Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil

Detalhes bibliográficos
Autor(a) principal: Silva, Luciano Pereira da
Data de Publicação: 2023
Outros Autores: Santiago, Edgo Jackson Pinto, Gomes-Silva, Frank, Silva, Antonio Samuel Alves da, Menezes, Rômulo Simões Cezar
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Acta scientiarum. Technology (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60621
Resumo: The Brazilian semi-arid region is recurrently affected by the scarcity of water that marks the landscape as it prints periods of severe drought. Therefore, rainfall in this region greatly influences plant growth in regional hydrological processes that affect droughts or floods. It is of practical interest to assess how changes in rainfall patterns occur to anticipate hydrological dynamics. However, this is not easy as climate change reshapes global hydrology. Thus, assertive forecasting has become rare and imputed estimates of a reasonable degree of uncertainty. The objective of this work was to verify from the mixture of exponential, gamma, beta, log-normal, Weibull, normal, log-logistic, and exponentiated log-logistic distributions, which best fits the monthly rainfall of the state of Pernambuco, Brazil. The data used came from 133 monthly rainfall series (1950 to 2012) distributed over the state of Pernambuco. The Maximum Likelihood Method estimated all parameters. The Kolmogorov-Smirnov adherence test was applied at 5% probability to assess the adjustments. The least rejected distributions in the adherence test were Weibull, gamma, and beta; October presented the smallest number of distributions considered adequate to model monthly rainfall. More than 99% of the rain gauge stations had some adequate probabilistic distribution to model monthly rainfall in March. For most months, except for March, the Weibull distribution was the most suitable for modeling the monthly rainfall in the vast majority of rain gauge stations of Pernambuco.
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spelling Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil rain; semi-arid; distribution mixture; modeling.rain; semi-arid; distribution mixture; modeling.The Brazilian semi-arid region is recurrently affected by the scarcity of water that marks the landscape as it prints periods of severe drought. Therefore, rainfall in this region greatly influences plant growth in regional hydrological processes that affect droughts or floods. It is of practical interest to assess how changes in rainfall patterns occur to anticipate hydrological dynamics. However, this is not easy as climate change reshapes global hydrology. Thus, assertive forecasting has become rare and imputed estimates of a reasonable degree of uncertainty. The objective of this work was to verify from the mixture of exponential, gamma, beta, log-normal, Weibull, normal, log-logistic, and exponentiated log-logistic distributions, which best fits the monthly rainfall of the state of Pernambuco, Brazil. The data used came from 133 monthly rainfall series (1950 to 2012) distributed over the state of Pernambuco. The Maximum Likelihood Method estimated all parameters. The Kolmogorov-Smirnov adherence test was applied at 5% probability to assess the adjustments. The least rejected distributions in the adherence test were Weibull, gamma, and beta; October presented the smallest number of distributions considered adequate to model monthly rainfall. More than 99% of the rain gauge stations had some adequate probabilistic distribution to model monthly rainfall in March. For most months, except for March, the Weibull distribution was the most suitable for modeling the monthly rainfall in the vast majority of rain gauge stations of Pernambuco.The Brazilian semi-arid region is recurrently affected by the scarcity of water that marks the landscape as it prints periods of severe drought. Therefore, rainfall in this region greatly influences plant growth in regional hydrological processes that affect droughts or floods. It is of practical interest to assess how changes in rainfall patterns occur to anticipate hydrological dynamics. However, this is not easy as climate change reshapes global hydrology. Thus, assertive forecasting has become rare and imputed estimates of a reasonable degree of uncertainty. The objective of this work was to verify from the mixture of exponential, gamma, beta, log-normal, Weibull, normal, log-logistic, and exponentiated log-logistic distributions, which best fits the monthly rainfall of the state of Pernambuco, Brazil. The data used came from 133 monthly rainfall series (1950 to 2012) distributed over the state of Pernambuco. The Maximum Likelihood Method estimated all parameters. The Kolmogorov-Smirnov adherence test was applied at 5% probability to assess the adjustments. The least rejected distributions in the adherence test were Weibull, gamma, and beta; October presented the smallest number of distributions considered adequate to model monthly rainfall. More than 99% of the rain gauge stations had some adequate probabilistic distribution to model monthly rainfall in March. For most months, except for March, the Weibull distribution was the most suitable for modeling the monthly rainfall in the vast majority of rain gauge stations of Pernambuco.Universidade Estadual De Maringá2023-04-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/6062110.4025/actascitechnol.v45i1.60621Acta Scientiarum. Technology; Vol 45 (2023): Publicação contínua; e60621Acta Scientiarum. Technology; v. 45 (2023): Publicação contínua; e606211806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60621/751375155825Copyright (c) 2023 Acta Scientiarum. Technologyhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSilva, Luciano Pereira da Santiago, Edgo Jackson Pinto Gomes-Silva, FrankSilva, Antonio Samuel Alves da Menezes, Rômulo Simões Cezar 2023-05-25T13:57:17Zoai:periodicos.uem.br/ojs:article/60621Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2023-05-25T13:57:17Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
title Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
spellingShingle Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
Silva, Luciano Pereira da
rain; semi-arid; distribution mixture; modeling.
