Chuvas intensas no Estado da Bahia

Silva, Demétrius D. da; Filho, Raimundo Rodrigues Gomes; Pruski, Fernando Falco; Pereira, Sílvio Bueno; Novaes, Luciano Farias de

doi:10.1590/s1415-43662002000200030

Cited by 44 publications

(45 citation statements)

References 2 publications

(1 reference statement)

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“…The highest ratio between maximum and minimum average values was 2.1, recorded for 50 min events, in Florianópolis and Itajaí. In a study involving 19 rain gauge stations in the state of Bahia, Brazil, Silva et al (2002) observed that the maximum/minimum ratio varied from 1.28 to 1.76, which is lower than observed in this study.…”

Section: Table 2 Average Values Of the Annual Maximum Precipitation contrasting

confidence: 92%

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Heavy rainfall equations for Santa Catarina, Brazil

Back

Henn²,

Oliveira³

2011

Rev. Bras. Ciênc. Solo

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Knowledge of intensity-duration-frequency (IDF) relationships of rainfall events is extremely important to determine the dimensions of surface drainage structures and soil erosion control. The purpose of this study was to obtain IDF equations of 13 rain gauge stations in the state of Santa Catarina in Brazil: Chapecó, Urussanga, Campos Novos, Florianópolis, Lages, Caçador, Itajaí, Itá, Ponte Serrada, Porto União, Videira, Laguna and São Joaquim. The daily rainfall data charts of each station were digitized and then the annual maximum rainfall series were determined for durations ranging from 5 to 1440 min. Based on these, with the Gumbel-Chow distribution, the maximum rainfall was estimated for durations ranging from 5 min to 24 h, considering return periods of 2, 5, 10, 20, 25, 50, and 100 years,. Data agreement with the Gumbel-Chow model was verified by the Kolmogorov-Smirnov test, at 5 % significance level. For each rain gauge station, two IDF equations of rainfall events were adjusted, one for durations from 5 to 120 min and the other from 120 to 1440 min. The results show a high variability in maximum intensity of rainfall events among the studied stations. Highest values of coefficients of variation in the annual maximum series of rainfall were observed for durations of over 600 min at the stations of the coastal region of Santa Catarina.Index terms: heavy rainfall, hydrology, drainage.

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Section: Table 2 Average Values Of the Annual Maximum Precipitation contrasting

confidence: 92%

“…In this sense, in places where pluviograph data are available, it is possible to fit the equation of heavy rainfall, also called IDF equation. The studies of Fendrich (1998), Costa & Brito (1999), Oliveira et al (2000), Silva et al (2002) and Soprani & Reis (2007) deal with this matter.…”

Section: Introductionmentioning

confidence: 99%

Heavy rainfall equations for Santa Catarina, Brazil

Back

Henn²,

Oliveira³

2011

Rev. Bras. Ciênc. Solo

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“…Sampaio (2011) utilizou a distribuição de probabilidades de Gumbel para a espacialização dos coeficientes de chuvas intensas de 130 postos distribuídos em bacias hidrográficas do Rio Grande do Sul. Silva et al (2002) comprovaram, para estimativa das equações de chuvas intensas para 19 postos pluviográficos do estado da Bahia, que o modelo de Gumbel foi o que apresentou melhor ajuste. Silva et al (2003) testaram as distribuições de Gumbel, Log-Normal a 2 e 3 parâmetros, Pearson e Log-Pearson Tipo III para dados de pluviográficos de 10 estações do Tocantins, constatando que Gumbel foi a melhor ajustada.…”

Section: Resultsunclassified

“…Os dados observados em escala diária permitiram computar séries de precipitação máxima diária anual, as quais foram ajustadas às distribuições de probabilidade Log-Normal a 2 parâmetros (LN-2P), Log-Normal a 3 parâmetros (LN-3P) e Gumbel que, dentre outras, são recomendadas por Naghettini & Pinto (2007) e já foram aplicadas por diversos autores na modelagem probabilística de eventos extremos de chuva (Trefry et al, 2000;Back, 2001;Silva et al, 2002;Sansigolo, 2008;Ben-Zvi, 2009;Santos et al, 2009;Back et al, 2011;Souza et al, 2012;Aragão et al, 2013;Mello & Viola, 2013).…”

Section: Methodsunclassified

“…A distribuição de Gumbel tem sido bastante utilizada para modelagem probabilística de valores extremos (Back, 2001;Silva et al, 2002Silva et al, , 2003Back et al, 2011;Sampaio, 2011;Souza et al, 2012;Mello & Viola, 2013); no entanto, a escolha e a definição da distribuição que melhor se ajusta ao conjunto de dados em análise devem ser realizadas comparando-se as frequências teóricas às frequências observadas por meio de testes estatísticos não paramétricos (Mello & Silva, 2013). Ressalta-se que os resultados desses testes dependem, de acordo com Back (2001), dos parâmetros da distribuição de probabilidades e da equação de posição de plotagem podendo, ainda, indicar mais de uma distribuição adequada.…”

Section: Introductionunclassified

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Modelagem probabilística de eventos de precipitação extrema no estado do Rio Grande do Sul

Caldeira

Beskow

Mello

et al. 2015

Rev. bras. eng. agríc. ambient.

