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REVIEW 3 major objections 6 minor 57 references

Large Effects of Particle Size Heterogeneity on Dynamic Saltation Threshold

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Pith's one-line read Sand beds with mixed grain sizes need 60 to 250 percent stronger wind to stop saltation than uniform sand of the same median size.

desk verdict A real and important result about particle-size heterogeneity inflating the dynamic saltation threshold, but the headline 249% number is softer than it looks; the qualitative claim holds, the quantitative ceiling needs a re-analysis of the roughness method. read the letter →

arxiv 1909.00132 v2 pith:EEFTCHNY submitted 2019-08-31 physics.geo-ph physics.ao-phphysics.flu-dyn

Wei Zhu , Xinghui Huo , Jie Zhang , Peng Wang , Thomas Pähtz , Ning Huang , Zhiguo He This is my paper · ORCID
classification physics.geo-phphysics.ao-phphysics.flu-dyn
keywords dynamicsaltationthresholdparticlesizeheterogeneitypoorlysortedsandroughnessmethodvisualwindtunnelaeoliandustaerosolemission
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that the wind speed at which saltation stops depends strongly on how mixed the sand bed is: for beds with similar median grain diameter $d_{50}$, poorly sorted sands have dynamic saltation thresholds 60–250 percent higher than well-sorted sands, measured both visually and by a surface-roughness regime-shift method. It also argues that sufficiently heterogeneous beds have more than one dynamic threshold: a lower, visually observed threshold at which only relatively fine grains saltate, and a higher roughness-based threshold at which the whole grain mixture participates. Even rescaling the threshold by the 90th percentile diameter $d_{90}$ instead of $d_{50}$ does not erase the difference, indicating that a bed's very coarse tail exerts a strong control. If correct, models of dust emission, dune dynamics, and planetary surface evolution that use a median-diameter threshold would predict saltation at winds where real heterogeneous beds have already stopped.

What carries the argument

The paper's central measuring device is the roughness-regime-shift method, which exploits that absent transport the bed surface roughness $z_o$ stays roughly constant, so the free-stream wind velocity and shear velocity obey $u_* = \alpha_1 U_{\infty}$, while saturated transport suppresses near-bed wind and raises roughness exponentially (the Bagnold-focus approximation), giving $u_* = \alpha_2 (U_{\infty} - u_f)$. The dynamic threshold is the intersection $u_t^{zo} = \alpha_1 \alpha_2 u_f /(\alpha_2 - \alpha_1)$. Complemented by the classic visual method, this yields the two thresholds compared across samples; the results are nondimensionalized as $A_{50} = u_t / \sqrt{(\rho_p/\rho_a - 1) g d_{50}}$ and $A_{90}$ with $d_{90}$ to separate median-size scaling from the effect of the coarse tail.

What would settle it

Collect and size the grains that are saltating in the wind-speed window between the visual and roughness thresholds for a poorly sorted bed (e.g., with a trap or high-speed imaging): the multi-threshold claim predicts they should be markedly finer than the bed's coarse tail; finding coarse grains in that cloud would refute the size-selective interpretation.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that the dynamic saltation threshold (the minimal shear velocity that keeps saltation going once started) is not a property of median grain size alone. In wind-tunnel experiments comparing four well-sorted and two poorly sorted sand beds with overlapping $d_{50}$, both the visual method and the roughness method give thresholds for the poorly sorted beds that are 60–250 percent larger. The paper attributes part of this to hiding effects: fine grains sheltered by coarse neighbors are harder to keep rebounding, and coarse grains require stronger flow to stay airborne, so a heterogeneous bed resists sustained transport until the wind is much stronger. A second finding is that poorly sorted beds exhibit two distinct cessation thresholds—a lower visual threshold marking the stop of fine-particle saltation and a higher roughness threshold marking the stop of the full ensemble—and the paper interprets the gap as size-selective, erosion-limited, undersaturated transport sustained by aerodynamic entrainment rather than the splash feedback that saturates uniform beds.

Load-bearing premise

The claim that heterogeneous beds have distinct thresholds for fine grains and the whole ensemble stands on the visual identification that only relatively fine particles saltate between the two thresholds; no size-resolved sampling of the saltating grains was made to confirm that identification.

