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Evaporation is one of the most important parameters of meteorological science. Therefore, predicting evaporation is necessary for both water resources and planning management. The present study uses Bayesian Model Averaging (BMA) based on developed and optimized Kernel Extreme Learning Machine (KELM) models for predicting daily evaporation in different provinces of Iran with different climates. The Water Strider Algorithm, Salp Swarm Algorithm, Shark Algorithm, and Particle Swarm Optimization were combined with the KELM to predict daily evaporation in the Hormozgan, Mazandaran, Fars, Yazd, and Isfahan provinces. The models' inputs were average temperature, rainfall, number of sunny hours, wind speed, and relative humidity. The introducing a new hybrid gamma test for determining the adequate inputs, using hybrid and optimized KELM based on developing ELM for predicting evaporation, integrating individual models for predicting evaporation, and quantifying the uncertainty of outputs are the main innovations of the current study. Multiple error indices were used to evaluate the ability of models for predicting evaporation. The standalone and optimized KELM models were used to predict daily evaporation in the first level. In the next level, the BMA based on outputs of standalone and optimized KELM models predicted daily pan evaporation. The general results indicated that the BMA provided the best accuracy among other models in all stations. This study also introduced the new hybrid gamma test (GT-WSA) for choosing the best input combinations. The hybrid GT-WSA gave the best input combination without computing all input combinations (25 − 1). The uncertainty analysis of models also indicated that the uncertainty of BMA and optimized KELM models was lower than that of the KELM model. [ABSTRACT FROM AUTHOR]

The aim of the study was to establish the strength and direction of the relationship between daily temperature of river water and air with the use of selected estimation methods. The relationship was assessed for the River Noteć and its tributaries (Western Poland), using the cross-correlation function and Granger causality. The study established cause-and-effect relations for "water–air" and "air–water" directions of influence. It was confirmed that forecasting the pattern of flowing water temperature from changes in air temperature yields better results when done based on data from the previous day. Results of modelling the relationship between data series with the use of the linear and natural cubic splines models confirmed the presence of a nonlinear relation. It was also established that there is a statistically significant correlation of random fluctuations for both temperature series on the same days. This made it possible to confirm the occurrence of short-term connections between water and air temperature. The results can be used to determine the qualities of thermal regimes and to predict temperature of river waters in the conditions of climate change. [ABSTRACT FROM AUTHOR]

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]