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Rainfall interception process is an important part of the biohydrological cycle, in which vegetation plays an important role by regulating the amount and dynamics of rainfall reaching the ground. In this paper, an event-based analysis is performed to discuss the influence of vegetation on dynamic of temporal response of soil volumetric water content (VWC) in the upper soil layer during rainfall events. More specifically, six events that occurred between 19 November 2021 and 30 June 2022, characterized by different hydro-meteorological and vegetation conditions, are analyzed based on continuous measurements of VWC in the open and below groups of two deciduous (Betula pendula Roth.) and two coniferous trees (Pinus nigra Arnold), as well as rainfall in the open and throughfall on an urban experimental plot in Ljubljana, Slovenia. VWC values at the upper depth (16 cm) were the highest under the birch tree, followed by the location in the open and under the pine tree. However, in the lowest depth (74 cm) VWC values were the lowest under the birch tree. VWC responses to rainfall and throughfall showed seasonal patterns related to the pre-event wetness conditions, with a faster occurrence of maximum VWC values in the leafless period. Additionally, rainfall amount and its dynamics during the event significantly affect the response, as VWC in general reaches its peak after the occurrence of more intense rainfall. Such an event-based analysis, offering an insight into the dynamics of the event development, is crucial and very beneficial for understanding of the biohydrological processes.

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.

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.

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.

Observational data scarcity often limits the potential of rainfall-runoff modelling around the globe. In ungauged catchments, earth observations or reanalysis products could be used to replace missing ground-based station data. However, performance of different datasets needs to be thoroughly tested, especially at finer temporal resolutions such as hourly time steps. This study evaluates the performance of ERA5-Land and COSMO-REA6 precipitation reanalysis products (PRPs) using 16 meso-scale catchments (41–460 km 2) located in Slovenia, Europe. These two PRPs are firstly compared with a gridded precipitation dataset that was constructed based on ground observational data. Secondly, a comparison of the temperature data of these reanalysis products with station-based air temperature data is conducted. Thirdly, several data combinations are defined and used as input for the rainfall-runoff modelling using the GR4H model. A special focus is on the application of an additional snow module. Both tested PRPs underestimate, for at least 20 %, extreme rainfall events that are the driving force of natural hazards such as floods. In terms of air temperature, both tested reanalysis products show similar deviations from the observational dataset. Additionally, air temperature deviations are smaller in winter compared to summer. In terms of rainfall-runoff modelling, the ERA5-Land yields slightly better performance than COSMO-REA6. If a recalibration with PRP has been carried out, the performance is similar compared to the simulations where station-based data were used as input. Model recalibration proves to be essential in providing relatively sufficient rainfall-runoff modelling results. Hence, tested PRPs could be used as an alternative to the station-based data in case precipitation or air temperature data are lacking, but model calibration using discharge data would be needed to improve the performance. [ABSTRACT FROM AUTHOR]