Editorial extensions

If this is right

  • Threshold predictions for dust emission and dune mobility that use only $d_{50}$ will systematically underpredict the wind needed to stop transport on heterogeneous desert soils and will overpredict transport just above the median-based threshold.
  • A heterogeneous bed can have a wind window in which fine grains saltate while coarse grains stay put, so single-threshold transport laws cannot describe the cessation branch there.
  • Because $d_{90}$ does not collapse the data, the relevant characteristic size for thresholds lies above $d_{90}$ or is a fuller function of the size distribution, not a single percentile.
  • The extrapolation method for inferring thresholds from transport-rate measurements should use weighted least squares that preserve near-threshold data; unweighted fits can shift inferred thresholds by up to a factor of 1.7.
  • Field observations that find a single, size-independent threshold may simply reflect well-sorted beds; the size-selective behavior is expected to appear only when bed sorting is poor (roughly $d_{90}/d_{50} > 2$ in this study).

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The coarse-tail control suggests a testable generalization: for mixed beds, the threshold should scale with a high percentile such as $d_{84}$ or $d_{90}$ plus a sorting parameter; reanalyzing existing threshold data sets with sorting metadata would reveal whether a universal two-parameter curve collapses aeolian and fluvial thresholds.
  • The 'more than one threshold' picture implies hysteresis in heterogeneous soils: once full-ensemble saltation is running, transport can continue down to the lower visual threshold, so onset and cessation differ not only by turbulence but by grain-size selectivity; dust-emission schemes may need a memory or state variable for bed armoring.
  • If the visual threshold is indeed a fine-particle subset threshold, then ripple-crest armoring and the saltation threshold are coupled: surface sorting changes the threshold as transport proceeds, so threshold is not a fixed bed property but co-evolves with bed texture.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This paper presents wind tunnel measurements of the dynamic saltation threshold for four well-sorted and two poorly sorted sand beds. The threshold is determined by two methods: a visual method and a roughness-based method that locates the intersection of constant-roughness and Bagnold-focus branches in the u* versus U∞ plane (Eqs. 5a, 5b, 6). The authors report that both methods yield thresholds 60–250% larger for poorly sorted beds than for well-sorted beds with similar median diameter, that the visual and roughness thresholds differ for the two poorly sorted samples (which they interpret as evidence for more than one dynamic threshold), and that rescaling by the 90th percentile diameter d90 does not eliminate the difference. They also revisit the extrapolation method for transport-rate data and argue that threshold estimates depend strongly on the fitting procedure.

Significance. The central claim—that sand size heterogeneity can substantially raise the dynamic saltation threshold beyond what d50-based scaling predicts—is physically important and, if well supported, has consequences for dust-emission modeling and planetary geomorphology. The paper has notable strengths: the paired comparisons (Samples 1–5 and 4–6) show threshold differences well beyond the reported 95% confidence intervals; both measurement methods lean in the same direction; and the underlying velocity-profile data are publicly archived on Zenodo (record 2550975). The paper is also candid about the transitional-region limitation of the roughness method. However, the quantitative range (60–250%) and the 'more than one threshold' interpretation rest on assumptions (two-line intersection; fine-subset identification by visual inspection) that require additional support, so the present version needs revision before the claims can be accepted at face value.

