Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines

There is a clear need to improve and update landslide susceptibility models across the Philippines. This is challenging, as landslides in this region are frequently triggered by temporally and spatially disparate typhoon events, and it remains unclear whether such spatially and/or temporally distinct typhoon events cause similar landslide responses, i.e. whether the landslide susceptibility for one typhoon event is similar for another. Here, we use logistic regression to develop four landslide susceptibility models based on three typhoon-triggered landslide inventories for the 2009 Typhoon Parma (local name Typhoon Pepeng), the 2018 Typhoon Mangkhut (local name Typhoon Ompong), and the 2019 Typhoon Kammuri (local name Typhoon Tisoy)

FSLAM: A QGIS plugin for fast regional susceptibility assessment of rainfall-induced landslides

Shallow slope failures triggered by rainfall commonly pose considerable risks in mountainous areas. In order to delineate areas where landslides are more prone to occur within a region, we have designed and developed a Python QGIS plugin named Fast Shallow Landslide Assessment Model (FSLAM). The plugin integrates a simplified hydrological model and a geotechnical model based on the infinite slope theory and contains two principal modules: runoff and slope stability modelling. It can output up to 15 raster maps describing the hydrological and stability conditions in a short computational time. Firstly, we explain the design of graphical user interface and the elements of the plugin. Then, the Berguedà area in NE Spain is used as case study to present the procedure of the plugin application. The results show that the accuracy of landslide susceptibility assessment performed by FSLAM-plugin is high and the computing time is only a few minutes