No abstract

The main objectives of the present work are to investigate the hydraulic characteristics of the dam discharge flow and its impact downstream. Building information modeling technology is adopted to generate the terrain entity and hydraulic structures. The calculation of the dam discharge and flood routing simulation is achieved by employing Reynolds-Averaged Navier-Stokes equations with the RNG k-ε eddy viscosity model for its turbulence closure, as well as the Volume of Fluid method. An urban flood experiment and the field measurement records are utilized and validated the model accuracy. The flow field is obtained to assess the dam working conditions under different water levels. The results show that the maximum downstream flow depth, the maximum discharge capacity and the hydraulic jump length under normal water level is 18.6 m, 13,800 m 3 /s, and 108 m, respectively. The dam satisfies the safety demand under different water levels but close attention should be paid to the dam foundation, especially around the incident points of the discharge flow. Complex turbulent flow patterns, including collision, reflection, and vortices, are captured by three-dimensional simulation. The numerical simulation can assist the reservoir management vividly, so as to guarantee the stability of the dam operation. 2 of 20 experiments and confinements of empirical equations, researchers have attempted to adopt numerical simulation to check the working conditions of hydraulic structures [9-11]. Li et al. [12] simulated the joint flood discharge with the surface outlet and bottom outlet of a dam. The energy dissipation rate is enhanced by effective operation rules to stabilize the downstream flow regimes.Rapidly varied flow having large streamline curvatures exerts non-hydrostatic pressure distribution over the dam discharge structure surface. The enormous three-dimensional (3D) effect of dam discharge flow reveals that two-dimensional (2D) assumptions in solving such problems are inadequate [13][14][15]. 3D numerical simulation comes into sight gradually because it can yield a high-resolution outcome and vividly display the variation of physical parameters in the flow field [16]. In fact, 3D flood numerical simulation can be used to judge disaster losses in terms of visual experience. More concretely, it can qualitatively and quantitatively assess flood hazards and render visual reference for the development of flood control schemes, providing an important foundation for flood forecasting, dam design, and flood control system application [17]. The 3D simulation is approximately equivalent to the reality in terms of landform and boundary conditions. Therefore, the results are more accurate and convincing, and it is widely available in practical engineering [18][19][20][21].Dam discharge simulation requires an integrated tool that can handle both terrain entities and hydraulic structures. The building information modeling (BIM) method renders an effective approach for the generation of these models. It can provide detailed structure ...

Abstract. An accurate estimation of river channel conveyance capacity and the water exchange at the river–floodplain interfaces is pivotal for flood modelling. However, in large-scale models limited grid resolution often means that small-scale river channel features cannot be well-represented in traditional 1D and 2D schemes. As a result instability over river and floodplain boundaries can occur, and flow connectivity, which has a strong control on the floodplain hydraulics, is not well-approximated. A subgrid channel (SGC) model based on the local inertial form of the shallow water equations, which allows utilization of approximated subgrid-scale bathymetric information while performing very efficient computations, has been proposed as a solution, and it has been widely applied to calculate the wetting and drying dynamics in river–floodplain systems at regional scales. Unfortunately, SGC approaches to date have not included the latest developments in numerical solutions of the local inertial equations, and the original solution scheme was reported to suffer from numerical instability in low-friction regions such as urban areas. In this paper, for the first time, we implement a newly developed diffusion and explicit adaptive weighting factor in the SGC model. Adaptive artificial diffusion is explicitly included in the form of an upwind solution scheme based on the local flow status to improve the numerical flux estimation. A structured sequence of numerical experiments is performed, and the results confirm that the new SGC model improved the model performance in terms of water level and inundation extent, especially in urban areas where the Manning parameter is less than 0.03 m-1/3 s. By not compromising computational efficiency, this improved SGC model is a compelling alternative for river–floodplain modelling, particularly in large-scale applications.

Abstract. An accurate estimation of river channel conveyance capacity and the water exchange at the river-floodplain interfaces is pivotal for flood modelling. However, in large-scale models limited grid resolution often means that small-scale river channel features cannot be well represented in traditional 1D/2D schemes. As a result instability over river and floodplain boundaries can occur, and flow connectivity, which has a strong control on the floodplain hydraulics, is not well-approximated. A subgrid channel model (SGC) based on the local inertial form of the shallow water equations, which allows utilization of approximated sub-grid scale bathymetric information while performing very efficient computations has been proposed as a solution, and it has been widely applied to calculate the wetting and drying dynamics in river-floodplain systems at regional scales. Unfortunately, SGC approaches to date have not included latest developments in numerical solutions of the local inertial equations, and the original solution scheme was reported to suffer from numerical instability in low friction regions such as urban areas. In this paper, for the first time, we implement a newly developed diffusion and explicit adaptive weighting factor in the SGC model. An adaptive artificial diffusion is explicitly included in the form of an upwind solution scheme based on the local flow status to improve the numerical flux estimation. A structured sequence of numerical experiments is performed, and the results confirm that the new SGC model improved the model performance in terms of water level and inundation extent, especially in urban areas where the Manning parameter is less than 0.03 m−1/3 s. By not compromising computational efficiency, this improved SGC model is a compelling alternative for river-floodplain modelling, particularly in large-scale applications.

Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.