  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 the 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. [..]
