A tracking algorithm to identify slab and weak layer combinations for assessing snow instability and avalanche problem type

2021 ◽  
Author(s):  
Benjamin Reuter ◽  
Léo Viallon-Galinier ◽  
Stephanie Mayer ◽  
Pascal Hagenmuller ◽  
Samuel Morin
Keyword(s):  
Snow Cover ◽  
Winter Season ◽  
Essential Element ◽  
Problem Type ◽  
Model Output ◽  
Avalanche Forecasting ◽  
Weak Layer ◽  
Problem Types ◽  
Snow Stratigraphy ◽  
Wet Snow

<p>Snow cover models have mostly been developed to support avalanche forecasting. Recently developed snow instability metrics can help interpreting modeled snow cover data. However, presently snow cover models cannot forecast the relevant avalanche problem types – an essential element to describe avalanche danger. We present an approach to detect, track and assess weak layers in snow cover model output data to eventually assess the related avalanche problem type. We demonstrate the applicability of this approach with both, SNOWPACK and CROCUS snow cover model output for one winter season at Weissfluhjoch. We introduced a classification scheme for four commonly used avalanche problem types including new snow, wind slabs, persistent weak layers and wet snow, so different avalanche situations during a winter season can be classified based on weak layer type and meteorological conditions. According to the modeled avalanche problem types and snow instability metrics both models produced weaknesses in the modeled stratigraphy during similar periods. For instance, in late December 2014 the models picked up a non-persistent as well as a persistent weak layer that were both observed in the field and caused widespread instability in the area. Times when avalanches released naturally were recorded with two seismic avalanche detection systems, and coincided reasonably well with periods of low modeled stability. Moreover, the presented approach provides the avalanche problem types that relate to the observed natural instability which makes the interpretation of modeled snow instability metrics easier. As the presented approach is process-based, it is applicable to any model in any snow avalanche climate. It could be used to anticipate changes in avalanche problem type due to changing climate. Moreover, the presented approach is suited to support the interpretation of snow stratigraphy data for operational forecasting.</p>

scholarly journals Modeling spatially distributed snow instability at a regional scale using Alpine3D

2021 ◽  
pp. 1-16
Author(s):  
Bettina Richter ◽  
Jürg Schweizer ◽  
Mathias W. Rotach ◽  
Alec van Herwijnen
Keyword(s):  
Laser Scanning ◽  
Meteorological Data ◽  
Regional Scale ◽  
Winter Season ◽  
Horizontal Resolution ◽  
Distributed Modeling ◽  
Avalanche Forecasting ◽  
Weak Layer ◽  
Snow Stratigraphy ◽  
Spatially Distributed

Abstract Assessing the avalanche danger level requires snow stratigraphy and instability data. As such data are usually sparse, we investigated whether distributed snow cover modeling can be used to provide information on spatial instability patterns relevant for regional avalanche forecasting. Using Alpine3D, we performed spatially distributed simulations to evaluate snow instability for the winter season 2016–17 in the region of Davos, Switzerland. Meteorological data from automatic weather stations were interpolated to 100 m horizontal resolution and precipitation was scaled with snow depth measurements from airborne laser scanning. Modeled snow instability metrics assessed for two different weak layers suggested that the weak layer closer to the snow surface was more variable. Initially, it was less stable than the weak layer closer to the ground, yet it stabilized faster as the winter progressed. In spring, the simulated snowpack on south-facing slopes stabilized faster than on north-facing slopes, in line with the regional avalanche forecast. In the winter months January to March 2017, simulated instability metrics did not suggest that the snowpack on south-facing slopes was more stable, as reported in the regional avalanche forecast. Although a validation with field data is lacking, these model results still show the potential and challenges of distributed modeling for supporting operational avalanche forecasting.

scholarly journals Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK

2019 ◽  
Vol 13 (12) ◽  
pp. 3353-3366 ◽  
Author(s):  
Bettina Richter ◽  
Jürg Schweizer ◽  
Mathias W. Rotach ◽  
Alec van Herwijnen
Keyword(s):  
Grain Size ◽  
Crack Propagation ◽  
Crack Length ◽  
Snow Cover ◽  
Field Experiments ◽  
Critical Crack ◽  
Critical Crack Length ◽  
Avalanche Forecasting ◽  
Weak Layer ◽  
Snow Stratigraphy

