11 Aug 2023
 | 11 Aug 2023
Status: this preprint is open for discussion.

Snow mechanical properties variability at the slope scale, implication for snow mechanical modeling

Francis Meloche, Francis Gauthier, and Alexandre Langlois

Abstract. Snow avalanches represent a natural hazard for infrastructures and backcountry recreationists. Risk assessment of avalanche danger is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to the decision-making and route finding in avalanche terrain for mountain users. Snow cover models simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5–50 m) remains critical given that slope stability and the possible size of an avalanche are governed by such scale. In order to better understand and predict the spatial variability at the slope scale, this work explores existing linkages between snow mechanical properties and microtopographic indicators. First, we compared covariance models and scaling properties. Then, we predicted snow mechanical properties, including point snow stability, using GAM spatial models (Generalized Additives Models) with microtopographic indicators as covariates. Snow mechanical properties such as snow density, elastic modulus, shear modulus and snow microstructural strength were measured at multiple locations over several studied slopes using a high-resolution snow penetrometer (SMP), in Rogers Pass, British-Columbia, and Mt Albert, Québec. Point snow stability such as the skier crack length, critical propagation crack length and a skier stability index were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators such as the topographic position index (TPI), vegetation height and proximity, Up-wind slope index (wind exposed/sheltered area) and potential radiation index were derived from Unmanned Aerial Vehicles (UAV) surveys with sub-meter resolution. We computed the variogram and log-log variogram of snow mechanical properties. The comparison showed some similarities in correlation distances fractal dimensions between the slab depth and slab snow density and also between the weak layer microstructural strength and the stability metrics. GAM models suggested several significant covariates such as TPI, VRM, Winstral index, vegetation height and distance to vegetation. The point snow stability maps generated represents good teaching material in avalanche skill training and awareness course. The difference in spatial pattern between the slab and the weak layer should be considered in snow mechanical modeling.

Francis Meloche et al.

Status: open (until 21 Oct 2023)

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  • RC1: 'Comment on egusphere-2023-1586', Anonymous Referee #1, 18 Aug 2023 reply

Francis Meloche et al.

Francis Meloche et al.


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Short summary
Snow avalanches are a dangerous natural hazard. Backcountry recreationists and avalanche practitioners try to predict the avalanche hazard based on the stability of the snow cover. However, the snow cover is variable in space and snow stability observations can vary within several meters. We measure the snow stability several times on a small slope to create high-resolution maps of snow cover stability. These results help us to understand the snow variation for scientists and practitioners.