• Climate change;
  • discharge;
  • flood frequency analysis;
  • method of L-moments;
  • method of moments;
  • seasonality;
  • statistical tests;
  • trends


The identification of statistical trends and seasonality is significant for understanding current global climate change. In this study, discharge data from the Litija gauging station on the Sava River were used to perform flood frequency and seasonality analyses and identify trends. A linear regression analysis and a Mann–Kendall (MK) test were performed to identify trends in differently defined samples, namely, annual maximum (AM) samples and peaks over threshold (POT) samples. Two types of discharge data series [discharge data with included local maxima (QM) and discharge data without local maxima (QD)] were considered. In the flood frequency analyses, the use of three-parameter distributions produced better test statistics than the use of distribution functions with two parameters. A statistically significant decreasing trend was detected in the mean monthly discharge values; however, the magnitudes of extreme events increased for the Litija station. The results of the study showed that the identified trends and their statistical significance depended on how the samples were defined.