Emprego dos métodos expeditos de Chow Gumbel e Bell para formulação de equações de chuvas intensas – uma avaliação de desempenho

2020

RBRH

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.

Alternative model of intense rainfall equation obtained from daily rainfall disaggregation

2020

RBRH

Chuvas extremas e equações intensidade-duração-frequência para o estado do Acre

Rev. Bras. Ciênc. Ambient. (Online)

Cadorin

2020

As chuvas intensas causam vários problemas, como alagamento, deslizamentoe erosão do solo, sendo importante conhecer as informações sobre as relaçõesintensidade-duração-frequência (IDF) de chuvas intensas locais. Em razão da carênciadessas informações para o estado do Acre, este trabalho teve como objetivos analisaras séries históricas de chuvas e ajustar as equações IDF por meio da desagregaçãoda chuva diária. Para o estudo utilizaram-se as estações pluviométricas da redehidrológica da Agência Nacional de Águas de 11 municípios do Acre. As sériesde máximas anuais apresentaram médias variando de 89,6 a 118,7 mm. Foramajustados os parâmetros da distribuição de Gumbel e da distribuição generalizada devalores extremos (GEV), utilizando os métodos dos momentos, o método da máximaverossimilhança, o método dos momentos-L (MML) e, ainda, o método de Chowpara a distribuição de Gumbel. A aderência das distribuições ajustadas às séries demáximas anuais foi avaliada pelos testes Kolmogorov-Smirnov e Anderson-Darling. Adistribuição GEV com parâmetros estimados pelo MML destacou-se como a melhordistribuição para 73% das estações. Foi observada grande variação nos valores dechuva máxima diária entre as estações, evidenciando a necessidade de determinaros valores com dados locais. As equações IDF obtidas apresentaram bom ajuste,com coeficientes de determinação acima de 0,99. As equações ajustadas permitemestimativa da intensidade da chuva com duração de 5 a 1.440 minutos e período deretorno de dois a cem anos com erro padrão inferior 6,40 mm h-1.

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

Rev. Bras. Ciênc. Ambient.

Bonfante

2021

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.