Abstract
Meandering rivers are distinguished by their characteristic sinuosity, which is subject to modulation through channel cutoff, resulting in the formation of oxbow lakes within the abandoned meander loops. Throughout the evolutionary course of a river, these cutoffs establish a connection between the channel and floodplain systems, both crucial to maintaining the dynamic equilibrium of the river system. Nonetheless, the interactive dynamic between the channel and floodplain and its influence on the transient behavior of the channel’s morphodynamics during a cutoff event are frequently reduced to simplistic representations in computational models. This study introduces a comprehensive numerical model that elucidates the adaptive processes of bed and planform during and subsequent to the inception of cutoff and oxbow lakes. The model is assessed through its application to a laboratory scale cutoff, before being employed to a real-world meandering river, specifically the Ucayali River in Peru, in order to gain understanding into channel development and the intricate patterns of planform dynamics following cutoff events. The model is able to capture the main modes of planform migration, translation and expansion for the case of the bend in the Ucayali River. During the neck cutoff, the model simulates the progression of erosional and depositional waves traveling in upstream and downstream directions respectively, underscoring the importance of incorporating both hydrodynamic and morphodynamic factors in characterizing the river dynamics associated with meander cutoffs.
1 Introduction
Meandering rivers are pervasive worldwide, providing corridors for navigation, delivering resources for industry, agriculture and urban water use. They interact with their valleys through floods and bank migration. Despite human alteration of rivers, natural erosion and deposition processes remain a dominant factor changing most fluvial systems (), thereby altering their floodplains. The morphodynamics ofriver migration generated by the interaction of water flow, sediment transport, erosion and sedimentation, is characterized by two repeating planform changing processes. One process is the short-term increase of sinuosity via meander elongation with downstream migration (sometimes upstream) of meander loops. The second process is the reduction of sinuosity with intermittent, long-term occurrence of channel cutoffs, where the flow bypasses the meander loop by a shorter path with the subsequent formation of an abandoned reach (). An example of the effect of such processes is illustrated by the reconstruction of the scrollbars and paleochannel of different ages distributed along the floodplain (following ’s methodology) of the Ucayali River shown in Figure 1. Notice the change in local sinuosity from 2.24 to 1.75 in the region where a cutoff occurred in 2014–2015 (determined from the satellite images from 2006 to 2017, when the bend was finally abandoned). It shows that bend expansion and translation were the primary modes for cutoff formation. During the lifetime of a meandering river, where migration and cutoffs interact in space and time, dynamic-equilibrium conditions occur and morphometrics (such as sinuosity, lateral migration rates, and rate of cutoff occurrence) reach their statistical steady-states (; ; ; ). Given the recurring and profound social-ecological impacts caused by the occurrence of channel cutoffs in meandering rivers (; ), fluvial geomorphologists have developed theories and tools to help explain this process (; ; ). Yet, a predictive model of channel cutoff has not been sufficiently developed to simulate the interaction between rivers and related alluvial floodplains ().
FIGURE 1
Cutoffs are sporadic events. They may be triggered by hydrologic events (
Sustainable river management and restoration planning require spatiotemporal modeling of channels (
There are processes of river meandering that morphodynamic models ignore, such as the period where the old bend is still active transporting water and developing aggradation, flow variability, influence of riparian vegetation, overbank processes (
2 Methods
The meander cutoff model comprises three main processes: i) the initial mesh generation and the assignment of the initial river bank lines; ii) the computation of the two-dimensional depth-averaged hydrodynamics with the model TELEMAC-2D (
2.1 Initialization of computational mesh and river bank lines
The initial mesh generation divides the computation domain into two regions. The first is the channel region, where the mesh has a higher spatial resolution since it is where key morphodynamic processes occur. The maximum size of the triangular elements in the channel region, ds is defined as ds = b/5, where b is the river channel half-width. The second region is the floodplain region, where a coarser resolution is used, with a size of triangular elements, df between 3ds and 5ds to reduce computational effort. The mesh size transition between the two regions is progressively increased to minimize numerical instability.
