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This archive contains MATLAB code for mapping and classification of meander bends, including example data for the Beatton River, Canada, as described in two articles in Journal of Geophysical Research: Earth Surface:   Limaye, A. B., 2025, A geometric algorithm to identify river meander bends: 1. Effect of perspective Limaye, A. B., 2025, A geometric algorithm to identify river meander bends: 2. Test for characteristic shapes 

ABSTRACT Braided rivers distribute sediment across landscapes, often forming wide channel belts that are preserved in stratigraphy as coarse-grained deposits. Theoretical work has established quantitative links between the depth distribution of formative channels in a braided river and the geometry of their preserved strata. However, testing these predictive relationships between geomorphic process and stratigraphic product requires examining how braided rivers and their deposits coevolve, with high resolution in both space and time. Here, using a series of four runs of a physical experiment, we examine the controls of water discharge and slope on the resulting geometry of preserved deposits. Specifically, we focus on how a twofold variation in water discharge and initial riverbed slope affects the spatiotemporal distribution of channel depths and the geometry of preserved deposits of a braided river. We find that the channel depths in the laboratory experiment are described by a two-parameter gamma distribution and the deepest scours correspond to zones of erosion at channel-belt margins and channel-thread confluences in the channel belt. We use a reduced-complexity flow model to reconstruct flow depths, which were shallower compared to channel thalweg depths. Synthetic stratigraphy built from timeseries of topographic surfaces shows that the distribution of cut-and-fill-unit thickness is invariant across the experiments and is determined by the variability in scour depths. We show that the distribution of cut-and-fill-unit thickness can be used to reconstruct formative-channel-depth distributions and that the mean thickness of these units is 0.31 to 0.62 times the mean formative flow depth across all experiments. Our results suggest that variations in discharge and slope do not translate to measurable differences in preserved cut-and-fill-unit thickness, suggesting that changes in external forcings are likely to be preserved in braided river deposits only when they exceed a certain threshold of change. 

This is the archive of the numerical model and river centerline data used for analyzing the timescale related to meandering channel migration, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers. 

Bank erosion commonly occurs in alluvial rivers, shaping landscapes and riparian habitats and impacting water quality and infrastructure. Several models have been proposed that link shear stresses to bank erosion. However, data to test these hypotheses for characteristic geometries of meandering channels are sparse and technically challenging to acquire. Here we present results from a controlled experiment in a naturalistic channel to isolate the relationships between turbulent flow and nascent bank erosion. We ran the experiments at the Outdoor StreamLab (St Anthony Falls Laboratory, University of Minnesota) and gathered high-precision, contemporaneous measurements of the turbulent flow field and topography near a standardized, erodible bank at five locations along a single meander. The measurements show that the rate of bank erosion varied both along the channel and vertically and, local bank erosion was not correlated with any single hydrodynamic parameter. Upstream of the meander apex, erosion correlated with the near-bank time-averaged streamwise velocity magnitude while downstream of the apex, bank erosion correlated more strongly with near-bank turbulence parameters and depth. These results support field measurements that suggest that fluid shear contributions to outer bank erosion reflect multiple components of turbulent flow structure in river meanders. 

This data repository includes (1) the original observations for channel bank positions of 143 meandering reaches in 89 rivers across the continental US in the dataset of the National Cooperative Highway Research Program Project (NCHRP) 24-26 (Lagasse et al., 2004), and (2) the geospatial data for centerlines of 103 meandering reaches in 70 rivers recovered from the original NCHRP 24-26 dataset. Reference: Lagasse, P. F., Zevenbergen, L. W., Spitz, W. J., & Thorne, C. R., 2004. Methodology for predicting channel migration. Washington, DC: Transportation Research Board. https://doi.org/10.17226/23352 

Abstract 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. 

Abstract 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. 

Abstract 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.