Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall

Extreme rain events can cause social and economic impacts in various sectors. Knowing the risk of occurrences of extreme events is fundamental for the establishment of mitigation measures and for risk management. The analysis of frequencies of historical series of observed rain through theoretical p...

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Autores principales:	Álvaro José Back, Fernanda Martins Bonfante
Formato:	article
Lenguaje:	EN
Publicado:	Associação Brasileira de Engenharia Sanitária e Ambiental 2021
Materias:	heavy rain drainage probability territorial management Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/32229f1ac6d249bcbb89c47f8ba88a1f
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spelling	oai:doaj.org-article:32229f1ac6d249bcbb89c47f8ba88a1f2021-11-19T01:33:19ZEvaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall1808-45242176-947810.5327/Z217694781015https://doaj.org/article/32229f1ac6d249bcbb89c47f8ba88a1f2021-09-01T00:00:00Zhttp://rbciamb.com.br/index.php/Publicacoes_RBCIAMB/article/view/1015https://doaj.org/toc/1808-4524https://doaj.org/toc/2176-9478Extreme rain events can cause social and economic impacts in various sectors. Knowing the risk of occurrences of extreme events is fundamental for the establishment of mitigation measures and for risk management. The analysis of frequencies of historical series of observed rain through theoretical probability distributions is the most commonly used method. The generalized extreme value (GEV) and Gumbel probability distributions stand out among those applied to estimate the maximum daily rainfall. The indication of the best distribution depends on characteristics of the data series used to adjust parameters and criteria used for selection. This study compares GEV and Gumbel distributions and analyzes different criteria used to select the best distribution. We used 224 series of annual maximums of rainfall stations in Santa Catarina (Brazil), with sizes between 12 and 90 years and asymmetry coefficient ranging from -0.277 to 3.917. We used the Anderson–Darling, Kolmogorov-Smirnov (KS), and Filliben adhesion tests. For an indication of the best distribution, we used the standard error of estimate, Akaike’s criterion, and the ranking with adhesion tests. KS test proved to be less rigorous and only rejected 0.25% of distributions tested, while Anderson–Darling and Filliben tests rejected 9.06% and 8.8% of distributions, respectively. GEV distribution proved to be the most indicated for most stations. High agreement (73.7%) was only found in the indication of the best distribution between Filliben tests and the standard error of estimate.Álvaro José BackFernanda Martins BonfanteAssociação Brasileira de Engenharia Sanitária e Ambientalarticleheavy raindrainageprobabilityterritorial managementEnvironmental sciencesGE1-350ENRevista Brasileira de Ciências Ambientais, Vol 56, Iss 4, Pp 654-664 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	heavy rain drainage probability territorial management Environmental sciences GE1-350
spellingShingle	heavy rain drainage probability territorial management Environmental sciences GE1-350 Álvaro José Back Fernanda Martins Bonfante Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall
description	Extreme rain events can cause social and economic impacts in various sectors. Knowing the risk of occurrences of extreme events is fundamental for the establishment of mitigation measures and for risk management. The analysis of frequencies of historical series of observed rain through theoretical probability distributions is the most commonly used method. The generalized extreme value (GEV) and Gumbel probability distributions stand out among those applied to estimate the maximum daily rainfall. The indication of the best distribution depends on characteristics of the data series used to adjust parameters and criteria used for selection. This study compares GEV and Gumbel distributions and analyzes different criteria used to select the best distribution. We used 224 series of annual maximums of rainfall stations in Santa Catarina (Brazil), with sizes between 12 and 90 years and asymmetry coefficient ranging from -0.277 to 3.917. We used the Anderson–Darling, Kolmogorov-Smirnov (KS), and Filliben adhesion tests. For an indication of the best distribution, we used the standard error of estimate, Akaike’s criterion, and the ranking with adhesion tests. KS test proved to be less rigorous and only rejected 0.25% of distributions tested, while Anderson–Darling and Filliben tests rejected 9.06% and 8.8% of distributions, respectively. GEV distribution proved to be the most indicated for most stations. High agreement (73.7%) was only found in the indication of the best distribution between Filliben tests and the standard error of estimate.
format	article
author	Álvaro José Back Fernanda Martins Bonfante
author_facet	Álvaro José Back Fernanda Martins Bonfante
author_sort	Álvaro José Back
title	Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall
title_short	Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall
title_full	Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall
title_fullStr	Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall
title_full_unstemmed	Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall
title_sort	evaluation of generalized extreme value and gumbel distributions for estimating maximum daily rainfall
publisher	Associação Brasileira de Engenharia Sanitária e Ambiental
publishDate	2021
url	https://doaj.org/article/32229f1ac6d249bcbb89c47f8ba88a1f
work_keys_str_mv	AT alvarojoseback evaluationofgeneralizedextremevalueandgumbeldistributionsforestimatingmaximumdailyrainfall AT fernandamartinsbonfante evaluationofgeneralizedextremevalueandgumbeldistributionsforestimatingmaximumdailyrainfall
_version_	1718420601253658624

Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall

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