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dc.contributor.authorBazo Zambrano, Juan Carlos
dc.date.accessioned2021-11-10T14:52:51Z
dc.date.available2021-11-10T14:52:51Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/20.500.12867/4568
dc.description.abstractDisaster planning has historically allocated minimal effort and finances toward advanced preparedness; however, evidence supports reduced vulnerability to flood events, saving lives and money, through appropriate early actions. Among other requirements, effective early action systems necessitate the availability of high-quality forecasts to inform decision making. In this study, we evaluate the ability of statistical and physically based season-ahead prediction models to appropriately trigger flood early preparedness actions based on a 75 % or greater probability of surpassing the 80th percentile of historical seasonal streamflow for the flood-prone Marañón River and Piura River in Peru. The statistical prediction model, developed in this work, leverages the asymmetric relationship between seasonal streamflow and the ENSO phenomenon. Additionally, a multi-model (least-squares combination) is also evaluated against current operational practices. The statistical prediction demonstrates superior performance compared to the physically based model for the Marañón River by correctly triggering preparedness actions in three out of four historical occasions, while both the statistical and multi-model predictions capture all four historical events when the required threshold exceedance probability is reduced to 50 %, with only one false alarm. For the Piura River, the statistical model proves superior to all other approaches, correctly triggering 28 % more often in the hindcast period. Continued efforts should focus on applying this season-ahead prediction framework to additional flood-prone locations where early actions may be warranted and current forecast capacity is limited.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherCopernicus Publicationses_PE
dc.relation.ispartofseriesNatural Hazards and Earth System Sciences;vol. 21, n° 7 (2021)
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/es_PE
dc.sourceRepositorio Institucional - UTPes_PE
dc.sourceUniversidad Tecnológica del Perúes_PE
dc.subjectPrevention modelses_PE
dc.subjectFloodses_PE
dc.subjectPrevention planses_PE
dc.subjectPlanes de prevenciónes_PE
dc.subjectInundacioneses_PE
dc.subjectPerúes_PE
dc.titleLeveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Perues_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journalNatural Hazards and Earth System Scienceses_PE
dc.identifier.doihttps://doi.org/10.5194/nhess-21-2215-2021
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.05.00es_PE
dc.description.sedeCampus Lima Centroes_PE
dc.publisher.countryDEes_PE
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE


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