Abstract. Observed snow stratigraphy and snow stability are of key importance for avalanche forecasting. Such observations are rare and snow cover models can improve the spatial and temporal resolution. To evaluate snow stability, failure initiation and crack propagation have to be considered. Recently, a new stability criterion relating to crack propagation, namely the critical crack length, was implemented into the snow cover model SNOWPACK. The critical crack length can also be measured in the field with a propagation saw test, which allows for an unambiguous comparison. To validate and improve the parameterization for the critical crack length, we used data from 3 years of field experiments performed close to two automatic weather stations above Davos, Switzerland. We monitored seven distinct weak layers and performed in total 157 propagation saw tests on a weekly basis. Comparing modeled to measured critical crack length showed some discrepancies stemming from model assumption. Hence, we replaced two variables of the original parameterization, namely the weak layer shear modulus and thickness, with a fit factor depending on weak layer density and grain size. With these adjustments, the normalized root-mean-square error between modeled and observed critical crack lengths decreased from 1.80 to 0.28. As the improved parameterization accounts for grain size, values of critical crack lengths for snow layers consisting of small grains, which in general are not weak layers, become larger. In turn, critical weak layers appear more prominently in the vertical profile of critical crack length simulated with SNOWPACK. Hence, minimal values in modeled critical crack length better match observed weak layers. The improved parameterization of critical crack length may be useful for both weak layer detection in simulated snow stratigraphy and also providing more realistic snow stability information – and hence may improve avalanche forecasting.

scholarly journals A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting

1992 ◽  
Vol 38 (128) ◽  
pp. 13-22 ◽  
Author(s):  
E. Brun ◽  
P. David ◽  
M. Sudul ◽  
G. Brunot
Keyword(s):  
Numerical Model ◽  
Snow Cover ◽  
Winter Season ◽  
Weather Conditions ◽  
Internal Layers ◽  
Avalanche Forecasting ◽  
Snow Metamorphism ◽  
Wet Snow ◽  
Made In ◽  
Snow Samples

AbstractLaws of snow metamorphism have been introduced in a numerical model which simulates the evolution of temperature, density and liquid-water profiles of snow cover as a function of weather conditions.To establish these laws, the authors have summarized previous studies on temperature gradient and on wet-snow metamorphism and they have also conducted metamorphism experiments on dry or wet fresh-snow samples. An original formalism was developed to allow a description of snow with parameters evolving continuously throughout time.The introduction of laws of metamorphism has improved significantly the derivation of the settlement of internal layers and of snow-covered albedo, which depend on the simulated stratigraphy, i.e. the type and size of snow grains of different layers of the snow cover.The model was tested during a whole winter season without any re-initialization. Comparison between the simulated characteristics of the snow cover and the observations made in the field are described in detail. The model proved itself to be very efficient in simulating accurately the evolution of the snow-cover stratigraphy throughout the whole winter season.

Simulating snow instability in complex terrain

2020 ◽  
Author(s):  
Bettina Richter ◽  
Alec van Herwijnen ◽  
Mathias W. Rotach ◽  
Jürg Schweizer
Keyword(s):  
Spatial Variability ◽  
Snow Cover ◽  
Complex Terrain ◽  
Laser Scanning ◽  
Winter Season ◽  
Temporal Variations ◽  
Full Potential ◽  
Avalanche Forecasting ◽  
Snow Stratigraphy ◽  
Operational Forecasting

<p><span>Numerical snow cover models are increasingly used in operational avalanche forecasting. While these models can provide snow stratigraphy and some snow instability information, their full potential is not yet exploited in forecasting. We investigated, how well the snow cover model Alpine3D simulated spatial and temporal variations in snow instability. Therefore, simulations were performed in highly varying complex terrain for the winter season 2016-2017 in the region of Davos, Switzerland for an area of about 21 km x 21 km. Alpine3D was forced with data from several automatic weather stations within the region, which were interpolated to a resolution of 100 m. To reproduce observed spatial variability, we scaled precipitation input with snow height measurements derived with airborne laser scanning. For comparison, we also simulated the snowpack without scaling. The simulation with scaling precipitation showed significantly higher spatial variability in modeled snow instability than the simulation without scaling. However, when information was aggregated to aspect and elevation dependent information for the whole region, as it is done for operational forecasting, this variability vanished and scaling precipitation seems unnecessary. At the beginning of the season and towards the end, snow instability depended on aspect, while in the winter months December to March, differences between different aspects were small. The simulations with scaling precipitation revealed a strong influence of snow depth on snow instability, although the various snow instability criteria provided inconsistent results. Simulated profiles, which were classified as rather favourable were rated as rather unstable and vice versa. A comparison to traditional snow profiles shows that snow stratigraphy was reproduced well, but assessing snow instability from stratigraphy alone is not feasible.</span></p>

