Quantifying snowfall and avalanche release synchronization: A case study

Abstract

We quantify the synchronization between snowfall and natural avalanches in relation to terrain properties at the detachment zone. We analyze field statistics of 549 avalanche events in terms of slope, aspect, timing, coordinate, and release area, identified by a georeferencing procedure applied on terrestrial photography. The information from the digital pictures, together with associated meteorological data, provides us with the input needed for model calibration, namely, the magnitude of snowfall, the snow compaction rate, and the timing of precipitation and of avalanche events. Synchronization between snowfall and avalanches is established for different slope categories. We obtain an average probability of release after a snow event of 30% and 16% for the high‐ and low‐slope categories (average slope 44° and 36°, respectively). Using the notion of information entropy, we quantify the uncertainty in predicting avalanche occurrence from a snow event. The steeper slopes correspond to a larger entropy in avalanche prediction. Further, the presented method allows us to establish the return period of avalanches without requiring a long series of data. When considering events regardless of their release depth, the avalanches had a return period of 48 days (higher slopes) and 88 days (lower slopes). Finally, we determine the average daily detachment rate as a function of snow depth and the return period of avalanches as a function of the release depth.

Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes

“…Many natural and engineered systems may be conceptualized as perturbed by external noise. Examples span geology 1 , seismology 2 , forestry 3 , medicine 4 , hydrology 5 , hurricane climatology 6 , avalanche prediction 7 , ecology 8 , insurance 9 , trade 10 , and social sciences 11 , among others. In such systems under variable forcing, understanding return periods to desirable (resilience) or undesirable (risk) states is of paramount importance for design and decision making.…”

Avalanche Susceptibility Mapping by Investigating Spatiotemporal Characteristics of Snow Cover Based on Remote Sensing Imagery along the Pemo Highway—A Critical Transportation Road in Tibet, China

“…Historical studies have indicated that snow is a key factor contributing to avalanche occurrences along the Pemo Highway [13][14][15][16]. Therefore, it is of great significance to assess avalanche susceptibility with snow as the main conditioning factor after obtaining the spatial and temporal characteristics of the snow cover along the Pemo Highway.…”

Morphodynamic Analysis and Statistical Synthesis of Geomorphic Data: Application to a Flume Experiment

“…Hence, the output of these models varies as a function of the random draw of input parameters. Stochastic models have been used in the Earth sciences to describe, for example, the occurrence of landslides and their impact on topography (Convertino et al, 2013;Moon et al, 2015), the synchronization of snowfall and avalanche release (Crouzy et al, 2015), the transport of sediment on hillslopes (Furbish et al, 2018) and in rivers (Ancey et al, 2008;Furbish et al, 2012;Liang et al, 2015;Lopez, 2003), or the buildup of stratigraphy (Straub & Wang, 2013). Compared with deterministic models, stochastic models are commonly less constrained to the physics governing the modeled system and are more likely to produce unrealistic configurations.…”