Physically-based distributed models are implemented for landslide susceptibility and hazard assessment around the world. Probabilistic methodologies are considered appropriate to study and quantify the uncertainties derived from the input parameters of these models. In this paper, three sets of Monte Carlo simulations, each one with 10,000 iterations, were applied for a slope stability analysis in a small basin of Envigado (Colombia), using the TRIGRS model, to characterise the uncertainty in the landslide assessment. Different parameters to determine the minimum number of realizations required to ensure a small variation in the failure probability were proposed and analyzed. The quality of the landslide susceptibility assessment was studied. Unexpected and probably erroneous results that may be common in the maps generated using this and other similar methodologies were identified and explained. Additionally, the distribution of the factor of safety was calculated for different grid cells of the basin, showing that the probability density function with the best adjustment to the frequency histogram of the factor of safety can vary between grid cells. The assumption of a normal distribution for the factor of safety would be inappropriate and would lead to miscalculations in this case study.

Landslides triggered by rainfall are one of the most frequent causes of disasters in tropical countries and mountainous terrains. Recent studies show an upsurge in landslide occurrence as an expected impact of human-induced climate change. This paper presents the analysis and implementation of two different physically-based models, SHALSTAB and TRIGRS, to evaluate the effect of rainfall on landslide hazard assessment in the north-western Colombian Andes. Intensity-Duration-Frequency curves were used in climate change scenarios for different return periods. According to the results, although higher rainfall intensities increase, landslide occurrence does not escalate in a direct or proportional relationship. Considering a steady infiltration process (SHALSTAB), the results show an expansion of d unstable areas, compared with a transient infiltration process (TRIGRS). A greater influence of rainfall duration instead of rainfall intensity was observed. The results highlight the need for studies that incorporate the scenarios of variability and climate change in the hazard assessment and land planning in the long term.

An analysis of the rainfall conditions and the influence of the infiltration processes on the slope stability are important in tropical mountain terrains due to the wide occurrence of rainfall-induced landslides. Distributed physical-based models can be coupled with techniques that relate the factor of safety with rainfall thresholds associated with the occurrence of this phenomenon. This study presents a methodology that determines the thresholds of rainfall intensity and duration, for the prediction of shallow landslides, using a distributed physical model called TRIGRS. In the analysis, it is used a percentage of the critical area of failure defined for a study area. The conditions of mean intensity and duration of rain that induce this failing area to be reached are adjusted to a potential equation, within certain ranges of duration and intensity. The model is used in a sub-basin of the Valle de Aburrá (Colombia) and the results are compared with rainfall thresholds defined with another distributed physical model. The obtained results reaffirm that the conditions of rainfall intensity and duration that trigger landslides in a region exhibit scaling properties determined by relationships of potential equations, like those obtained by other researchers in different places of the world.