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Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale

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10.1016/j.catena.2021.105213
 
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hdl:2117/362041

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Medina Iglesias, Vicente César deMés informacióMés informacióMés informació
Hurlimann Ziegler, MarcelMés informacióMés informacióMés informació
Guo, Zizheng
Lloret Morancho, AntonioMés informacióMés informacióMés informació
Vaunat, JeanMés informacióMés informacióMés informació
Document typeArticle
Defense date2021-06-01
Rights accessRestricted access - publisher's policy (embargoed until 2023-06-20)
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Rainfall-induced landslides represent an important threat in mountainous areas. Therefore, a physically-based model called “Fast Shallow Landslide Assessment Model” (FSLAM) was developed to calculate large areas (>100 km2 ) with a high-resolution topography in a very short computational time. FSLAM applies a simplified hydrological model and the infinite slope theory, while the two most sensitive soil properties regarding slope stability (cohesion and friction angle) can be stochastically included. The model has five necessary input raster files including information of soil properties, vegetation, elevation and rainfall. The principal output is the probability of failure (PoF) map. The Principality of Andorra was selected as case study, where FSLAM was successfully applied and validated using the existing landslide inventory. The PoF raster file of Andorra (including 19 million cells) was calculated in only 2 min. Therefore, an accurate calibration of the input parameters was easy, which strongly improved the final outcomes.
CitationDe Medina, V. [et al.]. Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale. "Catena", 1 Juny 2021, vol. 201, núm. 105213, p. 105213: 1-105213: 16. 
URIhttp://hdl.handle.net/2117/362041
DOI10.1016/j.catena.2021.105213
ISSN0341-8162
Publisher versionhttps://www.sciencedirect.com/science/article/abs/pii/S0341816221000722
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