Suchergebnisse

Abstract. In this work, we apply a physically based model, namely the
HIRESSS (HIgh REsolution Slope Stability Simulator) model, to forecast the
occurrence of shallow landslides at the regional scale. HIRESSS is a physically
based distributed slope stability simulator for analyzing shallow landslide
triggering conditions during a rainfall event. The modeling software is made up of two
parts: hydrological and geotechnical. The hydrological model is based on an
analytical solution from an approximated form of the Richards equation, while
the geotechnical stability model is based on an infinite slope model that
takes the unsaturated soil condition into account. The test area is a portion
of the Aosta Valley region, located in the northwest of the Alpine mountain chain. The
geomorphology of the region is characterized by steep slopes with elevations
ranging from 400 m a.s.l. on the Dora Baltea River's floodplain to
4810 m a.s.l. at Mont Blanc. In the study area, the mean annual
precipitation is about 800–900 mm. These features make the territory
very prone to landslides, mainly shallow rapid landslides and rockfalls.
In order to apply the model and to increase its reliability, an in-depth
study of the geotechnical and hydrological properties of hillslopes
controlling shallow landslide formation was conducted. In particular, two
campaigns of on site measurements and laboratory experiments were performed
using 12 survey points. The data collected contributed to the generation of an input map
of parameters for the HIRESSS model. In order to consider the effect of
vegetation on slope stability, the soil reinforcement due to the presence of
roots was also taken into account; this was done based on vegetation maps and
literature values of root cohesion. The model was applied using back analysis
for two past events that affected the Aosta Valley region between 2008 and
2009, triggering several fast shallow landslides. The validation of the
results, carried out using a database of past landslides, provided good
results and a good prediction accuracy for the HIRESSS model from both a
temporal and spatial point of view.