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A framework for the quantitative assessment of new data streams in avalanche forecasting
(Montana State University - Bozeman, College of Letters & Science, 2023) Haddad, Alexander Sean; Co-chairs, Graduate Committee: Eric A. Sproles and Jordy Hendrikx
Data used by avalanche forecasters are typically collected using weather stations, manual field-based observations (e.g., avalanche events, snow profiles, stability tests, personal observations, public observations, etc.) and weather forecasts ("traditional observations"). Today, snow cover observations can be delivered via remote sensing (e.g., satellite data, UAV, TLS, time-lapse camera etc.). Forecasting operations can also use statistical forecasting, weather models, and physical modeling to support decisions. This paper presents a framework and methodology to quantify the impact these new, complex data streams have on the formulation of, and associated uncertainty of, avalanche forecasting. We use data from a case study in Norway. Avalanche forecasters in Norway assessed size (D), likelihood, avalanche problem, and hazard level for a highway corridor in Grasdalen, Stryn Norway. The control groups were given access to traditional observations. The experimental groups were given access to the same traditional data, but also near-real-time snow surface LiDAR data ("RS+"). In case study one the RS+ (n=10) consensus findings were a hazard level two steps lower than the control group (n=10). In case study two the traditional (n=10) and RS+ groups' (n=7) consensus findings assessed the northeastern avalanche path at the same hazard level. Assessing the southwestern slide path, the traditional group (n=10) and RS+ group (n=9) had the same consensus finding for hazard level. In 2 of 3 case studies, the RS+ groups had fewer selections for size, likelihood, and avalanche problem which indicates reduced uncertainty in their forecasts. Throughout the 2022-2023 winter season Norwegian Public Roads Administration avalanche forecasters performed a real-time experiment throughout the season - with and without additional RS+ data when forecasting. They agreed on hazard level in 6 of 10 forecasts. In the other 4 forecasts, RS+ forecasters assessed the hazard level higher than traditional data forecasts. When RS+ data reveals aspects of conditions that traditional observations did not detail, RS+ forecasters adjust their selections in the hazard matrix, resulting in greater clustering of their predictions, indicating reduced uncertainty. Due to uncertainty associated with avalanche forecasting, this framework for assessment should be used to track avalanche forecast efficacy and build a qualitative and quantitative historical record.
Meteorological controls on wind slab properties
(Montana State University - Bozeman, College of Letters & Science, 2023) de Leeuw, Nathalie Marika; Chairperson, Graduate Committee: Jordy Hendrikx
Snow avalanches are dangerous phenomena, which can be made increasingly consequential through wind transport of snow. Wind-deposited snow has a broad range of possible physical and mechanical properties which can vary greatly over short distances, creating inconsistent and thus difficult avalanche conditions. This variability causes particular challenges for avalanche workers in data-sparse regions where important snowpack information at desired scales may be unavailable. Instead, snowpack properties are commonly inferred from available meteorological data. Though wind slab properties vary in space and time as meteorological conditions change, previous work has not explicitly studied these relationships at the slope-scale. In this research I aim to better understand how changes in meteorological variables relate to changes in wind slab physical properties. During two winters I recorded temperature, humidity, and wind speed at study sites in Montana's Madison Range (45.237, -111.424) and collected snowpack data during or immediately following blowing snow events. I found that average wind speeds at 0.5m and 1.5m above the snow surface were significantly higher during hard wind slab formation than soft wind slab formation, while unobstructed wind speed, maximum gust, and the length of time of wind transport were not associated with wind slab hardness. Temperature was higher during hard than soft wind slab formation, while humidity was not different between the two hardness categories. Although wind speed at 1.5m had a significant positive linear relationship with both wind slab density and blade hardness gauge force, it was a poor predictor of actual values for both of these parameters. Our findings help improve the understanding of the impact of near surface winds on wind slabs, which will aid avalanche forecasting and mitigation planning particularly in windy climates.
