Abstract
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. Here we show how remote sensing can accurately
inventory large avalanche depositional zones every year in a large basin
using a 32-year 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 the actual depositional zones of avalanches identified
in Google Earth and in the field (probability of detection 0.77 and positive
predictive value 0.96). A total of 810 000 large depositional zones of
avalanches have occurred since 1990 within an area of 28 500 km2 with a mean
frequency of 0.88 avalanches per square kilometre per year, 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 especially capable of
filling data voids in snow avalanche impacts in inaccessible regions.
Subject
Earth-Surface Processes,Water Science and Technology
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