07 Feb 2022
07 Feb 2022
Status: a revised version of this preprint is currently under review for the journal TC.

Snow Avalanche Frequency Estimation (SAFE): 32 years of remote hazard monitoring in Afghanistan

Arnaud Caiserman1, Roy C. Sidle1, and Deo Raj Gurung2 Arnaud Caiserman et al.
  • 1Mountain Societies Research Institute – University of Central Asia, Khorog, 736000, Tajikistan
  • 2Aga Khan Agency of Habitat, Dushanbe, 734013, Tajikistan

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.

Arnaud Caiserman et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Reviewer Comment on tc-2022-15', Yves Bühler, 04 Mar 2022
    • AC1: 'Reply on RC1', Arnaud Caiserman, 11 Mar 2022
    • AC2: 'Reply on RC1', Arnaud Caiserman, 26 Apr 2022
  • RC2: 'Comment on tc-2022-15', Hannah Vickers, 21 Mar 2022
    • CC1: 'Reply on RC2', Arnaud Caiserman, 01 Apr 2022
    • AC3: 'Reply on RC2', Arnaud Caiserman, 26 Apr 2022

Arnaud Caiserman et al.

Arnaud Caiserman et al.


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Short summary
Snow avalanches cause considerable material and human damage in all mountain regions of the world. Here, we present the first model to automatically inventory avalanches at the scale of a catchment area – here the Amu Darya in Afghanistan – every year since 1990. This model called Snow Avalanche Frequency Estimation (SAFE) is available online on the Google Engine interface. SAFE has been designed to be simple and universal to use. Nearly 30,000 avalanches were detected over the 32 years studied.