Modelling seasonal landslide motion: Does it only depend on fluctuations in normal effective stress?

Landslide motion is often simulated with interface-like laws able to capture changes in frictional strength caused by the growth of the pore water pressure and the consequent reduction of the effective stress normal to the plane of sliding. Here it is argued that, although often neglected, the evolution of all the 3D stress components within the basal shear zone of landslides also contributes to changes in frictional strength and must be accounted for to predict changes in seasonal velocity. For this purpose, an augmented sliding-consolidation model is proposed which allows for the computation of excess pore pressure development and downslope sliding with any constitutive law with 3D stress evolution. Simulations of idealised infinite slope models subjected to hydrologic forcing are used to study the role of in-situ stress conditions and stress rate multiaxiality. Specifically, a Drucker-Prager perfectly plastic model is used to replicate frictional failure and shear deformation at the base of landslides. The model reveals that conditions amenable to the shearing of a frictional interface are met only after numerous rainfall cycles, that is, when multiaxial stress rates are suppressed. In this case, the landslide is predicted to move through a seasonal ratcheting controlled only by the effective stress component normal to the plane of sliding. By contrast, in newly formed landslides, the multiaxial stress evolution is found to produce further regimes of motion, from plastic shakedown to cyclic failure, neither of which can be captured by interface-like frictional laws. Notably, the model suggests that a transition across these regimes can emerge in response to an aggravation of the magnitude of forcing, implying that (i) fluctuations in climate may alter the seasonal trends of motion observed today; (ii) our ability to quantify landslide-induced risks is impaired unless proper geomechanical models are used to examine their long-term dynamics.