scholarly journals A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting

1992 ◽  
Vol 38 (128) ◽  
pp. 13-22 ◽  
Author(s):  
E. Brun ◽  
P. David ◽  
M. Sudul ◽  
G. Brunot
Keyword(s):  
Numerical Model ◽  
Snow Cover ◽  
Winter Season ◽  
Weather Conditions ◽  
Internal Layers ◽  
Avalanche Forecasting ◽  
Snow Metamorphism ◽  
Wet Snow ◽  
Made In ◽  
Snow Samples

AbstractLaws of snow metamorphism have been introduced in a numerical model which simulates the evolution of temperature, density and liquid-water profiles of snow cover as a function of weather conditions.To establish these laws, the authors have summarized previous studies on temperature gradient and on wet-snow metamorphism and they have also conducted metamorphism experiments on dry or wet fresh-snow samples. An original formalism was developed to allow a description of snow with parameters evolving continuously throughout time.The introduction of laws of metamorphism has improved significantly the derivation of the settlement of internal layers and of snow-covered albedo, which depend on the simulated stratigraphy, i.e. the type and size of snow grains of different layers of the snow cover.The model was tested during a whole winter season without any re-initialization. Comparison between the simulated characteristics of the snow cover and the observations made in the field are described in detail. The model proved itself to be very efficient in simulating accurately the evolution of the snow-cover stratigraphy throughout the whole winter season.

scholarly journals Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK

2019 ◽  
Author(s):  
Bettina Richter ◽  
Jürg Schweizer ◽  
Mathias W. Rotach ◽  
Alec van Herwijnen
Keyword(s):  
Crack Length ◽  
Snow Cover ◽  
Field Experiments ◽  
Detection Method ◽  
Probability Of Detection ◽  
Critical Crack ◽  
Critical Crack Length ◽  
Avalanche Forecasting ◽  
Snow Stratigraphy ◽  
Abstract Data

Abstract. Data on snow stratigraphy and snow instability are of key importance for avalanche forecasting. Snow cover models can improve the spatial and temporal resolution of such data, especially if they also provide information on snow instability. Recently, a new stability criterion, namely a parameterization for the critical crack length, was implemented into the snow cover model SNOWPACK. To validate and improve this parameterization, we therefore used data from three years of field experiments performed close to two automatic weather station above Davos, Switzerland. Monitoring the snowpack on a weekly basis allowed to investigate limitations of the model. Based on 145 experiments we replaced two variables of the original parameterization, which were not sufficiently well modeled, with a fit factor thereby decreasing the normalized root mean square error from 1.80 to 0.28. With this fit factor, the improved parameterization accounts for the grain size resulting in lower critical crack lengths for snow layers with larger grains. This also improved an automatic weak layer detection method using a simple local minimum by increasing the probability of detection from 0.26 to 0.91 and decreased the false alarm ratio from 0.89 to 0.47.

scholarly journals Measurements of mid-winter spatial distribution of meltwater saturation

2010 ◽  
Vol 51 (54) ◽  
pp. 146-152
Author(s):  
J.C. Kapil ◽  
Anupam Kumar ◽  
P.S. Negi
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Snow Cover ◽  
Measurement Techniques ◽  
Three Dimensional ◽  
Dielectric Measurement ◽  
Snow Stratigraphy ◽  
Wet Snow ◽  
Water Contents ◽  
Saturation Profiles

AbstractUnder melt–freeze conditions crusts may evolve within a snowpack, which may favour avalanche initiation by forming a hard bed surface for weakly bonded faceted grains. We used a parallel-probe saturation profiler (PPSP) to record the distribution of water contents within the snowpack. Diurnal effects of melt–freeze action on the growth of crusts were monitored with the help of the PPSP device. Saturation profiles were collected from a partially wet snow cover. Snow stratigraphy was conducted manually in the morning, after overnight freezing, to identify the location and the granular compositions of the crusts that had evolved. A one-to-one correspondence between the saturation spikes collected using the PPSP and the actual positions of the crusts was established. The PPSP was also used to monitor three-dimensional variations in the maximum percolation depths within a south-facing snowpack. The operation of the PPSP is faster than existing dielectric measurement techniques, so it was applied to study the spatial variability of maximum percolation depths on the slopes of different aspects.

