Building physically-based models for assessing rainfall-induced shallow landslide hazard at the catchment scale: the case study of the Sorrento Peninsula (Italy)

Brunella Balzano, Alessandro Tarantino, Marco Valerio Nicotera, Giovanni Forte, Melania de Falco, Antonio Santo

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The assessment of rainfall-induced shallow landslide hazard at the catchment scale poses significant challenge. Traditional empirical approaches for landslide hazard assessment often assume that conditions having caused failure in the past won't change in the future. This assumption may not hold in a climate change scenario. Physically-based models (PBMs) therefore represent the natural approach to include changing climate effects. PBMs would in principle require the combination of a 3-D mechanical and water-flow model. However, a full 3-D finite element model at the catchment scale, with relatively small elements required to capture the pore-water pressure gradients, would have a significant computational cost. For this reason, simplifications to the mechanical (i.e. infinite slope) and water-flow model (i.e. 1-D or hybrid 3-D) are introduced, often based on a-priori assumptions and not corroborated by experimental evidence. The paper presents a methodology to build a PBM in a bottom-up fashion based on geological surveys and geotechnical investigation. The PBM is initially set as simple as possible and then moved to a higher level of complexity if the model is not capable of simulating past landslide events. The approach is presented for the case study of Sorrento Peninsula and two main landslides events recorded during winter 1996-1997.
LanguageEnglish
Number of pages42
JournalCanadian Geotechnical Journal
Early online date22 Nov 2018
DOIs
Publication statusE-pub ahead of print - 22 Nov 2018

Fingerprint

Landslides
Catchments
Rain
landslide
Hazards
hazard
catchment
rainfall
water flow
Water
Geological surveys
climate effect
hazard assessment
Pressure gradient
pressure gradient
Climate change
geological survey
porewater
climate change
methodology

Keywords

  • rainfall
  • flow-like landslides
  • unsaturated soil
  • water flow

Cite this

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title = "Building physically-based models for assessing rainfall-induced shallow landslide hazard at the catchment scale: the case study of the Sorrento Peninsula (Italy)",
abstract = "The assessment of rainfall-induced shallow landslide hazard at the catchment scale poses significant challenge. Traditional empirical approaches for landslide hazard assessment often assume that conditions having caused failure in the past won't change in the future. This assumption may not hold in a climate change scenario. Physically-based models (PBMs) therefore represent the natural approach to include changing climate effects. PBMs would in principle require the combination of a 3-D mechanical and water-flow model. However, a full 3-D finite element model at the catchment scale, with relatively small elements required to capture the pore-water pressure gradients, would have a significant computational cost. For this reason, simplifications to the mechanical (i.e. infinite slope) and water-flow model (i.e. 1-D or hybrid 3-D) are introduced, often based on a-priori assumptions and not corroborated by experimental evidence. The paper presents a methodology to build a PBM in a bottom-up fashion based on geological surveys and geotechnical investigation. The PBM is initially set as simple as possible and then moved to a higher level of complexity if the model is not capable of simulating past landslide events. The approach is presented for the case study of Sorrento Peninsula and two main landslides events recorded during winter 1996-1997.",
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Building physically-based models for assessing rainfall-induced shallow landslide hazard at the catchment scale : the case study of the Sorrento Peninsula (Italy). / Balzano, Brunella; Tarantino, Alessandro; Nicotera, Marco Valerio ; Forte, Giovanni ; de Falco, Melania; Santo, Antonio .

In: Canadian Geotechnical Journal, 22.11.2018.

Research output: Contribution to journalArticle

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