Oltrepo Pavese, which is located in the Northern Apennines of Italy, is characterized by high density of landslides and has historically suffered from widespread damage from landslides. The mass movements often developed in clayey-marly formations and are classified as complex movements. More recently extreme rainfall events have triggered shallow landslides in areas, which were not yet affected by these types of landslides. An important event happened subsequently to rapid snowmelt and intense rainfall in April 2009. The main objective of this research is to evaluate the predictability of shallow landslide occurrence of April 2009 event using three physically based models: SHALSTAB (Montgomery and Dietrich, 1994), SINMAP (Pack et al., 1998) and TRIGRS (Baum et al., 2002). The results obtained from the three models are compared in order to highlight potential and limitation of these models for the forecasting of the potential source areas.

Shallow landslides susceptibility assessment: application and comparison of physically based models in an area of Oltrepo Pavese

MEISINA, CLAUDIA;ZIZIOLI, DAVIDE
2012-01-01

Abstract

Oltrepo Pavese, which is located in the Northern Apennines of Italy, is characterized by high density of landslides and has historically suffered from widespread damage from landslides. The mass movements often developed in clayey-marly formations and are classified as complex movements. More recently extreme rainfall events have triggered shallow landslides in areas, which were not yet affected by these types of landslides. An important event happened subsequently to rapid snowmelt and intense rainfall in April 2009. The main objective of this research is to evaluate the predictability of shallow landslide occurrence of April 2009 event using three physically based models: SHALSTAB (Montgomery and Dietrich, 1994), SINMAP (Pack et al., 1998) and TRIGRS (Baum et al., 2002). The results obtained from the three models are compared in order to highlight potential and limitation of these models for the forecasting of the potential source areas.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/547442
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