Spatio-temporal analysis of large magnitude avalanches using dendrochronology
(Montana State University - Bozeman, College of Letters & Science, 2020) Peitzsch, Erich Hans; Chairperson, Graduate Committee: Jordy Hendrikx; Jordy Hendrikx, Daniel K. Stahle, Gregory T. Pederson, Karl W. Birkeland and Daniel B. Fagre were co-authors of the article, 'A regional spatio-temporal analysis of large magnitude snow avalanches using tree rings' submitted to the journal 'Natural hazards and Earth systems sciences' which is contained within this dissertation.; Gregory T. Pederson, Jordy Hendrikx, Karl W. Birkeland and Daniel B. Fagre were co-authors of the article, 'Trends in regional large magnitude snow avalanche occurrence and associated climate patterns in the U.S. northern Rocky Mountains' submitted to the journal 'Journal of climate' which is contained within this dissertation.; Chelsea Martin-Mikle, Jordy Hendrikx, Gregory T. Pederson, Karl W. Birkeland and Daniel B. Fagre were co-authors of the article, 'Vegetation characterization in avalanche paths using LIDAR and satellite imagery' submitted to the journal 'Arctic, antarctic, and alpine research' which is contained within this dissertation.
Snow avalanches are a natural hazard to humans and infrastructure as well as an important landscape disturbance affecting mountain ecosystems. In many mountainous regions, records of avalanche frequency and magnitude are sparse or non-existent. Inferring historic avalanche patterns to improve forecasting and understanding requires the use of dendrochronological methods. In this dissertation, we examine a regional tree-ring derived large magnitude avalanche dataset from northwest Montana in the northern Rocky Mountains, USA, to produce avalanche chronologies at three distinct scales (path, sub-region, and region), assess seasonal climate drivers of years with large magnitude avalanche occurrence on a regional scale, and characterize vegetation in select avalanche paths. By implementing a strategic spatial sampling design and collecting a large dataset of tree-ring samples, we: (1) assessed scaling in the context of a regional avalanche chronology, reconstructed avalanche chronologies for 12 avalanche paths in four subregions, and examined the effects of two methods of sampling indexing on the resultant avalanche chronology; (2) identified specific climate drivers of large magnitude avalanche years across a region and identified trends in avalanche year probability through time; and (3) tested the feasibility of using remote sensing products to quantify vegetation types in avalanche paths and characterized the vegetation composition based on return periods within specific avalanche paths. This dissertation is organized into 3 key chapters/manuscripts (Chapters 2, 3, and 4) and two supporting chapters (Chapters 1 and 5) that address the problem of assessing large magnitude avalanche frequency at various spatio-temporal scales using a tree-ring dataset. The results contribute toward a better understanding of reconstructing regional avalanche chronologies, a more accurate assessment of avalanche-climate relationships, and improved methods to characterize vegetation characteristics within avalanche path return periods. This work has applications for regions with sparse avalanche records.
Snow avalanche identification using Sentinel-1: detection rates and controlling factors
(Montana State University - Bozeman, College of Letters & Science, 2021) Keskinen, Zachary Marshall; Chairperson, Graduate Committee: Jordy Hendrikx; Jordy Hendrikx, Karl Birkeland and Markus Eckerstorfer were co-authors of the article, 'Snow avalanche identification using Sentinel-1 backscatter imagery: detection rates and controlling factors' submitted to the journal 'Natural hazards and Earth system sciences' which is contained within this thesis.
