An open-source and QGIS-integrated physically based model for Spatial Prediction of Rainfall-Induced Shallow Landslides (SPRIn-SL). (February 2023)

• Record Type: Journal Article
• Authors: Raimondi, Luca; Pepe, Giacomo; Firpo, Marco; Calcaterra, Domenico; Cevasco, Andrea
• Is Part Of: Environmental modelling & software. Volume 160(2023)
• Publication Date: 2023-02
• Subjects: Cinque Terre -- DEM resolution -- Open source -- Physically based model -- QGIS -- Shallow landslides; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105587
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: 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.