Abstract
Physically-based models have been used to assess landslide susceptibility, hazard, and risk in many regions worldwide. They have also been regarded as valuable tools for landslide prediction and the development or improvement of landslide early warning systems. They are usually validated to demonstrate their predictive capacity, but they are not deeply studied regularly to understand the sensitivity of the input variables and the behavior of the models under many different rainfall scenarios. In this research paper, we studied two distributed physically-based models for shallow landslides: SLIP and Iverson. For this, the first-order second-moment (FOSM) method was used to calculate the contribution of random input variables (soil strength, unit weight, and permeability parameters) to the variance of the factor of safety. Different intensity and duration rainfall events were simulated to assess the response of the models to those rainfall conditions in terms of the factor of safety and failure probability. The results showed that the shear strength (cohesion and friction angle, in order of significance) parameters have the largest contribution to the variance in both models, but they vary depending on geological, geotechnical, and topographic conditions. The Iverson and SLIP models respond in different ways to the variation of rainfall conditions: for shorter durations (e.g. ≤ 8 h), increasing the intensity caused more unstable areas in the SLIP model, while for longer durations the unstable areas were considerably higher for the Iverson model. Understanding those behaviors can be useful for practical and appropriate implementation of the models in landslide assessment projects.
Highlights
Physically-based models have been used in the last few decades to assess landslide susceptibility and hazard in many regions worldwide (Baum et al, 2005; Lin et al, 2021; Michel et al, 2014; Montrasio et al, 2011)
The results indicate that the parameter that has the greatest influence on the Iverson stability model is the cohesion (c’)
The Iverson and SLIP models respond in different ways to the variation of rainfall conditions: for shorter durations (e.g. ≤ 8 h), increasing the intensity caused more unstable areas in the SLIP model, while for longer durations the unstable areas were considerably higher for the Iverson model
Summary
Physically-based models have been used in the last few decades to assess landslide susceptibility and hazard in many regions worldwide (Baum et al, 2005; Lin et al, 2021; Michel et al, 2014; Montrasio et al, 2011). Even though the spatial and temporal prediction of landslides is considered a very difficult (almost impossible in many cases) task due to the high variability and that many factors (with great uncertainty) intervene in their occurrence, landslide early warning systems in different regions worldwide have adopted different methods trying to forecast landslide occurrence (Guzzetti et al, 2020). To make possible the implementation of physicallybased models on an operational landslide early warning system (LEWS) validation of the predictive capacity of the model is preeminent. It is usually done by comparing the spatial and temporal occurrence of landslides in a certain terrain area with the slope stability results obtained from simulations of the landslide triggering events (e.g., antecedent or intensity-duration rainfall event) incorporated as an input variable of the model. It is very important to understand the sensitivity of the input parameters in the model because assumptions and simplifications are commonly required (e.g., homogeneous soil layers and constant mechanical parameters for a complete geological unit)
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