Kalle Kronholm

Verification of regional snowpack stability and avalanche danger

Verification of snowpack stability and avalanche danger is a prerequisite for the improvement of avalanche forecasting and the development of models. Although avalanche danger is based on snowpack stability, little is known about the variation of regional snowpack stability at a given danger level. Verification can be done by observation of avalanche occurrence and/or stability tests. To verify avalanche forecasts, and to get a more detailed picture of regional snowpack stability patterns at different danger levels, a large-scale field study has been performed. On four occasions during the winter of 2002 stability data were collected in the region of Davos. During each 1- to 3-day sampling period between 50 and 70 full snow profiles with rutschblock tests were recorded, primarily on shady slopes. At the same time the avalanche danger was estimated based on observations in the field. For analysis the profiles were assigned to one of five stability classes: Very Poor, Poor, Fair, Good, Very Good. Relating the stability to the prevailing (verified) danger level showed distinct patterns of stability. At the danger level Low, 90% of the profiles were rated as Good, or Very Good, whereas at the danger level Considerable, more than 50% showed Poor or Very Poor stability. The coefficient of variation was about 20% independent of the danger level. Significant differences in aspect and elevation existed. Some of the variation could be explained by differences in snow depth and snowpack consolidation (ram resistance). A preliminary analysis of failure layers showed that a persistent weak layer of large faceted crystals above a crust could be found in the majority of the profiles during certain periods. Despite a generally large variation in stability this weak layer was very widespread, and strongly influenced snow stability during the course of the winter, even 2 months after its formation. Due to the stability variation found in this study, verification of avalanche forecasts based on single stability tests cannot be recommended.

Snow stability variation on small slopes

Abstract The spatial variability of snowpack mechanical properties strongly influences the fracture initiation and fracture propagation properties of the snowpack, thereby largely controlling the avalanche formation process. To investigate variations in stability on the slope scale, we measured stability with stuffblock and rammrutsch tests on eight small potential avalanche slopes above timberline near Davos, Switzerland. On each slope, 17–26 point stability tests arranged in predefined arrays were done. The median, the spread and the spatial structure of the stability was investigated for 16 weak layers. Significant slope scale trends in stability were found in six weak layers. The quartile coefficient of variation for the drop heights was around 40% overall, 20% if the slope scale linear trend was removed. Auto-correlation in drop height was found in eight layers. In none of these layers a range of spatial auto-correlation could be determined. Depth of the fracture layer partly explained variations in stability. A stability rating scheme based on the median, the spread and the spatial structure of stability test results predicted the layers that were most critical for slope stability.

Spatial variability of micropenetration resistance in snow layers on a small slope

Abstract The mechanisms leading to dry-snow slab release are influenced by the three-dimensional variability of the snow cover. We measured 113 profiles of penetration resistance with a snow micropenetrometer on an alpine snow slope. Seven distinct layers were visually identified in all snow micropenetrometer profiles. The penetration resistance of adjacent layers did not change abruptly, but gradually across layer boundaries that were typically 2 mm thick. In two layers, penetration resistance varied around 200% over the grid, possibly due to wind effects during or after layer deposition. Penetration resistance varied around 25%in five layers. Statistically significant slope-scale linear trends were found for all layers. The semivariogram was used to describe the spatial variation. Penetration resistance was autocorrelated, but the scale of variation was layer-specific. A buried layer of surface hoar was the most critical weak layer. It had little spatial variation. The layers in the slab above had higher spatial variation. The penetration resistance of each snow layer had distinct geostatistical properties, caused by the depositional processes.

Field observations on spatial variability of surface hoar at the basin scale

inclination, and wind exposure within an area of about 3 km 2 . Four automatic weather stations were located within the study area: one on level terrain and three across a ridge. Despite the good instrumentation the correlation between surface hoar growth and calculated sublimation rate was poor. Distinct spatial patterns of surface hoar growth were found. Surface hoar crystals were frequently larger at the ridge site than in the surroundings of the automatic weather station on level terrain. The variation in surface hoar formation was mainly due to different prevailing wind regimes during the formation periods. The surroundings of the automatic weather station on level terrain were under the influence of local katabatic winds that dried up the air so that growth conditions were locally less favorable. Our observations suggest that predicting surface hoar formation for complex alpine terrain on the basis of data from an automatic weather station, the standard procedure in avalanche forecasting, seems nearly impossible unless at least the local wind regime is known at high resolution (� 10 m). For both surface hoar formation and surface hoar destruction observations suggest wind conditions to be most crucial for spatial variation.

Integrating spatial patterns into a snow avalanche cellular automata model

[1]xa0Snow avalanches are a major mountain hazard that kills hundreds of people and causes millions of dollars in damage worldwide annually. Yet, the relationship between the well-documented spatial variability of the snowpack and the avalanche release process is not well understood. We utilize a cellular automata model to show that the spatial structure of shear strength may be critically important for avalanche fracture propagation. Fractures through weak layers with large-scale spatial structure are much more likely to propagate over large areas than fractures through weak layers with smaller-scale spatial structure. Our technique of integrating spatial structure into the model can improve many cellular automata models that aim to explain and predict other natural hazards, such as forest fires, landslides and earthquakes.