major comments (3)
  1. [Section 3.2.2, Eq. (6), Fig. 4c] The roughness-method threshold is defined as the intersection of the two fitted branches (Eq. 6), but the paper itself acknowledges in Section 3.2.2 that this neglects the transitional region near the dynamic threshold and flags an 'obvious transitional region' for Sample 5 in Figure 4c. Because the headline 60–250% range and the upper-bound 249% ratio for Samples 4 and 6 depend on the roughness threshold for Sample 6 (65.2 ± 2.3 cm/s), and because the reported confidence intervals include only line-fit uncertainty and not model-form uncertainty from the two-line approximation, the quantitative claim is not yet robust. I request a sensitivity analysis on the archived u*–U∞ data—for example, varying the range of points included in each branch, excluding points nearest the intersection, or fitting a smooth transition function—to show how uzo_t and the Sample 4-versus-6 ratio change. Without such an analysis, the specific 60–250% values in the abstract should be treated as provisional.
  2. [Section 5.1] The conclusion that the visual threshold represents saltation of a fine-particle subset whereas the roughness threshold represents the whole ensemble is inferred from the visual observation that 'only relatively fine particles were saltating' in the intermediate range for Samples 5 and 6. No quantitative size-resolved data on the saltating grains are presented (e.g., particle tracking, image analysis, or size-selective traps), so the 'more than one dynamic threshold' interpretation is currently under-supported. Either provide direct evidence of size-selective transport or explicitly present the fine-subset interpretation as a hypothesis that is consistent with, but not uniquely determined by, the observations.
  3. [Section 4, Table 1] The heterogeneity effect is anchored on only two poorly sorted samples (Samples 5 and 6), and the 95% confidence interval for the Sample 4 visual threshold (16.3 ± 6.0 cm/s) overlaps with the well-sorted samples. The qualitative conclusion that heterogeneity raises the threshold is supported by the two paired comparisons, but the paper should avoid presenting '60–250%' as a precise empirical bound until the model-form uncertainty in the roughness method is addressed and more heterogeneity levels are tested. The abstract and conclusions should be reworded to reflect this uncertainty.
minor comments (6)
  1. [Figure 5] The color-coded symbols will not be distinguishable in greyscale printing; please add distinct symbol shapes or markers in addition to color.
  2. [Header] The header on each page reads 'Confidential manuscript submitted to JGR-Earth Surface'; if this is posted as a preprint, the header should be updated or removed.
  3. [Section 2] The supporting information Figure S1 referenced in Section 2 is not included; please make it available with the submitted version or remove the reference.
  4. [Section 5.1] The statement that 'for all our tested sand beds, the visually estimated dynamic threshold is smaller than the one estimated from the roughness method' is based on point estimates; for Samples 1–4 the differences are not statistically significant, so the sentence should be qualified to emphasize that the significant difference occurs only for the poorly sorted samples.
  5. [Equation (2)] The notation M(Θ) and Mc is introduced; consider defining Mc explicitly on first use (it is currently defined parenthetically in the text) to improve readability.
  6. [Figure 5b caption] In the caption of Figure 5b, specify which measurements lack known d90 values and are therefore excluded from the comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline 60–250% thresholds come from direct wind-tunnel measurements and are not derived from a transport law containing the target result.

full rationale

The central claim—that dynamic saltation thresholds are 60–250% larger for poorly sorted beds than for well-sorted beds with similar d50—rests on direct wind-tunnel measurements rather than on a transport law whose inputs contain the target result. The visual threshold is defined operationally as the mean of the adjacent free-stream velocities bracketing transport cessation (Section 3.2.1), and the roughness threshold is defined as the intersection of two fitted log-law branches (Eqs. 5a, 5b, 6). These are measurement protocols, not predictions from a first-principles model being tested, so no fitted parameter is renamed as a prediction. The 60–250% range is also not an artifact of the roughness fit alone: the visual thresholds show the same qualitative effect (e.g., Samples 4 vs 6: 16.3 vs 49.3 cm/s in Section 4). The extrapolation re-analysis in Section 2 uses the authors' own transport law (Eq. 2 from Pähtz and Durán, 2018b), but this section is peripheral—it only demonstrates sensitivity of an indirect method and is not needed for the headline comparisons. The paper itself flags the transitional-region limitation of the roughness method (Section 3.2.2: 'neglects the transitional region that occurs near the dynamic threshold'; Section 5.2.2: 'there is an obvious transitional region for Sample 5'), which is a model-form and robustness caveat rather than a circularity. Hence no load-bearing step reduces to its own inputs by construction.

Assumptions & free parameters 4 free parameters · 7 assumptions · 0 invented entities

The central measurement requires no invented entities. The roughness-method thresholds carry three fitted parameters per run (alpha1, alpha2, u_f), and the extrapolation re-analysis adds fit parameters (CQ, Theta_t). The interpretation leans on the authors' own sustained-rebound theory and on the Bagnold-focus roughness model from prior literature; the distinction between fine-subset and full-ensemble thresholds is an ad hoc interpretive assumption rather than a measured quantity.