Snow avalanches present a significant hazard that endangers lives and infrastructure. Consistent and accurate datasets of avalanche events is valuable for improving forecasting ability and furthering knowledge of avalanches' spatial and temporal patterns. Remote sensing-based techniques of identifying avalanche debris allow for continuous and spatially consistent datasets of avalanches to be acquired. This study utilizes expert manual interpretations of Sentinel-1 synthetic aperture radar (SAR) satellite backscatter images to identify avalanche debris and compares those detections against historical field records of avalanches in the transitional snow climates of Wyoming and Utah. This study explores the utility of Sentinel-1 (a SAR satellite) images to detect avalanche debris on primarily dry slab avalanches. The overall probability of detection (POD) rate for avalanches large enough to destroy trees or bury a car (i.e., D3 on the Destructive Size Scale) was 64.6%. There was a significant variance in the POD among the 13 individual SAR image pairs (15.4 - 87.0%). Additionally, this study investigated the connection between successful avalanche detections and SAR-specific, topographic, and avalanche type variables. The most correlated variables with higher detection rates were avalanche path lengths, destructive size of the avalanche, incidence angles for the incoming microwaves, slope angle, and elapsed time between the avalanche and a Sentinel-1 satellite passing over. This study provides an initial exploration of the controlling variables in the likelihood of detecting avalanches using Sentinel-1 backscatter change detection techniques. This study also supports the generalizability of SAR backscatter difference analysis by applying the methodology in different regions with distinct snow climates from previous studies.
Atmospheric processes related to deep persistent slab avalanches in the western United States
(Montana State University - Bozeman, College of Letters & Science, 2019) Schauer, Andrew Robert; Chairperson, Graduate Committee: Jordy Hendrikx
Deep persistent slab avalanches are a natural hazard that are particularly difficult to predict. These avalanches are capable of destroying infrastructure in mountain settings, and are generally unsurvivable by humans. Deep persistent slab avalanches are characterized by a thick (> 1 m) slab of cohesive snow overlaying a weak layer in the snowpack, which can fail due to overburden stress of the slab itself or to external triggers such as falling cornices, explosives, or a human. While formation of such snowpack structure is controlled by persistent weather patterns early in the winter, a snowpack exhibiting characteristics capable of producing a deep persistent slab avalanche may exist for weeks or months before a specific weather event such as a heavy precipitation or rapid warming pushes the weak layer to its breaking point. Mountain weather patterns are highly variable down to the local scale (1-10 m), but they are largely driven by atmospheric processes on the continental scale (1000 km). This work relates atmospheric circulation to deep persistent slab events at Mammoth, CA; Bridger Bowl, MT; and Jackson, WY. We classify 5,899 daily 500 millibar geopotential height maps into 20 synoptic types using Self-Organizing Maps. At each location, we examine the frequency of occurrence of each of the 20 types during November through January during major deep persistent slab seasons and compare those frequencies to seasons without deep persistent slab avalanches. We also consider the 72-hour time period preceding deep persistent slab avalanches at each location and identify synoptic types occurring frequently, as well as those rarely occurring prior to onset of activity. At each location, we find specific synoptic types that tend to occur at a higher rate during major deep persistent slab years, while minor years are characterized by different circulation patterns. We also find a small number of synoptic types dominating the 72-hour period prior to onset of deep slab activity. With this improved understanding of the atmospheric processes preceding deep persistent slab avalanches, we provide avalanche practitioners with an additional tool to better anticipate a difficult to predict natural hazard.
Validation of a coupled weather and snowpack model across western Montana and its application as a tool for avalanche forecasting
(Montana State University - Bozeman, College of Letters & Science, 2016) Van Peursem, Kyle Webb; Chairperson, Graduate Committee: Jordy Hendrikx
Predicting avalanche danger depends on knowledge of the existing snowpack structure and the current and forecasted weather conditions. In remote and data sparse areas this information can be difficult, if not impossible, to obtain, increasing the uncertainty and challenge of avalanche forecasting. In this study, we coupled the Weather Research and Forecasting (WRF) model with the snow cover model SNOWPACK to simulate the evolution of the snow structure for several mountainous locations throughout western Montana, USA during the 2014-2015 and 2015-2016 winter seasons. We then compared the model output to manual snow profiles and snow and avalanche observations to assess the quantitative and qualitative accuracy of several snowpack parameters (grain form, grain size, density, stratigraphy, etc.) during significant avalanche episodes. At our study sites, the WRF model tended to over-forecast precipitation and wind, which impacted the accuracy of the simulated snow depths and SWE throughout most of the study period. Despite this, the SNOWPACKWRF model chain managed to approximate the snowpack stratigraphy observed throughout the two seasons including early season faceted snow, the formation of various melt-freeze crusts, the spring transition to an isothermal snowpack, and the general snowpack structure during several significant avalanche events. Interestingly, the SNOWPACK-WRF simulation was statistically comparable in accuracy to a SNOWPACK simulation driven with locally observed weather data. Overall, the model chain showed potential as a useful tool for avalanche forecasting, but advances in numerical weather and avalanche models will be necessary for widespread acceptance and use in the snow and avalanche industry.
