Abstract

Snow avalanches are the predominant hazards in winter in high elevation mountains. They cause damage to both humans and assets but cannot be accurately predicted. Until now, only local maps to estimate snow avalanche risk have been produced. Here we show how remote sensing can accurately inventory large avalanches every year at a basin scale using a 32-yr snow index derived from Landsat satellite archives. This Snow Avalanche Frequency Estimation (SAFE) built in an open-access Google Engine script maps snow hazard frequency and targets vulnerable areas in remote regions of Afghanistan, one of the most data-limited areas worldwide. SAFE correctly detected of the actual avalanches identified on Google Earth and in the field (Probability of Detection 0.77 and Positive Predictive Value 0.96). A total of 810,000 large avalanches occurred since 1990 within an area of 28,500 km2 with a mean frequency of 0.88 avalanches/km2yr−1, damaging villages and blocking roads and streams. Snow avalanche frequency did not significantly change with time, but a northeast shift of these hazards was evident. SAFE is the first robust model that can be used worldwide and is capable of filling data voids on snow avalanche impacts in inaccessible regions.

Highlights

  • Snow avalanches are among the fastest, up to 61 m/s-1, and most dangerous natural hazards in mountain areas (Louge et al, 2012)

  • Casualties associated with avalanches are numerous; in 2021 alone, 37 fatalities occurred in the US (Colorado Avalanche Information Center, 2021) and 127 in Europe

  • Avalanche surveys amongst remote villages are sparse because regions are uninhabited; avalanches can block connecting roads every year since avalanche volumes range from hundreds to several tens of thousand cubic meters (Gubler, 1987)

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Summary

Introduction

Snow avalanches are among the fastest, up to 61 m/s-1, and most dangerous natural hazards in mountain areas (Louge et al, 2012). Casualties associated with avalanches are numerous; in 2021 alone, 37 fatalities occurred in the US (Colorado Avalanche Information Center, 2021) and 127 in Europe 2021), but avalanche monitoring is not consistent across the globe. Most remote mountain regions and communities are not systematically monitored for avalanche occurrence. Avalanche surveys amongst remote villages are sparse because regions are uninhabited; avalanches can block connecting roads every year since avalanche volumes range from hundreds to several tens of thousand cubic meters (Gubler, 1987). Avalanches can be predicted based on snow depth and other weather parameters (Greene et al, 2016). The global weather monitoring of mountainous areas is scattered and very sparse in developing nations

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