Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no Brasil
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFPB |
Texto Completo: | https://repositorio.ufpb.br/jspui/handle/123456789/23139 |
Resumo: | The study of the properties of rainfall events at various spatio-temporal scales is of great importance for the understanding of environmental processes and variables, as well as socioeconomic activities. These studies on rainfall characteristics, mainly in sub-hourly resolutions, are not carried out in large areas of South America due to lack of data. The National Center for Monitoring and Alerting Natural Disasters (CEMADEN) has gradually implemented, since 2011, a sub-hourly monitoring network, composed of approximately 3,500 automated rain gauges distributed in Brazil, opening new opportunities for hydrological studies in this vast tropical country. To fill this knowledge gap, this study analyzed the dynamics and spatio-temporal correlation of rainfall and its characteristics in Brazil on sub-daily and subhourly time scales, using seven years of data (from 2014 to 2020) provided by CEMADEN. The minimum time between events (MIT) and the minimum depth (MRD = 1 mm) were used to define the rainfall events. Seven MITs (i.e., 30, 60, 120, 180, 360, 720 and 1440 min) were considered to assess the characteristics of rainfall events and their interrelationship. The Gaussian Mixture Models (GMM) clustering algorithm was applied to identify regions with similar rainfall patterns according to the considered MIT. Six groups with similar rainfall patterns were identified in Brazil, and even though some groups are close in a specific property, they are considerably different in others. The results show that the MIT has a strong influence on the properties of precipitation, with dry weather (variation of 4,812%) and the number of events (variation of 45%) being the most sensitive variables to the variation of this parameter. The Northeast coast is the region where the most precipitation events occur, with more than 200 events per year (MIT < 60 min). In contrast, the Semiarid region presented the lowest number of events in Brazil, with an average of 69 per year and reaching only 38 events with na MIT of 1440 minutes. The Central regions (mainly), Semi-arid, North and the Southeast coast have very intense rains, which can cause, with greater ease; floods, inundations and inundations. The Northeast, South and Southeast Coasts have the volume of accumulated rain as the main alert factor. In terms of the spatial correlation between stations, the smaller the measurement interval, the smaller the correlation for the same distance. In terms of spatial correlation between the data from the stations, considering the resolution of 10 minutes, on average, 2.5 km is the distance necessary to guarantee a correlation of 0.7 between the rainfall stations in Brazil. The results of this study provide a better understanding of precipitation and its characteristics in Brazil. |
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Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no BrasilMínimo intervalo de tempoEventos chuvososVariabilidade espacialMinimum time intervalRainy eventsSpatial variabilityCNPQ::ENGENHARIAS::ENGENHARIA CIVILThe study of the properties of rainfall events at various spatio-temporal scales is of great importance for the understanding of environmental processes and variables, as well as socioeconomic activities. These studies on rainfall characteristics, mainly in sub-hourly resolutions, are not carried out in large areas of South America due to lack of data. The National Center for Monitoring and Alerting Natural Disasters (CEMADEN) has gradually implemented, since 2011, a sub-hourly monitoring network, composed of approximately 3,500 automated rain gauges distributed in Brazil, opening new opportunities for hydrological studies in this vast tropical country. To fill this knowledge gap, this study analyzed the dynamics and spatio-temporal correlation of rainfall and its characteristics in Brazil on sub-daily and subhourly time scales, using seven years of data (from 2014 to 2020) provided by CEMADEN. The minimum time between events (MIT) and the minimum depth (MRD = 1 mm) were used to define the rainfall events. Seven MITs (i.e., 30, 60, 120, 180, 360, 720 and 1440 min) were considered to assess the characteristics of rainfall events and their interrelationship. The Gaussian Mixture Models (GMM) clustering algorithm was applied to identify regions with similar rainfall patterns according to the considered MIT. Six groups with similar rainfall patterns were identified in Brazil, and even though some groups are close in a specific property, they are considerably different in others. The results show that the MIT has a strong influence on the properties of precipitation, with dry weather (variation of 4,812%) and the number of events (variation of 45%) being the most sensitive variables to the variation of this parameter. The Northeast coast is the region where the most precipitation events occur, with more than 200 events per year (MIT < 60 min). In contrast, the Semiarid region presented the lowest number of events in Brazil, with an