The impacts of climate change on rainfall modeling in the Pantanal of Mato Grosso do Sul

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-arling (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.

Automated summary

The study aimed to adjust different probability distribution models to monthly, seasonal and annual rainfall data in the Pantanal of Mato Grosso do Sul, Brazil, and evaluate their performance using quality and information criteria, finding that the 3-parameter Lognormal distribution performed the best in all twelve months. It was observed that the 3-parameter Lognormal distribution is suitable for spring and summer, while the 2-parameter Gamma and 3-parameter Gamma distributions are better suited for winter and autumn seasons, and the 3-parameter Weibull distribution is the best fit for annual observations.