In the present explorative study, different time-series analysis methods, such as moving average, deterministic methods (linear trend with seasonality), and non-parametric Mann–Kendall trend test, were applied to monthly precipitation data from January 1871 to December 2014, with the aim of comparing the results of these methods and detecting the signs of climate change. The data set was provided by the University of Pannonia, and it contains monthly precipitation data of 144 years of measurements (1,728 data points) from the Keszthely Meteorological Station. This data set is special because few stations in Hungary can provide such long and continuous measurements with detailed historical background. The results of the research can provide insight into the signs of climate change in the past for the region of West Balaton. Parametric methods (linear trend and t-test for slope) for analyzing time series are the simplest ones to obtain insight into the changes in a variable over time. These methods have a requirement for normal distribution of the residuals that can be a limitation for their application. Non-parametric methods are distribution-free and investigators can get a more sophisticated view of the variable tendencies in time series.
The River Tisza is one of Central Europe's most important rivers. In the last one and a half century numerous anthropogenic activities have influenced its watershed. As a result measures need to be taken to protect its water quality, necessitating a comprehensive picture of the spatial and temporal variability of its processes, which this study aims to extend further. In this study five sampling locations were analyzed in the upper section of the Tisza over the time interval 1974–2005, dealing with 24 parameters using multi-variate data analysis methods. Employing time series analysis and taking the river's tributaries into account, the strong influence of the River Szamos was pointed out, while stochastic connections indicated the influence of the Tiszalök Water Barrage System on the spatial variation of the Tisza's processes. Finally, by using principal component analysis (PCA), the different background factors were revealed in space and time (seasonal separation) as well. During summer the processes tended to be nitrogen-related, while during winter inorganic compounds play a greater role. Most importantly, spatial variety was observable in the factors.