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Large-eddy simulations of laboratory-scale bedrock rivers show that the constriction producing the strongest plunging flow shifts with discharge—35% at low flow, 50% at high flow—while maximum bed shear stress occurs at 35% constriction und

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2026-08-01 19:34 UTC pith:RQQ5IE3L

load-bearing objection A novel LES study of plunging flows that deserves review, but the central optimum-constriction claim is currently undermined by mass-conservation-inconsistent inlet velocities and a rigid-lid that undercuts the backwater mechanism. the 4 major comments →

arxiv 2607.16908 v1 pith:RQQ5IE3L submitted 2026-07-18 physics.flu-dyn

Large Eddy Simulation of Plunging Flows in Laboratory-Scale Bedrock Rivers

Jayanga T. Samarasinghe , Laura V. Alvarez , Max Hurson , Jeremy G. Venditti This is my paper
classification physics.flu-dyn
keywords plunging flowbedrock riverslarge eddy simulationvelocity inversionconstriction-pool-wideningbed shear stressturbulent kinetic energysubcritical flow
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper uses eddy-resolving computational fluid dynamics to reproduce, in simulation, the plunging flows previously seen in laboratory flume experiments of constricted bedrock river channels. It aims to establish that these flows, marked by a velocity inversion in which the fastest water runs near the bed, can be simulated at laboratory scale, and that constriction width and discharge act jointly, not independently, in controlling their strength. The central finding is that the most erosive condition is not the tightest constriction: at high discharge, the strongest velocity inversion occurs at a 50% lateral pinch, but maximum bed shear stress and turbulent kinetic energy occur at 35% constriction, because tighter pinches throttle the inlet velocity and spread stress more thinly. The paper also shows that plunging flows are intermittent, with the high-velocity core pulsing faster than the flanking recirculation eddies, implying that time-averaged views may miss erosive bursts.

Core claim

On its own terms, the paper claims that large-eddy simulation can reproduce laboratory-scale subcritical plunging flows in bedrock canyon morphologies, and that those simulations reveal a non-monotonic, discharge-dependent control of plunging-flow intensity by lateral constriction. At low flow, the best-defined velocity inversion appears near 35% constriction; at high flow, the most pronounced inversion appears near 50% constriction, where the flow superelevates and plunges as a submerged-hydraulic-jump-like feature. Yet the highest bed shear stresses and turbulent kinetic energy occur at 35% constriction under high flow, because stronger constrictions reduce inlet velocity and produce broad

What carries the argument

The central machinery is a large-eddy simulation of a laboratory-scale constriction-pool-widening bedrock-river flume, with a rigid-lid free-slip water surface, no-slip roughened bed and walls, and a one-equation subgrid-scale turbulence model. The controlled inputs are the percentage of lateral constriction and the inflow discharge and velocity; the key diagnostics are the inversion-strength metric (depth of the maximum-velocity core below the water surface), resolved bed shear stress, and turbulent kinetic energy. The constriction creates a jet that plunges toward the bed, and the simulations isolate the downstream pool entrance as the zone of maximum energy extraction and shear.

Load-bearing premise

The load-bearing premise is that fixing the water surface as a rigid, free-slip lid does not change which constriction-discharge combinations produce plunging flows, even though the paper's mechanism for the high-flow case invokes water-level superelevation and backwater effects that the rigid lid cannot represent.

