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| Titel |
Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach |
| VerfasserIn |
S. Raia, M. Alvioli, M. Rossi, R. L. Baum, J. W. Godt, F. Guzzetti |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 2 ; Nr. 7, no. 2 (2014-03-25), S.495-514 |
| Datensatznummer |
250115578
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| Publikation (Nr.) |
 copernicus.org/gmd-7-495-2014.pdf |
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| Zusammenfassung |
| Distributed models to forecast the spatial and temporal occurrence of
rainfall-induced shallow landslides are based on deterministic laws.
These models extend spatially the static stability models adopted in geotechnical
engineering, and adopt an infinite-slope geometry to balance the
resisting and the driving forces acting on the sliding mass. An
infiltration model is used to determine how rainfall changes
pore-water conditions, modulating the local stability/instability
conditions. A problem with the operation of the existing
models lays in the difficulty in
obtaining accurate values for the several variables that describe the
material properties of the slopes. The problem is particularly severe
when the models are applied over large areas, for which sufficient
information on the geotechnical and hydrological conditions of the
slopes is not generally available. To help solve the problem, we
propose a probabilistic Monte Carlo approach to the distributed
modeling of rainfall-induced shallow landslides. For this purpose, we
have modified the transient rainfall infiltration and grid-based
regional slope-stability analysis (TRIGRS) code. The new code
(TRIGRS-P) adopts a probabilistic approach to compute, on a cell-by-cell
basis, transient pore-pressure changes and related changes in the
factor of safety due to rainfall infiltration. Infiltration is modeled
using analytical solutions of partial differential equations
describing one-dimensional vertical flow in isotropic, homogeneous
materials. Both saturated and unsaturated soil conditions can be
considered. TRIGRS-P copes with the natural variability inherent to
the mechanical and hydrological properties of the slope materials by
allowing values of the TRIGRS model input parameters to be sampled
randomly from a given probability distribution. The range of variation
and the mean value of the parameters can be determined by the usual
methods used for preparing the TRIGRS input parameters. The outputs
of several model runs obtained varying the input parameters are
analyzed statistically, and compared to the original (deterministic)
model output. The comparison suggests an improvement of the predictive
power of the model of about 10% and 16% in two
small test areas, that is, the Frontignano (Italy) and the Mukilteo (USA)
areas. We discuss the computational requirements of
TRIGRS-P to determine the potential use of the numerical model to
forecast the spatial and temporal occurrence of rainfall-induced
shallow landslides in very large areas, extending for several hundreds
or thousands of square kilometers. Parallel execution of the code
using a simple process distribution and the message passing interface
(MPI) on multi-processor machines was successful, opening the possibly
of testing the use of TRIGRS-P for the operational forecasting of
rainfall-induced shallow landslides over large regions. |
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