Authors: Xinxin Sui, Andrew W. Wood, Guoqiang Tang, Rhys Pulling, Cenlin He–Colorado School of Mines
Title: Nationwide Screening Method for Submerged Hydraulic Jumps at Low-Head Dams
Presentation Type: Lightning Talk
Abstract: Low-head dams (LHDs) pose significant hazards to river recreationalists due to the formation of submerged hydraulic jumps—turbulent, recirculating currents known as
“drowning machines” that have taken the lives of numerous recreationalists, would-be rescuers, and even many well-trained emergency personnel. While inventories list
thousands of these structures, these inventories lack dynamic, flow-dependent hazard assessments. We present a method for predicting the range of dangerous flows at LHDs by
integrating continental-scale hydrography, hydrology, and topography into basic hydraulic models. The method uses the Automated Rating Curve (ARC) generator to estimate
downstream rating curves and classify hydraulic jumps (Types A through D). We validated model accuracy using simulated historical streamflow data from confirmed fatality dates in
the BYU Low-Head Dam Fatalities Database. Results indicate that both the global-scale and U.S.-scale models performed strongly, correctly predicting a submerged hydraulic
jump at 87.7% and 86.5% of 233 and 242 sites for 374 and 347 fatal incidents, respectively. Ultimately, this method will allow agencies to develop flow-duration curves to prioritize
mitigation e[orts, such as dam removal or automated warning systems for structures that most frequently produce deadly currents.