A variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropical
Ano de defesa: | 2020 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Tecnologia |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Ambiental
|
Departamento: |
Engenharia Ambiental
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/24904 |
Resumo: | The pluviometric precipitation presents a large spatial variability and it is a phenomenon characterized by its irregular distribution. Its data are available from a limited number of stations that provide only punctuations mediations. Being the setting of a pluviometric stations’ net it is one of the most important factor to data accuracy, is necessary the net’s optimization. This work aims to analyze the kriging variance as an indicator to determine possible places that need new pluviometric stations in Rio Grande do Sul and to understand the aspect that influence the variability of the pluviometric precipitation. The National Water Agency (ANA) and National Institute of Meteorology (INMET) are organs that make the pluviometric monitoring at Rio Grande do Sul State and available these data. For the study were selected only the stations that had available data for 30 years period (from 1989 to 2018), and containing 5 years or more of consecutive data, aiming to have more than 10% of the years and the minimum of data reliability in order to do not lose the spatial representativeness of raining variability. Thereby it were selected 259 ANA stations and 18 INMET stations, totalizing 277 pluviometric stations. After obtaining the daily precipitation data, the monthly climatological average was performed. From the kriging interpolation method estimated the kriging variance to verify regions that present larger variability, where could be inserted more stations becoming better the sampling area. It were done systemic reductions of the stations from 100% to 95%, 90% and 50% intending to modulate the maximum kriging variance in function of the stations number and the months of the year. The modeling by generalized additive models of position, scale and shape (GAMLSS) was used to verify the relationship of the maximum kriging variance as a function of the number of seasons and the months of the year. Through the kriging variance it was possible to observe the places with larger variability that need of pluviometric stations every month of the year. And through the GAMLSS modulations, it was verified that there isn’t evident relation of the kriging maximum variance with the stations number, so that not necessarily the increase of measurement stations, without the reorganization of the spatial mesh, would cause a significant improvement in predictions. |
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2022-06-20T18:06:22Z2022-06-20T18:06:22Z2020-07-09http://repositorio.ufsm.br/handle/1/24904The pluviometric precipitation presents a large spatial variability and it is a phenomenon characterized by its irregular distribution. Its data are available from a limited number of stations that provide only punctuations mediations. Being the setting of a pluviometric stations’ net it is one of the most important factor to data accuracy, is necessary the net’s optimization. This work aims to analyze the kriging variance as an indicator to determine possible places that need new pluviometric stations in Rio Grande do Sul and to understand the aspect that influence the variability of the pluviometric precipitation. The National Water Agency (ANA) and National Institute of Meteorology (INMET) are organs that make the pluviometric monitoring at Rio Grande do Sul State and available these data. For the study were selected only the stations that had available data for 30 years period (from 1989 to 2018), and containing 5 years or more of consecutive data, aiming to have more than 10% of the years and the minimum of data reliability in order to do not lose the spatial representativeness of raining variability. Thereby it were selected 259 ANA stations and 18 INMET stations, totalizing 277 pluviometric stations. After obtaining the daily precipitation data, the monthly climatological average was performed. From the kriging interpolation method estimated the kriging variance to verify regions that present larger variability, where could be inserted more stations becoming better the sampling area. It were done systemic reductions of the stations from 100% to 95%, 90% and 50% intending to modulate the maximum kriging variance in function of the stations number and the months of the year. The modeling by generalized additive models of position, scale and shape (GAMLSS) was used to verify the relationship of the maximum kriging variance as a function of the number of seasons and the months of the year. Through the kriging variance it was possible to observe the places with larger variability that need of pluviometric stations every month of the year. And through the GAMLSS modulations, it was verified that there isn’t evident relation of the kriging maximum variance with the stations number, so that not necessarily the increase of measurement stations, without the reorganization of the spatial mesh, would cause a significant improvement in predictions.A precipitação pluviométrica apresenta grande variabilidade espacial, e é um fenômeno caracterizado por sua distribuição irregular. Seus dados estão disponíveis a partir de um número limitado de estações, que fornecem apenas medições pontuais. Sendo a configuração de uma rede de estações pluviométrica é um dos fatores mais importantes para precisão dos dados, é necessário a otimização das redes. O objetivo do trabalho é analisar a variância da krigagem como indicador para determinar possíveis locais que necessitam de novas estações pluviométricas no Rio Grande do Sul, e para compreender os aspectos que influenciam a variabilidade da precipitação pluviométrica. A Agência Nacional de Águas (ANA) e o Instituto Nacional de Meteorologia (INMET) são órgãos que realizam o monitoramento pluviométrico no estado do Rio Grande do Sul e disponibilizam os dados. Para o estudo foram selecionadas somente as estações que possuíam dados disponíveis para um período de 30 anos (de 1989 até 2018), e que continham 5 anos ou mais de dados consecutivos, visando ter mais que 10% dos anos e o mínimo de confiabilidade dos dados, a fim de não perder a representatividade espacial da variabilidade da chuva. Com isso foram selecionadas 259 estações da ANA e 18 do INMET, totalizando 277 estações pluviométricas. Após a obtenção dos dados diários de precipitação, foi realizada a média climatológica mensal. A partir do método de interpolação da krigagem, estimou-se a variância da krigagem, para verificar regiões que apresentam maior variabilidade, onde poderiam ser inseridas mais estações, melhorando a área de amostragem. Foram realizadas reduções sistemáticas das estações de 100% para 95%, 90%, 75% e 50%, com a intenção de modelar a variância de krigagem máxima em função do número de estações e dos meses do ano. A modelagem por modelos aditivos generalizados de posição, escala e forma (GAMLSS) foi usada para verificar a relação da variância de krigagem máxima em função do número de estações e dos meses do ano. Através da variância da krigagem foi possível observar os locais com maior variabilidade, que necessitam de estações pluviométricas em cada mês do ano e no geral. E por meio da modelagem de GAMLSS, foi verificado que não há relação evidente da variância máxima da krigagem com o número de estações, de modo que não necessariamente o aumento de estações de medição, sem a reorganização da malha espacial, causaria uma melhora significativa nas predições.