The Spatial Distribution of Two Surface Hoar Events in the Chilkat and Takhinsha Mountains of Southeast Alaska (original) (raw)
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Predicting the spatial distribution and persistence of buried surface hoar layers is important when evaluating avalanche hazard. This study used weather-based models to predict the formation of surface hoar and investigated how buried layers change over time. Seven years of study plot observations from the Columbia Mountains of British Columbia were used to calibrate models for surface hoar formation. The latent heat flux was modelled with weather station data and forecasted data from the Canadian numerical weather prediction model (GEM15). A linear relationship was found between vapour mass flux and observed surface hoar crystal size (r 2 of 0.84 with weather station data and 0.70 with GEM15 data), and was used to predict crystal size over seven winters. Crystal size predictions had root mean square errors of 2.4 and 4.1 mm with weather station and GEM15 data, respectively. The model was compared with other empirical weather-based models. Layers of buried surface hoar were tracked with shear frame tests, compression tests (CT) and propagation saw tests (PST). PSTs and fracture character in CTs indicated that the propensity for propagation in layers of surface hoar remained high for up to six weeks. Layers with large crystals were found to weakly indicate low stability. Results from this study could be used to improve the representation of surface hoar layers in snow cover models and make spatial predictions with NWP data.
Modelling surface hoar formation and evolution on mountain slopes
Predicting the spatial distribution and persistence of surface hoar layers is a challenge to avalanche forecasters and backcountry recreationists. This study evaluates surface hoar size predictions made with empirical weather based models and discusses how buried layers change over time. Surface hoar layers were observed during the 2011-2012 winter at flat, north and south facing slopes in the Columbia Mountains. Two models were developed to predict crystal size, one using a constant growth rate and the other estimating vapour deposit from the surface energy balance. Weather station data and forecasted data from the GEM15 numerical weather prediction model were used to simulate the growth of layers over seven winters. Reasonable size predictions were made with growth rates of 2.1 and 2.6 mm per day (r 2 = 0.44), although specific rates appeared to change with season. The surface energy balance model made good predictions of crystal size with real station data (r 2 = 0.74) and reasonable predictions with the output of GEM15 (r 2 = 0.49). Time series of shear frame and propagation saw test results were made from observations on a buried surface hoar layer that formed in February 2012. The large crystals in this layer showed a steady increase in shear strength while the potential for fracture propagation remained high. The results of this study show promise for modelling surface hoar formation and evolution in snow cover models.
Winter terrain roughness as a new parameter to define size and location of avalanche release areas
Location and size of avalanche release areas are crucial inputs in modelling of avalanche dynamics as, together with fracture depth, they determine the initial avalanche volume. One difficulty in estimating avalanche release areas is that they vary in location and size within the same topographical basin due to variation in snow cover distribution. During the snow accumulation season, terrain features successively disappear leading to increasingly homogeneous deposition patterns during storm events and, thus, to a progressive smoothing of the terrain surface. These changing deposition patterns might therefore explain the differences in release areas. To characterize the smoothing effect of snow on terrain we use the concept of roughness. Roughness is calculated for several snow surfaces and their corresponding underlying terrain. To this end, elevation models of winter and summer terrain are derived from high-resolution measurements performed by airborne LIDAR. The winter datasets correspond to snow cover scenarios with varying snow depths ranging from 1m to 4m. For one scenario, six avalanches were artificially triggered and an additional laser scan was performed after the releases. We show that for both summer and winter surfaces, low roughness values are organized in clusters. Further, the clusters obtained from the snow scenario with avalanches are able to reproduce location and size of the observed release areas.
Meteorological, elevation, and slope effects on surface hoar formation
The Cryosphere
Failure in layers of buried surface hoar crystals (frost) can cause hazardous snow slab avalanches. Surface hoar crystals form on the snow surface and are sensitive to micro-meteorological conditions. In this study, the role of meteorological and terrain factors was investigated for three layers of surface hoar in the Columbia Mountains of Canada. The distribution of crystals over different elevations and aspects was observed on 20 days of field observations during a period of high pressure. The same layers were modelled over simplified terrain on a 2.5 km horizontal grid by forcing the snow cover model SNOWPACK with forecast weather data from a numerical weather prediction model. Modelled surface hoar growth was associated with warm air temperatures, high humidity, cold surface temperatures, and low wind speeds. Surface hoar was most developed in regions and elevation bands where these conditions existed, although strong winds at high elevations caused some model discrepancies. SNO...
