Suchergebnisse

Abstract. We communicate the results of a preliminary investigation aimed at
improving a state-of-the-art RSLEWS (regional-scale landslide early
warning system) based on rainfall thresholds by integrating mean
soil moisture values averaged over the territorial units of the
system. We tested two approaches. The simplest can be easily applied
to improve other RSLEWS: it is based on a soil moisture threshold
value under which rainfall thresholds are not used because
landslides are not expected to occur. Another approach deeply
modifies the original RSLEWS: thresholds based on antecedent
rainfall accumulated over long periods are substituted with soil
moisture thresholds. A back analysis demonstrated that both
approaches consistently reduced false alarms, while the second
approach reduced missed alarms as well.

Abstract. Central Asia is an area characterized by complex tectonics and active deformation; the related seismic activity controls the earthquake hazard level that, due to the occurrence of secondary and tertiary effects, also has direct implications for the hazard related to mass movements such as
landslides, which are responsible for an extensive number of casualties
every year. Climatically, this region is characterized by strong rainfall
gradient contrasts due to the diversity of climate and vegetation zones.
The region is drained by large, partly snow- and glacier-fed rivers that
cross or terminate in arid forelands; therefore, it is also affected by a
significant river flood hazard, mainly in spring and summer seasons. The
challenge posed by the combination of different hazards can only be tackled by
considering a multi-hazard approach harmonized among the different
countries, in agreement with the requirements of the Sendai Framework for
Disaster Risk Reduction. This work was carried out within the framework of
the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project as part of a multi-hazard approach and is
focused on the first landslide susceptibility analysis at a regional scale
for Central Asia. To this aim the most detailed landslide inventories,
covering both national and transboundary territories, were implemented in a
random forest model, together with several independent variables. The
proposed approach represents an innovation in terms of resolution (from 30
to 70 m) and extension of the analyzed area with respect to previous
regional landslide susceptibility and hazard zonation models applied in
Central Asia. The final aim was to provide a useful tool for land
use planning and risk reduction strategies for landslide scientists,
practitioners, and administrators.

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.