average of 69 per year and reaching only 38 events with na MIT of 1440 minutes. The Central regions (mainly), Semi-arid, North and the Southeast coast have very intense rains, which can cause, with greater ease; floods, inundations and inundations. The Northeast, South and Southeast Coasts have the volume of accumulated rain as the main alert factor. In terms of the spatial correlation between stations, the smaller the measurement interval, the smaller the correlation for the same distance. In terms of spatial correlation between the data from the stations, considering the resolution of 10 minutes, on average, 2.5 km is the distance necessary to guarantee a correlation of 0.7 between the rainfall stations in Brazil. The results of this study provide a better understanding of precipitation and its characteristics in Brazil.Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPqO estudo das propriedades dos eventos de chuva em várias escalas espaço temporais é de grande importância para a compreensão de processos e variáveis ambientais, bem como atividades socioeconômicas. Esses estudos sobre as características das chuvas, principalmente em resoluções sub-horárias, não são realizados em grandes áreas da América do Sul devido à falta de dados. O Centro Nacional de Monitoramento e Alerta de Desastres Naturais (CEMADEN) implementou gradativamente desde 2011 uma rede de monitoramento subhorário, composta por aproximadamente 3.500 pluviômetros automatizados distribuídos no Brasil, abrindo novas oportunidades para estudos hidrológicos nesse vasto país tropical. Para preencher essa lacuna de conhecimento, este estudo analisou a dinâmica e a correlação espaçotemporal da chuva e suas características no Brasil em escalas temporais subdiárias e subhorárias, utilizando sete anos de dados (de 2014 a 2020) fornecidos pelo CEMADEN. O tempo mínimo entre eventos (MIT) e a lâmina mínima (MRD = 1 mm) foram usados para definir os eventos de chuva. Sete MIT’s (i.e., 30, 60, 120, 180, 360, 720 e 1440 min) foram considerados para avaliar as características dos eventos de chuva e sua inter-relação. O algoritmo de agrupamento Gaussian Mixture Models (GMM) foi aplicado para identificar regiões com padrões de chuva semelhantes de acordo com o MIT considerado. Foram identificados 6 grupos com padrões pluviométricos semelhantes no Brasil, e, mesmo havendo proximidade de alguns grupos em uma propriedade específica, eles são consideravelmente diferentes em outras. Os resultados mostram que o MIT tem forte influência sobre as propriedades da precipitação, sendo o tempo seco (variação de 4.812%) e o número de eventos (variação de 45%) as variáveis mais sensíveis a variação de tal parâmetro. A costa do Nordeste é a região onde mais ocorrem eventos de precipitação, com mais de 200 eventos por ano (MIT < 60 min). Em oposição, a região Semiárida apresentou o menor número de eventos do Brasil, com uma média de 69 por ano e chegando a apenas 38 eventos com MIT de 1440 minutos. As regiões Central (principalmente), Semiárida, Norte e a costa Sudeste possem chuvas muito intensas, podendo provocar, com maior facilidade; inundações, alagamentos e enchentes. A Costa do Nordeste, o Sul e o Sudeste possuem o volume de chuva acumulada como principal fator de alerta. Em termos de coeficiente de correlação espacial entre as estações, quanto menor o intervalo de medição, menor é a correlação para uma mesma distância. Em termos de correlação espacial entre os dados das estações, considerando a resolução de 10 minutos, em média, 2,5 km é a distância necessária para garantir um coeficiente de correlação de 0,7 entre as estações pluviométricas do Brasil. Os resultados deste estudo fornecem uma melhor compreensão da precipitação e suas características no Brasil.Universidade Federal da ParaíbaBrasilEngenharia Civil e AmbientalPrograma de Pós-Graduação em Engenharia Civil e AmbientalUFPBAlmeida, Cristiano das Neveshttp://lattes.cnpq.br/5858373824027435Coelho, Victor Hugo Rabelohttp://lattes.cnpq.br/5109911884566474Lemos, Filipe Carvalho2022-06-14T18:51:16Z2022-03-282022-06-14T18:51:16Z2022-03-21info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttps://repositorio.ufpb.br/jspui/handle/123456789/23139porAttribution-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nd/3.0/br/info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFPBinstname:Universidade Federal da Paraíba (UFPB)instacron:UFPB2022-06-15T13:45:27Zoai:repositorio.ufpb.br:123456789/23139Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufpb.br/PUBhttp://tede.biblioteca.ufpb.br:8080/oai/requestdiretoria@ufpb.br|| diretoria@ufpb.bropendoar:2022-06-15T13:45:27Biblioteca Digital de Teses e Dissertações da UFPB - Universidade Federal da Paraíba (UFPB)false |
dc.title.none.fl_str_mv |
Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no Brasil |
title |
Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no Brasil |
spellingShingle |
Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no Brasil Lemos, Filipe Carvalho Mínimo intervalo de tempo Eventos chuvosos Variabilidade espacial Minimum time interval Rainy events Spatial variability CNPQ::ENGENHARIAS::ENGENHARIA CIVIL |
title_short |
Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no Brasil |
