Viscoplastic modelling of rainfall-driven slow-moving landslides: application to California Coast Ranges

Abstract

Slow-moving landslides are widely observed in mountainous areas worldwide. While most of these landslides move slowly downslope over long periods of time, some ultimately accelerate rapidly and fail catastrophically. Simulating the landslide creep movement triggered by environmental factors such as precipitation, is therefore necessary to anticipate potential damaging effects on proximal infrastructure, habitat, and life. Here, we present a physically-based model that links pore-water pressure changes in the landslide mass with a new viscoplastic constitutive law designed to capture different temporal trends in slow-moving landslides. The model accounts for landslide velocity changes caused by rainfall infiltration through the Terzaghi's effective stress principle, thus directly resolving the deformation of the active shear zone. Calibration and validation of the computations benefited from both ground-based and remote sensing data for three active landslides in the California Coast Ranges, USA. We find that our model can accurately describe both slow quasi-continuous and episodic movement commonly displayed by active landslides. Although inherent limitations of the viscoplasticity framework did not enable us to describe catastrophic landslide acceleration, our model provides versatile tools that can be used to analyze and describe distinct types of slow-moving landslide dynamics.

Multiscale modeling of granular dynamics on flowslide triggering and runout

“…While the original SCM involved a one‐way coupling between pore pressure and frictional strength, later developments of Iverson 28 allowed full coupling between sliding and consolidation. In this context, the work by Chen and Buscarnera further expands the scope of the application of this class of models by allowing a versatile selection of the constitutive behavior for the active zone of landslide deformation, inelastic effects in the rate of excess pore pressure dissipation, and a streamlined analysis of the entire cycle of pre‐failure, triggering, and post‐failure landslide dynamics 29–31 . Hereafter, the key features of the SCM model proposed by Chen and Buscarnera are briefly described.…”

Rainfall Infiltration through Stratified Colluvial Deposits: Analytical Approach vs. Numerical Modelling

“…In both circumstances, the soil permeability (k), the rainfall intensity (I), and the duration (D), along with the pre-existing degree of saturation (S) of the soil, are decisive in determining the saturation process rate and the amount of water stored within the colluvial deposit [27,28]. Thanks to the widespread diffusion of numerical computation techniques and the extensive availability of numerical codes, the water infiltration process within a colluvial deposit can be effectively modelled, adopting the non-linear differential equation of Richards [29][30][31][32][33][34][35][36][37]. Otherwise, analytical (approximate) infiltration models can be adopted, which are hydrological models that try to reproduce the water seepage into the soil analytically, assuming simplified hypotheses compared to the real phenomenon [38].…”

Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network

“…Landslide is a geological phenomenon characterized by the downward movement of soil or rock mass on a slope. This movement occurs due to various factors, including rainfall, river erosion, groundwater activity, earthquakes, and human-induced slope cutting [20,[30][31][32]. At present, researchers comprehensively detect landslides using the associated characteristics describing landslides, such as the spectral features, other morphological and appearance characteristics from remote sensing images, and lithological, hydrological, and geological factors.…”