<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Relation of influencing variables and weather conditions on rainfall partitioning by birch and pine trees

Faculty of Civil and Geodetic Engineering

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

General weather conditions may have a strong influence on the individual elements of the hydrological cycle, an important part of which is rainfall...

Relation of influencing variables and weather conditions on rainfall partitioning by birch and pine trees

Online veröffentlicht: 15. Nov. 2021

Seitenbereich: 456 - 466

Eingereicht: 28. Mai 2021

Akzeptiert: 05. Aug. 2021

DOI: https://doi.org/10.2478/johh-2021-0023

Schlüsselwörter
Throughfall, Stemflow, Rainfall interception, Rainfall microstructure, Boosted regression trees, Random forest

© 2021 Katarina Zabret et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Relation of influencing variables and weather conditions on rainfall partitioning by birch and pine trees

Online veröffentlicht: 15. Nov. 2021

Seitenbereich: 456 - 466

Eingereicht: 28. Mai 2021

Akzeptiert: 05. Aug. 2021

DOI: https://doi.org/10.2478/johh-2021-0023

SchlüsselwörterThroughfall, Stemflow, Rainfall interception, Rainfall microstructure, Boosted regression trees, Random forest

© 2021 Katarina Zabret et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Throughfall, Stemflow, Rainfall interception, Rainfall microstructure, Boosted regression trees, Random forest