Mixture models of probability distributions applied to rainfall in the state of Pernambuco, Brazil
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
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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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) instacron:UEM |
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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1799315338065608704 |