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

Kronholm, Kalle; Schneebeli, Martin; Schweizer, Jürg

doi:10.3189/172756404781815257

Cited by 53 publications

(41 citation statements)

References 23 publications

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“…Previous studies of many point measurements at the slope scale using the SMP revealed, among other things, that typical weak layers are often continuously present but have clearly varying properties (e.g., Kronholm et al, 2004). However, relating spatial variations as derived from point measurements to slope stability has so far not been successful.…”

Section: Introductionmentioning

confidence: 99%

A new index combining weak layer and slab properties for snow instability prediction

Schweizer¹,

Reuter²

2015

Nat. Hazards Earth Syst. Sci.

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Abstract. Snow slope stability evaluation requires considering weak layer as well as slab properties -and in particular their interaction. We developed a stability index from snow micro-penetrometer (SMP) measurements and compared it to 129 concurrent point observations with the compression test (CT). The index considers the SMP-derived micro-structural strength and the additional load, which depends on the hardness of the surface layers. The new quantitative measure of stability discriminated well between point observations rated as either "poor" or "fair" (CT < 19) and those rated as "good" (CT ≥ 19). However, discrimination power within the intermediate range was low. We then applied the index to gridded snow micro-penetrometer measurements from 11 snow slopes to explore the spatial structure and possibly relate it to slope stability. Stability index distributions on the 11 slopes reflected various possible strength and load (stress) distributions that can naturally occur. Their relation to slope stability was poor, possibly because the index does not consider crack propagation. Hence, the relation between spatial patterns of point stability and slope stability remains elusive. Whereas this is the first attempt of a truly quantitative measure of stability, future developments should consider a better reference of stability and incorporate a measure of crack propagation.

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Section: Introductionmentioning

confidence: 99%

A new index combining weak layer and slab properties for snow instability prediction

Schweizer¹,

Reuter²

2015

Nat. Hazards Earth Syst. Sci.

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“…A time-consuming but essential part of spatial variability analysis is the manual matching of the stratigraphic sequence (e.g., Kronholm et al, 2004). For this reason, previous studies have focused on a relatively few number of layers (e.g., 11 layers by Kronholm et al, 2004, 5-10 layers by Sturm and Benson, 2004).…”

Section: Discussionmentioning

confidence: 99%

“…The true spatial variability on a decameter scale is mainly due to the wind in cold winter months . This factor leads to a continuous depth-variation of the snow layers but preserves the main features of the profiles, when these are measured within tens of meters distance in smooth terrain (Harper and Bradford, 2003;Kronholm et al, 2004;Tape et al, 2010). Spatial variation of snow layer depth may affect snow internal processes such as pressure sintering, and may therefore yield spatial variability in intensive properties such as density or hardness (Schweizer et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Harper and Bradford (2003), Benson (2004), Birkeland K. W. et al (2004), and Kronholm et al (2004) tracked layers manually from point measurements in order to assess snow spatial variability. Layer tracking was performed manually because no adequate automatic algorithms existed for layer identification (Birkeland K. W. et al, 2004;Kronholm et al, 2004). Tape et al (2010) tracked layers manually through the use of close-up infrared photography of a snow trench.…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Comparing and Matching Snow Profiles, Application for Profiles Measured by Penetrometers

Hagenmuller

Pilloix

2016

Front. Earth Sci.

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Hardness has long been recognized as a good predictor of snow mechanical properties and therefore as an indicator of snowpack stability at the measured point. Portable digital penetrometers enable the amassing of a large number of snow stratigraphic hardness profiles. Numerous probings can be performed to assess the snowpack spatial variability and to compensate for measurement errors. On a decameter scale, continuous internal layers are typically present in the snowpack. The variability in stratigraphic features observed in the measurement set mainly originates from the measured variations in internal layer thickness due to either a real heterogeneity in the snowpack or to errors in depth measurement. For the purpose of real time analysis of snowpack stability, a great amount of data collected by digital penetrometers must be quickly synthesized into a characterization representative of the test site. This paper presents a method with which to match and combine several hardness profiles by automatically adjusting their layer thicknesses. The objectives are to synthesize the information collected by several profiles into one representative profile of the measurement set, disentangle information about hardness and depth variabilities, and quantitatively compare hardness profiles measured by different penetrometers. The method was tested by using co-located hardness profiles measured with three different penetrometers-the snow micropenetrometer (SMP), the Avatech SP1 and the ramsonde-during the winter 2014-2015 at two sites in the French Alps. When applied to the SMP profiles of both sites, the method reveals a low spatial variability of hardness properties, which is usually masked by depth variations. The developed algorithm is further used to evaluate the new portable penetrometer SP1. The hardness measured with this instrument is shown to be in good agreement with the SMP measurements, but errors in the recovered depth are notable, with a standard deviation of 7.8 cm and a maximum absolute error of 20 cm at one site. Combining several SP1 profiles with our algorithm reduces depth errors to a standard deviation of 3.5 cm and a maximum of 10 cm. On ramsonde profiles, the method is less effective as substantial variability in ram hardness arises from differences between operators.

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“…The layering affects the thermal conductivity and may direct and block the flow of water vapor and melt water (Colbeck, 1991). Layers formed during windy conditions are likely to be more spatially variable than other layers (Kronholm et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variability in summer snow pack in Dronning Maud Land, Antarctica

et al. 2011

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Abstract.To quantify the spatial and temporal variability in the snow pack, field measurements were carried out during four summers in Dronning Maud Land, Antarctica. Data from a 310-km-long transect revealed the largest horizontal gradients in snow density, temperature, and hardness in the escarpment region. On the local scale, day-to-day temporal variability dominated the standard deviation of snow temperature, while the diurnal cycle was of second significance, and horizontal variability on the scale of 0.4 to 10 m was least important. In the uppermost 0.2 m, the snow temperature was correlated with the air temperature over the previous 6-12 h, whereas at the depths of 0.3 to 0.5 m the most important time scale was 3 days. Cloud cover and radiative fluxes affected the snow temperature in the uppermost 0.30 m and the snow density in the uppermost 0.10 m. Both on the intra-pit and transect scales, the ratio of horizontal to temporal variability increased with depth. The horizontal standard deviation of snow density increased rapidly between the scales of 0.4 and 2 m, and more gradually from 10 to 100 m. Inter-annual variations in snow temperature and density were due to interannual differences in air temperature and the timing of the precipitation events.

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

Cited by 53 publications

References 23 publications

A new index combining weak layer and slab properties for snow instability prediction

A new index combining weak layer and slab properties for snow instability prediction

A New Method for Comparing and Matching Snow Profiles, Application for Profiles Measured by Penetrometers

Spatial and temporal variability in summer snow pack in Dronning Maud Land, Antarctica

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