aran Valley. The ATES was developed by Parks Canada and the Canadian Avalanche Center in order to provide users with information on the terrain. Having tested the method and its acceptance by potential users, last season 33.000 ha were mapped, corresponding to the areas more heavily used in winter. The maps were made available to the users via web ( www.lauegi.conselharan.org , kmz format) and by means of panels in the main trail heads. In addition, an analysis of the accidents occurred in Aran Valley and in the Aiguestortes National Park (southern side of the range) during the last 25 years was performed. It was found that the majori- ty of the accidents occur in Complex (55%) or Challenging (26%) terrain when the danger is Consid- erable (3). During the 2012-13 season, the ATES was included in the daily Avalanche Report through Avaluator TM . The implementation of the ATES and the use of Avaluator TM in the Avalanche Reports have resulted in a better information about terrain, a better integration of terrain in decision making and, moreover, in a better danger rating. The good results obtained will encourage the spreading of the method to other areas of the Pyrenees

/pdf/integrating-the-ates-into-the-avalanche-information-in-aran-3t459u58s4.pdf

Integrating the ATES into the avalanche information in Aran Valley (Central Pyrenees)

Avalanche size is a key parameter in avalanche danger rating as well as in the communication between technicians. In 2009 the European Avalanche Warning Services (EAWS) adopted the Canadian Destructive Avalanche Size Scale (Perla 1980) but with some changes: for example, the numerical rating used in Canada and the US was substituted by the descriptive terms 'sluff', 'small', 'medium', 'large' and 'very large'. In 2010 an additional column was introduced in order to include the characteristics of the avalanche runout. To evaluate the uniformity in the use of the said scale, a survey questionnaire was sent out to all the EAWS avalanche centers and to different avalanche services in Canada. It comprised 18 avalanche cases with pictures, maps and basic morphological data. At the moment of performing this analysis, 70 surveys have been received back, 61 of which from 10 different European countries and 9 from Canada. Basic statistical description is hereby performed, including Mode, Mean and Standard Deviation. The results of the survey show a lack of uniformity in the classification of avalanche sizes within and between EAWS centers. In comparison, Canadian results are much more uniform. The cause of this lack of standardization in the European centers seems to be the absence of guidelines on how to use the scale as well as the additional parameters that were introduced in the European scale. The results of this survey are currently being used to improve the avalanche size scale used by the EAWS.

/pdf/how-big-is-big-results-of-the-avalanche-size-classification-4ccmlerhzk.pdf

How big is big: results of the avalanche size classification survey

Saharan dust outbreaks occur nearly every winter in the Pyrenees. It is well established that dust layers on the top of the snowpack reduce the snow albedo and hence potentially accelerate snow melt. In addition, such dust layers once buried in the snowpack may behave as critical layers and are often reported as associated to avalanche activity. However, studies addressing in detail why buried dust layers might form potentially dangerous layers causing significant avalanche release are scarce. A study involving both monitoring of natural deposited dust layers and a field experiment in the Pyrenees have been conducted to test and support previous observations that faceting and crust formation within the dust layer are key processes for avalanche release. The field experiment consisted in reproducing a pair of dust deposition events artificially on North and South aspects slopes at tree-line elevation, and monitoring the metamorphism and stability of these artificial dust layers with subsequence snowfalls in the snowpack. Both natural and artificial dust layers were investigated first in dry cold and then wet, isothermal snowpack.

/pdf/effects-of-saharan-dust-outbreaks-on-the-snow-stability-in-11hnno3tik.pdf

Effects of Saharan Dust Outbreaks on the Snow Stability in the Pyrenees

The highway C-28 is located in the Central Pyrenees and links the Aran valley with Catalonia along 20 km over the Bonaigua Pass. It constitutes a key access route for winter visitors. Most of the slopes affecting the road face to the south, with heights varying between 1600 and 2300 meters. We started from 12 years of meteorological and avalanche data collected by the local avalanche warning service of Aran Valley. Weather data were obtained from two automatic weather stations and a flowcapt, whereas avalanche activity was manually recorded in a GIS. We selected several weather parameters including snow drift, elapsed time, trend and categorical parameters. Using a classification tree method, we have developed a model to determine periods of significant avalanche activity in terms of the predefined avalanche day concept. The model is performed for the entire road in a combined analysis and also for three individual sub-areas within the Pass. Results showed that conventional factors describing snow depth were more significant than temperature and precipitation factors. Derived snow drift parameters from snow depth and water precipitacion showed more importance than drift data from the flowcapt. Radiation and wind direction variables had low importance in all the tests. The detailed analysis by subareas has not achieved the objective due to the reduction of the database. However, it has allowed to confirm the differences between one of the sub-areas and the dynamics of the rest of the highway.

/pdf/a-model-for-avalanche-forecasting-on-the-bonaigua-pass-spain-1mdkvcxl0n.pdf

Montse Bacardit

Papers

Integrating the ATES into the avalanche information in Aran Valley

Integrating the ATES into the avalanche information in Aran Valley (Central Pyrenees)

How big is big: results of the avalanche size classification survey

Effects of Saharan Dust Outbreaks on the Snow Stability in the Pyrenees

A Model for Avalanche Forecasting on the Bonaigua Pass, Spain, Using Classification Trees