Changes in the shear strength and micro-penetration hardness of a buried surface-hoar layer

Abstract We investigated a buried surface-hoar layer using the SnowMicroPen (SMP), an instrument designed to measure detailed snowpack profiles.We collected data from two adjacent parts of a slope 6 days apart. In addition, one manual snowpack profile was sampled each day, as well as 50 quantified loaded column tests (QLCTs) which provided an index of shear strength. For the SMP data, a 900 m2 area was sampled on both days in a grid with points 3 mapart, with some sub-areas of more closely spaced measurements. We collected 86 SMP profiles on the first day and 129 on the second day. Our analyses involved manually locating layer boundaries and calculating statistics for the force signal through the surface-hoar layer. The shear strength index increased by 40% between the two sampling days, but the SMP data show no statistical difference in layer thickness, and the mean, minimum, median, and a variety of percentile measures of the SMP force signal through the layer also do not change. Interestingly, the maximum hardness, and the variance and coefficient of variation of the SMP signal, increased. Since the small SMP tip might only break one or a couple of bonds as it passes through the weak layer, we interpret these changes as being indicative of increasing bond strength. Though we cannot specifically tie the increasing maximum hardness of the SMP signal to our QLCT results, our work suggests that the maximum SMP signal within buried surface-hoar layers may be useful for tracking increases in the shear strength of those layers.

Reliability of sampling designs for spatial snow surveys

Spatial patterns are an inherent property of most naturally occurring materials at a large range of scales. To describe spatial patterns in the field, several observations are made according to a certain sampling design. The spatial structure can be described by the semivariogram range, and nugget and sill variances. We test how reliably seven sampling designs estimate these parameters for simulated spatial fields with predefined spatial structures using a Monte Carlo approach. Five designs have been used previously in the field for snow cover sampling, whereas two designs with semi-random sampling locations have not been used in the field. The designs include 84-159 sampling locations covering small mountain slopes typical of snow avalanche terrain. The results from the simulations show that all designs: (a) give reasonably unbiased estimates of the semivariogram parameters when averaged over many simulations, and (b) show considerable spread in the semivariogram parameter estimates, causing large uncertainty in the semivariogram estimates. Our results suggest that any comparisons of the estimated semivariogram parameters made with the sampling designs will be associated with large uncertainties. To remedy this, we suggest that optimal sampling designs for sampling slope scale snow cover parameters must include more sampling locations and a stratified randomized sampling design in the future.

Field measurements of sintering after fracture of snowpack weak layers

[1]xa0This research documents two cases where field workers unintentionally fractured a snowpack weak layer, but no avalanche released. Measurements from before and after the fractures provide unique data sets on the temporal change of snow stability. Shear strength decreased immediately after fracture on both slopes. Subsequent strengthening occurred in both cases, though the rates differed presumably due to the characteristics of the weak layers. Our results have two important implications. First, they suggest the sub-critical weak layer fractures assumed as a prerequisite in some snow slab avalanche release models are transient features, and future modeling efforts must take this into account. Second, they provide insights into interpreting snow stability tests and assessing the stability of slopes with fractured weak layers.

The effectiveness of avalanche airbags

AIMnAsphyxia is the primary cause of death among avalanche victims. Avalanche airbags can lower mortality by directly reducing grade of burial, the single most important factor for survival. This study aims to provide an updated perspective on the effectiveness of this safety device.nnnMETHODSnA retrospective analysis of avalanche accidents involving at least one airbag user between 1994 and 2012 in Austria, Canada, France, Norway, Slovakia, Switzerland and the United States. A multivariate analysis was used to calculate adjusted absolute risk reduction and estimate the effectiveness of airbags on grade of burial and mortality. A univariate analysis was used to examine causes of non-deployment.nnnRESULTSnBinomial linear regression models showed main effects for airbag use, avalanche size and injuries on critical burial, and for grade of burial, injuries and avalanche size on mortality. The adjusted risk of critical burial is 47% with non-inflated airbags and 20% with inflated airbags. The adjusted mortality is 44% for critically buried victims and 3% for non-critically buried victims. The adjusted absolute mortality reduction for inflated airbags is -11 percentage points (22% to 11%; 95% confidence interval: -4 to -18 percentage points) and adjusted risk ratio is 0.51 (95% confidence interval: 0.29 to 0.72). Overall non-inflation rate is 20%, 60% of which is attributed to deployment failure by the user.nnnCONCLUSIONnAlthough the impact on survival is smaller than previously reported, these results confirm the effectiveness of airbags. Non-deployment remains the most considerable limitation to effectiveness. Development of standardized data collection protocols is encouraged to facilitate further research.