The initial river bathymetry and floodplain topography are mapped onto the initial mesh within the framework of a Cartesian coordinate system. In cases where digital elevation model (DEM) data for the floodplain is not accessible, an alternative method for defining the valley slope, Sv, is based on the equation Sv = Sc ⋅Ω, where Sc is the longitudinal channel slope and Ω is the channel sinuosity. The initial demarcation of the river bank lines is required to separate the channel and floodplain domains. These bank lines are subject to dynamic modifications enacted by the channel migration submodel.
2.2 Hydrodynamics and bed morphodynamics
TELEMAC-2D and SISYPHE are mature open-source models developed by EDF-R&D (France). More details about TELEMAC-2D and SISYPHE can be found in the two manuals by
TELEMAC-2D solves the two-dimensional time-dependent shallow water equations (Eqs. 1–3) using the h-type finite element method (
SISYPHE computes the sediment transport (as bedload and suspended load for non-cohesive or cohesive sediments) and the evolution of the riverbed (
The Exner equation (Eq. 5) is used in the SISYPHE model to solve bed evolution:where zb is the bed elevation; t is the time; λ is the material porosity; Qb = (Qbx, Qby) is the vector of unit sediment discharges in x- and y-direction, respectively.
The formulation of
The computation of the flow field is performed by TELEMAC-2D and the computation of Exner equation is addressed by SISYPHE. Flow field and bed morphology have different time scales; hydraulic perturbations occur faster than the bed evolution processes. For that reason, the computation of flow and bed evolution can be decoupled. Once the flow variables are solved, they are passed to SISYPHE to solve bedload and bed evolution.
2.3 Channel migration and cutoff detection
Planform channel migration occurs by fluvial erosion and bank geotechnical processes causing bank retreat, and by depositional processes then produce bank advance (
Besides the flow field and bed evolution, bank migration (planform dynamics) is involved in the morphological evolution of rivers and it has the largest time scale. The algorithm proposed for the erosion and accretion processes also has a different coupling period from bed morphology and flow field computations. During the step of the computation of the erosion and accretion rates, we can obtain the migration distances of the river through Eq. 8. The migration of the banks determined by d, triggers adaptation of the elements of the mesh and reconfiguration of its topology (adaptation of nodes and reconfiguration of elements of the mesh).where d is the migration vector; Δt is time step in the model; and en is the unit vector normal to the bank.
Previous studies have proposed an algorithm for detecting imminent cutoffs in numerical modeling (e.g.,
;
). In this study, we utilized a modified version of
’s algorithm, which uses the outer-bank line instead of the channel centerline and optimizes the sweeping range. These modifications significantly reduce iteration times compared to enumerating all nodes to calculate their distances and locate the minimum distance that identifies an imminent cutoff. The algorithm is briefly described below (
Figure 2):
1. The outer-bank lines are discretized at equal length intervals (ds in Figure 2) and n nodes that are sorted onto a uniform square grid board. The size of each cell is exactly equal to the threshold distance of cutoff dc, where dc is set to be equal to 2ds in our model, i.e., 20% channel width or 0.4b.
2. Approximately n operations are needed to traverse all outer-bank nodes. From upstream to downstream, when a node (Pi in Figure 2) is being examined, except for the cell that Pi is in (namely the center cell), only five cells contain nodes and will be swept. In other words, for the case illustrated in Figure 2, standing at the center cell, the five cells to be traversed are the west, east, southwest, south, and southeast cells.
3. Then, all nodes within the center cell are swept to identify if a node exists that fulfills two conditions, first, a Cartesian distance to node Pi lower than dc, and second, streamwise distance between these two nodes larger than 5dc. Then the same search statements are executed in the five nearby cells.
4. If the node fulfills the conditions to trigger a cutoff, the two nodes where a cutoff occurs are recorded and both the oxbow lake and the new shortened channel are formed.
FIGURE 2

Schematic of the cutoff detection algorithm: an outer-bank line is discretized into a finite number of nodes, P1, P2, …, Pn; an arbitrary node, Pi (orange node), is being scanned for possible cutoff; the distance between Pi and Pj (blue node) is found less than dc, as well as the streamwise distance of these two nodes are larger than 5dc, indicating a cutoff has been detected.
