Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/4775 |
Resumo: | In developed countries it is common the use of rainfall estimatives by using tools like Remote Sensing, but in technically less advanced countries, like in Brazil, this usage still limited. The study area focuses on the center-west of the country, which has a restricted network of rainfall stations. The State of Goiás and Distrito Federal has a wide area and its network of rainfall stations available is distributed irregularly, having distances, producing only point data, which can cause the use of inadequate information in the region, committing the management of stormwater in urban and rural areas. With the advent of technology, data from the Tropical Rainfall Measuring Mission satellite - (TRMM) has provided more consistency in relation to rainfall data, because it uses a global coverage, eliminating the effects of prior data points. This work has as main objective to develop a database of rainfall from the TRMM satellite (3B42 algorithm - version 7) and rainfall data measured in the field (rainfall stations), on the interval of 1998 to 2011. From this data was possible the development of rainfall data bank consisting of four bands: Band 1, which was composed by maps of rainfall stations; Band 2, represented by maps of rainfall from TRMM; Band 3 consists on maps of rainfall product, which is to merge the data from rainfall stations with TRMM data, and finally the database was represented by the Band 4, which is the metadata; this map is the pixel values used in band 3 The results of this study showed that there is an agreement between the data of rainfall stations (band 1) and TRMM (band 2) in periods of low rainfall frequency, unlike the rainy season, where the TRMM tends to overestimate the values of rainfall stations, showing correlation of maximum 94.09% in 2005, and at least 25.52% registered in the year 2006. When analyzing the composition of the (band 3), it was observed that during the dry period, a total of up to 100% of data from maps of rainfall stations was used, and in the rainy season it was used by 30% of data from the TRMM rainfall maps, which can be proven by the band 4. These maps resulted in a considerable solution for use mainly in the rainy seasons, where the rainfall stations do not always manage to capture the total precipitation. The TRMM represented 30% of the data in the rainy seasons, covering possible errors of rainfall stations. |
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Ferreira, Nilson Clementinohttp://lattes.cnpq.br/6466969611652630Ferreira, Nilson ClementinoNazareno, Nilton Ricetti Xavier deSiqueira, Eduardo Queija dehttp://lattes.cnpq.br/8510425534480223Morais, Lidiane Batista de2015-10-26T13:48:26Z2014-08-27MORAIS, L. B. Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal. 2014. 110 f. Dissertação (Mestrado em Engenharia do Meio Ambiente) - Universidade Federal de Goiás, Goiânia, 2014.http://repositorio.bc.ufg.br/tede/handle/tede/4775In developed countries it is common the use of rainfall estimatives by using tools like Remote Sensing, but in technically less advanced countries, like in Brazil, this usage still limited. The study area focuses on the center-west of the country, which has a restricted network of rainfall stations. The State of Goiás and Distrito Federal has a wide area and its network of rainfall stations available is distributed irregularly, having distances, producing only point data, which can cause the use of inadequate information in the region, committing the management of stormwater in urban and rural areas. With the advent of technology, data from the Tropical Rainfall Measuring Mission satellite - (TRMM) has provided more consistency in relation to rainfall data, because it uses a global coverage, eliminating the effects of prior data points. This work has as main objective to develop a database of rainfall from the TRMM satellite (3B42 algorithm - version 7) and rainfall data measured in the field (rainfall stations), on the interval of 1998 to 2011. From this data was possible the development of rainfall data bank consisting of four bands: Band 1, which was composed by maps of rainfall stations; Band 2, represented by maps of rainfall from TRMM; Band 3 consists on maps of rainfall product, which is to merge the data from rainfall stations with TRMM data, and finally the database was represented by the Band 4, which is the metadata; this map is the pixel values used in band 3 The results of this study showed that there is an agreement between the data of rainfall stations (band 1) and TRMM (band 2) in periods of low rainfall frequency, unlike the rainy season, where the TRMM tends to overestimate the values of rainfall stations, showing correlation of maximum 94.09% in 2005, and at least 25.52% registered in the year 