Machine Learning Reveals the Contrasting Roles of Rainfall and Canopy Structure Metrics on the Formation of Canopy Drip and Splash Throughfall

Abstract

Throughfall is a significant majority of the total precipitation reaching the ground in forested areas. This study revealed biotic and abiotic factors influencing the throughfall generation process, with the throughfall partitioning into free throughfall, splash throughfall, and canopy drip created at foliar surface drip points (FSDPs) and occasional woody surface drip points (O-WSDPs), utilizing machine learning. Using a large-scale rainfall simulator, throughfall drops were simultaneously measured at 19 locations under a mix of deciduous and coniferous tree species in both foliated and unfoliated states. Random forest modeling showed that biotic factors, such as foliage amount, primarily affected the development and volume fraction of canopy drip in foliated trees. In contrast, for unfoliated trees, canopy drip volume fraction was mainly influenced by abiotic factors, including drop size and kinetic energy of open rainfall. The formation and volume fraction of splash throughfall were primarily influenced by abiotic factors for both foliated and unfoliated trees. From the comparison between the foliated and unfoliated states, the generation process of canopy drip was separately clarified between FSDPs and O-WSDPs. More and larger canopy drip was generated by more foliage with a more wetted canopy with less fluctuation at the FSDPs, whereas a less wetted canopy and/or higher drop impact energy generated more and larger canopy drip at O-WSDPs. This study underscores the importance of canopy structure and meteorological conditions in determining throughfall partitioning. The findings contribute to a nuanced understanding of rainwater redistribution in forest ecosystems.