Floods are natural and dynamic phenomenon. Floods in Slovenia are mostly caused by intensive\ud
rainfall. Active and passive flood mitigations are performed to lower flood threat. Floods can endanger\ud
human lives, therefore effective and quality flood frequency analysis are important and in addition\ud
they are also precondition for flood mitigations.\ud
Flood frequency analysis can be carried out with annual maximum series method or peaks over\ud
threshold method. The main advantage of annual maximum series method is simplicity. Independence\ud
criterion and threshold selection are two important properties of POT method. Due to these difficulties\ud
POT method remains unpopular and underemployed in the practice of design flood estimation. POT\ud
sample is compounded from all peaks above a certain threshold level. Annual maximum series sample\ud
contains only maximum flood of each year.\ud
First part of graduation thesis consist theoretical background of partial duration and annual maximum\ud
series methods. Goodness of fit tests which can be used for testing hypothesis and distributions\ud
comparison are introduced. Confidence intervals are also discussed in thesis.\ud
In practical part of thesis flood frequency analysis are performed. Data from gauging station Litija 1\ud
on river Sava was used for analysis. Some frequently used probability distributions and three different\ud
parameter estimation techniques were used. Method of moments, method of L-moments and\ud
maximum likelihood method were applied to Litija 1 data. POT analyses were carried out for different\ud
threshold values and influence of threshold selection on analysis results was discussed. Goodness of fit\ud
tests were used for determination of the best fit distribution and for comparison of parameter\ud
estimation techniques. We tried to define the optimal threshold value. Analyses results were compared\ud
and we find out that POT method gave better results as annual maximum series method. Log-Pearson\ud
type 3 distribution with parameters estimated with method of L-moments gave the best fit to data.\ud
Method of L-moments gave better results in most of the applied probability distributions as method of\ud
moments and maximum likelihood method
