Suchergebnisse

Abstract. Shaanxi Province, located in northwest China and spanning multiple
hydroclimatic and geological zones, has many areas largely suffering from
rainfall-induced landslide and debris flow. The objectives of this study are
to reveal the spatiotemporal characteristics of the two hazards and identify
their major controlling factors in this region based on a region-wide,
comprehensive ground-survey-based hazard inventory dataset from 2009 to 2012.
We investigated the spatiotemporal characteristics of the two hazards and
quantified the relationships between the occurrence rates of the two hazards
and their influencing factors, including antecedent rainfall amount, rainfall
duration, rainfall intensity, terrain slope, land cover type and soil type.
The results show that landslide has a higher occurrence rate and more
extensive distribution than debris flow in this region, while the two hazards
are both concentrated in the south with ample rainfall and steep terrains.
Both of the hazards show clear seasonalities: July–September for landslide
and July for debris flow. Rainfall characteristics (amount, duration and
intensity) and slope are the dominant factors controlling slope stability
across this region. Debris flow is more sensitive to these rainfall metrics
on the high-value ranges than landslide in this region. Land cover is another
influencing factor but soil type does not appear to impose consistent impacts
on the occurrence of the two hazards. This study not only provides
important inventory data for studying the landslide and debris flow hazards
but also adds valuable information for modeling and predicting the two
hazards to enhance resilience to these hazards in this region.

Abstract. Conventional outputs of physics-based landslide
forecasting models are presented as deterministic warnings by calculating
the safety factor (Fs) of potentially dangerous slopes. However, these models
are highly dependent on variables such as cohesion force and internal
friction angle which are affected by a high degree of uncertainty especially
at a regional scale, resulting in unacceptable uncertainties of Fs. Under
such circumstances, the outputs of physical models are more suitable if
presented in the form of landslide probability values. In order to develop
such models, a method to link the uncertainty of soil parameter values with
landslide probability is devised. This paper proposes the use of Monte Carlo
methods to quantitatively express uncertainty by assigning random values to
physical variables inside a defined interval. The inequality Fs<1 is
tested for each pixel in n simulations which are integrated in a unique
parameter. This parameter links the landslide probability to the
uncertainties of soil mechanical parameters and is used to create a
physics-based probabilistic forecasting model for rainfall-induced shallow
landslides. The prediction ability of this model was tested in a case study,
in which simulated forecasting of landslide disasters associated with heavy
rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan
province, China, was performed. The proposed model successfully forecasted
landslides in 159 of the 176 disaster points registered by the
geo-environmental monitoring station of Sichuan province. Such testing
results indicate that the new model can be operated in a highly efficient way
and show more reliable results, attributable to its high prediction accuracy.
Accordingly, the new model can be potentially packaged into a forecasting
system for shallow landslides providing technological support for the
mitigation of these disasters at regional scale.