Evaluating the influence of grass distribution patterns on runoff and sediment yield dynamics: A flow path length perspective

Vegetation distribution patterns exert a first-order control on hillslope hydrology and erosion; however, the mechanisms by which spatial heterogeneity in vegetation regulates runoff generation and sediment yield remain inadequately understood. This knowledge gap constrains the development of physically based erosion models and effective soil conservation strategies. To elucidate how heterogeneous vegetation distributions govern hillslope hydrological connectivity and associated runoff–erosion processes, rainfall–runoff plot experiments were conducted under five vegetation distribution patterns—vertical strips (VS), horizontal strips (HS), X-shaped strips (XS), chessboard uniform distribution (CD), and random patchy distribution (RP)—with a bare slope (BS) serving as the control. Hydrological connectivity was quantified using relative flow path length (RFL), allowing systematic assessment of its influence on overland flow hydraulics, runoff and sediment yield. Results show that key hydrodynamic parameters respond nonlinearly to RFL and are well described by quadratic relationships (adjusted R² > 0.70). Mean flow velocity (v), stream power (ω), and unit energy (E) initially increased and subsequently declined with increasing RFL, reaching extreme values at RFL = 1. Under rainfall intensities of 60–120 mm·h⁻¹ , v, ω, and E increased by 100–114 %, 54–79 %, and 18–38 %, respectively. In contrast, flow resistance (f) and shear stress (τ) exhibited inverse responses, decreasing by 78–85 % and 11–29 % under the same conditions. Erosion rate (ER) also displayed a pronounced nonlinear response to RFL: as RFL increased from 0.513 to 1, ER rose by 65–118 %, with the sensitivity of ER to RFL diminishing at higher rainfall intensities. Building on these relationships, an erosion rate model coupling stream power ω and RFL was developed and validated using multi-source datasets. The model exhibited strong predictive skill and robustness, with adjusted R² and Nash–Sutcliffe efficiency (NSE) values exceeding 0.75, and substantially outperformed the Water Erosion Prediction Project (WEPP) model (adjusted R² = 0.316; NSE = −0.283). Overall, this study establishes a clear mechanistic link between vegetation-induced heterogeneity in hillslope hydrological connectivity and erosion dynamics, providing new insights for improving erosion modeling and designing vegetation-based soil conservation measures.