This study, which includes both experimental and numerical-modelling components, investigates the potentially dangerous conditions that can often occur when low-head dams (or weirs) are overtopped and ‘submerged’-type hydraulic jumps subsequently form downstream of them. The combination of high local turbulence levels, air entrainment, and strong surface currents associated with submerged jumps pose a significant risk to safety of boaters and swimmers. In this study, a wide range of flow regimes and different experimental conditions (i.e. crest length and downstream apron elevation) were considered. The experimental phase involved physical model testing to determine: (i) the hydraulic conditions that govern submerged jump formation, and (ii) the hydrodynamic characteristics of the submerged vortex. The numerical model, developed using OpenFOAM, was validated with the obtained experimental data. This research seeks to help develop improved guidelines for the design and safe operation of low-head dams. The experimental phase of the study involved physical model testing to determine: (i) the hydraulic conditions that govern submerged jump formation, and (ii) the hydrodynamic characteristics of the submerged vortex. The numerical modelling work involved using interFoam (OpenFOAM toolbox) for simulating the experimental results. InterFoam is an Eulerian 3-D solver for multiphase incompressible fluids that employs the Volume of Fluid approach (VOF) to capture the water-air interface. The developed numerical model was subsequently validated using the experimental data collected and processed by the author of this study. The range of tailwater depths associated with submerged hydraulic jump formation is dramatically reduced when a broad-crested weir is coupled with an elevated downstream apron, especially under high flow rate conditions. However sharp-crested weirs induced vortices which displayed reduced velocities and decreased spatial development, which were judged to be safer than broad crest lengths under the same discharge conditions. The classical formulation for the degree of submergence was not explicative when used to evaluate “how submerged the vortex was”. Consequently, a new normalized formulation which compares the local tailwater depth to the lower and upper tailwater limits for the submerged hydraulic jump is proposed. The numerical model developed for this study demonstrated the existence of residual turbulent kinetic energy at downstream sections located within the vortex’s extension, at instants coinciding with the presence of a fully formed roller. This turbulent energy is arguably responsible for the stationary nature of the vortex under constant flow conditions. Residual vertical and horizontal velocities at points located within the vortex’s domain are indicative of the existence of the free surface current.

Identifying Low-head dams (LHD) and creating an inventory become a priority as fatalities continue to occur at these structures. Because obstruction inventories do not specifically identify LHDs, and they are not assigned a hazard classification, there is not an official inventory of LHD. However, there is a multi-agency taskforce that is creating an inventory of LHD. All efforts have been performed by manually identifying LHD on Google Earth Pro (GE Pro). The purpose of this paper is to assess whether a machine learning approach can accelerate the national inventory. We used a machine learning approach to implement a high-resolution remote sensing data and a Convolutional Neural Network (CNN) architecture. The model achieved 76% accuracy on identifying LHD (true positive) and 95% accuracy identifying NLHD (true negative) on the validation set. We deployed the trained model into the National Hydrologic Geospatial Fabric (Hydrofabric) flowlines on the Provo River watershed. The results showed a high number of false positives and low accuracy in identifying LHD due to the mismatch between Hydrofabric flowlines and actual waterways. We recommend improving the accuracy of the Hydrofabric waterway tracing algorithms to increase the percentage of correctly classified LHD.