rain; semi-arid; distribution mixture; modeling.
title_short Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
title_full Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
title_fullStr Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
title_full_unstemmed Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
title_sort Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
author Silva, Luciano Pereira da
author_facet Silva, Luciano Pereira da
Santiago, Edgo Jackson Pinto
Gomes-Silva, Frank
Silva, Antonio Samuel Alves da
Menezes, Rômulo Simões Cezar
author_role author
author2 Santiago, Edgo Jackson Pinto
Gomes-Silva, Frank
Silva, Antonio Samuel Alves da
Menezes, Rômulo Simões Cezar
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Silva, Luciano Pereira da
Santiago, Edgo Jackson Pinto
Gomes-Silva, Frank
Silva, Antonio Samuel Alves da
Menezes, Rômulo Simões Cezar
dc.subject.por.fl_str_mv rain; semi-arid; distribution mixture; modeling.
rain; semi-arid; distribution mixture; modeling.
topic rain; semi-arid; distribution mixture; modeling.
rain; semi-arid; distribution mixture; modeling.
description The Brazilian semi-arid region is recurrently affected by the scarcity of water that marks the landscape as it prints periods of severe drought. Therefore, rainfall in this region greatly influences plant growth in regional hydrological processes that affect droughts or floods. It is of practical interest to assess how changes in rainfall patterns occur to anticipate hydrological dynamics. However, this is not easy as climate change reshapes global hydrology. Thus, assertive forecasting has become rare and imputed estimates of a reasonable degree of uncertainty. The objective of this work was to verify from the mixture of exponential, gamma, beta, log-normal, Weibull, normal, log-logistic, and exponentiated log-logistic distributions, which best fits the monthly rainfall of the state of Pernambuco, Brazil. The data used came from 133 monthly rainfall series (1950 to 2012) distributed over the state of Pernambuco. The Maximum Likelihood Method estimated all parameters. The Kolmogorov-Smirnov adherence test was applied at 5% probability to assess the adjustments. The least rejected distributions in the adherence test were Weibull, gamma, and beta; October presented the smallest number of distributions considered adequate to model monthly rainfall. More than 99% of the rain gauge stations had some adequate probabilistic distribution to model monthly rainfall in March. For most months, except for March, the Weibull distribution was the most suitable for modeling the monthly rainfall in the vast majority of rain gauge stations of Pernambuco.
publishDate 2023
dc.date.none.fl_str_mv 2023-04-28
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60621
10.4025/actascitechnol.v45i1.60621
url http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60621
identifier_str_mv 10.4025/actascitechnol.v45i1.60621
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/60621/751375155825
dc.rights.driver.fl_str_mv Copyright (c) 2023 Acta Scientiarum. Technology
http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2023 Acta Scientiarum. Technology
http://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual De Maringá
publisher.none.fl_str_mv Universidade Estadual De Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Technology; Vol 45 (2023): Publicação contínua; e60621
Acta Scientiarum. Technology; v. 45 (2023): Publicação contínua; e60621
1806-2563
1807-8664
reponame:Acta scientiarum. Technology (Online)
instname:Universidade Estadual de Maringá (UEM)
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instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Acta scientiarum. Technology (Online)
collection Acta scientiarum. Technology (Online)
repository.name.fl_str_mv Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv ||actatech@uem.br
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