Vegetation–Debris Synergy in Alternate Sandbar Morphodynamics: Flume Experiments on the Impacts of Density, Layout, and Debris Geometry

Predicting how vegetation–debris interactions reshape alternate sandbars under a steady subcritical flow remains poorly understood in laboratory-to-field scaling. This study quantified how vegetation density and layout interact with debris geometry to control scouring and deposition and developed an empirical tool to predict normalized bed-level changes. Flume experiments investigated how vegetation–debris interactions regulate the hydromorphodynamics of non-migrating alternate sandbars under a steady subcritical flow (Q = 0.003 m3/s; slope = 1/200). Vegetation patches were configured in two spatial layouts—upstream (apex) and river line (edge), at varying densities, with and without debris (I-type: wall-like; U-type: horseshoe-shaped). Results indicated that dense upstream vegetation combined with I-type debris produced the strongest morphodynamic response, generating maximum scour, corresponding to the maximum bed-elevation changes (Δz) normalized by water depth (h) (dimensionless Δz/h) values of −1.55 and 1.05, and sustaining more than 70% of the downstream morphodynamic amplitude. In contrast, U-type debris promoted distributed deposition with a milder scour, while sparse vegetation yielded weaker, more transient responses. Debris geometry-controlled flow partitioning: the I-type enhanced frontal acceleration, whereas the U-type facilitated partial penetration and redistribution. To integrate these findings into predictive frameworks, an empirical regression model was developed to estimate Δz/h from the vegetation density, distribution, and debris geometry, with an additional blockage index to capture synergistic effects. The model achieved 87.5% prediction within ±20% error, providing a practical tool for anticipating scour and deposition intensity across eco-hydraulic configurations. These insights advance intelligent water management by linking morphodynamic responses with predictive modeling, supporting flood-resilient river engineering, adaptive channel stability assessments, and nature-based solutions.