What would settle it

Run the same constriction-pool geometry at 50% constriction and high discharge with a deformable free surface (e.g., a volume-of-fluid or level-set simulation); if the strong velocity inversion disappears or shifts to another constriction, the rigid-lid assumption is the cause and the optimal-constriction claims fail. Alternatively, an experiment that measures the water-surface profile through the constriction and shows no superelevation would undercut the proposed plunging mechanism.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If these simulations are right, bedrock incision models that assume steady, uniform flow will systematically underestimate erosion in constriction-pool-widening reaches, because the erosive stress is concentrated, intermittent, and localized at the pool entrance.
  • The constriction-discharge optimum implies that no single constriction severity can be called 'most erosive'; the same geometry can be erosive at one discharge and less so at another, so landscape evolution models must couple hydraulics with discharge history.
  • Because maximum shear stress occurs at moderate (35%) constriction rather than the tightest (50%) pinch, canyon evolution models should account for the trade-off between constriction-driven acceleration and the throttling of inlet velocity by upstream backwater.
  • The subcritical character of the simulated plunging flows suggests the same modeling approach could be extended to field-scale canyons, where plunging flows are universally subcritical, opening a path to simulate erosive events at discharges too high to measure in the field.
  • The observed intermittency—pulsing of the velocity core at roughly 0.7-second periods amid longer eddy oscillations—implies that short-duration measurements and time-averaged surveys may miss the peak erosive phases of plunging flows.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The rigid-lid setup may be suppressing the very backwater the authors invoke; a deformable-free-surface run of the 50%-constriction high-flow case would show whether the velocity inversion survives without the fixed lid.
  • Because the inversion-strength maximum (50% constriction) and the shear-stress maximum (35%) do not coincide, future work should separate 'plunging-flow strength' as a kinematic quantity from erosive power when comparing field, lab, and model results.
  • The pulsing frequencies reported here (≈1.4 Hz in the core, ≈0.85 Hz in the eddies) could be searched for in field velocity records in real bedrock canyons; their presence at similar nondimensional frequencies would suggest the intermittency is not a lab-scale artifact.
  • The two discharges studied bracket a small slice of the parameter space; a systematic sweep of constriction versus discharge would yield a phase diagram of plunging-flow regimes that could directly feed incision models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 2 minor

Summary. This paper presents large-eddy simulations of open-channel flow through laboratory-scale bedrock canyon constriction-pool-widening geometries, based on the Hunt et al. (2018) flume experiments. The authors vary lateral constriction (0–50%) and two nominal discharges (0.003 and 0.006 m3/s) and compare simulated velocity fields with digitized dye-trace observations. They report that LES reproduces velocity inversions, that the optimal constriction for plunging flow is ~35% at low flow and ~50% at high flow, that maximum bed shear stress and TKE occur at 35% constriction at high flow, and that plunging flows are intermittent, with implications for bedrock incision.

Significance. If the results are correct, the paper is a valuable step toward eddy-resolving, three-dimensional characterization of plunging flows in bedrock rivers, with direct implications for bedrock incision modeling. The systematic variation of constriction and discharge, the reported resolution metric (kres/ktotal ≥ 80%), and the public availability of processed data are notable strengths. However, the central quantitative claims are compromised by an apparent violation of mass conservation at the inlet and by an internal inconsistency in the low-flow optimal-constriction result. The interpretation also relies on backwater effects that the rigid-lid, free-slip water surface cannot represent. These issues are load-bearing and require substantive revision.

major comments (4)
  1. [§2.2.2 / Table 2] The inlet boundary conditions are internally inconsistent. With channel width W=0.2032 m and upstream depth d1=0.08 m (Table 1), continuity requires U_in = Q/(W d1). For LF0 the tabulated Q=0.003 m3/s requires U_in=0.185 m/s, but U_in=0.591 m/s is listed, implying an inlet discharge of 0.0096 m3/s (factor 3.2). For C18, U_in=0.385 implies 0.0063 m3/s instead of 0.003; for HF0, U_in=0.680 implies 0.0111 m3/s instead of 0.006; C21 implies 0.0097 m3/s. Consequently the simulated flows are not at the stated discharges, so the 'low flow' versus 'high flow' comparison and the constriction dependence of inversion strength are not for the claimed flow conditions. The authors must correct this, either by using consistent inlet velocities or by reporting the actual simulated discharges, and re-evaluate the optimal-constriction conclusions.
  2. [§3.2.3 vs §4.2/Abstract] The paper states two different low-flow optimal constrictions. §3.2.3 says 'C19 (40% constriction) exhibited the strongest velocity inversion', while §4.2 says 'at low flow, the best-defined plunging flow happens at 35% constriction' and the abstract claims ~35%. These cannot both be true; the central claim about an optimal constriction at low flow is therefore internally inconsistent. Please reconcile and specify the metric used to define 'best-defined'.
  3. [§4.3 / §3.2.2] The simulations use a rigid-lid, free-slip water surface based on the observed surface, which the authors acknowledge 'limited the possible expression of a strong backwater'. Nevertheless, §3.2.2 attributes the lower inlet velocity in highly constricted cases to a backwater effect that raises upstream water level, and §4.2/4.3 invoke 'water-level superelevation above the constriction' and hydrostatic pressure recovery as part of the plunging mechanism. With a rigid lid, the free surface cannot deform, so the model cannot generate the backwater feedback that is part of the proposed explanation. The physical mechanism invoked is therefore not simulated; either a free-surface-capable solver should be used, or the interpretation must be revised to what a rigid-lid pressure field can actually represent.
  4. [§3.1 / Table 3] The validation evidence for the low-flow cases, which underpin the ~35% optimal-constriction claim, is weak. Table 3 shows that the 35% low-flow case (C18) is not within the experimental uncertainty bounds, and only 2 of 5 low-flow cases (C19, C20) fall within bounds, yet the text states 'approximately 60% of CFD predictions fell within the experimental uncertainty envelope'. The 60% figure is not supported by Table 3. In addition, the model is validated against the same Hunt et al. experiments from which the inlet velocities were extracted (with Venditti as co-author on both), so the validation is partly in-sample. The authors should state this explicitly and temper claims of reproduction for low-flow conditions.
minor comments (2)
  1. [Global] There are recurring typos and inconsistencies in notation: 'RSME' should be 'RMSE'; the variable index assigns 'v' to the vertical direction but 'w' to the lateral direction, while the text uses u and w as streamwise and vertical in Eqs. (13)–(17); please standardize. Location coordinates in §2.5 (e.g., z = 0.4 m) appear inconsistent with the stated flow depths (~0.13 m).
  2. [References] The reference 'Kusack, Tingan Li, and Jeremy G Venditti' appears to be missing the first author's initials or has an incorrect citation format; please verify. Also, Eq. (12) uses ν_t to compute bed shear stress; since ν_t is the SGS eddy viscosity, the computed 'bed shear stress' may not equal the physical wall shear stress, especially if the wall model already provides a wall shear stress. Please clarify the definition and whether ν_t or total viscosity is appropriate.

