Flow dynamics were characterized and stable zones in diversions were quantified using physical modeling, in situ experiments, and 3D numerical simulations. ADV (1 cm spatial resolution) and water-level probes (0.01 cm spatial resolution) were used in the physical experiments in a rectangular channel. ADCP (resolution of 50 cm) was employed for in situ validation at a northern China hub. Numerical simulations using ANSYS 2022R2 Fluent software with RNG k-ε and VOF showed little error (<15%) compared to the experiments. The results quantified the diversion zone into four sub-regions: acceleration (length 0.8–1.2 h); stabilization (1.2–3.5 h); diffusion deceleration (3.5–5.0 h); and stagnation (localized eddies, diameter 0.3–0.8 d). The stable zone length was dominantly controlled by the nonlinear coupling of geometric (B_s/B_m, 42%) and hydraulic (Fr, 28%) parameters. Upstream and downstream stable zone empirical models showed high accuracy (R² = 0.83 and 0.76, p < 0.01), with an average relative error <15%. Based on the proposed zoning principles and flow characteristics, measurement facilities in the irrigation area are presented. These tools enhance irrigation diversion design and management for improved water efficiency.

Canal discharge identification is essential for irrigation water metering, water management, and ecological protection. With the rapid advancement of UAV aerial photogrammetry, UAV-based large-scale field flow field observations have emerged as a prominent research focus. Given the capability to accurately retrieve canal surface flow velocities, a critical challenge remains in UAV remote sensing-based canal hydrological monitoring: how to identify key cross-sections, obtain high-resolution surface flow field information, and enable timely canal cross-section discharge estimation during sudden flood events. To address the aforementioned challenges, this manuscript combines deep learning algorithms with Kalman filtering and monocular ranging techniques. Artificial square sheet tracers are released into the canal, based on which a YOLO-DeepSort deep tracking framework is constructed. Based on the established UAV-based canal flow velocity perception platform using deep learning, this manuscript achieves mAP@0.5 of 0.995, with precision and recall both reaching 1.0 for real-time tracer detection and flow velocity identification via UAV low-altitude remote sensing. The average relative error of velocity estimation is within 7%, and discharge inversion errors are 1.7%, 6.4%, and 4.6% for the three canal sections, respectively. The surface flow field and cross-sectional velocity distribution of the observed sections are obtained accurately. This manuscript is expected to provide a systematic scientific basis for UAV low-altitude remote sensing-based canal discharge monitoring.

Periphytic algae, as representative aquatic epiphytic communities, play a vital role in the material cycling and energy flow in rivers. Through physiological processes such as photosynthetic carbon fixation and nutrient absorption, they perform essential ecological functions in water self-purification, maintenance of primary productivity, and microhabitat formation. This study investigates the interaction mechanisms between these highly flexible organisms and the hydrodynamic environment, thereby addressing the limitations of traditional hydraulic theories developed for rigid vegetation. By incorporating the coupled effects of biological flexibility and water flow, an innovative nonlinear resistance model with velocity-dependent response is developed. Building upon this model, a coupled governing equation that integrates water flow dynamics, periphytic algae morphology, and layered Reynolds stress is formulated. An analytical solution for the vertical velocity distribution is subsequently derived using analytical methods. Through Particle Image Velocimetry (PIV) measurements conducted under varying flow velocity conditions in an experimental tank, followed by comprehensive error analysis, the accuracy and applicability of the model were verified. The results demonstrate strong agreement between predicted and measured values, with the coefficient of determination R² greater than 0.94, thereby highlighting the model’s predictive capacity in capturing flow velocity distributions influenced by periphytic algae. The findings provide theoretical support for advancing the understanding of ecological hydrodynamics and establish mechanical and theoretical foundations for river water environment management and vegetation restoration. Future research will build upon the established nonlinear resistance model to investigate the dynamic coupling mechanisms between multi-species periphytic algae communities and turbulence-induced pulsations, aiming to enhance the predictive modelling of biotic–hydrodynamic feedback processes in aquatic ecosystems.