Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment. (February 2020)

• Record Type: Journal Article
• Authors: Wang, Sheng; Zhang, Ke; van Beek, Ludovicus P.H.; Tian, Xin; Bogaard, Thom A.
• Is Part Of: Environmental modelling & software. Volume 124(2020)
• Publication Date: 2020-02
• Subjects: Landslide prediction -- Hydrological model -- Infinite slope model -- Scaling; 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.2019.104607
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Rainfall-triggered shallow landslides are widespread natural hazards around the world, causing many damages to human lives and property. In this study, we focused on predicting landslides in a large region by coupling a 1 km-resolution hydrological model and a 90 m-resolution slope stability model, where a downscaling method for soil moisture via topographic wetness index was applied. The modeled hydrological processes show generally good agreements with the observed discharges: relative biases and correlation coefficients at three validation stations are all <20% and >0.60, respectively. The derived scaling law for soil moisture allows for near-conservative downscaling of the original 1-km soil moisture to 90-m resolution for slope stability assessment. For landslide prediction, the global accuracy and true positive rate are 97.2% and 66.9%, respectively. This study provides an effective and computationally efficient coupling method to predict landslides over large regions in which fine-scale topographical information is incorporated. Highlights: Downscaling soil moisture using topographical attributes. Coupling hydrological model and slope stability model with different spatial resolutions. Developed a useful and efficient method to conduct landslide hazard prediction. The coupled model is more robust than the conventional rainfall threshold method.