Snow gliding, a slow movement downhill of snow cover, is complex to forecast and model and yet is extremely important, because it drives snowpack dynamics in the pre-avalanching phase. Despite recent interest in this process and the development of some studies therein, this phenomenon is poorly understood and represents a major point of uncertainty for avalanche forecasting. This study presents a data-driven, physically based, time-dependent 1D model, Poli-Glide, able to predict the slow movement of snowpacks along a flow line at the daily scale. The objective of the work was to create a useful snow gliding model, requiring few, relatively easily available input data, by (i) modeling snowpack evolution from measured precipitation and air temperature, (ii) evaluating the rate and extent of movement of the snowpack in the gliding phase, and (iii) assessing fracture (i.e., avalanching) timing. Such a model could be then used to provide hazard assessment in areas subject to gliding, thereby, and subsequent avalanching. To do so, some simplifying assumptions were introduced, namely that (i) negligible traction stress occurs within soil, (ii) water percolation into snow occurs at a fixed rate, and (iii) the micro topography of soil is schematized according to a sinusoidal function in the absence of soil erosion. The proposed model was then applied to the “Torrent des Marais-Mont de La Saxe” site in Aosta Valley, monitored during the winters of 2010 and 2011, featuring different weather conditions. The results showed an acceptable capacity of the model to reproduce snowpack deformation patterns and the final snowpack’s displacement. Correlation analysis based upon observed glide rates further confirmed dependence against the chosen variables, thus witnessing the goodness of the model. The results could be a valuable starting point for future research aimed at including more complex parameterizations of the different processes that affect gliding.

Physical Modeling of Snow Gliding: A Case Study in the NW Italian Alps

Daniele Bocchiola
2021-01-01

Abstract

Snow gliding, a slow movement downhill of snow cover, is complex to forecast and model and yet is extremely important, because it drives snowpack dynamics in the pre-avalanching phase. Despite recent interest in this process and the development of some studies therein, this phenomenon is poorly understood and represents a major point of uncertainty for avalanche forecasting. This study presents a data-driven, physically based, time-dependent 1D model, Poli-Glide, able to predict the slow movement of snowpacks along a flow line at the daily scale. The objective of the work was to create a useful snow gliding model, requiring few, relatively easily available input data, by (i) modeling snowpack evolution from measured precipitation and air temperature, (ii) evaluating the rate and extent of movement of the snowpack in the gliding phase, and (iii) assessing fracture (i.e., avalanching) timing. Such a model could be then used to provide hazard assessment in areas subject to gliding, thereby, and subsequent avalanching. To do so, some simplifying assumptions were introduced, namely that (i) negligible traction stress occurs within soil, (ii) water percolation into snow occurs at a fixed rate, and (iii) the micro topography of soil is schematized according to a sinusoidal function in the absence of soil erosion. The proposed model was then applied to the “Torrent des Marais-Mont de La Saxe” site in Aosta Valley, monitored during the winters of 2010 and 2011, featuring different weather conditions. The results showed an acceptable capacity of the model to reproduce snowpack deformation patterns and the final snowpack’s displacement. Correlation analysis based upon observed glide rates further confirmed dependence against the chosen variables, thus witnessing the goodness of the model. The results could be a valuable starting point for future research aimed at including more complex parameterizations of the different processes that affect gliding.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1191639
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