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de TecnologiaPrograma de Pós-Graduação em Engenharia AmbientalUFSMBrasilEngenharia AmbientalAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessEstações pluviométricasVariância da krigagemGAMLSSPluviometric stationsKriging varianceCNPQ::ENGENHARIASA variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropicalThe kriging variance for understanding the pluviometric precipitation behavior in subtropical Brazilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisSeidel, Enio Júniorhttp://lattes.cnpq.br/7115995033005231Piccilli, Daniel Gustavo AllasiaRossoni, Diogo Franciscohttp://lattes.cnpq.br/5234387104934517Pisoni, Alana300000000009600600600600600bd3d465a-223f-468a-a744-31df8d283e100dc13207-ec58-4c5d-93f8-c71d81f42a00e31d67c6-0682-4c0c-8651-2b206bcfa970ec60096e-a6c8-4798-a72e-7fa5da7cc198reponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGEA_2020_PISONI_ALANA.pdfDIS_PPGEA_2020_PISONI_ALANA.pdfDissertação de mestradoapplication/pdf2505605http://repositorio.ufsm.br/bitstream/1/24904/1/DIS_PPGEA_2020_PISONI_ALANA.pdff8aabb92faa301fc7967894578254743MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.ufsm.br/bitstream/1/24904/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81956http://repositorio.ufsm.br/bitstream/1/24904/3/license.txt2f0571ecee68693bd5cd3f17c1e075dfMD531/249042022-06-20 15:06:22.444oai:repositorio.ufsm.br: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ório Institucionalhttp://repositorio.ufsm.br/PUBhttp://repositorio.ufsm.br/oai/requestopendoar:39132022-06-20T18:06:22Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.por.fl_str_mv |
A variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropical |
dc.title.alternative.eng.fl_str_mv |
The kriging variance for understanding the pluviometric precipitation behavior in subtropical Brazil |
title |
A variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropical |
spellingShingle |
A variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropical Pisoni, Alana Estações pluviométricas Variância da krigagem GAMLSS Pluviometric stations Kriging variance CNPQ::ENGENHARIAS |
title_short |
A variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropical |
title_full |
A variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropical |
title_fullStr |
A variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropical |
title_full_unstemmed |
A variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropical |
title_sort |
A variância de krigagem na compreensão do comportamento da precipitação pluviométrica no Brasil subtropical |
author |
Pisoni, Alana |
author_facet |
Pisoni, Alana |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Seidel, Enio Júnior |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/7115995033005231 |
dc.contributor.referee1.fl_str_mv |
Piccilli, Daniel Gustavo Allasia |
dc.contributor.referee2.fl_str_mv |
Rossoni, Diogo Francisco |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/5234387104934517 |
dc.contributor.author.fl_str_mv |
Pisoni, Alana |
contributor_str_mv |
Seidel, Enio Júnior Piccilli, Daniel Gustavo Allasia Rossoni, Diogo Francisco |
dc.subject.por.fl_str_mv |
Estações pluviométricas Variância da krigagem GAMLSS |
topic |
Estações pluviométricas Variância da krigagem GAMLSS Pluviometric stations Kriging variance CNPQ::ENGENHARIAS |
dc.subject.eng.fl_str_mv |
Pluviometric stations Kriging variance |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS |
description |
The pluviometric precipitation presents a large spatial variability and it is a phenomenon characterized by its irregular distribution. Its data are available from a limited number of stations that provide only punctuations mediations. Being the setting of a pluviometric stations’ net it is one of the most important factor to data accuracy, is necessary the net’s optimization. This work aims to analyze the kriging variance as an indicator to determine possible places that need new pluviometric stations in Rio Grande do Sul and to understand the aspect that influence the variability of the pluviometric precipitation. The National Water Agency (ANA) and National Institute of Meteorology (INMET) are organs that make the pluviometric monitoring at Rio Grande do Sul State and available these data. For the study were selected only the stations that had available data for 30 years period (from 1989 to 2018), and containing 5 years or more of consecutive data, aiming to have more than 10% of the years and the minimum of data reliability in order to do not lose the spatial representativeness of raining variability. Thereby it were selected 259 ANA stations and 18 INMET stations, totalizing 277 pluviometric stations. After obtaining the daily precipitation data, the monthly climatological average was performed. From the kriging interpolation method estimated the kriging variance to verify regions that present larger variability, where could be inserted more stations becoming better the sampling area. It were done systemic reductions of the stations from 100% to 95%, 90% and 50% intending to modulate the maximum kriging variance in function of the stations number and the months of the year. The modeling by generalized additive models of position, scale and shape (GAMLSS) was used to verify the relationship of the maximum kriging variance as a function of the number of seasons and the months of the year. Through the kriging variance it was possible to observe the places with larger variability that need of pluviometric stations every month of the year. And through the GAMLSS modulations, it was verified that there isn’t evident relation of the kriging maximum variance with the stations number, so that not necessarily the increase of measurement stations, without the reorganization of the spatial mesh, would cause a significant improvement in predictions. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-07-09 |
dc.date.accessioned.fl_str_mv |
2022-06-20T18:06:22Z |
dc.date.available.fl_str_mv |
2022-06-20T18:06:22Z |
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 |
http://repositorio.ufsm.br/handle/1/24904 |
url |
http://repositorio.ufsm.br/handle/1/24904 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
300000000009 |
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600 600 600 600 600 |
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dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Ambiental |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Engenharia Ambiental |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
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