Integrating the above algorithm, the model can detect incipient cutoffs. As described in earlier sections, the present model uses an irregular mesh based on triangular elements for the main channel and the floodplain; thus, it is crucial to keep track of the bank lines during meander evolution. Therefore, we define a binary variable to track the channel–floodplain regions. For the initial mesh, all channel nodes are given the value of 0, and all floodplain nodes are given the value of 1. The boundaries of “0 region” and “1 region” are two polylines representing the left and right bank lines (see Figure 3A). After several time steps, especially at the moment of cutoff, the “0 region” will be enlarged and forms a new left bank line or a right bank line (see Figure 3B). Next, the model will continuously run using the new configurations. Flow velocities in the newly-formed oxbow lake will reduce when time advances; thus, the “1 region” will gradually shrink and eventually form a new channel (see the process in Figures 3C–F).
FIGURE 3

A demonstration of mesh adaptation for pre-cuotoff (A), cutoff (B,C), and post-cutoff (D–F) condition. Notice that the finer mesh along the main channel is maintained during the transitional stage of the cutoff process. Herein, the water depth (sub-panels) is shown only when flow velocity magnitude is above a tolerance (1e-3 m/s).
2.4 Relevant timescales and computation strategy
Three categories of physical processes are involved: hydraulics, sediment transport/bed evolution, and migration of the river banks. Each of them has different timescales and for that reason, different time steps can be used for the numerical solution of the flow field, the bed evolution, and the computation of the migration of banks. The decoupling of the hydraulics and bed evolution is handled internally by SISYPHE. The time step is specified in term of multiples of the time used for the hydraulics (one, two or three orders of magnitude are typically utilized). The time step for the migration of the banks is managed by MEANDRE; the time step for the migration of the banks is set as several times the time step used to evolve the river bed. In cases where local processes are acting (e.g., bank collapse) at different rates than reach-averaged rates, a coupling between the different modules has to be accounted for.
3 Results
3.1 Modeling Han and Endreny’s laboratory cutoff experiment
The validation of the methodology formulated here was performed first using the results of the experiment of
FIGURE 4

Modeling results compared against experiment results. Flow is from left to right. (A) Experiment results (bed elevation and water surface elevation). M4-M6 are temporal stages. Note that the bed elevation data at M5 was not captured to avoid the potential interference of draining the river. (B) Modeling results (bed elevation, water depth, and flow velocity). Cycle = 0, 8 and 24 correspond to M4, M5, and M7 in the experiment, and cycle = 89 refers to the formation of the new straight channel after cutoff, which is an extension of the experiment. For simplicity the floodplain topography is not shown.
The cutoff modeling approach described herein was utilized to simulate this experiment and the results of flow velocity, bed shear stress, water depth and bed topography are shown in Figure 4B for the experimental meander stages M4, M5 and M6, which corresponded with the model Cycle 0, 8 and 24. A cycle is a single timestep where the river banks are adapted by the processes of bank erosion and accretion, and the mesh is generated again if necessary for a better representation of the river banks position. The model also simulated beyond M6 (Cycle 89), corresponding to a stage when the cutoff had transitioned to its lowest sinuosity. The water depth and velocity vectors at M5 (also shown at Figure 4) capture the development of the cutoff breaching the channel outer banks, and the changes in water depth between the stages M5 and M7 capture the transition from oxbow lake to paleo-channel. During cutoff, shear stresses increased in the cutoff region; these larger shear stresses then spatially expanded and cut a deeper, straighter channel through the meander neck, which resulted in their decrease.
