Lingqi Yi

Quantifying flow resistance in open channels with mixed‐height vegetation is essential for predicting stage–discharge relationships and assessing conveyance and habitat conditions in vegetated floodplains. In staggered tall–short vegetation, strong lateral velocity contrasts and inter‐patch momentum transfer alter the flow structure and hinder the direct use of conventional roughness formulations. Based on flume experiments with rigid cylindrical surrogates arranged as laterally adjacent tall and short patches in a staggered layout, this study develops a physics‐based procedure to estimate the drag coefficient and the equivalent Manning coefficient across emergent to submerged conditions. A two‐patch momentum‐balance model is formulated by explicitly accounting for the measured water‐surface slope, vegetation drag within each patch and an apparent shear stress representing interfacial momentum exchange between the tall and short patches. Closed‐form expressions are then derived to invert patch‐averaged drag coefficients from readily measurable hydraulic variables and flow‐facing area metrics, providing a tractable alternative to purely empirical calibration. The analytically inverted drag coefficients are further consolidated with literature data recast under consistent definitions of Reynolds number and a density descriptor, yielding an empirical predictor C _d ( R e, λ ) for rigid vegetation in staggered canopies. Finally, a practical linkage between the mean vegetation drag coefficient and Manning coefficient is established by incorporating vegetation area density and hydraulic‐radius scaling, enabling computation of an equivalent Manning coefficient for mixed‐height, staggered vegetation within the tested parameter range. The proposed framework offers a parameter‐consistent basis for resistance estimation in laterally heterogeneous vegetated channels and supports hydraulic assessment and management of vegetated floodplains.

Accurate prediction of drag coefficient ( C _D ) and terminal settling velocity for irregular, bio‐origin particles is essential for dispersal assessment and eco‐hydraulic simulation; however, substantial uncertainty persists for living Oncomelania hupensis because its conical–spiral shell, surface roughness and heterogeneous mass distribution are not adequately represented by classical spherical or generic non‐spherical correlations. We quantified the settling of living O. hupensis collected from representative middle‐Yangtze habitats, obtaining 70 valid individuals and stratifying them into three size classes by major‐axis length. Tests were conducted in a transparent cylindrical column with a mid‐column observation window. Terminal settling segments were objectively delineated from the near‐zero‐acceleration intervals of vertical displacement–time records, enabling the back‐calculation of C _D and particle Reynolds number ( R _e ) via force–balance inversion. Measured terminal velocities spanned 7.08–15.324 cm/s, with corresponding C _D ranging 0.52–2.97 over R _e = 0.5–15. Building on the two‐term Haider–Levenspiel structure, a morphology‐aware drag correlation was developed in which model coefficients were parameterized as functions of shape descriptors (e.g., sphericity and Corey shape factor), supplemented by a smoothness‐stabilized correction and a nonlinear morphology–drag coupling term, and it was calibrated using constrained nonlinear least squares with multi‐start initialization. Relative to baseline spherical and commonly used non‐spherical formulas applied to the same dataset, the proposed correlation increased the coefficient of determination from 0.45 to 0.76 and reduced mean square error from 0.23 to 0.11. Size‐resolved results indicated the tightest C _D – R _e distributions for medium individuals, whereas small and large classes exhibited higher dispersion, consistent with posture/orientation variability and morphological heterogeneity. The resulting correlation and reproducible parameter‐inference workflow provide practically accurate inputs for particle‐tracking modules, improving predictions of settling distance, residence time and deposition hot spots of O. hupensis to support habitat management and schistosomiasis control strategies in river–floodplain systems.