Stability algorithm for snow micro-penetrometer measurements

Abstract

AbstractInformation on snow-cover stability is important for predicting avalanche danger. Traditionally, stability evaluation is based on manual observations of snow stratigraphy and stability tests, which are time-consuming. The SnowMicroPen (SMP) is a high-resolution, constant-speed penetrometer to measure penetration resistance. We have analysed the resistance signal to derive snow stability. The proposed stability algorithm was developed by comparing 68 SMP force–distance profiles with the corresponding manual profiles, including stability tests. The algorithm identifies a set of four potentially weak layers by taking into account changes in structure and rupture strength of microstructural elements that make up snow layers as derived from the SMP signal. In 90% of the cases, one of the four potentially weak layers proposed by the algorithm coincided with the failure layer observed in the stability test. To select the critical layer from the four potential weaknesses was more difficult. With fully automatic picking of the critical layer, agreement with the failure layer observed in the stability test was reached in 60% of the cases. To derive a stability classification, we analysed weak-layer as well as slab properties. These predictor variables allow the SMP signal to be classified into two stability classes, poor and fair-to-good, with an accuracy of ∼75% when compared with observed stability. The SMP, in combination with the proposed algorithm, shows high potential for providing snow-cover stability information at high resolution in time and space.