Review of spatial variability of snowpack properties and its importance for avalanche formation

Schweizer, Jürg; Kronholm, Kalle; Jamieson, J. Bruce; Birkeland, Karl W.

doi:10.1016/j.coldregions.2007.04.009

Cited by 133 publications

(102 citation statements)

References 62 publications

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“…Accordingly, avalanche forecasters must possess an indepth understanding of the interaction between terrain and snowpack processes. Schweizer et al (2008) describe that forecasters develop sophisticated, inductive processing techniques that search for terrain correlated patterns, and relate them to avalanche formation processes.…”

Section: Location In the Terrainmentioning

confidence: 99%

“…Static terrain factors such as slope angle, shape, aspect and elevation directly influence both in situ snowpack development, and the impact of weather factors such as precipitation, air temperature and wind. Terrain is the constant modifier on all factors that influence avalanche formation (Schweizer et al 2008), and understanding where a particular avalanche problem is located in the terrain is crucial for effectively managing the associated risk. For backcountry travel, the exposure component of risk (people's time and position in terrain) is the single most important consideration for controlling risk (Statham 2008).…”

Section: Location In the Terrainmentioning

confidence: 99%

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A conceptual model of avalanche hazard

Statham

Haegeli

Greene³

et al. 2017

Nat Hazards

114

112

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This conceptual model of avalanche hazard identifies the key components of avalanche hazard and structures them into a systematic, consistent workflow for hazard and risk assessments. The method is applicable to all types of avalanche forecasting operations, and the underlying principles can be applied at any scale in space or time. The concept of an avalanche problem is introduced, describing how different types of avalanche problems directly influence the assessment and management of the risk. Four sequential questions are shown to structure the assessment of avalanche hazard, namely: (1) What type of avalanche problem(s) exists? (2) Where are these problems located in the terrain? (3) How likely is it that an avalanche will occur? and (4) How big will the avalanche be? Our objective was to develop an underpinning for qualitative hazard and risk assessments and address this knowledge gap in the avalanche forecasting literature. We used judgmental decomposition to elicit the avalanche forecasting process from forecasters and then described it within a risk-based framework that is consistent with other natural hazards disciplines.

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Section: Location In the Terrainmentioning

confidence: 99%

Section: Location In the Terrainmentioning

confidence: 99%

A conceptual model of avalanche hazard

Statham

Haegeli

Greene³

et al. 2017

Nat Hazards

114

112

View full text Add to dashboard Cite

show abstract

“…For the latter it has been shown that the snow distribution at the winter maximum before the beginning of the melting period strongly determines the temporal evolution of the remaining snow resources and -if converted to snow water equivalent (Jonas et al, 2010) -the potential melt water runoff during the melting period . Several studies reported a very high spatial variability of snow depth and other snow pack parameters at different spatial scales in mountainous regions (e.g., Elder et al, 1991;Schweizer et al, 2008;Lehning et al, 2008;Grünewald et al, 2010;Egli, 2011). This high variation in snow cover distribution on very small scales requires a high spatial resolution of snow samples to measure different parameters of the snow pack such as, e.g., the areal mean snow depth on complex alpine topography and the temporal evolution of snow-covered areas during melt with high areal representativeness and low absolute uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Snow depth mapping in high-alpine catchments using digital photogrammetry

Bühler¹,

et al. 2015

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Abstract. Information on snow depth and its spatial distribution is crucial for numerous applications in snow and avalanche research as well as in hydrology and ecology. Today, snow depth distributions are usually estimated using point measurements performed by automated weather stations and observers in the field combined with interpolation algorithms. However, these methodologies are not able to capture the high spatial variability of the snow depth distribution present in alpine terrain. Continuous and accurate snow depth mapping has been successfully performed using laser scanning but this method can only cover limited areas and is expensive. We use the airborne ADS80 optoelectronic scanner, acquiring stereo imagery with 0.25 m spatial resolution to derive digital surface models (DSMs) of winter and summer terrains in the neighborhood of Davos, Switzerland. The DSMs are generated using photogrammetric image correlation techniques based on the multispectral nadir and backward-looking sensor data. In order to assess the accuracy of the photogrammetric products, we compare these products with the following independent data sets acquired simultaneously: (a) manually measured snow depth plots; (b) differential Global Navigation Satellite System (dGNSS) points; (c) terrestrial laser scanning (TLS); and (d) ground-penetrating radar (GPR) data sets. We demonstrate that the method presented can be used to map snow depth at 2 m resolution with a vertical depth accuracy of ±30 cm (root mean square error) in the complex topography of the Alps. The snow depth maps presented have an average accuracy that is better than 15 % compared to the average snow depth of 2.2 m over the entire test site.

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“…HS can be also measured using ultrasonic [3] or laser [4] sensors, while SWE can be monitored using snow pillows [5] or cosmic rays [4]. The significance of local measurements has been often debated [6][7][8][9][10], especially in view of the marked spatial variability of snow processes [11][12][13][14]. To partially take this variability into account, snow manual measurements are often performed along snow courses and then averaged to provide a more representative estimation of available SWE and snow depth [15].…”

Section: Introductionmentioning

confidence: 99%

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

et al. 2018

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Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20-0.30 m, but decreases to 0.06-0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

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Review of spatial variability of snowpack properties and its importance for avalanche formation

Cited by 133 publications

References 62 publications

A conceptual model of avalanche hazard

A conceptual model of avalanche hazard

Snow depth mapping in high-alpine catchments using digital photogrammetry

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

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