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With the increasing frequency of high intensity rainfall events, landslides on natural slopes have become a critical concern from a disaster management perspective. Rainfall-induced landslides are caused by the reduction in the soil shear strength due to the increased pore water pressure induced by rainfall and/or rapid snowmelt. It is important to understand the mechanism of failure for employing reliable early warning and effective risk reduction strategies. Geotechnical slope stability analysis can be carried out easily on a slope scale, however, extending this at a regional scale is demanding due to the spatial variability of hydrological and geotechnical properties. Physics-based landslide susceptibility models are designed with the explicit goal of using hydrological mechanisms for the identification of possible landslide source areas, primarily computing factor of safety (FS) values on a grid. However, given that the majority of these models operate independently, integrating them into a fully automated Landslide Early Warning Systems (LEWS) remains a significant technical challenge. This work proposes a methodology that leverages meteorological forecasts sourced from the MET Weather Application Programming Interface (API), in conjunction with topographical and soil properties, to project Factor of Safety (FS) values on an hourly basis. A case study from Norway has been used as a pilot for the demonstration of the method proposed. The forecasted FS values are dynamically visualized in real-time within the data platform of the Norwegian Geotechnical Institute, NGI Live, which can also be used as a map overlay for other infrastructure projects in the study area. The proposed method holds the promise of providing physics-based decision support for disaster risk reduction and critical infrastructure management efforts.
Abraham et al. (Fri,) studied this question.