Avalanche Prediction and Dynamics using Temperature Variance , Grain Size Variance and Flow Regimes

TL;DR

This study explores how temperature variance, grain size, and flow regimes affect avalanche behavior, utilizing SVMs and the Material Point Method to improve understanding and prediction of avalanche dynamics.

Contribution

It introduces the combined use of temperature and grain size variance analysis, MPM simulations, and SVM-based forecasting for enhanced avalanche prediction.

Findings

Avalanche size patterns are scale-free and unaffected by temperature.

MPM effectively models snow avalanche flow regimes.

SVMs identify key meteorological and snowpack features for forecasting.

Abstract

We investigate the effects of temperature variance, grain size variation, flow regimes, and the use of Support Vector Machines (SVMs) in avalanche studies. The temperature variance experiments involved ice single crystals and polycrystals, revealing that the scale-free pattern of avalanche sizes remains consistent regardless of temperature. The dynamics of dislocations in polycrystals were found to be independent of stress level and temperature. The Material Point Method (MPM) was used to explore snow avalanche behavior and identify flow regimes. The MPM accurately represented various flow patterns of snow avalanches, although challenges remained in capturing powder clouds. SVMs were employed for avalanche forecasting, using meteorological and snowpack variables as input features. The selected features provided insights into snowfall characteristics, snow accumulation, rain interaction, snowdrift patterns, cloud dynamics, snowpack mechanics, and temperature distribution within the snowpack. The findings contribute to a better understanding of avalanche dynamics and offer potential improvements in avalanche trend predictions.