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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| Format: | Article |
| Language: | English |
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Associação Brasileira de Engenharia Sanitária e Ambiental
2021-09-01
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| Series: | Revista Brasileira de Ciências Ambientais |
| Subjects: | |
| Online Access: | https://www.rbciamb.com.br/Publicacoes_RBCIAMB/article/view/1015 |
| _version_ | 1811258461135044608 |
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| author | Álvaro José Back Fernanda Martins Bonfante |
| author_facet | Álvaro José Back Fernanda Martins Bonfante |
| author_sort | Álvaro José Back |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T18:14:44Z |
| format | Article |
| id | doaj.art-32229f1ac6d249bcbb89c47f8ba88a1f |
| institution | Directory Open Access Journal |
| issn | 1808-4524 2176-9478 |
| language | English |
| last_indexed | 2024-04-12T18:14:44Z |
| publishDate | 2021-09-01 |
| publisher | Associação Brasileira de Engenharia Sanitária e Ambiental |
| record_format | Article |
| series | Revista Brasileira de Ciências Ambientais |
| spelling | doaj.art-32229f1ac6d249bcbb89c47f8ba88a1f2022-12-22T03:21:41ZengAssociação Brasileira de Engenharia Sanitária e AmbientalRevista Brasileira de Ciências Ambientais1808-45242176-94782021-09-0156465466410.5327/Z217694781015560Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfallÁlvaro José Back0https://orcid.org/0000-0002-0057-2186Fernanda Martins Bonfante1https://orcid.org/0000-0002-9773-4742Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina (Epagri) - BrazilUniversidade do Extremo Sul Catarinense (UNESC) - BrazilExtreme 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.https://www.rbciamb.com.br/Publicacoes_RBCIAMB/article/view/1015heavy raindrainageprobabilityterritorial management |
| spellingShingle | Álvaro José Back Fernanda Martins Bonfante Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall Revista Brasileira de Ciências Ambientais heavy rain drainage probability territorial management |
| title | 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_short | 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 |
| topic | heavy rain drainage probability territorial management |
| url | https://www.rbciamb.com.br/Publicacoes_RBCIAMB/article/view/1015 |
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