Meteorological metrics associated with deep slab avalanches on persistent weak layers
(Montana State University - Bozeman, College of Letters & Science, 2014) Marienthal, Alex Grayson; Chairperson, Graduate Committee: Jordy Hendrikx
Snow avalanches are a potentially fatal and highly destructive natural hazard. Snow slab avalanches occur in steep alpine terrain due to an unstable layered snowpack. When a consolidated layer of snow forms a slab above a weak layer of snow the slab may collapse and slide downhill due to gravitational and applied forces (e.g., the weight of a skier, explosive, or new snowfall). Persistent weak layers form in the snowpack due to strong vapor pressure gradients, and they can last for weeks to months as a slab builds above them. Avalanches on persistent weak layers become less frequent, yet are typically larger and more destructive the longer and deeper the layer is buried. Deep slab avalanches on persistent weak layers pose a difficult forecasting problem due to their low likelihood of occurrence and potentially high consequences. This thesis aims to identify meteorological metrics that are associated with deep slabs on persistent weak layers. We used univariate analysis, classification trees, and random forests to explore relationships between seasons with deep slabs and summaries of meteorological metrics over the beginning of the season during weak layer formation. We also looked at the relationship between days with these avalanches and summaries of meteorological metrics over the days prior to them. In addition, we reviewed a case study of a season that had multiple deep slabs on a persistent weak layer and a historic wet slab avalanche cycle on the same layer, at Bridger Bowl ski area. Seasons with deep slabs typically had relatively low precipitation throughout the early part of the season (i.e., November - January), and a snowpack in the beginning of the season that was sufficiently deep, but shallow enough for a weak layer to develop. Our results also showed warmer twenty-four hour temperatures and more precipitation over seven day prior to days with dry deep slabs, and extended periods of above freezing temperatures were seen prior to days with deep wet slabs. These results are in line with previous research and are suggestive of meteorological summaries that may be useful to forecast deep slab avalanches on persistent weak layers.
An investigation of the spatial variability and efficacy of the Extended Column Test
(Montana State University - Bozeman, College of Letters & Science, 2014) Hoyer, Ian Richard Steele; Chairperson, Graduate Committee: Jordy Hendrikx; Jordy Hendrikx, Karl W. Birkeland and Kathryn M. Irvine were co-authors of the article, 'Spatial variability of extended column test results at the slope scale' submitted to the journal 'Cold regions science and technology' which is contained within this thesis.
Most avalanche accidents are the results of an avalanche triggered by the victim, or a member of the victim's party. Many of these accidents are the result of uncertainty regarding the stability of the snowpack. Spatial variability of snow stability is a significant cause of this uncertainty. There has been significant previous work on the spatial variability of snow stability at multiple spatial scales, but most of these studies have focused on measures of fracture initiation. This study investigates the spatial variability of Extended Column Test (ECT) results (an index of fracture propagation). We measured the spatial variability of ECT results in 23 grids across southwest Montana over the course of two winters. These slopes were all topographically uniform, wind sheltered clearings, with snowpacks relatively undisturbed by skiers or snowmobiles. Twenty eight ECTs were spaced across each grid in a standardized layout with a 30 m x 30 m extent. Our results are consistent with previous work, with some grids showing high levels of variability as well as other grids with relatively homogenous results. We found no consistent spatial pattern to our test results. We tested slopes with a variety of weak layers (surface hoar, depth hoar, new snow, and near surface facets), slab characteristics (slab hardness, slab depth), and snow depths and found no correlations with ECT results. We found a relationship between the forecasted regional avalanche danger and the percent of ECTs showing propagation in a grid. As the regional danger increases the percent of ECTs propagating in a grid does as well. ECT results are most variable under moderate danger. When the regional avalanche danger is either considerable or low, results are likely to be more consistent. The key practical implication of our results is that ECTs, like all other stability tests, should be interpreted with an appropriate level of caution and in consideration of all other relevant variables. The spatial variability of this test has the potential to be high on some slopes, while on other slopes test results will be entirely in agreement.
