A process-based approach to estimate point snow instability

• Published in: The Cryosphere
• Main Authors: B. Reuter, J. Schweizer, A. van Herwijnen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2015
• Subjects: envir; geo; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-9-837-2015; http://www.the-cryosphere.net/9/837/2015/tc-9-837-2015.pdf; https://doaj.org/article/b8a724abaf4c46008cf9adddb258adc6

Snow instability data provide information about the mechanical state of the snow cover and are essential for forecasting snow avalanches. So far, direct observations of instability (recent avalanches, shooting cracks or whumpf sounds) are complemented with field tests such as the rutschblock test, since no measurement method for instability exists. We propose a new approach based on snow mechanical properties derived from the snow micro-penetrometer that takes into account the two essential processes during dry-snow avalanche release: failure initiation and crack propagation. To estimate the propensity of failure initiation we define a stress-based failure criterion, whereas the propensity of crack propagation is described by the critical cut length as obtained with a propagation saw test. The input parameters include layer thickness, snow density, effective elastic modulus, strength and specific fracture energy of the weak layer – all derived from the penetration-force signal acquired with the snow micro-penetrometer. Both instability measures were validated with independent field data and correlated well with results from field tests. Comparisons with observed signs of instability clearly indicated that a snowpack is only prone to avalanche if the two separate conditions for failure initiation and crack propagation are fulfilled. To our knowledge, this is the first time that an objective method for estimating snow instability has been proposed. The approach can either be used directly based on field measurements with the snow micro-penetrometer, or be implemented in numerical snow cover models. With an objective measure of instability at hand, the problem of spatial variations of instability and its causes can now be tackled.