Circularity Check

2 steps flagged

Partial circularity: inlet velocities are taken from the Hunt et al. (2018) dataset used for validation, and the claimed optimal-constriction result is largely encoded in those prescribed inlet velocities, several of which violate the stated discharges.

specific steps
  1. fitted input called prediction [§2.2.2 (Table 2), §2.3, §3.1]
    ""For the present simulations, we used inlet velocity values extracted from Hunt’s original experimental dataset at the actual channel inlet, which corresponds to our model’s upstream boundary." (§2.2.2) ... "The validation of the numerical simulations was performed using data from the laboratory experiments that employed dye tracing ... generated velocity profiles that could be directly compared to the numerical simulation." (§2.3)"

    The model's inflow boundary condition is taken from the same Hunt et al. (2018) experiments whose dye-trace velocities are then used as the validation target. The agreement reported in §3.1 is therefore a test of internal consistency between the prescribed inlet data and downstream measurements, not an independent prediction. This is partial circularity: the simulation is partly conditioned on the very data it is claimed to reproduce.

  2. fitted input called prediction [§3.2.2 / Table 2; §4.2 and §4.3]
    ""This reduction occurred because higher downstream resistance in the more constricted cases caused an increase in upstream water level, which in turn lowered the inlet velocity to maintain the same discharge." (§3.2.2) ... "the LES simulations employed a rigid-lid approach based on the observed water surface, which limited the possible expression of a strong backwater" (§4.3)"

    The backwater-driven decrease in U_in is invoked to explain why the 'optimal' constriction shifts with discharge, but the free surface is rigid, so this decrease cannot emerge from the simulation; it is imposed via Table 2. The imposed U_in values are not consistent with the stated discharges: with W=0.2032 m and d1=0.08 m, continuity gives U_in=0.185 m/s for Q=0.003 m3/s and 0.369 m/s for Q=0.006 m3/s, while Table 2 lists 0.319–0.680 m/s. The central 'optimal constriction' result is therefore substantially encoded in the prescribed boundary velocities rather than produced by the modeled flow.

full rationale

Most of the paper is a conventional LES study: the filtered Navier–Stokes equations are solved with OpenFOAM, grid resolution is checked via kres/ktotal, and comparison with external experiments would normally be a score-0–2 situation. The circularity is partial. First, the inlet velocities in Table 2 were extracted from the Hunt et al. (2018) experimental dataset, and the same experiments' dye-trace velocities are used as the validation target (§2.2.2, §2.3, §3.1). The model is thus conditioned on the data it is then said to 'predict'; this is not a blind test. Second, and more importantly, the central discovery—an 'optimal' constriction of ~35% at low flow and ~50% at high flow—is explained through backwater-driven reductions of inlet velocity with constriction (§3.2.2), but the rigid-lid free surface (§4.3) cannot produce that backwater; the lower U_in values are imposed inputs in Table 2. Continuity with the stated Q=0.003/0.006 m3/s, W=0.2032 m, and d1=0.08 m would require U_in=0.185/0.369 m/s, while Table 2 lists 0.319–0.680 m/s, so the simulated discharges are not the claimed ones. The optimal-constriction pattern is therefore substantially an artifact of prescribed inlet conditions rather than an emergent flow prediction. I do not see a self-citation uniqueness argument, and the Hunt et al. self-citation is not load-bearing in itself; the issue is using the same dataset as both boundary condition and validation target. Overall score 4: some conditioning/partial circularity; the LES core still has independent content.

