Análise de Frequência dos Dados Pluviométricos Observados em 2011 e 2013 na Região Serrana, Estado do Rio de Janeiro

Ottero, Clarisse R.; Chargel, Leonardo Tristão; Hora, Mônica de Aquino Galeano Massera da

doi:10.1590/0102-7786331007

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…However, in applications, we usually adjusted distribution parameters based on the series to be tested. Even with these limitations, the KS test is widely used often as the only adhesion test (Al-Suhili and Khanbilvardi, 2014;Pereira et al, 2017;Silva Neto et al, 2017;Ottero et al, 2018) and also commonly used as criterion for selecting the best distribution (Caldeira et al, 2015;Back and Cadorin, 2020). AD and Rf tests proved to be more rigorous and should be favored.…”

Section: Resultsmentioning

confidence: 99%

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

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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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Section: Resultsmentioning

confidence: 99%

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

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Bonfante

2021

Rev. Bras. Ciênc. Ambient.

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“…These results show that, for this data series, all distributions tested could be used to estimate the maximum daily rainfall. Several papers showed that the annual maximum rainfall series fit well for the Gumbel distribution (Back et al, 2011), and often this distribution is used without performing the adhesion test (Oliveira et al, 2005;Ottero et al, 2018), or without checking for another distribution that fits best. The generalized distribution of extreme values (GEV) has been widely accepted for the discretion of natural maximum events.…”

Section: Resultsmentioning

confidence: 99%

Alternative model of intense rainfall equation obtained from daily rainfall disaggregation

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2020

RBRH

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The objective of this work was to propose an alternative model of IDF equation based on the disaggregation of daily rainfall. Data from the rainfall gauge station code 02649018 of the Brazilian National Water Agency from the period 1968 to 2011 were used. Several extreme event probability distributions were adjusted. Rainfall intensity with a return period of 2 to 100 years and durations of 5 to 1440 minutes were estimated using the relationships between rainfall for different durations. The four coefficients of the traditionally used IDF equation were adjusted, obtaining a sum of squares of deviations equal to 695.1 (mm.h-1)2 and standard error of estimation equal to 2.69 mm.h-1. For the model of the intense rainfall equation proposed, that relates the rain intensity and duration with the maximum daily rainfall, the sum of squares of the deviations was 33.6 (mm.h-1)2 and the standard estimated error was 0.59 mm.h-1. Including the return period in the model, we obtained squares of the deviations of 129.4 (mm.h-1)2, with a standard error of estimation of 1.16 mm.h-1. The models proposed have, besides better estimation accuracy, the advantages of facilitating the updating only by updating the maximum daily precipitation and sub serve the spatial representation.

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“…The GEV distribution obtained by the Lmoments method was highlighted with the best fitting in 32 (73%) historical series. The Gumbel distribution is widely used for maximum annual rainfall (Ottero et al, 2018;Mistry and Suryanarayana, 2019). Notwithstanding, there are studies indicating that the GEV distribution has been shown to be superior to the Gumbel distribution (Beskow et al, 2015;Namitha and Vinothkumar, 2019).…”

Section: Resultsmentioning

confidence: 99%

Extreme rainfall and IDF equations for Alagoas State, Brazil

2020

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Intensity-duration-frequency (IDF) equations have important applications in several engineering areas such as urban drainage designs, hydrological modeling, and soil conservation projects. This study analyzes the annual maximum series and fits IDF equations for 44 rainfall stations in Alagoas State, Brazil. We adjusted parameters of the Gumbel distribution (GD) and the Generalized Extreme Value (GEV) distribution. The fitting of the observed data to the probability distributions, as well as the selection of the best distribution, were based on the Kolmogorov-Smirnov and Anderson-Darling tests at a 5% significance level. The GEV distribution with parameters obtained by the L-moments method was considered the best in 73% of rainfall stations. The estimated IDF equations showed a good fit, with determination coefficients above 0.991. The maximum rainfall intensities have spatial variation following the climatic zones of the state. The fitted equations allow estimating rainfall intensities from 5 minutes to 24 hours with a return period of 2 to 100 years, and standard error of less than 6.83 mm h-1.

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Análise de Frequência dos Dados Pluviométricos Observados em 2011 e 2013 na Região Serrana, Estado do Rio de Janeiro

Cited by 12 publications

References 10 publications

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

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

Alternative model of intense rainfall equation obtained from daily rainfall disaggregation

Extreme rainfall and IDF equations for Alagoas State, Brazil

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