Surface hoar observations at the scale of a helicopter skiing operation
(Montana State University - Bozeman, College of Letters & Science, 2014) Borish, Matthew John; Chairperson, Graduate Committee: Stephan G. Custer
Understanding what controls coarse scale snowpack properties, such as surface hoar distribution, is imperative for predicting snow avalanches. Due in part to the inherent difficulties of winter travel in mountainous terrain, most spatial variability investigations of snow properties have been limited to relatively fine scales. To quantify snow surface spatial variability at the basin, region, and mountain range scales, a team of heli-skiing guides recorded observations describing surface hoar presence or absence coordinates, crystal size, and elevation throughout four major surface hoar formation periods over two heli-skiing seasons in rugged alpine terrain near Haines, Alaska across an extent of nearly 60 km. Geostatistical analysis yielded spherical semivariogram autocorrelation ranges from approximately 3-25 km, which is similar in size to many of the basins and regions within the study area. Kriging models built from the semivariograms were produced to aid geographic visualization of coarse scale snowpack processes. Geographically Weighted Regression revealed a positive relationship between elevation and surface hoar crystal size with adjusted R 2 values averaging near 0.40. The results of this research suggest it may be possible to identify areas with greater surface hoar growth and persistence potentials as a consequence of synoptic onshore or offshore flow, and glacially influenced katabatic winds. Additionally, larger surface hoar crystals may be found in the higher elevation avalanche starting zones in the alpine glaciated terrain near Haines, Alaska. These results can help in future efforts to forecast snow stability patterns over large areas.
March wet avalanche prediction at Bridger Bowl ski area, Montana
(Montana State University - Bozeman, College of Letters & Science, 2004) Romig, Jeannette M.; Chairperson, Graduate Committee: Stephan G. Custer
Wet avalanches are a safety concern for all ski areas because they are difficult to control artificially and the shift from safe to dangerous wet snow conditions can happen very quickly. Forecasting for wet avalanche conditions in intermountain ski areas, such as Bridger Bowl, Montana, can be especially difficult because intermountain snow climates can exhibit wet avalanche characteristics of either maritime or continental snow climates. Various statistical models have been developed for avalanche prediction; however, most are tailored specifically for dry avalanche forecasting. Archived meteorological, snowpack and avalanche data for the month of March from 1968 to 2001 (1996 data unavailable) were used to develop 68 possible predictor variables related to temperature, snowpack settlement, and precipitation characteristics. The original Bridger Bowl dataset was divided into a ♯new snowα and an ♯old snowα dataset. A ♯new snowα day has newly fallen snow that is less than 48 hours old; an ♯old snowα day has newly fallen snow that is more than 48 hours old. The two datasets were used to determine whether the factors that influence ♯old snowα and ♯new snowα wet avalanche occurrence differ. Hypotheses were developed and tested to determine which ♯old snowα and ♯new snowα variables behaved significantly different on days with wet avalanches compared to days with no wet avalanches. The 33 ♯old snowα significant variables and the 22 ♯new snowα significant variables were analyzed with binomial logistic regression to produce one prediction model for ♯old snowα wet avalanche conditions and another prediction model for ♯new snowα wet avalanche conditions. The ♯old snowα model uses the prediction day minimum temperature and the two day change in total snow depth as predictor variables. This model has a 75% success rate for calculating accurate wet avalanche probabilities for ♯old snowα days. The ♯new snowα model uses the prediction day minimum temperature as well as the three day cumulative new snow water equivalent as predictor variables. This model has a 72% success rate for calculating accurate wet avalanche probabilities for ♯new snowα days.

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