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 ORCID: https://orcid.org/0000-0001-7868-8835, Tarantino, Alessandro, Nicotera, Marco Valerio, Forte, Giovanni, De Falco, Melania and Santo, Antonio 2019. Building physically-based models for assessing rainfall-induced shallow landslide hazard at the catchment scale: the case study of the Sorrento Peninsula (Italy). Canadian Geotechnical Journal 56 (9) , pp. 1291-1303. 10.1139/cgj-2017-0611

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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.

Item Type:	Article
Date Type:	Publication
Schools:	Engineering
Publisher:	NRC Research Press (Canadian Science Publishing)
ISSN:	0008-3674
Date of First Compliant Deposit:	6 December 2018
Date of Acceptance:	14 November 2018
Last Modified:	28 Nov 2024 16:00
URI:	https://orca.cardiff.ac.uk/id/eprint/117454

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