The model simulated the post-cutoff sequence through Cycle 89 to track activity in the upstream erosional and downstream depositional processes, extending beyond M7, after the laboratory experiment terminated. This additional tracking allowed us to outline a conceptual model of the development of the longitudinal bed profile during and after the cutoff (Figure 5), which is based on the river geometry and other bend wavelengths and amplitudes found during cutoff evolution (
FIGURE 5

3D view of bed morphology: (A) pre-cutoff condition, (B) connectivity between the bends is initiated, (C) the erosional and depositional waves are propagated, (C) to (D) width and bed adjustment take place. The quality of the mesh (along the main channel and the floodplain) is maintained during the transitional cutoff process. Vertical exaggeration is 2. The conceptual model of morphological response after cutoff: i) profile before cutoff, ii) profile right after cutoff, iii) the river tends to return to equilibrium conditions by producing erosional (upstream) and depositional (downstream) waves (
The evolution of bed shear stress and bedload for the numerical modeling of
FIGURE 6

Analysis of sediment transport computed for
3.2 Modeling a cutoff in the Ucayali River
Following the model application in the laboratory scale, we conducted the simulation on a natural cutoff in the Ucayali River. In the satellite imagery provided by the Landsat Missions, a massive number of cutoffs, oxbow lakes, scroll bars, and paleo-channels can be observed along the Ucayali River (see the example reach illustrated in Figure 1). A cutoff event that occurred between 2014 and 2015 was considered for modeling with the approach presented in this study. The cutoff was located approximately 200 km downstream of Pucallpa City, eastern Peru. From the satellite imagery of 2006, 2010, 2013, 2014, 2015, 2016, and 2017, it was determined that the channel banks migrated at approximately 50–200 m/yr (see Figure 1). To set up the cutoff model based on the 2013 river geometry, several assumptions were made: 1) Since the goal was to reproduce the main physical processes of the cutoff (bank migration, riverbed erosion and sedimentation), the initial river geometry was considered of constant width of 800 m instead of the spatially varying river widths. The width was eventually self-adjusted during the simulation; 2) Due to the limitation of available data, the initial river bathymetry was obtained from a synthetic riverbed topography model proposed by
Figure 7 shows the modeled migration of the banks between 2013 and 2017 in the analyzed reach; at Cycle 35, the cutoff was initiated. An abrupt drop in flow velocity is observed at the bend, that is, being abandoned. In Cycle 60 of the simulation, which corresponds chronologically to the satellite imagery of 2015, an oxbow lake was fully formed since the velocity is zero although the oxbow is still not infilled. There is a coincidence in the development of bars when comparing the modeling results; the bars marked with a to c indicated in the results of cycle 60 illustrated in Figure 7, are similar to the ones observed from the satellite imagery of 06/2015. During Cycle 100 the simulation developed a large bar just downstream of the cutoff point, which reconfigures the flow distribution and promotes bank erosion in front of such bar. This is also observed in the satellite imagery (image of 2017 in Figure 7). After the cutoff developed, the new shortened channel will progressively self-adjust to a new meander bend, whose channel sinuosity will gradually increase due to channel migration, same as the rest of the upstream and downstream bends (Figure 8). The simulation took 120 h for the Ucayali River in a high-performance cluster using 32 cores of computation. The channel (floodplain) was discretized using triangular elements with maximum size of 50 m (250 m) edge length.
FIGURE 7

The Ucayali River. Modeling results compared against Landsat imageries, flow goes from left to right. Modeled bed elevation, flow velocity and water depth are shown at the initial condition (Oct. 2013), Cycle #35 (cutoff initiation, no satellite imagery available), Cycle #60 (Jun. 2015) and Cycle #100 (Apr. 2017).
FIGURE 8

The Ucayali River. Simulated bank line migration [in (A)] agrees with the trend of the Landsat imageries-derived centerline migration [in (B)]. Hollow arrows indicate the regular migration patterns without cutoff impact: translation and expansion. Solid arrows indicate the cutoff-induced channel migration: channel widening, shortened waterway formation and oxbow lake sedimentation.
The process of bed evolution also determines how the cutoff bend interacts temporarily with the flow before being completely abandoned. Such interaction is observed in the modeled flow velocity and bed elevation (Figure 7), where the velocity after the cutoff gradually diminished while the original bed was aggrading. The abandonment of the original bend occurs progressively. For instance,
4 Discussion
4.1 Model novelties/advantages and limitations
4.1.1 Novelties/advantages
Long-term simulations (from hundreds of years to geologic time scales) were usually performed with linearized models (
4.1.2 Limitations
Horizontal and vertical heterogeneity of floodplain material is not accounted for in the present model. The rate of bank retreat in rivers, however depends on the interaction of forcing factors (shear stress exerted by the flow and bank instability) with the resistance to erosion and geomechanical properties of the bank material. In the present modeling framework, we have simplified the bank erosion/accretion processes and ignored the bank collapse events. Horizontal (floodplain soils of different properties) and vertical (layers of soils of different properties) heterogeneities may have important effects on the dynamics of meandering rivers (
4.2 Complex morphodynamic processes
The morphodynamic processes prior, during and after cutoff processes involve several complex patterns which can be addressed by the present modeling framework.
