Abstract
This paper presents a new open-source and physically based model for Spatial Prediction of Rainfall-Induced Shallow Landslides (SPRIn-SL) through the Quantum GIS (QGIS) software. SPRIn-SL consists of a set of shell scripts developed using the Python language that can be directly run from the QGIS processing toolbox through a user-friendly graphical interface. The tool implements the infinite slope method by incorporating the TOPOG and the Green-Ampt models to consider groundwater flow and transient rainfall infiltration, respectively. Furthermore, DEM pre-processing procedures to extract reliable terrain morphometric features, a new statistical method for modelling soil depth and a procedure for predictive accuracy evaluation, were implemented. By using a 1-m resolution DEM, the developed model was tested in a small coastal catchment of Cinque Terre (Liguria, Italy), providing accurate outcomes, and proving to be an easy-to-use tool for landslide susceptibility zoning which can have useful implications on the risk reduction.
•QGIS module written in Python for spatially predicting shallow landslides.•DEM pre-processing procedures to extract reliable morphometric predisposing factors.•A new statistical method for modelling soil depth is implemented.•High accuracy in simulating rainfall-induced shallow landslides in a test basin.•Easy-to-use physically based tool for landslide susceptibility zoning.