@InProceedings{CostaOlKaXaSaCa:2017:AnEsEx,
               author = "Costa, Tassia Alves and Oliveira, Kenny Delmonte and Kapiche, 
                         Allys Larissa Amiti Fagundes and Xavier, Alexandre C{\^a}ndido 
                         and Sanches, Ieda Del Arco and Camargo, Eduardo Celso Gerbi",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and {} 
                         and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "An{\'a}lise espa{\c{c}}o-temporal dos extremos de 
                         precipita{\c{c}}{\~a}o para o estado do Esp{\'{\i}}rito 
                         Santo",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5896--5903",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Among the main global climate change characteristics is the 
                         increase of the extreme weather events, mainly related to unusual 
                         droughts and heavy rainfall, which causes huge social and economic 
                         damages. In this context, the development of techniques that can 
                         help identify risk areas and contribute to the mitigation of the 
                         impacts is of substantial value for the planning actions of 
                         monitoring and reduction of injuries. The aims of this work were 
                         to analyze the space-time distribution of the variables 
                         controlling the magnitude for extreme precipitations of the State 
                         of Esp{\'{\i}}rito Santo, to represent them in several scenarios 
                         of return period and to point out the uncertainties generated by 
                         the indicator kriging to the probability distribution function. 
                         The results showed that the distribution curve of the probability 
                         density functions generated increases drastically in the 1 to 10 
                         years period and later stabilizes. The uncertainty map created by 
                         indicator kriging has the advantage of being fixed at time, that 
                         is, the uncertainty is the same for any return period, and can 
                         also support the decision about the major errors; and important 
                         application of the variation coefficient uncertainty map (%) in 
                         the decision making for future studies with maximum precipitation 
                         events.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60103",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMBST",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMBST",
           targetfile = "60103.pdf",
                 type = "Meteorologia e climatologia",
        urlaccessdate = "27 abr. 2024"
}