Elucidating spatio-temporal throughfall dynamics with ULS derived forest structure density metrics

Water fluxes in forests inherit complex dynamics remains poorly understood. Precipitation in forests is intercepted by the canopy and spatially redistributed, resulting in distinct patterns of throughfall and stemflow. Canopy throughfall creates spatially heterogeneous water flux patterns beneath the forest canopy, which leads to 'hot spots' of water and nutrient input to the ground and affects soil infiltration, groundwater recharge and runoff. Despite its importance, the influence of forest structure on vegetation driven water partitioning and on the emerging spatio-temporal patterns remains poorly understood. The quantification of forest morphology in high spatial and temporal resolution is a challenge. as direct approaches are labour-intensive and often require destructive sampling (e.g. count total leaf number). Light Detection and Ranging (LiDAR) sensing from Uncrewed Aerial Vehicles (UAVs) has emerged as an effective technique to measure the three-dimensional forest structures.

Previous studies considering LiDAR derived structural metrics investigated throughfall on forest stand level with airborne laser scanning (ALS)(Schumacher & Christiansen (2015)), developed models on throughfall kinetic energy (Senn et al. (2020)) and identified  water drip points in branch architecture with TLS (Wischmeyer et al. (2024)). However, these studies are limited in their spatio-temporal resolution. Recent advances of UAV based LiDAR sensor technologies (ULS) enabled the representation of forest structures both with adequate temporal and spatial detail. Such data may be the key to track and understand precipitation dynamics in forests.

Here, we present an innovative approach that combines ULS-derived forest structure metrics and in-situ-derived throughfall measurements to explore the relationship between changes in forest structure and spatio-temporal throughfall dynamics. The ULS datasets was collected starting from April 2024 with 1-2 flights per month over a forest plot in the black forest, Germany. The dataset captures forest morphology variation within the year including tree growth and changes in structure and foliage density. Continuous throughfall measurements were collected in the center (0,4 ha area) of the forest plot. 100 tipping bucket units of an automated throughfall sampling network were mounted along transects on the ground of a mixed and pure Beech and Douglas fir stand monitoring throughfall from precipitation events of different sizes, starting November 2024. Classic trough throughfall measurements starting summer 2024 complement the dataset, which covers throughfall during dormant- and vegetation period. From the LiDAR data, we derive different metrics describing forest morphology, from voxel based point densities to experimental occlusion-related permeability metrics.

In a combined correlation analysis of density- and permeability metrics with corresponding daily spatial throughfall, the influence of phenological changes on throughfall patterns at a high spatial and temporal resolution is investigated. With this study, we aim to identify the potential of LiDAR-derived metrics of forest structure from multi-temporal datasets for forest hydrology research and to develop approaches on how to integrate metrics that are suitable descriptors for complex forest canopy structures into investigation of water fluxes in forest.