The detailed analysis of individual flood event elements, including peak discharge (Q), flood event volume (V), and flood event duration (D), is an important step for improving our understanding of complex hydrological processes. More than 2,500 flood events were defined based on the annual maximum (AM) peak discharge from 50 Slovenian gauging stations with catchment areas of between 10 and 10,000 km2 . After baseflow separation, the stations were clustered into homogeneous groups and the relationships between the flood event elements and several catchment characteristics were assessed. Different types of flood events were characteristic of different groups. The flashiness of the stream is significantly connected with mean annual precipitation and location of the station. The results indicate that some climatic factors like mean annual precipitation and catchment related attributes as for example catchment area have notable influence on the flood event elements. When assessing the dependency between the pairs of flood event elements (Q, V, D), the highest correlation coefficients were obtained for the Q-V pair. The smallest correlations or no correlations were observed between the Q and D variables.
General weather conditions may have a strong influence on the individual elements of the hydrological cycle, an important part of which is rainfall interception. The influence of general weather conditions on this process was analysed, evaluating separately the influence of various variables on throughfall, stemflow, and rainfall interception for a wet (2014), a dry (2015), and an average (2016) year. The analysed data were measured for the case of birch and pine trees at a study site in the city of Ljubljana, Slovenia. The relationship between the components of rainfall partitioning and the influential variables for the selected years was estimated using two statistical models, namely boosted regression trees and random forest. The results of both implemented models complemented each other well, as both indicated the rainfall amount and the number of raindrops as the most influential variables. During the wet year 2014 rainfall duration seems to play an important role, correlating with the previously observed influence of the variables during the wetter leafless period. Similarly, during the dry year 2015, rainfall intensity had a significant influence on rainfall partitioning by the birch tree, again corresponding to the influences observed during the drier leafed period.
Influence of the pattern of effective rainfall on modeled hydrograph was investigated in the study. The modelling was performed with the U.S. Army Corps of Engineers hydrograph package HEC-HMS 3.2 and calibrated and validated on measured hydrographs of Glinscica watershed. Six different models of rainfall loss were applied and their effect on modeled hydrograph was evaluated. Peak discharge, time of peak discharge and runoff volume were compared. The best results with the lowest RMSE in the study was obtained with the SCS curve number loss method. Also synthetic hyetographs of different probability and duration were used. Three positions of the maximum rainfall intensity at 25, 50 and 75 % of the rainfall duration were applied. The results showed essential differences in simulated time to peak and also differences in peak discharge. The differences in time to peak increases considerably with the increasing of the rainfall duration. Finally, the results of constant intensity distribution of rainfall of different durations were compared with those obtained with typical rainfall distribution with the position of the maximum intensity at 50 %. Results showed considerable differences in peak discharge and time to peak by longer durations of the rainfall. and Práca obsahuje výsledky výskumu vplyvu efektívnych zrážok na modelovaný hydrograf. Odtok bol modelovaný pomocou nástroja U.S. Army Corps of Engineers hydrograph package HEC-HMS 3.2, potom kalibrovaný a verifikovaný na meraných hydrografoch povodia Glinscica. Vplyv zrážok na modelovaný hydrograf bol vypočítaný pre šesť rôznych modelov priebehu zrážok. Porovnali sme maximálne prietoky, časy ich trvania a odtečené množstvá. Najlepšie výsledky s najnižším RMSE sme získali s SCS modelom odtoku. Použili sme tiež syntetické hyetografy rozdielnej pravdepodobnosti a trvania. Použili sa tri polohy maximálnych intenzít zrážok; pre 25, 50 a 75 % ich trvania. Výsledky ukázali zásadný rozdiel v simulovaných časoch maximálneho prietoku a tiež rozdiely v maximálnych prietokoch. Rozdiely v časoch dosiahnutia maximálnych odtokov sa výrazne zvyšovali s časom trvania zrážky. Nakoniec sme porovnali výsledky výpočtov s konštantnými intenzitami rozdelenia s rôznym trvaním zrážky s tými, ktoré boli vypočítané s použitím typických rozdelení, s polohou maximálnej intenzity zrážok pri 50 % ich trvania. Výsledky ukazujú významné rozdiely v maximálnych prietokoch a v časoch ich dosiahnutia v závislosti od trvania zrážky.
Substantial evidence shows that the frequency of hydrological extremes has been changing and is likely to continue to change in the near future. Non-stationary models for flood frequency analyses are one method of accounting for these changes in estimating design values. The objective of the present study is to compare four models in terms of goodness of fit, their uncertainties, the parameter estimation methods and the implications for estimating flood quantiles. Stationary and non-stationary models using the GEV distribution were considered, with parameters dependent on time and on annual precipitation. Furthermore, in order to study the influence of the parameter estimation approach on the results, the maximum likelihood (MLE) and Bayesian Monte Carlo Markov chain (MCMC) methods were compared. The methods were tested for two gauging stations in Slovenia that exhibit significantly increasing trends in annual maximum (AM) discharge series. The comparison of the models suggests that the stationary model tends to underestimate flood quantiles relative to the non-stationary models in recent years. The model with annual precipitation as a covariate exhibits the best goodness-of-fit performance. For a 10% increase in annual precipitation, the 10-year flood increases by 8%. Use of the model for design purposes requires scenarios of future annual precipitation. It is argued that these may be obtained more reliably than scenarios of extreme event precipitation which makes the proposed model more practically useful than alternative models.