2006. When analyzing the composition of the (band 3), it was observed that during the dry period, a total of up to 100% of data from maps of rainfall stations was used, and in the rainy season it was used by 30% of data from the TRMM rainfall maps, which can be proven by the band 4. These maps resulted in a considerable solution for use mainly in the rainy seasons, where the rainfall stations do not always manage to capture the total precipitation. The TRMM represented 30% of the data in the rainy seasons, covering possible errors of rainfall stations.Em países desenvolvidos é comum o uso de estimativas de precipitação utilizando ferramentas como o Sensoriamento Remoto, mas em países menos evoluídos tecnicamente, como o Brasil, este uso ainda é limitado. A área de estudo se concentra na região centro-oeste do país, que possui uma restrita rede de estações pluviométricas. Os Estados de Goiás e Distrito Federal possuem uma área ampla e sua rede de estações pluviométricas disponível é distribuída irregularmente, possuindo distanciamentos, produzindo apenas dados pontuais, o que pode causar a utilização de informações inadequadas da região, comprometendo o gerenciamento das águas pluviais em áreas urbanas e rurais. Com o advento da tecnologia, os dados do satélite Tropical Rainfall Measuring Mission– (TRMM) veio proporcionar mais coerência em relação aos dados pluviométricos, pois se trata de uma cobertura global, eliminando os efeitos dos dados antes pontuais. Este trabalho possui como objetivo principal formular um banco de dados de precipitação provenientes do satélite TRMM (algoritmo 3B42), e dados pluviométricos medidos em campo (estações pluviométricas), no intervalo temporal do ano de 1998 a 2011. A partir destes dados foi possível a elaboração do banco de dados pluviométricos, composto por 4 bandas: Banda 1, que foi composto de mapas de chuvas das estações pluviométricas; Banda 2, representado por mapas de chuvas do TRMM; Banda 3 composto pelos mapas de chuvas produto, que é a junção dos dados das estações pluviométricas com os dados TRMM, e finalmente o banco de dados foi representado pela Banda 4, que são os metadados; neste mapa se encontra os valores dos pixels utilizados na banda 3. Os resultados deste estudo mostraram que existe uma concordância entre os dados de estações pluviométricas (banda 1) e TRMM (banda 2) no período de menor frequência de precipitações, diferentemente no período chuvoso, onde o TRMM tende a superestimar aos valores das estações pluviométricas, mostrando correlação de no máximo 94,09% no ano de 2005, e no mínimo 25,52% registrado no ano de 2006. Quando analisada a composição da (banda 3), foi observado que no período seco, foi utilizado um total de até 100% de dados provenientes dos mapas de chuvas das estações pluviométricas, e no período chuvoso utilizou-se até 30% de dados dos mapas de chuvas do TRMM, o que pode ser comprovado pela banda 4. Estes mapas produtos resultaram em uma considerável solução para serem utilizados principalmente nas estações chuvosas, onde nem sempre as estações pluviométricas conseguem captar a precipitação total. O TRMM representou até 30% dos dados nas estações de chuvas, cobrindo possíveis erros das estações pluviométricas.Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-26T13:46:11Z No. of bitstreams: 2 Dissertação - Lidiane Batista de Morais - 2014.pdf: 4727753 bytes, checksum: 70260752148030b1e54f898d23d5c762 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-26T13:48:26Z (GMT) No. of bitstreams: 2 Dissertação - Lidiane Batista de Morais - 2014.pdf: 4727753 bytes, checksum: 70260752148030b1e54f898d23d5c762 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Made available in DSpace on 2015-10-26T13:48:26Z (GMT). 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dc.title.por.fl_str_mv |
Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal |
dc.title.alternative.eng.fl_str_mv |
Pluviometric data bank integrated by the TRMM platform and rainfall stations in the state of Goiás and Distrito Federal |
title |
Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal |
spellingShingle |
Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal Morais, Lidiane Batista de Sensoriamento remoto Pluviometria TRMM Estações pluviométricas Remote sensing Pluviometry TRMM Rainfall stations ENGENHARIA DE AGUA E SOLO::CONSERVACAO DE SOLO E AGUA |
title_short |
Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal |
title_full |
Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal |
title_fullStr |
Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal |
title_full_unstemmed |
Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal |
title_sort |
Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal |
author |
Morais, Lidiane Batista de |
author_facet |
