Quantification of vegetation and meteorological variables influencing the kinetic energy of raindrops

Abstract

The process of interception, whereby vegetation partitions rainfall, largely influences natural processes such as soil erosion. The kinetic energy of rainfall plays a crucial role in evaluating this impact. In this study, we measured rainfall characteristics using three disdrometers placed above and below vegetation, specifically under two distinct tree species (birch and pine) in an urban area in Ljubljana, Slovenia. The study period extends over two years, subdivided into a dry and a wet sub-period. The investigation encompasses the effects of vegetation characteristics, raindrops characteristics and meteorological variables on the kinetic energy of throughfall. Two methods, namely boosted regression trees and random forest, were used to evaluate the influence of vegetation and meteorological variables on raindrop characteristics and their kinetic energy. The results indicate that, in general, pine reduces the kinetic energy of raindrops to a much greater extent than birch, and that birch exerts a positive effect on the reduction of kinetic energy only during the leafed period. Both applied machine learning models confirmed that the amount of throughfall has the greatest influence on kinetic energy, regardless of vegetation type. Furthermore, rainfall intensity and median-volume drop diameter exhibited a greater influence in the case of pine compared to the birch tree. Additionally, the findings indicate that the influence of the event duration on kinetic energy of throughfall differs depending on the presence of foliage on the tree canopy.