Axiom & Free-Parameter Ledger

4 free parameters · 6 axioms · 0 invented entities

The paper introduces no new physical entities. The free parameters are mostly boundary-condition and closure choices; the most consequential are the unreported wall roughness and the experimental inlet velocities, both of which condition the shear-stress and inversion-strength results. The rigid-lid assumption is a domain assumption that directly limits the physical mechanisms the simulation can represent.

free parameters (4)
  • Wall roughness height ks / z0 = unreported
    Section 2.2.2 sets z0 = 0.033 ks (Garcia 2008) but never gives ks; bed shear stress magnitudes depend directly on this choice.
  • Inlet velocity U_in (per case) = 0.319–0.680 m/s (Table 2)
    Extracted from Hunt et al.'s original experimental dataset; imposes momentum and discharge at the inlet, so all central results are conditional on these values.
  • SGS model constant Ck = not stated (standard Yoshizawa value)
    One-equation kEqn SGS model uses Ck; the paper does not report the value or sensitivity to it.
  • Time-averaging window for mean fields = not stated (25–85 s used for spectra)
    The averaging duration for mean velocity, Reynolds stress, and shear stress is not specified; turbulence statistics may depend on it.
axioms (6)
  • standard math Incompressible Navier-Stokes equations with LES filtering
    Section 2.2.1, Equations 1–2; standard formulation.
  • domain assumption One-equation eddy-viscosity SGS model (kEqn) adequately represents unresolved stresses
    Section 2.2.1; model accuracy is central to turbulence predictions.
  • domain assumption No-slip rough-wall log-law wall function with unknown ks
    Section 2.2.2; used as the bed boundary condition; roughness height unreported.
  • domain assumption Rigid-lid free-slip water surface
    Section 4.3 states the rigid-lid approach, which prevents free-surface deformation and backwater; yet the mechanism discussion invokes superelevation.
  • domain assumption Dye traces represent centerline maximum velocity
    Section 2.3; basis of the validation dataset; may introduce bias from dispersion and tracking uncertainty.
  • domain assumption Laboratory-scale flow dynamics transfer to field-scale subcritical plunging flows
    Section 4.1; the paper acknowledges the unresolved discrepancy in Froude number between lab and field.

pith-pipeline@v1.3.0-alltime-deepseek · 25964 in / 15607 out tokens · 169295 ms · 2026-08-01T19:34:00.131767+00:00 · methodology

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read the original abstract

Non-uniform flow dynamics in bedrock-bound channel morphologies play a critical role in landscape evolution because these reaches are locations along river long profiles where active bedrock incision occurs. Field observations indicate that plunging flows, characterized by velocity inversions within bedrock-bound constriction-pool-widening (CPW) channel morphologies, drive incision at the local scale. These flows generate high shear stresses that promote sediment transport and contribute to the development and maintenance of CPW morphology. Previous studies of plunging flows have relied on coarse-scale field observations and labor-intensive laboratory experiments to investigate their dynamics. Here, we use eddy-resolving computational fluid dynamics models to examine plunging-flow behavior, building on experimental evidence that lateral channel constriction induces plunging flows. Using large-eddy simulations (LES) of laboratory-scale flows, we found that the optimal constriction for generating plunging flows is approximately 35% under lower-flow conditions but increases to 50% at higher flows because of changes in inlet velocity and flow depth. At higher discharge rates, channel constriction further amplifies the plunging effect, producing substantial shear stresses near the point of velocity inversion. Increasing constriction also leads to greater velocity variance and more intermittent pulsing of plunging flows, both of which are likely to enhance incision potential. These findings highlight the need to refine bedrock incision models to better represent the dynamic and complex nature of plunging flows, moving beyond the simplified steady-flow assumptions that underpin most landscape evolution models.

Figures

Figures reproduced from arXiv: 2607.16908 by Jayanga T. Samarasinghe, Jeremy G. Venditti, Laura V. Alvarez, Max Hurson.

