Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions

The inherent uncertainty in hydro-geotechnical parameters presents a significant challenge for accurately predicting rainfall-triggered shallow landslides in mountainous regions. In this study, a novel probabilistic framework was developed and implemented in the “Py.GIS-FSLAM-FORM” software, designe...

Descripción completa

Detalles Bibliográficos
Autores: Cui, Hongzhi, Medina Iglesias, Vicente César de|||0000-0001-5578-3848, Hurlimann Ziegler, Marcel|||0000-0003-0119-1438, Ji, Jian
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/408620
Acceso en línea:https://hdl.handle.net/2117/408620
https://dx.doi.org/10.1016/j.compgeo.2024.106400
Access Level:acceso abierto
Palabra clave:Landslides
Landslide susceptibility assessment
Hydrological conditions
Physically-based probabilistic modelling
First-order reliability method
Cross-negative-correlation
Esllavissades
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de sòls
id ES_3fe12eb6c12155fcdb4dc0f2bf5ac9f6
oai_identifier_str oai:upcommons.upc.edu:2117/408620
network_acronym_str ES
network_name_str España
repository_id_str
spelling Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditionsCui, HongzhiMedina Iglesias, Vicente César de|||0000-0001-5578-3848Hurlimann Ziegler, Marcel|||0000-0003-0119-1438Ji, JianLandslidesLandslide susceptibility assessmentHydrological conditionsPhysically-based probabilistic modellingFirst-order reliability methodCross-negative-correlationEsllavissadesÀrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de sòlsThe inherent uncertainty in hydro-geotechnical parameters presents a significant challenge for accurately predicting rainfall-triggered shallow landslides in mountainous regions. In this study, a novel probabilistic framework was developed and implemented in the “Py.GIS-FSLAM-FORM” software, designed to address the complexities associated with parameter uncertainty, correlation, and distribution. By combining the Fast Shallow Landslide Assessment Model (FSLAM) with the First-Order Reliability Method (FORM), we have enhanced the traditional probabilistic approach to create more accurate landslide susceptibility maps. This study emphasizes the uncertainly of geotechnical parameters and the critical influence of hydrological conditions on landslide susceptibility, especially focusing on the interaction between antecedent recharge (qa) and event rainfall (Pe). In our study area (Val d’Aran, Spain), the probabilistically based results revealed that areas of very high susceptibility are significantly affected by event rainfall, particularly on slopes of 30–40 degrees and aspects between 100 and 250 degrees. The variability in geotechnical parameters, especially the coefficient of variation (COV) in cohesion and friction angle, plays a crucial role in landslide susceptibility assessment, with increased COVs leading to greater landslide uncertainty. Additionally, cross-negative correlations and non-normal distributions of geotechnical parameters substantially influence the spatial distribution of landslides, notably when combining antecedent recharge with event rainfall. These results highlight the importance of incorporating parameter variability and hydrological conditions in susceptibility models to improve the precision of regional landslide forecasts. While the study was performed in Val d'Aran, its methodologies and conclusions are relevant to mountainous areas worldwide, offering insights for refining landslide prediction models and susceptibility assessments, contributing to global efforts in landslide disaster prevention.Peer ReviewedElsevier20242024-08-0120242024-05-24journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/408620https://dx.doi.org/10.1016/j.compgeo.2024.106400reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4086202026-05-27T15:37:01Z
dc.title.none.fl_str_mv Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions
title Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions
spellingShingle Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions
Cui, Hongzhi
Landslides
Landslide susceptibility assessment
Hydrological conditions
Physically-based probabilistic modelling
First-order reliability method
Cross-negative-correlation
Esllavissades
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de sòls
title_short Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions
title_full Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions
title_fullStr Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions
title_full_unstemmed Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions
title_sort Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions
dc.creator.none.fl_str_mv Cui, Hongzhi
Medina Iglesias, Vicente César de|||0000-0001-5578-3848
Hurlimann Ziegler, Marcel|||0000-0003-0119-1438
Ji, Jian
author Cui, Hongzhi
author_facet Cui, Hongzhi
Medina Iglesias, Vicente César de|||0000-0001-5578-3848
Hurlimann Ziegler, Marcel|||0000-0003-0119-1438
Ji, Jian
author_role author
author2 Medina Iglesias, Vicente César de|||0000-0001-5578-3848
Hurlimann Ziegler, Marcel|||0000-0003-0119-1438
Ji, Jian
author2_role author
author
author
dc.subject.none.fl_str_mv Landslides
Landslide susceptibility assessment
Hydrological conditions
Physically-based probabilistic modelling
First-order reliability method
Cross-negative-correlation
Esllavissades
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de sòls
topic Landslides
Landslide susceptibility assessment
Hydrological conditions
Physically-based probabilistic modelling
First-order reliability method
Cross-negative-correlation
Esllavissades
Àrees temàtiques de la UPC::Enginyeria civil::Geotècnia::Mecànica de sòls
description The inherent uncertainty in hydro-geotechnical parameters presents a significant challenge for accurately predicting rainfall-triggered shallow landslides in mountainous regions. In this study, a novel probabilistic framework was developed and implemented in the “Py.GIS-FSLAM-FORM” software, designed to address the complexities associated with parameter uncertainty, correlation, and distribution. By combining the Fast Shallow Landslide Assessment Model (FSLAM) with the First-Order Reliability Method (FORM), we have enhanced the traditional probabilistic approach to create more accurate landslide susceptibility maps. This study emphasizes the uncertainly of geotechnical parameters and the critical influence of hydrological conditions on landslide susceptibility, especially focusing on the interaction between antecedent recharge (qa) and event rainfall (Pe). In our study area (Val d’Aran, Spain), the probabilistically based results revealed that areas of very high susceptibility are significantly affected by event rainfall, particularly on slopes of 30–40 degrees and aspects between 100 and 250 degrees. The variability in geotechnical parameters, especially the coefficient of variation (COV) in cohesion and friction angle, plays a crucial role in landslide susceptibility assessment, with increased COVs leading to greater landslide uncertainty. Additionally, cross-negative correlations and non-normal distributions of geotechnical parameters substantially influence the spatial distribution of landslides, notably when combining antecedent recharge with event rainfall. These results highlight the importance of incorporating parameter variability and hydrological conditions in susceptibility models to improve the precision of regional landslide forecasts. While the study was performed in Val d'Aran, its methodologies and conclusions are relevant to mountainous areas worldwide, offering insights for refining landslide prediction models and susceptibility assessments, contributing to global efforts in landslide disaster prevention.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-08-01
2024
2024-05-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/408620
https://dx.doi.org/10.1016/j.compgeo.2024.106400
url https://hdl.handle.net/2117/408620
https://dx.doi.org/10.1016/j.compgeo.2024.106400
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406690670018560
score 15.301603

Similares en LA Referencia