@Article{TomasSoJoMeFrSa:2022:CaStSa,
               author = "Tomas, Livia Rodrigues and Soares, Giovanni Guarnieri and Jorge, 
                         Aurelienne Aparecida Souza and Mendes, Jeferson Feitosa and 
                         Freitas, Vander L. S. and Santos, Leonardo Bacelar Lima",
          affiliation = "{Centro Nacional de Monitoramento e Alertas de Desastres Naturais 
                         (CEMADEN)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Estadual Paulista (UNESP)} and {Universidade Federal 
                         de Ouro Preto (UFOP)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Flood risk map from hydrological and mobility data: A case study 
                         in Sao Paulo (Brazil)",
              journal = "Transactions in GIS",
                 year = "2022",
               volume = "26",
               number = "5",
                pages = "2341--2365",
                month = "Aug.",
             abstract = "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 Sao 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.",
                  doi = "10.1111/tgis.12962",
                  url = "http://dx.doi.org/10.1111/tgis.12962",
                 issn = "{1467-9671;} and 1361-1682",
             language = "en",
           targetfile = "Transactions in GIS - 2022 - Tom s - Flood risk map from 
                         hydrological and mobility data A case study in S o Paulo 
                         Brazil.pdf",
        urlaccessdate = "16 jun. 2024"
}