Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)

Detalhes bibliográficos
Autor(a) principal: Tomás, Lívia Rodrigues
Data de Publicação: 2022
Outros Autores: Soares, Giovanni Guarnieri, Jorge, Aurelienne A. S., Mendes, Jeferson Feitosa [UNESP], Freitas, Vander L. S., Santos, Leonardo B. L.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1111/tgis.12962
http://hdl.handle.net/11449/240268
Resumo: Cities increasingly face flood risk primarily due to extensive changes of the natural land cover to built-up areas with impervious surfaces. In urban areas, flood impacts come mainly from road interruption. This article proposes an urban flood risk map from hydrological and mobility data, considering the megacity of São Paulo, Brazil, as a case study. We estimate the flood susceptibility through the Height Above the Nearest Drainage algorithm; and the potential impact through the exposure and vulnerability components. We aggregate all variables into a regular grid and then classify the cells of each component into three classes: Moderate, High, and Very High. All components, except the flood susceptibility, have few cells in the Very High class. The flood susceptibility component reflects the presence of watercourses, and it has a strong influence on the location of those cells classified as Very High.
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spelling Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)Cities increasingly face flood risk primarily due to extensive changes of the natural land cover to built-up areas with impervious surfaces. In urban areas, flood impacts come mainly from road interruption. This article proposes an urban flood risk map from hydrological and mobility data, considering the megacity of São Paulo, Brazil, as a case study. We estimate the flood susceptibility through the Height Above the Nearest Drainage algorithm; and the potential impact through the exposure and vulnerability components. We aggregate all variables into a regular grid and then classify the cells of each component into three classes: Moderate, High, and Very High. All components, except the flood susceptibility, have few cells in the Very High class. The flood susceptibility component reflects the presence of watercourses, and it has a strong influence on the location of those cells classified as Very High.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Deutsche ForschungsgemeinschaftFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Research Department National Center for Monitoring and Early Warning of Natural Disasters (Cemaden), São PauloPostgraduate Program in Applied Computing - CAP National Institute for Space Research (INPE), São PauloDepartment of Environmental Engineering São Paulo State UniversityDepartment of Computing Federal University of Ouro Preto, Minas GeraisDepartment of Environmental Engineering São Paulo State UniversityNational Center for Monitoring and Early Warning of Natural Disasters (Cemaden)National Institute for Space Research (INPE)Universidade Estadual Paulista (UNESP)Federal University of Ouro PretoTomás, Lívia RodriguesSoares, Giovanni GuarnieriJorge, Aurelienne A. S.Mendes, Jeferson Feitosa [UNESP]Freitas, Vander L. S.Santos, Leonardo B. L.2023-03-01T20:09:22Z2023-03-01T20:09:22Z2022-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article2341-2365http://dx.doi.org/10.1111/tgis.12962Transactions in GIS, v. 26, n. 5, p. 2341-2365, 2022.1467-96711361-1682http://hdl.handle.net/11449/24026810.1111/tgis.129622-s2.0-85132177753Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengTransactions in GISinfo:eu-repo/semantics/openAccess2023-03-01T20:09:22Zoai:repositorio.unesp.br:11449/240268Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-03-01T20:09:22Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)
title Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)
spellingShingle Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)
Tomás, Lívia Rodrigues
title_short Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)
title_full Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)
title_fullStr Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)
title_full_unstemmed Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)
title_sort Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)
author Tomás, Lívia Rodrigues
author_facet Tomás, Lívia Rodrigues
Soares, Giovanni Guarnieri
Jorge, Aurelienne A. S.
Mendes, Jeferson Feitosa [UNESP]
Freitas, Vander L. S.
Santos, Leonardo B. L.
author_role author
author2 Soares, Giovanni Guarnieri
Jorge, Aurelienne A. S.
Mendes, Jeferson Feitosa [UNESP]
Freitas, Vander L. S.
Santos, Leonardo B. L.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv National Center for Monitoring and Early Warning of Natural Disasters (Cemaden)
National Institute for Space Research (INPE)
Universidade Estadual Paulista (UNESP)
Federal University of Ouro Preto
dc.contributor.author.fl_str_mv Tomás, Lívia Rodrigues
Soares, Giovanni Guarnieri
Jorge, Aurelienne A. S.
Mendes, Jeferson Feitosa [UNESP]
Freitas, Vander L. S.
Santos, Leonardo B. L.
description Cities increasingly face flood risk primarily due to extensive changes of the natural land cover to built-up areas with impervious surfaces. In urban areas, flood impacts come mainly from road interruption. This article proposes an urban flood risk map from hydrological and mobility data, considering the megacity of São Paulo, Brazil, as a case study. We estimate the flood susceptibility through the Height Above the Nearest Drainage algorithm; and the potential impact through the exposure and vulnerability components. We aggregate all variables into a regular grid and then classify the cells of each component into three classes: Moderate, High, and Very High. All components, except the flood susceptibility, have few cells in the Very High class. The flood susceptibility component reflects the presence of watercourses, and it has a strong influence on the location of those cells classified as Very High.
publishDate 2022
dc.date.none.fl_str_mv 2022-08-01
2023-03-01T20:09:22Z
2023-03-01T20:09:22Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1111/tgis.12962
Transactions in GIS, v. 26, n. 5, p. 2341-2365, 2022.
1467-9671
1361-1682
http://hdl.handle.net/11449/240268
10.1111/tgis.12962
2-s2.0-85132177753
url http://dx.doi.org/10.1111/tgis.12962
http://hdl.handle.net/11449/240268
identifier_str_mv Transactions in GIS, v. 26, n. 5, p. 2341-2365, 2022.
1467-9671
1361-1682
10.1111/tgis.12962
2-s2.0-85132177753
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Transactions in GIS
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 2341-2365
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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