Preprints
https://doi.org/10.5194/nhess-2022-271
https://doi.org/10.5194/nhess-2022-271
 
17 Nov 2022
17 Nov 2022
Status: this preprint is currently under review for the journal NHESS.

Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models

Stefan Steger1, Mateo Moreno1,2, Alice Crespi1, Peter James Zellner1, Stefano Luigi Gariano3, Maria Teresa Brunetti3, Massimo Melillo3, Silvia Peruccacci3, Francesco Marra4, Robin Kohrs1,5, Jason Goetz5, Volkmar Mair6, and Massimiliano Pittore1 Stefan Steger et al.
  • 1Institute for Earth Observation, Eurac Research, Bolzano-Bozen, Italy
  • 2University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands
  • 3CNR IRPI, Perugia, Italy
  • 4Institute of Atmospheric Sciences and Climate, National Research Council (CNR-ISAC), Bologna, Italy
  • 5Department of Geography, Friedrich Schiller University Jena, Germany
  • 6Office for Geology and Building Materials Testing, Autonomous Province of Bolzano-South Tyrol, Cardano, Italy

Abstract. The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at 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.

Stefan Steger et al.

Status: open (until 29 Dec 2022)

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Stefan Steger et al.

Stefan Steger et al.

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Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to a denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false alarm rates.
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