Open AccessThe impacts of climate change on rainfall modeling in the Pantanal of Mato Grosso do Sul
Author(s) -
Ivana Pobočíková,
Amaury de Souza,
Marcel Carvalho Abreu,
José Francisco de OliveiraJúnior,
Cícero Manoel dos Santos,
Taynã Maria Pinto Lins,
Flávio Aristone,
Pedro Ramos
Publication year - 2021
Publication title -
acta scientiarum. technology/acta scientiarum. technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.183
H-Index - 17
eISSN - 1807-8664
pISSN - 1806-2563
DOI - 10.4025/actascitechnol.v43i1.55112
Subject(s) - akaike information criterion , log normal distribution , weibull distribution , bayesian information criterion , precipitation , statistics , environmental science , mathematics , gamma distribution , mean squared error , climatology , meteorology , geography , geology
The most significant and influential meteorological element in environmental conditions and human activities is precipitation. The objective of this study was to adjust eight probability distributions to monthly, seasonal and annual rainfall data in the Pantanal of Mato Grosso do Sul, Brazil, using a time series of data (1983-2013) by the National Meteorological Water Agency (ANA). The performance evaluation of different probability distribution models was assessed by the quality of fit of the selected probability distributions for precipitation data. Quality tests as chi-square, Kolmogorov-Smirnov (KS) and Anderson-Darling (AD), the information criteria as Akaike (AIC) and the Bayesian criterion (BIC) were used. Then the mean root square error (RMSE) and the coefficient of determination (R2) were applied. The analyzes were made monthly, annually and by seasons. The 3-parameter Lognormal distribution performs the best for all twelve months and provides the best-fit to the monthly rainfall data. Thus characterizing a dry period that runs from May to September and a rainy period between the months of October and April, it was observed that the 3-parameter Lognormal distribution has best adjustment for spring and summer, and for winter and autumn the 2-parameter Gamma and 3-parameter Gamma distribution performed better. For annual observations, the function that best fits is 3-parameter Weibull distribution.