title_full |
Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no Brasil |
title_fullStr |
Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no Brasil |
title_full_unstemmed |
Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no Brasil |
title_sort |
Caracterização espaço-temporal da precipitação nas escalas subhorária e subdiária no Brasil |
author |
Lemos, Filipe Carvalho |
author_facet |
Lemos, Filipe Carvalho |
author_role |
author |
dc.contributor.none.fl_str_mv |
Almeida, Cristiano das Neves http://lattes.cnpq.br/5858373824027435 Coelho, Victor Hugo Rabelo http://lattes.cnpq.br/5109911884566474 |
dc.contributor.author.fl_str_mv |
Lemos, Filipe Carvalho |
dc.subject.por.fl_str_mv |
Mínimo intervalo de tempo Eventos chuvosos Variabilidade espacial Minimum time interval Rainy events Spatial variability CNPQ::ENGENHARIAS::ENGENHARIA CIVIL |
topic |
Mínimo intervalo de tempo Eventos chuvosos Variabilidade espacial Minimum time interval Rainy events Spatial variability CNPQ::ENGENHARIAS::ENGENHARIA CIVIL |
description |
The study of the properties of rainfall events at various spatio-temporal scales is of great importance for the understanding of environmental processes and variables, as well as socioeconomic activities. These studies on rainfall characteristics, mainly in sub-hourly resolutions, are not carried out in large areas of South America due to lack of data. The National Center for Monitoring and Alerting Natural Disasters (CEMADEN) has gradually implemented, since 2011, a sub-hourly monitoring network, composed of approximately 3,500 automated rain gauges distributed in Brazil, opening new opportunities for hydrological studies in this vast tropical country. To fill this knowledge gap, this study analyzed the dynamics and spatio-temporal correlation of rainfall and its characteristics in Brazil on sub-daily and subhourly time scales, using seven years of data (from 2014 to 2020) provided by CEMADEN. The minimum time between events (MIT) and the minimum depth (MRD = 1 mm) were used to define the rainfall events. Seven MITs (i.e., 30, 60, 120, 180, 360, 720 and 1440 min) were considered to assess the characteristics of rainfall events and their interrelationship. The Gaussian Mixture Models (GMM) clustering algorithm was applied to identify regions with similar rainfall patterns according to the considered MIT. Six groups with similar rainfall patterns were identified in Brazil, and even though some groups are close in a specific property, they are considerably different in others. The results show that the MIT has a strong influence on the properties of precipitation, with dry weather (variation of 4,812%) and the number of events (variation of 45%) being the most sensitive variables to the variation of this parameter. The Northeast coast is the region where the most precipitation events occur, with more than 200 events per year (MIT < 60 min). In contrast, the Semiarid region presented the lowest number of events in Brazil, with an average of 69 per year and reaching only 38 events with na MIT of 1440 minutes. The Central regions (mainly), Semi-arid, North and the Southeast coast have very intense rains, which can cause, with greater ease; floods, inundations and inundations. The Northeast, South and Southeast Coasts have the volume of accumulated rain as the main alert factor. In terms of the spatial correlation between stations, the smaller the measurement interval, the smaller the correlation for the same distance. In terms of spatial correlation between the data from the stations, considering the resolution of 10 minutes, on average, 2.5 km is the distance necessary to guarantee a correlation of 0.7 between the rainfall stations in Brazil. The results of this study provide a better understanding of precipitation and its characteristics in Brazil. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-06-14T18:51:16Z 2022-03-28 2022-06-14T18:51:16Z 2022-03-21 |
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.uri.fl_str_mv |
https://repositorio.ufpb.br/jspui/handle/123456789/23139 |
url |
https://repositorio.ufpb.br/jspui/handle/123456789/23139 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
Attribution-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nd/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal da Paraíba Brasil Engenharia Civil e Ambiental Programa de Pós-Graduação em Engenharia Civil e Ambiental UFPB |
publisher.none.fl_str_mv |
Universidade Federal da Paraíba Brasil Engenharia Civil e Ambiental Programa de Pós-Graduação em Engenharia Civil e Ambiental UFPB |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da UFPB instname:Universidade Federal da Paraíba (UFPB) instacron:UFPB |
instname_str |
Universidade Federal da Paraíba (UFPB) |
instacron_str |
UFPB |
institution |
UFPB |
reponame_str |
Biblioteca Digital de Teses e Dissertações da UFPB |
collection |
Biblioteca Digital de Teses e Dissertações da UFPB |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da UFPB - Universidade Federal da Paraíba (UFPB) |
repository.mail.fl_str_mv |
diretoria@ufpb.br|| diretoria@ufpb.br |
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1801842994772443136 |