Rainfall Partitioning Dynamics in Xerophytic Shrubs: Interplays Between Self-Organization and Meteorological Drivers

Rainfall partitioning, a crucial process in shaping the local hydrological cycle, governs canopy interception and subsequent soil water recharge. While canopy structure and meteorological conditions fundamentally regulate this process, the role of plant self-organization and its interactions with meteorological drivers (non-precipitation variables in particular) remain underexplored. To address this gap, we investigated rainfall partitioning components, including the amount, intensity, efficiency, and temporal dynamics of throughfall and stemflow, in clumped and scattered Vitex negundo L. var. heterophylla (Franch.) Rehder shrubs in the Yangjuangou catchment of the Chinese Loess Plateau during the 2021–2022 rainy seasons. Despite comparable net precipitation (clumped: 83.5% vs. scattered: 84.2% of incident rains), divergent rainfall partitioning strategies emerged. Clumped V. negundo exhibited greater stemflow (8.6% vs. 5.2%), characterized by enhanced intensity, efficiency, and favorable temporal dynamics. Conversely, scattered shrubs favored throughfall generation (79.0% vs. 74.9%). Consistent with previous research, rainfall amount was recognized as the primary control on partitioning rains. Furthermore, our integrated analysis, combining machine learning with variance decomposition, highlighted the critical roles of antecedent canopy wetness (4 h pre-event leaf wetness) and wind speed thresholds (e.g., low wind vs. gust) in regulating partitioning efficiency and temporal dynamics. These findings advance the mechanistic understanding of the interplay between plant self-organization and hydrological processes, demonstrating how morphological adaptations in V. negundo optimize water harvesting in semi-arid ecosystems. This addressed the need to incorporate dynamic interplays between plant structure (specifically, self-organized patterns) and meteorological factors (particularly non-precipitation variables) into ecohydrological models, especially for improved predictions in water-limited regions.