Rainfall-Induced Landslides forecast using local precipitation and global climate indexes

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datacite.alternateIdentifier.citationNatural Hazards, 102 (1), 131-115, 2020
datacite.alternateIdentifier.doi10.1007/s11069-020-03913-0
datacite.alternateIdentifier.issn1573-0840
datacite.creatorFustos, Ivo
datacite.creatorAbarca-del-Río, Rodrigo
datacite.creatorMoreno-Yaeger, Pablo
datacite.creatorSomos-Valenzuela, Marcelo A.
datacite.date2020
datacite.rightsRegistro bibliográfico
datacite.subjectEnso-aao Variability
datacite.subjectLogistic Regression
datacite.subjectRainfall-induced Landslides
datacite.subjectAir-sea Interaction
datacite.subjectClimate Effect
datacite.subjectEl Nino-southern Oscillation
datacite.subjectForecasting Method
datacite.subjectGlobal Climate
datacite.subjectLandslide
datacite.subjectPrecipitation (climatology)
datacite.subjectRainfall
datacite.subjectRegional Climate
datacite.subjectRegression Analysis
datacite.subjectChile
datacite.subjectPacific Ocean
datacite.subjectPacific Ocean (south)
datacite.titleRainfall-Induced Landslides forecast using local precipitation and global climate indexes
dc.description.abstractWe analyse RIL events between 1950 and 2002 to investigate the role played by climate variability, using the El Niño-Southern Oscillation (ENSO), the Antarctic Oscillation (AAO) and local precipitation as predictors, through logistic and probabilistic (Logit and Probit) modelling. From the probabilistic regression analysis, it is clear that rain plays a major role, since its weight in the regression is almost 50%. However, we show that integrating South Pacific climate variability represented by ENSO/AAO significantly increases predictability, reaching over 87%. Moreover, sensitivity and specificity analyses confirm that although local rainfall is the main triggering factor, adding the two macroclimate variables increases the ability to predict true positive and negative occurrences by almost 80%. This confirms the need to integrate macroclimatic variables to make assertive local predictions. Surprisingly, and contrary to what might have been expected considering ENSO's recognized role in regional climate variability, the integration of AAO variability significantly improves RIL prediction capacity, while on average ENSO can be considered a second-order predictor. These results, obtained through a simple logistic regression methodology (Logit and/or Probit), can contribute to better risk management in the middle-latitude zones of Chile. The methodology can be extended to other areas of the world that do not have high-density hydrometeorological information to support preventive decision-making through logistic RIL forecasting. © 2020 Elsevier B.V., All rights reserved.
dc.description.ia_keywordclimate, variability, enso, local, role, through, logistic
dc.identifier.issn0921-030X
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/3897
dc.language.isoen
dc.publisherSpringer Nature
dc.relationinstname: ANID
dc.relationreponame: Repositorio Digital RI2.0
dc.rights.driverinfo:eu-repo/semantics/openAccess
dc.sourceNatural Hazards
dc.subject.ia_odsODS 13: Acción por el clima
dc.subject.ia_oecd1nCiencias Naturales
dc.subject.ia_oecd2nMatemáticas y Estadística
dc.subject.ia_oecd3nEstadística
dc.type.driverinfo:eu-repo/semantics/article
dc.type.driverhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.openaireinfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
oaire.citationEdition2020
oaire.citationEndPage131
oaire.citationIssue1
oaire.citationStartPage115
oaire.citationTitleNatural Hazards
oaire.citationVolume102
oaire.fundingReferenceANID FONDECYT 11180500 (Regular)
oaire.fundingReferenceDIUFRO DI18-0060
oaire.licenseConditionCopyright © Springer Nature, 2020
oaire.resourceTypeArtículo
oaire.resourceType.enArticle
uct.catalogadorjvu
uct.comunidadIngenieríaen_US
uct.departamentoDepartamento de Obras Civiles y Geología
uct.facultadFacultad de Ingeniería
uct.indizacionScience Citation Index Expanded - SCIE
uct.indizacionScopus
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