Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
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Media type:
E-Article
Title:
Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
Description:
<jats:p>Abstract. The increasing availability of long-term observational data can lead to the
development of innovative modelling approaches to determine landslide
triggering conditions at a regional scale, opening new avenues for landslide
prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models
(GAMMs) to develop an interpretable approach that identifies seasonally
dynamic precipitation conditions for shallow landslides. The model builds
upon a 21-year record of landslides in South Tyrol (Italy) and separates
precipitation that induced landslides from precipitation that did not. The
model accounts for effects acting at four temporal scales: short-term
“triggering” precipitation, medium-term “preparatory” precipitation,
seasonal effects, and across-year data variability. It provides relative
landslide probability scores that were used to establish seasonally dynamic
thresholds with optimal performance in terms of hit and false-alarm rates,
as well as additional thresholds related to user-defined performance scores.
The GAMM shows a high predictive performance and indicates that more
precipitation is required to induce a landslide in summer than in
winter/spring, which can presumably be attributed mainly to vegetation and
temperature effects. The discussion illustrates why the quality of input
data, study design, and model transparency are crucial for landslide
prediction using advanced data-driven techniques.
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