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Global climate change is likely to influence evapotranspiration (ET); as a result, many ET calculation methods may not give accurate results under different climatic conditions. The main objective of this study is to verify the suitability of machine learning (ML) models as calculation methods for pan evaporation modeling on the macro-regional scale. The most significant PE changes in the different agroclimatic zones of the Slovak Republic were compared, and their considerable impacts were analyzed. On the basis of the agroclimatic zones, 35 meteorological stations distributed across Slovakia were classified into six macro-regions. For each of the meteorological stations, 11 variables were applied during the vegetation period in the years from 2010 to 2020 with a daily time step. The performance of eight different ML models—the neural network (NN) model, the autoneural network (AN) model, the decision tree (DT) model, the Dmine regression (DR) model, the DM neural network (DM NN) model, the gradient boosting (GB) model, the least angle regression (LARS) model, and the ensemble model (EM)—was employed to predict PE. It was found that the different models had diverse prediction accuracies in various geographical locations. In this study, the results of the values predicted by the individual models are compared.

Assessment of the land use impact on the processes of water balance in the river basin should be an indispensable part of integrated river basins management. This paper compares climatic conditions occurring during the long-term period (1951-1980), following the situation immediately after dry conditions (1993-1999) and extremely rainy dates (2009-2012) with emphasis to estimate the runoff components in the Žitava river basin: the Obyce sub-catchment, situated in its upper part (74.5 km2) in the Slovak Republic. Modelling of the land use change effect on the total hydrology balance of the river basin characteristics was performed using the hydrological model WaSiM-ETH. The model was applied to evaluate the vegetation type influence and the water balance change in the presently mostly forested river basin (1), altering its replacement by the permanent grasses (2) and bushes (3), with emphasis to different total water balance characteristics change. The present state land use data were taken from the Corine Land Cover of the Slovak Republic. Model results show that actual evapotranspiration would decrease from -1.3% in case of bushes in 2009 up to -32.5% in case of grass in 2011. However, 13.3% rise was considered for bushes in 2010. Total annual discharge shows its increment in all observed changes from 5.9% for bushes in 2010 up to 65.3% for grass in 2012. Only in case of bushes in 2011 there was observed slight decrease of about -3.1%. Regarding the very expected land use change, especially in connection with the ongoing global climate change, the estimation of the hydrology balance components is of utmost significance.