Best-fit probability distribution models for monthly rainfall of Northeastern Brazil

The analysis of precipitation data is extremely important for strategic planning and decision-making in various natural systems, as well as in planning and preparing for a drought period. The drought is responsible for several impacts on the economy of Northeast Brazil (NEB), mainly in the agricultu...

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Autores principales:	Patricia de Souza Medeiros Pina Ximenes, Antonio Samuel Alves da Silva, Fahim Ashkar, Tatijana Stosic
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	goodness of fit northeastern brazil precipitation data probability distributions Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/c2170751d38044969c2285171a6d9e05
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spelling	oai:doaj.org-article:c2170751d38044969c2285171a6d9e052021-11-06T11:22:38ZBest-fit probability distribution models for monthly rainfall of Northeastern Brazil0273-12231996-973210.2166/wst.2021.304https://doaj.org/article/c2170751d38044969c2285171a6d9e052021-09-01T00:00:00Zhttp://wst.iwaponline.com/content/84/6/1541https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732The analysis of precipitation data is extremely important for strategic planning and decision-making in various natural systems, as well as in planning and preparing for a drought period. The drought is responsible for several impacts on the economy of Northeast Brazil (NEB), mainly in the agricultural and livestock sectors. This study analyzed the fit of 2-parameter distributions gamma (GAM), log-normal (LNORM), Weibull (WEI), generalized Pareto (GP), Gumbel (GUM) and normal (NORM) to monthly precipitation data from 293 rainfall stations across NEB, in the period 1988–2017. The maximum likelihood (ML) method was used to estimate the parameters to fit the models and the selection of the model was based on a modification of the Shapiro-Wilk statistic. The results showed the chosen 2-parameter distributions to be flexible enough to describe the studied monthly precipitation data. The GAM and WEI models showed the overall best fits, but the LNORM and GP models gave the best fits in certain months of the year and regions that differed from the others in terms of their average precipitation. HIGHLIGHTS Real monthly precipitation data from 293 rainfall stations in Northeastern Brazil.; The selection of the model was based on a modification of the Shapiro-Wilk statistic.; The gamma and Weibull distributions showed the best fits compared to the others.;Patricia de Souza Medeiros Pina XimenesAntonio Samuel Alves da SilvaFahim AshkarTatijana StosicIWA Publishingarticlegoodness of fitnortheastern brazilprecipitation dataprobability distributionsEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 84, Iss 6, Pp 1541-1556 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	goodness of fit northeastern brazil precipitation data probability distributions Environmental technology. Sanitary engineering TD1-1066
spellingShingle	goodness of fit northeastern brazil precipitation data probability distributions Environmental technology. Sanitary engineering TD1-1066 Patricia de Souza Medeiros Pina Ximenes Antonio Samuel Alves da Silva Fahim Ashkar Tatijana Stosic Best-fit probability distribution models for monthly rainfall of Northeastern Brazil
description	The analysis of precipitation data is extremely important for strategic planning and decision-making in various natural systems, as well as in planning and preparing for a drought period. The drought is responsible for several impacts on the economy of Northeast Brazil (NEB), mainly in the agricultural and livestock sectors. This study analyzed the fit of 2-parameter distributions gamma (GAM), log-normal (LNORM), Weibull (WEI), generalized Pareto (GP), Gumbel (GUM) and normal (NORM) to monthly precipitation data from 293 rainfall stations across NEB, in the period 1988–2017. The maximum likelihood (ML) method was used to estimate the parameters to fit the models and the selection of the model was based on a modification of the Shapiro-Wilk statistic. The results showed the chosen 2-parameter distributions to be flexible enough to describe the studied monthly precipitation data. The GAM and WEI models showed the overall best fits, but the LNORM and GP models gave the best fits in certain months of the year and regions that differed from the others in terms of their average precipitation. HIGHLIGHTS Real monthly precipitation data from 293 rainfall stations in Northeastern Brazil.; The selection of the model was based on a modification of the Shapiro-Wilk statistic.; The gamma and Weibull distributions showed the best fits compared to the others.;
format	article
author	Patricia de Souza Medeiros Pina Ximenes Antonio Samuel Alves da Silva Fahim Ashkar Tatijana Stosic
author_facet	Patricia de Souza Medeiros Pina Ximenes Antonio Samuel Alves da Silva Fahim Ashkar Tatijana Stosic
author_sort	Patricia de Souza Medeiros Pina Ximenes
title	Best-fit probability distribution models for monthly rainfall of Northeastern Brazil
title_short	Best-fit probability distribution models for monthly rainfall of Northeastern Brazil
title_full	Best-fit probability distribution models for monthly rainfall of Northeastern Brazil
title_fullStr	Best-fit probability distribution models for monthly rainfall of Northeastern Brazil
title_full_unstemmed	Best-fit probability distribution models for monthly rainfall of Northeastern Brazil
title_sort	best-fit probability distribution models for monthly rainfall of northeastern brazil
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/c2170751d38044969c2285171a6d9e05
work_keys_str_mv	AT patriciadesouzamedeirospinaximenes bestfitprobabilitydistributionmodelsformonthlyrainfallofnortheasternbrazil AT antoniosamuelalvesdasilva bestfitprobabilitydistributionmodelsformonthlyrainfallofnortheasternbrazil AT fahimashkar bestfitprobabilitydistributionmodelsformonthlyrainfallofnortheasternbrazil AT tatijanastosic bestfitprobabilitydistributionmodelsformonthlyrainfallofnortheasternbrazil
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Best-fit probability distribution models for monthly rainfall of Northeastern Brazil

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