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This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007–2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov–Smirnov test, the corrected Akaike criterion AICc, the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28–32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes. [ABSTRACT FROM AUTHOR]

• Evaporation trends of tropical reservoirs in Brazil were investigated. • Both positive and negative trends in evaporation were observed. • In the industrial region, the negative trend was potentially caused by the impact of pollutant released to the atmosphere. • In the coastal region, the positive trend was related to increasing air temperature. This paper aims to analyze the trends (Mann-Kendall test) of evaporation and climatological variables, including air temperature, relative humidity and wind speed in four reservoirs located in the Brazilian Northeast. The evaporation was estimated through remote sensing using the Surface Energy Balance System for Water (AquaSEBS) model from 1985 to 2018. In addition, it was verified which meteorological parameter has the highest correlation (R2) with the evaporation in order to assess why this process is changing throughout the years in each reservoir. The model performed well, with root mean square error (RMSE) ≤ 1.25 mm/day, percent bias (PBIAS) ≤ 13.70% and coefficient of determination (R2) ≥ 0.51. One reservoir located closer to a coastal zone presented a positive trend (+0.24 mm/34 years), corroborating the measurements of a reference class A pan, and Penman-Monteith equation, while the others, located closer to industrial areas, displayed negative trends (−0.26 to −0.080 mm/34 years). This reduced evaporation was attributed to the impact of industrial pollutant release to the atmosphere, while the increasing effect in the other reservoir was due to its proximity to the coast, which implies an air renewal as the sea breeze contributes for an improvement in the air quality. The meteorological data also presented positive and negative correlations: Temperature exhibited a positive trend (+0.57 °C/34 years), whereas humidity (−4.83%) and wind speed (−0.29 m/s), a negative trend. Evaporation displayed moderate coefficient of determination (R2 > 0.31) with wind speed and humidity. Yet, the reduction of evaporation may be associated with a possible counterbalancing effect of wind speed over humidity. Overall, the results contribute to a better understanding of evaporation trends in a water-scarce region and can be used in a water resources management context as the increasing/reduction of evaporation might significantly impact water availability in this area. [ABSTRACT FROM AUTHOR]