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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]

The aim of this paper is to investigate the teleconnection of large-scale climatic signals with river flow discharge in northern Iran. To this end, the Garmrud, Alishrud, and Babolrud rivers were selected: their monthly data set during the years 1993–2018 was received from the regional water company of the Mazandaran Province. Climate indices including the Southern Oscillation Index, extreme eastern tropical Pacific sea surface temperature (Niño 1 + 2), Global Mean Temperature Index, North Pacific pattern (NP), Pacific Decadal Oscillation, and North Atlantic Oscillation were investigated. The results of the cross-correlation, and the Spearman's test showed that there is a significant relationship at a 0.01 level between the indices and the river flow discharge. The most correlated indices were the NP and Niño 1 + 2. The NP index was inversely correlated and the Niño 1 + 2 was directly correlated with the rivers' flow variations, and the time lag of these two indices' maximum effectiveness was 2 months and 1 month, respectively. The adaptive neuro-fuzzy inference system and multilayer perceptron (MLP) models were used as the predictor models. The inputs were selected from the time lags of both river discharge and climatic indices. According to the normalized root mean square error (NRMSE) values (0.14–0.22), the predictions are evaluated as having moderate and appropriate qualities. The best performance of all models, R = 0.824 and RMSE = 0.350 CMS, was achieved for the MLP model. The results showed that consideration of the climatic indices as inputs may improve monthly flow predictability by an average of 24.1% (11.4–38.4% among the rivers and models). [ABSTRACT FROM AUTHOR]

The temperature of river water (TRW) is an important factor in river ecosystem predictions. This study aims to compare two different types of numerical model for predicting daily TRW in the Warta River basin in Poland. The implemented models were of the stochastic type—Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)—and the artificial intelligence (AI) type—Adaptive Neuro Fuzzy Inference System (ANFIS), Radial Basis Function (RBF) and Group Method of Data Handling (GMDH). The ANFIS and RBF models had the most fitted outputs and the AR, ARMA and ARIMA patterns were the most accurate ones. The results showed that both of the model types can significantly present suitable predictions. The stochastic models have somewhat less error with respect to both the highest and lowest TRW deciles than the AIs and were found to be better for prediction studies, with the GMDH complex model in some cases reaching Root Mean Square Error (RMSE) = 0.619 °C and Nash-Sutcliff coefficient (NS) = 0.992, while the AR(2) simple linear model with just two inputs was partially able to achieve better results (RMSE = 0.606 °C and NS = 0.994). Due to these promising outcomes, it is suggested that this work be extended to other catchment areas to extend and generalize the results. [ABSTRACT FROM AUTHOR]