Extremos Intense rainfall study of Goiânia/GO by modeling maximum annual events using Gumbel and Generalized Extreme Value distributions

Filho, José Alexandre Pinto Coelho; Melo, Denise Christina de Rezende; Araújo, Maisa de Lourdes Martins

doi:10.5935/ambiencia.2017.01.05

Cited by 6 publications

(6 citation statements)

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“…Analyzing the results of the Anderson-Darling test at a significance level of 5%, it was found that the best adjustment to the AMDR series (Figure 3) was given by the Kappa (72.1%), the GEV (27.1%), and the 2P-LN distributions, which was better for only 2 (0.8%) of the 247 series. These results corroborate the studies by Coelho Filho et al (2017), Ye et al (2018), and Back and Cadorin (2020), who also found that multiparametric distributions perform better when compared to commonly used ones, such as 2P-LN and Gumbel. Table 2 shows the number of AMDR series whose adjustments were suitable or not for each probability distribution tested, according to the Anderson-Darling test results.…”

Section: Resultssupporting

confidence: 91%

“…The probability distribution parameters were adjusted using the Method of L-Moments. This method has been used to estimate parameters distribution in hydrological studies (Beskow et al, 2015;Alemaw and Chaoka, 2016;Coelho Filho et al, 2017) due to producing better estimates for small samples, which are the ones generally available for environmental studies, besides not being influenced by gaps in rainfall series (Parida, 1999;Ganora and Laio, 2015).…”

Section: • Gumbelmentioning

confidence: 99%

“…To overcome this obstacle, IDF equations can be adjusted using pluviometric data, employing the probabilistic modeling of the Annual Maximum Daily Rainfall (AMDR) (Peleg et al, 2018;Alemaw and Chaoka, 2016;Coelho Filho et al, 2017;Switzman et al, 2017), associated to the technique of Daily Rainfall Disaggregation (DRD) (Coutinho et al, 2019;Silva Cruz et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Intensity-Duration-Frequency equations for Rio Grande do Sul - Brazil, based on stationary rainfall series

et al. 2023

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Heavy rainfall information is essential for environmental studies and water engineering. This study therefore aimed to adjust Intensity-Duration-Frequency (IDF) equations for 247 locations in the Rio Grande do Sul (RS) using stationary rainfall series. Mann-Kendall’s test was applied to identify the temporal trends in the Annual Maximum Daily Rainfall (AMDR) series of 271 rain gauges in RS. The Kappa, Generalized Extreme Value (GEV), Gumbel, two-parameters Log-Normal and three-parameters Log-Normal probabilistic distributions were adjusted to the AMDR series without significant temporal trend. The best distribution fit was given by Anderson-Darling’s test, so the AMDR was discretized up to 5 minutes. IDF equations coefficients were adjusted in RStudio, using Nash-Sutcliffe’s Coefficient and the Root-Mean-Square Error to evaluate them. In conclusion: the most suitable distributions for the AMDR were the multiparametric Kappa and GEV; the IDF equations coefficients adjustment was classified as “excellent”; coefficients a and b varied across the RS and are correlated with the AMDR and geographical positions; and the c and d coefficients were practically constant. Keywords: goodness of fit test, Kappa probabilistic distribution, trends test.

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Section: Resultssupporting

confidence: 91%

Section: • Gumbelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intensity-Duration-Frequency equations for Rio Grande do Sul - Brazil, based on stationary rainfall series

et al. 2023

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“…For GEV distribution, differences above 20% were only observed with a return period of more than 100 years. These results are in consonance with Coelho et al (2017), who reported differences greater than 18% in the maximum rainfall calculated with the GEV and Gumbel distributions with parameters estimated by the methods of moments and L-moments method. Back (2018), who analyzed maximum flow estimates with different probability distributions, observed that for the 10-year return period, differences were below 10%, while differences could be above 20% for the 100-year return period.…”

Section: Rbciambsupporting

confidence: 91%

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

Back

Bonfante

2021

Rev. Bras. Ciênc. Ambient.

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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.

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“…Conhecer as precipitações máximas resulta em diversas aplicações no campo da engenharia de recursos hídricos, podendo ser utilizada como base para o dimensionamento de obras hidráulicas, tais como canais, bueiros e vertedores (Coelho Filho, 2017). Segundo Silva et al (2018) e Gardiman et al (2012), para a caracterização das precipitações é necessário conhecer a sua intensidade, duração e frequência de ocorrência em determinado período de retorno (curvas IDF), pois essas informações permitem projetar, de forma mais segura, estruturas hidráulicas como barragens; canais escoadouros, obras de drenagem, projetos de irrigação e projetos agrícolas.…”

Section: Introductionunclassified

Análise De Métodos De Interpolação Espacial Para Curvas Idf Da Bacia Hidrográfica Do Rio Madeira

Silva

Cordeiro²,

Costa³

et al. 2021

Jour. Hypers. Rem. Sensg.

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The maximum rainfall data result in several applications in the field of water resources engineering and can be used as a basis for the dimensioning of hydraulic works. Its spatialization and the realization of interpolation methods are of enormous importance for the Amazon, with many isolated areas that make it difficult to implement measurement tools. Thus, the objective of the research was to generate maps with the spatialization of intensity, duration and frequency (IDF) curves for the Madeira River basin, using the Bell methods together with the Inverse Distance Weighting (IDW) and Kriging (KRG). The results obtained by the IDW methods showed that there is the formation of "islands" in the spatialization maps. The maps interpolated by KRG showed greater smoothing in isohyets and more regular lines, allowing a better identification of intensity ranges. The KRG method presented, for the Madeira River basin, better interpolation results than the results presented by the IDW method, mainly in the identification of differences between return times and duration. It is concluded that performing the spatialization of IDF curve data in a hydrographic basin is a tool that can help in a better management of water resources, for the development of future works in regions where there is no monitoring of rainfall data.

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Extremos Intense rainfall study of Goiânia/GO by modeling maximum annual events using Gumbel and Generalized Extreme Value distributions

Cited by 6 publications

References 1 publication

Intensity-Duration-Frequency equations for Rio Grande do Sul - Brazil, based on stationary rainfall series

Intensity-Duration-Frequency equations for Rio Grande do Sul - Brazil, based on stationary rainfall series

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

Análise De Métodos De Interpolação Espacial Para Curvas Idf Da Bacia Hidrográfica Do Rio Madeira

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