Dimensionamento amostral para a estimação da média de precipitação pluvial mensal em locais do Estado do Mato Grosso do Sul

Torres, Francisco Eduardo; Filho, Alberto Cargnelutti; Teodoro, Paulo Eduardo; Corrêa, Caio Cézar Guedes; Ribeiro, Larissa Pereira; Cunha, Elias Rodrigues da

doi:10.1590/0103-8478cr20150348

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…Given these high percentages with fit to normal distribution, it can be concluded that the use of normal distribution has credibility for estimating the monthly rainfall in the Mato Grosso do Sul State, agreeing with the results obtained by Torres et al (2016). The greatest probable precipitation values at 75% probability by normal distribution occurred between the months of November to April, which allows us to classify these months as the rainiest months of the year in Mato Grosso do Sul (Figure 1).…”

Section: Resultssupporting

confidence: 84%

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Probable monthly rainfall associated with distinct biomes of Mato Grosso do Sul state

Teodoro¹,

Silva²,

Oliveira‐Júnior³

et al. 2017

Biosci. J.

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ABSTRACT:The aim of this study was to determine the probable monthly rainfall for the state of Mato Grosso do Sul, considering the level of 75% probability, and study the spatial distribution associated with its different biomes. The rainfall data of 32 stations (sites) in the state of Mato Grosso do Sul were collected in the period 1954-2013. In each of the 384 series, the average monthly rainfall was calculated, for at least 30 years of observation. The Kolmogorov-Smirnov adhesion test was applied to the rainfall time series to check the fit of the data to a normal distribution. The likely fallout was estimated at 75% probability, using the normal probability distribution and, subsequently, it was adopted the method of Ordinary Kriging interpolation mathematics to spatial data. Based on the likely monthly precipitation estimated, the State of Mato Grosso do Sul possess three distinct periods, with the precipitation associated with different biomes: the rainy season (between the months November to March, where increased precipitation occurred in the Savanna biome), dry season (between the months from

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Section: Resultssupporting

confidence: 84%

“…Probable rainfall was estimated at 75% probability, using the normal probability distribution according Torres et al (2016), also named Gaussian curve, which shows two parameters and its probability density function is defined by the equation (HASTINGS;PEACOCK, 1975):…”

Section: Estimate Of the Probable Rainfallmentioning

confidence: 99%

Probable monthly rainfall associated with distinct biomes of Mato Grosso do Sul state

Teodoro¹,

Silva²,

Oliveira‐Júnior³

et al. 2017

Biosci. J.

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“…The Brazilian State of Mato Grosso do Sul plays a significant role in the national agriculture where information on rainfall is of vital importance (Correa et al ., 2014; Teodoro et al ., 2016; Torres et al ., 2016). Approximately 65% of the area of Pantanal, a particular and important biome and the largest continental wetland in the world (Ioris et al ., 2014), is located in the northwest of the state.…”

Section: Introductionmentioning

confidence: 99%

Analysis of monthly and annual rainfall variability using linear models in the state of Mato Grosso do Sul, Midwest of Brazil

Abreu

Souza

Lyra

et al. 2020

Intl Journal of Climatology

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This study aims at fitting polynomial models based on latitude, longitude and altitude coordinates to estimate the monthly and annual mean rainfall in Mato Grosso do Sul state, Midwest region of Brazil. Furthermore, its target is to verify whether hydrologically similar regions provide statistical improvement in the regression fit for rainfall estimation. To create the monthly and annual rainfall for 32 rain gauge stations, there were used at least 15 years long data records for analysis with a percentage of gaps at most 10%. Generally, the monthly and annual rainfall models present suitable statistical validation coefficients. The number of predictor variables enhances the performance of the regression method when estimating monthly and annual rainfall. Fitting regressions in hydrologically similar groups through cluster analysis tends to increase regression performance; however, the limited number of rain gauge stations in Brazil makes this technique difficult to apply because the number of parameters of the regression models may be greater than the number of rain gauge stations in the cluster. The first degree polynomial regression proved to be the most adequate to represent the mean monthly and annual rainfall because of the equivalence between observed and predicted values of rainfall and because of the statistical analysis. Fitting polynomial models presents suitable method for practical applications, forming an important tool for environmental management in the Mato Grasso do Sul State.

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“…De maneira geral, dentre os elementos climáticos, é possível observar que a precipitação é o elemento que possui menor precisão das estimativas tomando como base trinta anos de observação (Cargnelutti Filho et al, 2009). Um estudo recente observou que trinta anos são suficientes para prever a precipitação na maioria dos meses do ano, com exceção de junho, julho e agosto, uma vez que para esses meses podem ser necessários até sessenta e três anos (Torres et al, 2016).…”

Section: Introductionunclassified

Aplicação da Distribuição Nakagami na Análise de Dados de Precipitação

Mazucheli

Emanuelli

2019

Rev. bras. meteorol.

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Resumo Este trabalho teve como objetivo avaliar o desempenho da distribuição Nakagami na análise de séries de precipitação mensal, ao longo de vários anos, visando à seleção de uma distribuição útil para o planejamento e gestão de atividades dependentes dos índices de precipitação na Região Sul do Brasil. Para tanto, compara-se a mesma com cinco distribuições alternativas: Weibull, Gama, Log-Normal, Log-Logística e Inversa-Gaussiana. Foram utilizadas séries históricas de 33 estações meteorológicas observadas entre janeiro de 1970 a dezembro de 2014, totalizando 396 séries (33 estações × 12 meses). Para a escolha da distribuição, que forneceu o melhor ajuste, foram utilizados os valores dos critérios de informação de Akaike, de Kolmogorov-Smirnov, de Anderson-Darling e de Cramér-von Mises. Segundo estes critérios se encontrou que as distribuições Nakagami e Weibull foram selecionadas o maior número de vezes (Nakagami: 146 vezes e Weibull: 100 vezes). Embora a distribuição Nakagami não seja muito utilizada, na descrição de dados de precipitação, recomenda-se sua utilização na descrição do comportamento da pluviosidade mensal como alternativa para distribuições tradicionalmente utilizadas.

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Dimensionamento amostral para a estimação da média de precipitação pluvial mensal em locais do Estado do Mato Grosso do Sul

Abstract: RESUMO

Cited by 7 publications

References 6 publications

Probable monthly rainfall associated with distinct biomes of Mato Grosso do Sul state

Probable monthly rainfall associated with distinct biomes of Mato Grosso do Sul state

Analysis of monthly and annual rainfall variability using linear models in the state of Mato Grosso do Sul, Midwest of Brazil

Aplicação da Distribuição Nakagami na Análise de Dados de Precipitação

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