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Abstract:Part of precipitation is intercepted by forest canopies, while the rest reaches the ground as throughfall or stemflow. This process is influenced by various meteorological variables, of which we have mainly focused on drop diameter and velocity. Rainfall in the open and throughfall under birch and pine trees have both been measured since 2014 in Ljubljana, Slovenia. The results demonstrate that the total throughfall during 3.5 years was 73% and 53% of rainfall under birch and pine trees, respectively. During the 236 analysed events, the median volume diameter was 1.8 mm (±1.7 mm), and kinetic energy between 0.01 mJ/cm 2 and 23.3 mJ/cm 2 was recorded. We closely analysed the effect of rainfall microstructure on throughfall under pine and birch trees during three specific rainfall events. The increase in drop diameter and fall velocity during a rainfall event instantaneously increased throughfall under pine trees between 25% and 47%, whereas no such changes were observed under birch trees. This may be the consequence of different tree properties of the two species. Additionally, in the case of a saturated canopy, throughfall under pine trees exceeded rainfall in the open after an onset of larger and faster drops.

Urbanization changes the natural environment, alters land use, and affects the hydrological cycle. Due to decreased infiltration, runoff appears faster with higher flow peaks. A nature‐based solution is to plant trees because they intercept precipitation and help to reduce water reaching the ground, forming surface runoff. Rainfall partitioning for birch (Betula pendula Roth.) and pine (Pinus nigra Arnold) trees is measured in the city of Ljubljana, Slovenia, during 2014 and 2015. The measured values for two consecutive years are used to estimate potential surface runoff reduction due to planting of the trees at a parking lot. The results demonstrate that birch and pine trees intercepted 23 and 45% of gross rainfall, respectively. Both tree species intercept more rainfall in the leafed period. Additionally, rainfall interception during wet (2014) and dry (2015) years has been compared. In 2014 rainfall interception is highly influenced by rainfall intensity, while it has a negligible impact on rainfall interception in 2015, when air temperature is more influential. The scenario of covering 10% of the parking lot area with the trees results in runoff reduction of up to 7.3% per year. In general, runoff reduction is higher in a wet rather than a dry year. The new findings about the performance of different tree species in different climate conditions can offer valuable information for the decision makers and landscape designers about the benefits of trees in urban areas.

The process of urbanisation leads to significant changes in surface cover, which influence the hydrological properties of an area. The infiltration of precipitation into the soil is reduced, so that both surface water runoff and the velocity at which water travels have increased drastically. In recent decades climate change has also been observed to affect precipitation trends. Many studies have shown that the amount of rainfall is increasing and that heavy rainfall events are becoming more frequent. These changes are producing more runoff, which has to be drained. Urban trees can reduce the amount of precipitation reaching the ground due to rainfall interception, and are becoming increasingly recognized as an effective means for the regulation of storm water volumes and costs. The study measured rainfall interception in an urban area. It shows that Betula pendula can intercept 20.6% of annual rainfall, whereas Pinus nigra could intercept as much as 51.0% of annual rainfall. The advantage of rainfall interception was shown in the case of a parking lot where the planting of trees was able to reduce runoff by up to 17%.

The influence of tree characteristics and meteorological variables on spatial variability of throughfall under a single silver birch and black pine tree was evaluated. During the year 2016 throughfall was measured at 11 points under each tree canopy. For 30 analysed events total throughfall under the birch tree accounted for 73% and under the pine tree 56% of the rainfall in the open. The coefficient of variation of point throughfall was 30% and 40% for the birch and pine tree, respectively. In case of the birch tree both the distance from the stem and canopy coverage influenced throughfall spatial variability, which also showed different patterns during leafed and leafless periods. Additionally, the amount of rainfall and its microstructure influenced the spatial variability of throughfall under the birch tree. However, among the considered tree characteristics only canopy coverage was recognized as a parameter influencing spatial variability of throughfall under the pine. Furthermore, its spatial patterns were specified by meteorological variables, namely the amount of rainfall and its intensity.

In recent decades, an increase in flood damage has been observed; therefore, the assessment of potential damage is becoming more important. Estimation of potential flood damage can be achieved with various existing models. However, their transferability is questionable and the actual availability of input data is often limited. To overcome these shortcomings, a new model has been developed to effectively estimate flood damage in Croatia. The proposed model uses only publicly available data such as the CORINE data set and information from censuses. The model was developed in an open source Geographic Information System programme. Validation of the model's performance was performed using data from the extreme May 2014 flood that occurred in the Balkan Peninsula. Furthermore, the model has already been used in practice and has proven to be user‐friendly due to the minimum input required from open source data, allowing for insights to be gained from data updates.

Rainfall interception is an important process of the water cycle that can have significant influence on surface runoff and groundwater storage. Since rainfall interception measurements are rare and time consuming, rainfall interception estimation can be made indirectly using different meteorological variables. Experimental data of rainfall interception for birch and pine trees was measured at an experimental plot located in an urban area of Ljubljana, Slovenia in this study. A copula model was applied to predict the rainfall interception using meteorological variables, namely air temperature and vapour pressure deficit data. The copula model performance was compared to some other models such as decision trees, multiple linear regressions, and exponential functions. Using random sampling, we found that the copula model where Khoudraji-Liebscher copula functions were used yielded slightly smaller root mean square error (RMSE) and mean absolute error (MAE) values than other tested methods (i.e., RMSE and MAE results for birch trees were 24.2% and 18.2%, respectively and RMSE and MAE results for pine trees were 25.0% and 19.6%, respectively). The results demonstrate that the copula-based proposed method and other tested models could be used for the prediction of rainfall interception at the considered plot and in the wider surroundings. Furthermore, these models could also be applied for the prediction of rainfall interception for these two tree species in other locations under similar vegetation and meteorological conditions.

Urban trees play an important role in the built environment, reducing the rainfall reaching the ground by rainfall interception. The amount of intercepted rainfall depends on the meteorological and vegetation characteristics. By applying the multiple correspondence analysis (MCA), we analysed the influence of rainfall amount, intensity and duration, the number of raindrops, the mean volume diameter (MVD), wind speed and direction on rainfall interception. The analysis was based on data from 176 events collected over more than three years of observations. Measurements were taken under birch (Betula pendula Roth.) and pine (Pinus nigra Arnold) trees located in an urban park in the city of Ljubljana, Slovenia. The results indicate that rainfall interception is influenced the most by rainfall amount and the number of raindrops. In general, the ratio of rainfall interception to gross rainfall decreases with longer and more intense rainfall events. The influence of the raindrop number depends also on their size (MVD), which is evident especially for the pine tree. For example, pine tree interception increases with smaller raindrops regardless of their number. In addition, MCA gives a new insight into the influence of wind characteristics, which was not visible using previous methods of data analysis (regression analysis, correlation matrices, regression trees, boosted regression trees). According to the nearby buildings, a wind corridor is sometimes created, decreasing rainfall interception by both tree species.