scholarly journals Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating snowpack model output for avalanche forecasting

2021 ◽  
Vol 14 (1) ◽  
pp. 239-258
Author(s):  
Florian Herla ◽  
Simon Horton ◽  
Patrick Mair ◽  
Pascal Haegeli
Keyword(s):  
Hazard Assessment ◽  
Assessment Process ◽  
Model Output ◽  
Time Warping ◽  
Avalanche Forecasting ◽  
Avalanche Hazard ◽  
Snow Stratigraphy ◽  
Operational Application ◽  
Dynamic Time ◽  
Profile Alignment

Abstract. Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant for their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models, which consist of multidimensional sequences describing the snow characteristics of grain type, hardness, and age. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. By emulating aspects of the human avalanche hazard assessment process, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build an understanding of how to interpret and trust operational snowpack simulations.

scholarly journals New categories for the climatic division of snowy areas in Japan

1998 ◽  
Vol 26 ◽  
pp. 131-137 ◽  
Author(s):  
Masaaki Ishizaka
Keyword(s):  
Temperature Gradient ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Depth ◽  
Winter Season ◽  
Critical Values ◽  
Absolute Value ◽  
Mean Temperature ◽  
Wet Snow ◽  
The Absolute

New categories for the climatic division of snowy areas according to their snow-cover character in mid-winter are proposed. They are a wet-snow region, a dry-snow region, an intermediate snow region and a depth-hoar region. The wet-snow region is defined as the region in which every layer of deposited snow is wet due to percolation of snowmelt water throughout the winter. In contrast, areas in which the snow cover is dry, at least in the coldest period of the winter season, are classified into two categories, that is the dry-snow region and the depth-hoar region. In the latter region, the small snow depth and low air temperature induce development of depth hoar. The intermediate snow region was introduced to indicate an intermediate character between the dry-snow and wet-snow regions. From the climatic dataset calculated by the Japanese Meteorological Agency and from snow surveys, it has been found that in snowy areas, which have a climatic monthly mean temperature in January (Tjan) higher than 0.3°C, snow would be expected to be wet throughout the winter and, in areas that have Tjan, lower than −1.1°C, to be dry at least in the coldest period. Snow covers, where Tjan is between these two values, are expected to have intermediate characters. Therefore, these temperatures are supposed to be critical values among the wet, dry and intermediate snow regions. The criterion that separates the depth-hoar region from the dry-snow areas was found to be given by a climatic mean temperature gradient. This value lies between 10 and 12°Cm−1, which is derived by dividing the absolute value of the average of the climatic monthly mean air temperature, which is always below 0°C, by the average of the monthly maximum snow depth during January and February.

scholarly journals New categories for the climatic division of snowy areas in Japan

1998 ◽  
Vol 26 ◽  
pp. 131-137 ◽  
Author(s):  
Masaaki Ishizaka
Keyword(s):  
Temperature Gradient ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Depth ◽  
Winter Season ◽  
Critical Values ◽  
Absolute Value ◽  
Mean Temperature ◽  
Wet Snow ◽  
The Absolute

New categories for the climatic division of snowy areas according to their snow-cover character in mid-winter are proposed. They are a wet-snow region, a dry-snow region, an intermediate snow region and a depth-hoar region. The wet-snow region is defined as the region in which every layer of deposited snow is wet due to percolation of snowmelt water throughout the winter. In contrast, areas in which the snow cover is dry, at least in the coldest period of the winter season, are classified into two categories, that is the dry-snow region and the depth-hoar region. In the latter region, the small snow depth and low air temperature induce development of depth hoar. The intermediate snow region was introduced to indicate an intermediate character between the dry-snow and wet-snow regions. From the climatic dataset calculated by the Japanese Meteorological Agency and from snow surveys, it has been found that in snowy areas, which have a climatic monthly mean temperature in January (Tjan ) higher than 0.3°C, snow would be expected to be wet throughout the winter and, in areas that have Tjan, lower than −1.1°C, to be dry at least in the coldest period. Snow covers, where Tjan is between these two values, are expected to have intermediate characters. Therefore, these temperatures are supposed to be critical values among the wet, dry and intermediate snow regions. The criterion that separates the depth-hoar region from the dry-snow areas was found to be given by a climatic mean temperature gradient. This value lies between 10 and 12°Cm−1, which is derived by dividing the absolute value of the average of the climatic monthly mean air temperature, which is always below 0°C, by the average of the monthly maximum snow depth during January and February.

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