Abstract
Current and ongoing changes in the climate are typified by a rise in global temperatures. Climate change can have a dramatic impact on the water cycle. The aim of this paper was to develop a model based on Thornthwaite-type monthly water balance estimations. The main goals were to calibrate the model parameters using a remote sensing-based evapotranspiration dataset. The calibrated model was used for projection on the basis of four climate model datasets (remo, dmihirham5, smhirca.bcm, knmiracmo2). The four main projection periods were: 1980-2010, 2010-2040, 2040-2070, and 2070-2100. The advantage of this model is its robust structure. It can be applied if temperature and precipitation time series are available. The key parameter is the water storage capacity of the soil (SOILMAX), which can be calibrated using the actual evapotranspiration data available. If the physical properties of the soil are known, the maximal rooting depth is also projectable. The model can be primarily used at the catchment level or for areas without additional amounts of water from below. For testing the model, a mixed parcel of land that is used as a cornfield near Mosonmagyaróvár and a small, forest-covered catchment near Sopron were successfully used as the datasets. Furthermore, we determined the water stress with the calculation of the relative extractable water (REW), soil water deficit (SWD), and the water stress index (IS).