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Many meandering rivers migrate, at rates that vary both along‐stream and inversely with the observation interval. Many numerical models have been developed to predict this migration; their success is usually evaluated statistically or by qualitative comparison to observations in map view. We propose a framework to test migration models that unites these statistical, spatial, and temporal perspectives. We measure model fit with a statistic that compares the magnitude and direction of migration between predictions and observations. Model fit is contextualized in space, using a dimensionless coordinate system based in the location along a half‐meander bend; and in time, using a dimensionless observation interval that accounts for channel scale and migration rate. We applied this framework to test predictions for a curvature‐driven model of channel migration, using data from seven rapidly migrating rivers in the Amazon Basin and 103 more slowly migrating rivers across the continental US, as reconstructed from a legacy data set. We find that across both datasets, channel migration rates peak slightly downstream of the bend apex. Migration rate underestimation/overestimation tends to occur when the observed rate is greater/less than its median along the channel. Predicted migration direction opposes observations for slowly migrating locations and upstream of the bend apex. Model forecasts break down if the channel migrates by more than its width. The analysis framework is portable to testing other models of channel migration, and can help improve predictions for the stability of infrastructure along rivers and for landscape change over geologic timescales.

 

Abstract River channels are among the most common landscape features on Earth. An essential characteristic of channels is sinuosity: their tendency to take a circuitous path, which is quantified as along-stream length divided by straight-line length. River sinuosity is interpreted as a characteristic that either forms randomly at channel inception or develops over time as meander bends migrate. Studies tend to assume the latter and thus have used river sinuosity as a proxy for both modern and ancient environmental factors including climate, tectonics, vegetation, and geologic structure. But no quantitative criterion for planform expression has distinguished between random, initial sinuosity and that developed by ordered growth through channel migration. This ambiguity calls into question the utility of river sinuosity for understanding Earth's history. We propose a quantitative framework to reconcile these competing explanations for river sinuosity. Using a coupled analysis of modeled and natural channels, we show that while a majority of observed sinuosity is consistent with randomness and limited channel migration, rivers with sinuosity ≥1.5 likely formed their geometry through sustained, ordered growth due to channel migration. This criterion frames a null hypothesis for river sinuosity that can be applied to evaluate the significance of environmental interpretations in landscapes shaped by rivers. The quantitative link between sinuosity and channel migration further informs strategies for preservation and restoration of riparian habitat and guides predictions of fluvial deposits in the rock record and in remotely sensed environments from the seafloor to planetary surfaces. 

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.

 

The Braiding Index (BI), defined as the average count of intercepted channels per cross‐section, is a widely used metric for characterizing multi‐thread river systems. However, it does not account for the diversity of channels (e.g., in terms of water discharge) within different cross‐sections, omitting important information related to system complexity. Here we present a modification ofBI,the Entropic Braiding Index (eBI), which augments the information content inBIby using Shannon Entropy to encode the diversity of channels in each cross section.eBIis interpreted as the number of “effective channels” per cross‐section, allowing a direct comparison with the traditionalBI. We demonstrate the potential of the ratioBI/eBIto quantify channel disparity, differentiate types of multi‐thread systems (braided vs. anastomosed), and assess the effect of discharge variability, such as seasonal flooding, on river cross‐section stability.