Abstract
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.
Highlights
Considered a national heritage site and a biosphere reserve, the Pantanal is the largest floodplain on the planet and is home to an invaluable ecosystem
Located in the center of South America (SA), the Pantanal is included in the continuous floodplain of the Upper Paraguay basin, which occupies 361,666 km2, where highly diverse flora and fauna are sustained by seasonal floods (Louzada, Bergier, & Assine, 2020)
Quantitative data are lacking as input into environmental planning, as well as various types of socioeconomic and socio-environmental modeling methods, which can be combined with ecological or hydrological data in integrated approaches to connect human systems to environmental indicators, mainly in Protected areas (PAs), where PAs are fundamental for biodiversity conservation, yet their impacts on nearby residents are contested (Gray et al, 2018; Santiago, Correia Filho, Oliveira-Júnior, & Silva Junior, 2019; Naidoo et al, 2019)
Summary
Considered a national heritage site and a biosphere reserve, the Pantanal is the largest floodplain on the planet and is home to an invaluable ecosystem. Located in the center of South America (SA), the Pantanal is included in the continuous floodplain of the Upper Paraguay basin, which occupies 361,666 km, where highly diverse flora and fauna are sustained by seasonal floods (Louzada, Bergier, & Assine, 2020). Its territory has approximately 160,000 km divided between Paraguay, Bolivia and Brazil (Silva & Abdon, 1998; Junk & Cunha, 2005; Teodoro et al, 2016). The Brazilian territory occupies about 40% of this basin. The Brazilian Pantanal is divided into 7 municipalities in the state of Mato Grosso (MT) - (35%) and 9 municipalities in the state of Mato Grosso do Sul (MS) - (65%) (Silva & Abdon, 1998). Quantitative data are lacking as input into environmental planning, as well as various types of socioeconomic and socio-environmental modeling methods, which can be combined with ecological or hydrological data in integrated approaches to connect human systems to environmental indicators, mainly in Protected areas (PAs), where PAs are fundamental for biodiversity conservation, yet their impacts on nearby residents are contested (Gray et al, 2018; Santiago, Correia Filho, Oliveira-Júnior, & Silva Junior, 2019; Naidoo et al, 2019)
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