2022
Climate change impact on avalanches is ambiguous. Fewer, wetter, and smaller avalanches are expected in areas where snow cover is declining, while in higher altitude areas where snowfall prevails, snow avalanches are frequently and spontaneously triggered. In the present paper, we assess 39 years (winters of 1979-1999 to 2002-2020) of avalanche activity related to meteorological and snow drivers in the Krkonoše Mountains, Czechia, Central Europe. The analysis is based on an avalanche occurrence dataset for mostly south, south-easterly oriented 60 avalanche paths and related meteorological and snowpack data. Since 1979, 179 / 531 wet-snow / slab avalanches have been recorded. The aim is to analyze changes in avalanche activity: frequency and magnitude, and detect driving weather variables of wet and slab avalanches with quantification of variable importance. Especially, the number of wet avalanches in February and March has increased in the last three decades, while the number of slab avalanches has decreased with decadal variability. Medium, large, and very large slab avalanches seem to decline with decadal variability since 1961. The results indicate that wet avalanches are influenced by 3-day maximum and minimum air temperature, snow depth, wind speed, wind direction, and rainfall. Slab avalanche activity is determined by snow depth, rainfall, new snow, and wind speed. Air temperature-related variables for slab avalanches were less important than rain and snow-related variables based on the balanced random forest (RF) method. Surprisingly, the RF analysis revealed less significant relationship between new snow sum and slab avalanche activity. This could be because of the wind redistributing snow in storms in low altitude mountains. Our analysis allows the use of the identified wet and slab avalanche driving variables to be included in the avalanche danger levels alerts. Although it cannot replace operational forecasting, machine learning can allow for additional insights for the decision-making process to mitigate avalanche hazard. 1 Introduction Snow avalanches are major natural hazards. As rapidly moving snow masses, snow avalanches pose a serious threat to people, property, and infrastructure. The growth in popularity of winter tourism has led to an increase in numbers of avalanche acci-1
Annals of the Association of American Geographers, 1990
We examine the roles of lithology, structure, and topography as determinants of the location and morphology of snow-avalanche paths in east-central Glacier National Park, Montana. Most models of avalanche path location emphasize topographic interactions with prevailing winds and ignore the role of geologic influences. Landsat Thematic Mapper digital data, covering the study area, were enhanced through a combination of directional and nondirectional spatial filters, principal components analysis, and channel ratioing to delineate the relationships between lithology, structure, topography, and avalanche-path location. We used CIS technology to examine these spatial relationships. Fieldwork confirmed the remote sensing and CIS interpretations. M o r e than 50 percent of the snow-avalanche paths are located directly beneath a widespread diorite sill, and roughly 25 percent and 20 percent of the additional paths were associated with topographic couloirs (probably structurally controlled) and structural lineaments respectively. Structural patterns also influenced slope aspect of the paths. Morphometric differences between these paths and those from a larger, previous study group are attributed to local climatic differences on opposite sides of the Continental Divide.
A Between-Storm Indicator of Avalanche Activity
Proceedings of the 2000 International Snow Science Workshop October 1 6 Big Sky Montana, 2000
Earlier studies examining relationships between weather factors and avalanche activity showed that the important between-storm factors include an index related to the minimum and maximum daily air temperatures. The index helped explain the differences in avalanche activity with similar storms, but with different circumstances affecting the snow between storms. This study used the results of the previous investigation to guide case studies taking a closer look at the temperature index, which has the name vapor gradient index (VGI). The VGI accumulates each day between storms by adding the current day's value VGI' with the accumulated value from the previous day. During storms the value remains constant, and is reset to zero when the snow stops falling. To calculate the VGI' for the current day, one requires two temperatures, the daily minimum temperature and the value midway between the daily minimum and maximum. For each temperature the one then uses Clausius-Clapeyron equation to calculate the saturation vapor pressure at each temperature. The difference between the two vapor pressures becomes the VGI', the value of the current day. We used the SNTHERM model of snow processes to calculate grain growth during a 17-day period between storms in the early winter,1994, at Mammoth Mountain, California. During this period the VGI showed a high correlation to grain growth.
Cold Regions Science and Technology, 1999
Using classification and regression tree models, we evaluated 31 factors in terms of their importance to explaining avalanche activity indices at two ski areas: Alta, UT and Mammoth Mountain, CA. This study derived new empirical factors that combined wind velocity with new snow amount, air temperatures with time, and total snow depth with time. The analyses created over-fit tree models in exploring structures inherent in the data to obtain the relative ranking and scores of various combinations of the 31 factors. Avalanche activity indices included maximum size, number of releases and sum of sizes of released avalanches. Results showed that time lagged conventional factors describing snowfall and derived wind-drift parameters ranked highest in all tests. Snow drift factors segregated into prominent wind directions showed only moderate importance. Among the non-storm factors, the starting snow depth of a particular year ranked highest showing the importance of interannual variability. This was followed by the accumulated vapor pressure difference, which we formulated to better describe the conditioning of old snow with age. The average snow depth increase and other factors followed in importance.