Morais, Lidiane Batista de |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Ferreira, Nilson Clementino |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/6466969611652630 |
dc.contributor.referee1.fl_str_mv |
Ferreira, Nilson Clementino |
dc.contributor.referee2.fl_str_mv |
Nazareno, Nilton Ricetti Xavier de |
dc.contributor.referee3.fl_str_mv |
Siqueira, Eduardo Queija de |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/8510425534480223 |
dc.contributor.author.fl_str_mv |
Morais, Lidiane Batista de |
contributor_str_mv |
Ferreira, Nilson Clementino Ferreira, Nilson Clementino Nazareno, Nilton Ricetti Xavier de Siqueira, Eduardo Queija de |
dc.subject.por.fl_str_mv |
Sensoriamento remoto Pluviometria TRMM Estações pluviométricas |
topic |
Sensoriamento remoto Pluviometria TRMM Estações pluviométricas Remote sensing Pluviometry TRMM Rainfall stations ENGENHARIA DE AGUA E SOLO::CONSERVACAO DE SOLO E AGUA |
dc.subject.eng.fl_str_mv |
Remote sensing Pluviometry TRMM Rainfall stations |
dc.subject.cnpq.fl_str_mv |
ENGENHARIA DE AGUA E SOLO::CONSERVACAO DE SOLO E AGUA |
description |
In developed countries it is common the use of rainfall estimatives by using tools like Remote Sensing, but in technically less advanced countries, like in Brazil, this usage still limited. The study area focuses on the center-west of the country, which has a restricted network of rainfall stations. The State of Goiás and Distrito Federal has a wide area and its network of rainfall stations available is distributed irregularly, having distances, producing only point data, which can cause the use of inadequate information in the region, committing the management of stormwater in urban and rural areas. With the advent of technology, data from the Tropical Rainfall Measuring Mission satellite - (TRMM) has provided more consistency in relation to rainfall data, because it uses a global coverage, eliminating the effects of prior data points. This work has as main objective to develop a database of rainfall from the TRMM satellite (3B42 algorithm - version 7) and rainfall data measured in the field (rainfall stations), on the interval of 1998 to 2011. From this data was possible the development of rainfall data bank consisting of four bands: Band 1, which was composed by maps of rainfall stations; Band 2, represented by maps of rainfall from TRMM; Band 3 consists on maps of rainfall product, which is to merge the data from rainfall stations with TRMM data, and finally the database was represented by the Band 4, which is the metadata; this map is the pixel values used in band 3 The results of this study showed that there is an agreement between the data of rainfall stations (band 1) and TRMM (band 2) in periods of low rainfall frequency, unlike the rainy season, where the TRMM tends to overestimate the values of rainfall stations, showing correlation of maximum 94.09% in 2005, and at least 25.52% registered in the year 2006. When analyzing the composition of the (band 3), it was observed that during the dry period, a total of up to 100% of data from maps of rainfall stations was used, and in the rainy season it was used by 30% of data from the TRMM rainfall maps, which can be proven by the band 4. These maps resulted in a considerable solution for use mainly in the rainy seasons, where the rainfall stations do not always manage to capture the total precipitation. The TRMM represented 30% of the data in the rainy seasons, covering possible errors of rainfall stations. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014-08-27 |
dc.date.accessioned.fl_str_mv |
2015-10-26T13:48:26Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
MORAIS, L. B. Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal. 2014. 110 f. Dissertação (Mestrado em Engenharia do Meio Ambiente) - Universidade Federal de Goiás, Goiânia, 2014. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/4775 |
identifier_str_mv |
MORAIS, L. B. Banco de dados pluviométricos integrados por dados da plataforma TRMM e estações pluviométricas nos estados de Goiás e Distrito Federal. 2014. 110 f. Dissertação (Mestrado em Engenharia do Meio Ambiente) - Universidade Federal de Goiás, Goiânia, 2014. |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/4775 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.program.fl_str_mv |
1009702091972874847 |
dc.relation.confidence.fl_str_mv |
600 600 600 |
dc.relation.department.fl_str_mv |
724087251626315585 |
dc.relation.cnpq.fl_str_mv |
-7776146459471652194 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
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application/pdf |
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UFG |
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Brasil |
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Universidade Federal de Goiás |
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