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In meandering rivers, interactions between flow, sediment transport, and bed topography affect diverse processes, including bedform development and channel migration. Predicting how these interactions affect the spatial patterns and magnitudes of bed deformation in meandering rivers is essential for various river engineering and geoscience problems. Computational fluid dynamics simulations can predict river morphodynamics at fine temporal and spatial scales but have traditionally been challenged by the large scale of natural rivers. We conducted coupled large‐eddy simulation and bed morphodynamics simulations to create a unique database of hydro‐morphodynamic data sets for 42 meandering rivers with a variety of planform shapes and large‐scale geometrical features that mimic natural meanders. For each simulated river, the database includes (a) bed morphology, (b) three‐dimensional mean velocity field, and (c) bed shear stress distribution under bankfull flow conditions. The calculated morphodynamics results at dynamic equilibrium revealed the formation of scour and deposition patterns near the outer and inner banks, respectively, while the location of point bars and scour regions around the apexes of the meander bends is found to vary as a function of the radius of curvature of the bends to the width ratio. A new mechanism is proposed that explains this seemingly paradoxical finding. The high‐fidelity simulation results generated in this work provide researchers and scientists with a rich numerical database for morphodynamics and bed shear stress distributions in large‐scale meandering rivers to enable systematic investigation of the underlying phenomena and support a range of river engineering applications.

Prediction of statistical properties of the turbulent flow in large‐scale rivers is essential for river flow analysis. The large‐eddy simulation (LES) provides a powerful tool for such predictions; however, it requires a very long sampling time and demands significant computing power to calculate the turbulence statistics of riverine flows. In this study, we developed encoder‐decoder convolutional neural networks (CNNs) to predict the first‐ and second‐order turbulence statistics of the turbulent flow of large‐scale meandering rivers using instantaneous LES results. We train the CNNs using a data set obtained from LES of the flood flow in a large‐scale river with three bridge piers—a training testbed. Subsequently, we employed the trained CNNs to predict the turbulence statistics of the flood flow in two different meandering rivers and bridge pier arrangements—validation testbed rivers. The CNN predictions for the validation testbed river flow were compared with the simulation results of a separately done LES to evaluate the performance of the developed CNNs. We show that the trained CNNs can successfully produce turbulence statistics of the flood flow in the large‐scale rivers, that is, the validation testbeds.

For most of the year, a dry‐bed desert wash is void of water flow. Intensive rain events, however, could trigger significant flash floods that bring about highly complicated hydrodynamics and morphodynamics processes within a desert stream. We present a fully coupled three‐phase flow model of air, water, and sediment to simulate numerically the propagation of a flash flood in a field‐scale fluvial desert stream, the so‐called Tex Wash located in the Mojave Desert, California, United States. The turbulent flow of the flash flood is computed using the three‐dimensional unsteady Reynolds‐averaged Navier–Stokes equations closed with the shear stress transportk−ωmodel. The free surface of the flash flood at the interface of air and water phases is computed with the level‐set method, which enables instantaneous tracking of the water surface as the flash flood propagates over the dry bed of the desert stream. The evolution of the desert fluvial stream's morphology, due to the action of the propagating flash flood on the mobile bed, is calculated using a Eulerian morphodynamics model based on the curvilinear immersed boundary method. The capabilities of the proposed numerical framework are demonstrated by applying it to simulate a flash flood event in a 0.65‐km‐long reach of the Tex Wash, the intricate channel morphology of which is obtained using light imaging detection and ranging technology. The simulated region of the stream includes a number of bridge foundations. The simulation results of the model for the flash flood event revealed the formation of a highly complex flow field and scour patterns within the stream. Moreover, our simulation results showed that most scour processes take place during the steady phase of the flash flood, that is, after the flash flood fills the stream. The transient phase of the flash flood is rather short and contributes to a very limited amount of erosion within the desert stream.