Tree destruction induced by heavy rainfall, an overlooked type of forest degradation, has been exacerbated along with global climate change. On the Chinese Loess Plateau, especially in afforested gully catchments dominated by Robinia pseudoacacia, destructive rainfall events have increasingly led to widespread forest damage. Previous study has manifested the severity of heavy rainfall-induced tree destruction and its association with topographic change, yet the contributions of tree structure and forest structure remain poorly understood. In this study, we quantified the destroyed trees induced by heavy rainfall using light detection and ranging (LiDAR) techniques. We assessed the influence of tree structure (tree height, crown diameter, and crown area), forest structure (tree density, gap fraction, leaf area index, and canopy cover), and terrain parameters (elevation, slope, and terrain relief) using machine learning models (random forest and logistic regression). Based on these, we aimed to clarify the respective and combined contributions of structural and topographic factors to rainfall-induced tree destruction. Key findings revealed that when considered in isolation, greater tree height, crown diameter, crown area, leaf area index (LAI), and canopy cover suppressed tree destruction, whereas higher gap fractions increased the probability of tree destruction. However, the synergistic increases of tree structural factors (tree height, crown diameter, and crown area) and forest structural factors (LAI and canopy cover) significantly promoted tree destruction, which can counteract the inhibitory effect of terrain on destruction. In addition, increases in tree structure or canopy density (LAI and canopy cover) also increased the probability of tree destruction at the same elevation. Our findings challenge conventional assumptions in forest management by demonstrating the interaction of tree structure and canopy density can significantly promote tree destruction during heavy rainfall. This highlights the need to avoid overly dense afforestation in vulnerable landscapes and supports more adaptive, climate-resilient restoration strategies.

Rainstorm events are becoming increasingly frequent due to the impacts of global warming, which results in widespread erosion disasters and related tree destruction. However, previous corresponding studies of forest damage have focused on typhoons or wildfires, ignoring the increasing risk of rainstorm erosion-induced tree destruction. It is unclear what scale of tree destruction can be caused by heavy rainfall. In this study, we used a tree segmentation method based on airborne light detection and ranging (LiDAR) technology to accurately quantify the tree destruction during heavy rainfall in a representative afforested catchment on the Chinese Loess Plateau. Additionally, topographic changes were calculated using pre- and post-heavy rainfall LiDAR datasets, and tree destruction was assessed by combining terrain information and tree structural parameters. The results showed that 3253 trees in the catchment (0.9 km2) were destroyed due to rainstorm erosion, among which 2845 trees were located on gully slope landform, accounting for 87.4 % of all destroyed trees. Tree destruction on steep gully slope (slope: 45.5 degrees-50.5 degrees) was mainly induced by rainstorm erosion, while that on both sides of the gully bed (altitude: 1137 m-1147 m) was mainly induced by sediment deposition. In the catchment, the deposition area that resulted in tree destruction (21265 m2) was greater than the erosion area (20020 m2). However, the damage caused by erosion was more destructive than that caused by deposition. There was a significant linear relationship between tree structural parameters and terrain in the forestland catchment. Our study provides a reference methodology for studies of forest damage due to extreme weather events worldwide, and has significant implications for ecosystem management and reforestation in the context of global change.