Figure 1
Figure 1. Figure 1: LES computational domain and boundary conditions. (a) Three-dimensional (3D) view of the full channel geometry, illustrating key features including the entrance, lateral constriction, scour pool, and outlet. (b) Plan view of the highlighted region in panel a, showing the entrance, constriction, and scour pool. (c) Front view along the centerline X–X’ (panel a). (d) Computational mesh from front view, showi… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Selected adaptive resolution of the computational domain and (b) fraction of resolved Turbulent Kinetic Energy kres/ktotal with the selected adaptive mesh resolution at 30s of simulation time for the C25 case. 9 [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of Froude number (F r), velocity, and inversion strength (δmax) under low-flow (a–c) and high-flow (d – f) conditions. 3.2.2 Mean flow field Flow fields from low-flow simulations reveal distinct patterns in velocity as lateral constriction increases (Figure 5a). For cases ranging from no lateral constriction (LF0) to 40% lateral constriction (C19), maximum velocities shifted toward the channel b… view at source ↗
Figure 5
Figure 5. Figure 5: Mean velocity magnitude for (a) low flow and (b) high flow simulations. 3.2.3 Elevation of maximum velocity and inversions The maximum velocity pathways in the simulations were determined for both low and high flows. For the low flow cases, increasing constriction and decreasing inflow velocity pushed the maximum velocity pathways toward the bed for cases C16–C19 (Figure 6a). However, in the 50% constricti… view at source ↗
Figure 6
Figure 6. Figure 6: Maximum velocity pathways for a) low flow (LF0, C16-C20) and b) high flow (HF0, C21-C25). (c) Non-dimensional velocity and relative depth of maximum velocity (δmax) for 50% constriction simulations at low flow (C20) and high flow (C25). 19 [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Instantaneous bed shear stress (Equation 12) magnitude for high flow scenario. 3.2.5 Visualization and analysis of streamlines Streamlines reveal patterns in the flow field that emerge with the introduction of the lateral constriction ( [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Variation in streamlines with high flow simulations with no constriction (HF0) a) on the surface and b) along a transect through the channel. Streamlines for simulations with a 50% constriction (C25) c) on the surface and d) along a transect through the channel. Locations A, B, and C are locations in the flow where we examined the oscillation patterns in turbulent flow field. 3.3 Turbulent flow field 3.3.1… view at source ↗
Figure 9
Figure 9. Figure 9: Distributions of a) urms along the channel centerline, b) urms for a cross-section at x = 2.2m (c) wrms along the centerline and (d) wrms for a cross-section at x = 2.2m. 3.3.2 LES-Resolved Stresses and Total Turbulent Kinetic Energy We examine the x – z stress tensor to better understand turbulent shear stress and momentum mixing in plunging flows. Under no-constriction conditions (HF0; Figure 10a), turbu… view at source ↗
Figure 10
Figure 10. Figure 10: Spatial distribution of turbulence in plunging flows developed at high flow, including a) the downstream and vertical component of Reynolds stress (ρu′w ′ ) and b) turbulent kinetic energy. Turbulent Kinetic Energy (TKE) represents the energy extracted from the mean flow by the motion of turbulent eddies (Bradshaw 1977; Klein 2005). TKE production involves interactions of the Reynolds stresses with mean v… view at source ↗
Figure 11
Figure 11. Figure 11: High flow distributions of a) Turbulence production along the channel centerline, b) turbulence production for a cross-section at x = 2.2m (c) eddy viscosity along the centerline and (d) eddy viscosity for a cross-section at x = 2.2m. 3.4.1 Counter rotating eddies and flow oscillations We explore the dynamics of turbulent eddies in the flow using velocity vectors fields. We chose to examine the high flow … view at source ↗
Figure 12
Figure 12. Figure 12: Velocity vectors for high flow with a 50% constriction (C25) along (a) at the water surface (b) an along stream transect through the plunging flow, (c) 2mm above the bed, and d) through a cross-section downstream of the plunging flow. To examine this oscillatory behavior in the counter rotating eddies and its effect on plunging flow, we selected three monitoring locations: Locations A and B correspond to … view at source ↗
Figure 13
Figure 13. Figure 13: Continuous wavelet analysis of velocity-magnitude oscillations for the 50% constriction case (C25) at Location A (a), Location B (b), and Location C (c). Panel (d) shows the normalized pre-multiplied spectrum, fP(f)/σ2 , as a function of frequency, representing the variance-preserving form of the spectral density. For the C25 case, the variance-preserving spectra shows clear spatial differences in the dom… view at source ↗

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