This study aims to investigate the precipitation trends in Keszthely (Western Hungary, Central Europe) through an examination of historical climate data covering the past almost one and a half centuries. Pettitt’s test for homogeneity was employed to detect change points in the time series of monthly, seasonal and annual precipitation records. Change points and monotonic trends were analysed separately in annual, seasonal and monthly time series of precipitation. While no break points could be detected in the annual precipitation series, a significant decreasing trend of 0.2–0.7 mm/year was highlighted statistically using the autocorrelated Mann-Kendall trend test. Significant change points were found in those time series in which significant tendencies had been detected in previous studies. These points fell in spring and winter for the seasonal series, and October for the monthly series. The question therefore arises of whether these trends are the result of a shift in the mean. The downward and upward shift in the mean in the case of spring and winter seasonal amounts, respectively, leads to a suspicion that changes in precipitation are also in progress in these seasons. The study concludes that homogeneity tests are of great importance in such analyses, because they may help to avoid false trend detections.

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The most essential requirement for water management is efficient and informative monitoring. Operating water quality monitoring networks is a challenge from both the scientific and economic points of view, especially in the case of river sections ranging over hundreds of kilometers. Therefore, spatio-temporal optimization is vital. In the present study, the optimization of the monitoring system of the River Tisza, the second largest river in Central Europe, is presented using a generally applicable and novel method, combined cluster and discriminant analysis (CCDA). This area for the study was chosen because, spatial inhomogeneity of a river's monitoring network can more easily be studied in a mostly natural watershed - as in the case of the River Tisza - since the effects of man-made obstacles: e.g water barrage systems, hydroelectric power plants, artificial lakes, etc. are more pronounced. Furthermore, since the temporal sampling frequency was bi-weekly, the opportunity of optimizing the monitoring system on a temporal (monthly) scale arose. In the research, 15 water quality parameters measured at 14 sampling sites in the Hungarian section of the River Tisza were assessed for the time period 1975-2005. First, four within-year sections ("hydrochemical seasons") were determined, characterized with unequal lengths, namely 2, 4, 2, and 4 months long starting with spring. Homogeneous groups of sampling sites were determined in space for every season, with the main separating factors being the tributaries and man-made obstacles. Similarly, an overall pattern of homogeneity was determined. As an overall result, the 14 sampling sites could be grouped into 11 homogeneous groups leading to the possibility of reducing the number of sampling locations and thus making the monitoring system more cost-efficient.

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Abstract⎯ Parametric methods (linear trend, t-test for slope) for analyzing time series are the simplest methods to get insight to the changes in a variable over time. These methods have a requirement for normal distribution of the population that can be a limit for application. Non-parametric methods are distribution-free methods, and investigators can have a more sophisticated view to the variable tendencies in time series. 144-year-long time series of precipitation data measured at the meteorological station in Keszthely, Hungary (latitude: 46°44′, longitude: 17°14′, elevation: 124 m above Baltic sea level) were analyzed by Mann-Kendall trend test for detecting tendencies in the time series. Sen's slope estimator was applied to estimate the slope of the linear changes. In average, 44 mm decline can be shown for 100 years in the annual sum, 29.7 mm and 25.7 mm in the precipitation sum of spring and autumn (in 100 years), respectively. The rainfall sum of winter increased by 15.4 mm. Sums of April, May, and October declined by 10.8 mm, 13 mm, and 20.9 mm, respectively, according to one-tailed Mann-Kendall tests. These results were compared to the previous results of the authors carried out by parametric methods. Results of two-tailed tests of parametric and non-parametric methods are easily comparable. Parametric method (linear trend) proved significant decreasing tendencies for spring, April, and October. Nonparametric Mann-Kendall tests show significant declining tendencies for spring, autumn, and October.