EGU24-9036, updated on 08 Mar 2024
https://doi.org/10.5194/egusphere-egu24-9036
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Temporal clustering of rainfall for landslides detection

Fabiola Banfi1, Emanuele Bevacqua2, Pauline Rivoire3,4, Sérgio C. Oliveira5, Joaquim G. Pinto6, Alexandre M. Ramos6, and Carlo De Michele1
Fabiola Banfi et al.
  • 1Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy
  • 2Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
  • 3Institute of Geography and Oeschger Centre for Climate Change Research, University of Bern, Switzerland
  • 4Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Switzerland
  • 5Centre for Geographical Studies, Institute of Geography and Spatial Planning (IGOT), Associated Laboratory TERRA, University of Lisbon, Lisbon, Portugal
  • 6Institute of Meteorology and Climate Research, Troposphere Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

Landslides are impactful and complex natural hazards, causing important damages in vulnerable areas. They can be related to several pre-existing conditions and triggering factors. The former are variables that do not directly cause the event but that increase its likelihood in the presence of a triggering variable. Example of the former are the slope or the aspect, of the latter precipitation, earthquakes, snowmelt, or human disturbances. Among the triggering factors the most important is rainfall. Usually deep-seated movement, characterized by a slip surface deeper than 1.5 m, are related to repeated moderate precipitation episodes while shallow landslides, characterized by a slip surface less deep than 1.5 m, to single and more intense episodes. Landslide detection is usually performed with the use of precipitation thresholds, either process-based or empirical ones. Here we introduce a new methodology to detect landslides based on temporal clustering of precipitation. Temporal clustering is a particular typology of compound event falling inside the category of temporal compounding events and it is defined as the occurrence of multiple events of the same type in close succession. The new method is compared with the use of empirical rainfall threhsolds considering as case study two landslide inventories in the Lisbon region, Portugal. The method shows a better sensitivity with respect to empirical rainfall thresholds and a performance in terms of precision variable dependending on the site. In general, the detection of deep landslides is better than of shallow landslide. The method requires only precipitation data and the selection of a precipitation quantile to identify events and it could help to improve the detection of rainfall-triggered landslides.

How to cite: Banfi, F., Bevacqua, E., Rivoire, P., Oliveira, S. C., Pinto, J. G., Ramos, A. M., and De Michele, C.: Temporal clustering of rainfall for landslides detection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9036, https://doi.org/10.5194/egusphere-egu24-9036, 2024.

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