Comparison of the Hydrodynamics of Open-Surface and Ice-Covered Rivers
During winter, river ice is a common phenomenon in cold areas, which affects river hydrodynamics by altering the vertical profile from the ice boundary to the bed. For ice-covered scenarios, the common literature for depth-averaged flow profiles under open surface conditions overestimates the profile of ice-covered rivers. This work develops a theoretical model on lateral momentum exchange and depth-averaged velocity profiles for both open-channel and ice-covered rivers using quartic eddy viscosity models. In addition, a series of field measurement campaigns (Acoustic Doppler Current Profiler) was conducted to provide a sufficient and accurate database for validating the theoretical models in the Red River of the North in Fargo, North Dakota, United States under the open surface and fully ice-coverage of summer and winter seasons of 2021 through 2025. We find that the lateral distribution of the depth-averaged velocity profile has its peak at the thalweg. These observations can be described by our theoretical model. Our results indicate that the impact of the ice cover on lateral distribution of depth-averaged velocity in comparison to open channel flow is significant in two ways: (i) suppressing the profile and (ii) impacts of secondary flow. The peak of depth-averaged velocity of the open channel is found to be higher than ice-covered rivers. Also, the effect of secondary flow in an open channel is higher than in ice-covered cases. Moreover, this developed model for lateral momentum transfer within ice-covered rivers forms a basis for future studies that can explore how the presence of ice coverage may alter sediment transport dynamics. This study is supported by National Science Foundation (NSF) CAREER award # 2239799.
Monitoring flow velocities in small streams using drone imagery
Migrating rivers can cause significant damage to civil infrastructures every year due to flooding and erosion. Therefore, monitoring river flow is crucial. While flow measurement is frequently carried out in large rivers, data is sparse in small streams as it is challenging to deploy large measurement devices in shallow areas. In this work, we aim at developing a novel methodology to use drone imagery to monitor flow velocity in small and medium streams. A framework for post-processing drone imagery data, and finally merging Drone imagery, LiDAR, and ADCP for topography reconstruction is developed. First, the Digital Terrain Model (DTM) of a river reach (Buffalo River, Minnesota, United States) and its vicinity is reconstructed from Acoustic Doppler Current Profiler (ADCP), Light Detection and Ranging (LiDAR), and drone photogrammetry data. Second, particle tracking is carried out to reconstruct flow field from drone images from individual particles on the water surface. The flow field derived from drone data is limited within a certain region close to the inner bank. Finally, two-dimensional numerical simulations are carried out using the DTM to compute the unsteady flow dynamics in the river reach to provide a benchmark flow data using the open-source HEC-RAS software. The obtained flow field from numerical simulations will be used to evaluate the accuracy of flow fields derived from drone images. The proposed methodology provides a new way of estimating flow profiles for small and medium-sized streams.Â