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R E S U M OA gestão de cheias em bacias hidrográficas brasileiras deve ser discutida e priorizada, porém o cenário atual indica lacunas quanto às informações hidrológicas com variabilidade espacial e temporal condizentes. A modelagem probabilística de eventos extremos de precipitação, buscando a extrapolação para uma frequência e duração, pode servir como excelente ferramenta de análise e tomada de decisões. O objetivo principal deste trabalho foi analisar o ajuste de diferentes modelos probabilísticos a séries de precipitação máxima diária anual no Rio Grande do Sul. Séries pluviométricas de 342 estações foram ajustadas às distribuições Log-Normal a 2 e 3 parâmetros e Gumbel e a adequação foi realizada pelos testes de Kolmogorov-Smirnov e Qui-Quadrado. Todas as distribuições de probabilidade consideradas foram adequadas; entretanto, a distribuição Log-Normal a 3 parâmetros apresentou os melhores ajustes segundo os resultados do teste Qui-Quadrado. Os parâmetros das distribuições de probabilidades apresentaram variabilidade ao longo do estado e forte relação com a localização, sugerindo que a regionalização de chuvas intensas pode ser empregada no Rio Grande do Sul como excelente ferramenta de gestão.Probabilistic modelling of extreme rainfall events in the Rio Grande do Sul state A B S T R A C T Flood management in Brazilian watersheds must be discussed and prioritized, however, the current scenario indicates that there are gaps in hydrological information with respect to its spatial and temporal variability. The probabilistic modelling of extreme rainfall events, having as goal to extrapolate values for a given frequency and duration, can be used as an excellent tool for analysis and decision-making. The main objective of this study was to analyse the adjustment of different probabilistic models for series of annual maximum daily rainfall in Rio Grande do Sul. Series of 342 rain gauges were adjusted to 2-parameter Log-Normal, 3-parameter Log-Normal and Gumbel probability distributions and goodnessof-fit tests were based on the Kolmogorov-Smirnov and Chi-Square procedures. It was found that all the distributions presented adequate results, however, 3-parameter LogNormal distribution had the best performance in accordance with the Chi-Square test. The parameters of probability distribution presented variability over the state and a pronounced relationship with the location of rain gauges. This suggests that regionalization of highintensity rainfall can be employed in Rio Grande do Sul as an excellent management tool. Palavras-chave:chuvas intensas distribuição de probabilidade regionalização hidrológica

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Multiparameter probability distributions for heavy rainfall modeling in extreme southern Brazil

Beskow

Caldeira

Mello

et al. 2015

Journal of Hydrology: Regional Studies

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a b s t r a c t Study region: The study was conducted in the Rio Grande do Sul state -Brazil. Study focus: Studies about heavy rainfall events are crucial for proper flood management in river basins and for the design of hydraulic infrastructure. In Brazil, the lack of runoff monitoring is evident, therefore, designers commonly use rainfall intensity-duration-frequency (IDF) relationships to derive streamflow-related information. In order to aid the adjustment of IDF relationships, the probabilistic modeling of extreme rainfall is often employed. The objective of this study was to evaluate whether the GEV and Kappa multiparameter probability distributions have more satisfying performance than traditional two-parameter distributions such as Gumbel and Log-Normal in the modeling of extreme rainfall events in southern Brazil. Such distributions were adjusted by the L-moments method and the goodness-of-fit was verified by the Kolmogorov-Smirnov, Chi-Square, Filliben and Anderson-Darling tests. New hydrological insights for the region: The Anderson-Darling and Filliben tests were the most restrictive in this study. Based on the Anderson-Darling test, it was found that the Kappa distribution presented the best performance, followed by the GEV. This finding provides evidence that these multiparameter distributions result, for the region of study, in greater accuracy for the generation of intensity-duration-frequency curves and the prediction of peak streamflows and design hydrographs. As a result, this finding can support the design of hydraulic structures and flood management in river basins.

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Chuvas intensas no Estado da Bahia

Cited by 44 publications

References 2 publications

Heavy rainfall equations for Santa Catarina, Brazil

Heavy rainfall equations for Santa Catarina, Brazil

Modelagem probabilística de eventos de precipitação extrema no estado do Rio Grande do Sul

Multiparameter probability distributions for heavy rainfall modeling in extreme southern Brazil

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