Review of spatial variability of snowpack properties and its importance for avalanche formation

• Published in: Cold regions science and technology 2008-02, Vol.51 (2), p.253-272
• Main Authors: Schweizer, Jürg, Kronholm, Kalle, Jamieson, J. Bruce, Birkeland, Karl W.
• Format: Article
• Language: English
• Subjects: Avalanche forecasting; Avalanche formation; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Exact sciences and technology; External geophysics; Natural hazards: prediction, damages, etc; Numerical modeling; Scale; Snow cover; Snow mechanical properties; Snow slope stability evaluation; Snow stratigraphy; Snow. Ice. Glaciers; Spatial variability
• ISSN: 0165-232X; 1872-7441
• DOI: 10.1016/j.coldregions.2007.04.009

The seasonal snow cover is spatially variable. Spatial variability of layer properties is due to various external and internal process drivers interacting with terrain and ground cover during and after the deposition process. Many processes that act as process drivers such as radiation and wind cause spatial variations of the snowpack at several scales. The most challenging process is probably wind that might hinder prediction of variability at the slope scale. The complexities and uncertainties involved in snow slope stability evaluation and avalanche prediction are largely due to the variable nature of the snow cover. Many studies have tried to quantify spatial variability. Different methods have been used and the studies covered a variety of scales. Accordingly, some results appear contradictory, suggesting that the degree of spatial variation varies widely. This is not surprising, and is partly due to the methods used and of course, due to varying natural conditions. For example, the variation will strongly depend on the measurement scale — the so-called support — of the method which varies from 10 − 4  m 2 for the SnowMicroPen to 3 m 2 for the rutschblock test. The layering was found to be less variable than, for example, the stability of small column tests. Whereas it is often perceived that the results of the studies were not conclusive, they completely changed our view of spatial variability. The importance of scale issues, in particular for avalanche formation became evident. Geostatistical analysis has been introduced and used to determine the length of spatial autocorrelation and to derive appropriate input data for numerical models. Model results suggest that spatial variation of strength properties has a substantial “knock-down” effect on slope stability and that the effect increases with increasing spatial correlation. The focus on scale has also revealed that spatial variations can promote instability or inhibit it. With the awareness of scale the causes of spatial variability can now be addressed. We will review the present state of knowledge, discuss consequences for avalanche forecasting and snow stability evaluation, and recommend future research directions.