A new shaft spillway composed of an inlet structure that includes inlet swirling-flow-generating piers, an annular weir, a vertical shaft, and an outlet structure is presented. Major factors influencing the inlet’s discharge coefficient, including the number of piers, pier weir angle, weir head, and pier height are discussed. An empirical discharge coefficient equation using a relative dimensionless weir head is presented in order to provide design guidance. Model experiments and numerical simulations are conducted regarding the flow behavior of the proposed inlet and vortex drop structure performance. Hydraulic characteristics such as the flow pattern, air core distribution, annular hydraulic jump position, pressure profiles, and water profiles of the outlet tunnel are obtained and agree well with the measured experimental data. Results show that the flow around the inlet is divided into a free-flow swirling region near the piers and submerged-flow region at the piers. The flow rate grows nearly linearly with the relative dimensionless weir head in the submerged region, and the discharge coefficient is approximately constant at 0.223. Experiments and simulations reveal the rotational flow movement mechanism of the self-regulating underwater swirl piers and the energy dissipation of the new vortex drop shaft spillway. Analytic calculations for the resultant velocity and water course thickness of the shaft sections correlate well with the results of a numerical simulation.

Forecasting of ice jams and their breakup is crucial to prevent or reduce flooding risk in cold regions. A back propagation (BP) neural network model improved by the Levenberg-Marquardt clustering method has been developed with air temperatures and precipitation as inputs and applied for ice-jam forecasting in a given year in the upper reaches of the Heilongjiang River (Amur River), where ice flooding occurs frequently during spring. The accuracy rate achieved was 85%, higher than that obtained using the conventional statistical method (62% accuracy), for ice-jam breakup forecasting. The BP model has a forecast period of 10 days with a maximum error of two days and a qualified rate of 100% for national standards breakup date forecasting. The forecast on the ice-jam breakup, which was released 24 days ahead, provided accurate results for the breakup date and the occurrence of ice jams in the spring of 2017.

Ice blasting with explosives is an important method for mitigating or preventing ice jam floods during the spring breakup of frozen rivers. Successful blasting relies on properly determining the relationships between parameters including blasting crater radius, explosive weight, ice cover thickness, and water depth, though variations in the final three factors have significant effects on the blasting crater radius. We conducted field experiments in an upper reach of the Heilong (Amur) River, which forms the border between China and Russia, in order to develop an empirical formula correlating these factors. The blasting crater radius determined by the proposed equation resulted in average errors of less than 8.5% when compared with the measured values. This formula was used for ice blasting along the upper Heilong River in spring 2016 and 2017, successfully preventing ice jam formation during river breakup and thus providing a scientific basis for the prevention of ice-related flooding in northern regions.

Accurate prediction of the discharge through radial gates is difficult. Existing calibration methods typically provide sufficient accuracy for free flows and submerged flows with jet Froude number at vena contracta larger than 1.7. However, their predictions are poor for submerged flows with jet Froude number less than 1.7. Based on the analysis of experimental data collected by the US Bureau of Reclamation, an empirical indicator was proposed to predict the approximate range of jet Froude number for submerged flows. Accordingly, submerged flows were subdivided into partially submerged flows with fully developed hydraulic jump, and totally submerged flows with undular or no jump. A new discharge calibration method, referred to as the identification method, was developed for the three distinct flow conditions. Verification of the proposed method indicated the discharge prediction accuracies were significantly improved for totally submerged flows.