Paleochannels and river migration: As observed in Figure 1, during the lifetime of a river, both recent geomorphic features (e.g., scroll bars and oxbow lakes) and ancient geomorphic features (e.g., paleochannels and terraces) are found. The floodplain is usually characterized of fine sediment deposits and vertical layers of heterogenous soils (
Forced and migrating free bars: the coexistence of forced and free bars in meandering channels (
4.3 Detailed monitoring of large scale processes
Recently, detailed field measurements in cutoffs were performed along different rivers; White River, Arkansas (United States) (
5 Conclusion
The present research introduces a computational model capable of predicting the transitional hydrodynamics and morphodynamics associated with a meander neck cutoff, considering the temporary interaction with the reach undergoing abandonment. The model was validated by means of a laboratory-controlled mobile bed experiment and an actual cutoff observed in the Ucayali River. It is shown to effectively reproduce cutoff dynamics and evaluate non-local morphological effects beyond the constraints of existing linear models, such as bar formation, sediment wave evolution, and two-dimensional flow field structure. The results of the model show widening of the cutoff cross-section, with the consequent formation of forced bars, adaptation of the main channel bed through upstream and downstream sediment waves, formation of oxbow lakes, and preservation of the resulting paleo-channel in the floodplain. In summary, the model demonstrates efficient cutoff detection and channel adaptation capabilities, thereby highlighting the importance of considering both hydrodynamics and morphodynamics for describing meander cutoff temporal evolution. The study also describes the need to develop physics-based simplified models for incorporating local effects of neck cutoffs into the long term river migration models.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/ZhiLiHydro/NeckCutoffModeling.
Author contributions
Conceptualization: ZL, AM, and JA; methods: ZL and AM. Experiment data: TE and BH; computational resources, EC and RD; formal analysis: ZL and AM; data curation: ZL and AM; interpretation and discussion, ZL, AM, JA, TE, BH, EC, and RD; writing–original draft preparation, ZL and AM; review and editing, JA, TE, BH, EC, and RD; visualization, ZL and AM; supervision, JA; funding acquisition, JA. All authors contributed to the article and approved the submitted version.
Funding
JA thanks partial funding from Gordon and Betty Moore Foundation (Grant GBMF7711), OTCA (Contract ANA/623/2021).
Acknowledgments
JA thanks long-term collaborations with the Directorate of Navigation and Hydrography of the Peruvian Navy, Senamhi-Peru (Marco Paredes), Yangtze University-China (Jingfu Shan), and UNAM-Mexico that nurture research ideas. The authors also thank the editors and three reviewers for their insightful comments and suggestions.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2023.1208782/full#supplementary-material
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Summary
Keywords
meandering river, neck cutoff, numerical modeling, bed morphodynamics, Ucayali River frontiers
Citation
Li Z, Mendoza A, Abad JD, Endreny TA, Han B, Carrisoza E and Dominguez R (2023) High-resolution modeling of meander neck cutoffs: laboratory and field scales. Front. Earth Sci. 11:1208782. doi: 10.3389/feart.2023.1208782
Received
19 April 2023
Accepted
29 August 2023
Published
12 September 2023
Volume
11 - 2023
Edited by
Jorge Lorenzo-Trueba, Montclair State University, United States
Reviewed by
Alvise Finotello, Ca’ Foscari University of Venice, Italy
Tian Dong, The University of Texas Rio Grande Valley, United States
Stefano Lanzoni, University of Padua, Italy
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© 2023 Li, Mendoza, Abad, Endreny, Han, Carrisoza and Dominguez.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhi Li, zhil2@illinois.edu
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