free parameters (4)
  • alpha1 (u* proportionality constant, absent transport) = fitted per run, not reported
    Slope of u* versus U_infinity for the no-transport regime, fitted from low-wind measurements (Eq. 5a). The roughness-method threshold in Eq. (6) depends on this fit.
  • alpha2 (u* proportionality constant, saturated transport) = fitted per run, not reported
    Slope of u* versus U_infinity for the saturated regime, fitted from high-wind measurements (Eq. 5b).
  • u_f (Bagnold focus velocity) = fitted per run, not reported
    Intercept parameter in Eq. (5b) used with alpha2; the threshold in Eq. (6) is a function of alpha1, alpha2, and u_f.
  • CQ and Theta_t (extrapolation fits) = not reported numerically
    In the re-analysis of Creyssels et al. (2009) and Ho et al. (2011) data, CQ and Theta_t are fit parameters for Eq. (1) and Theta_t for Eq. (2); their fitted values drive the conclusion that the extrapolation method is procedure-sensitive.
assumptions (7)
  • domain assumption The mean wind profile follows the log-law u_x(z) = (u*/kappa) ln(z/z_o) at the four measured elevations (4.2 to 30.1 cm).
    Invoked in Section 3.1 (Eq. 3) to extract u* and z_o from hot-wire profiles; the paper notes deviations for z > 30.1 cm.
  • domain assumption In saturated saltation, the surface roughness follows the Bagnold-focus form z_o = z_f exp(-kappa u_f/u*), while in absence of transport z_o is approximately constant.
    This is the basis of the roughness method (Section 3.2.2), taken from prior literature (Bagnold 1936; Creyssels et al. 2009; Ho et al. 2011; Durán et al. 2011). The paper uses it to derive Eqs. (5a)-(6).
  • domain assumption The free-stream velocity U_infinity equals the log-law velocity evaluated at a fixed height H proportional to the boundary-layer thickness.
    Used to derive Eqs. (5a)-(6) in Section 3.2.2; this links measured U_infinity to u* and z_o.
  • domain assumption Transport at the measurement position is saturated despite no sand feeding at the tunnel entrance.
    Stated in Section 3.1, justified by the 20 m fetch and a few upwind test profiles; the roughness method requires saturated transport for regime (5b).
  • domain assumption Successively decrementing U_infinity measures the dynamic cessation threshold, not a hysteretic static threshold.
    Section 3.2.1 reports one static-protocol test run for Sample 1 that gave a larger threshold, used to argue the visual method measures the dynamic threshold.
  • ad hoc to paper The visual threshold marks transport of a fine-particle subset while the roughness threshold marks transport of the whole ensemble for poorly sorted beds.
    This interpretive claim (Section 5.1) is supported only by visual observation that coarse grains creep while fine grains saltate; it is not measured by size-resolved sampling.
  • ad hoc to paper The rebound hypothesis of Pähtz and Durán (2018a), that the cessation threshold is set by energy compensation of rebounding particles, is assumed when interpreting the results.
    Section 5.2 uses this framework to explain why coarse-tail control arises; it is the authors' own prior theory, invoked but not re-derived here.

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Pith. "Pith review of Large Effects of Particle Size Heterogeneity on Dynamic Saltation Threshold." pith.science (2026). https://pith.science/paper/EEFTCHNY

@misc{pith2026190900132,
  author       = {Pith},
  title        = {Pith review of: Large Effects of Particle Size Heterogeneity on Dynamic Saltation Threshold},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EEFTCHNY}},
  note         = {Machine review of arXiv:1909.00132}
}
read the original abstract

Reliably predicting the geomorphology and climate of planetary bodies requires knowledge of the dynamic threshold wind shear velocity below which saltation transport ceases. Here we measure this threshold in a wind tunnel for four well-sorted and two poorly sorted sand beds by visual means and by a method that exploits a regime shift in the behavior of the surface roughness caused by momentum transfer from the wind to the saltating particles. For our poorly sorted sands, we find that these measurement methods yield different threshold values because, at the smaller visual threshold, relatively coarse particles do not participate in saltation. We further find that both methods yield threshold values that are much larger (60--250\%) for our poorly sorted sands than for our well-sorted sands with similar median particle diameter. In particular, even a rescaling of the dynamic saltation threshold based on the 90th percentile particle diameter rather than the median diameter cannot fully capture this difference, suggesting that relatively very coarse particles have a considerable control on the dynamic threshold. Similar findings were previously reported for water-driven sediment transport. Our findings have important implications for quantitative predictions of saltation transport-related geophysical processes, such as dust aerosol emission.

Figures

Figures reproduced from arXiv: 1909.00132 by the authors.

Figure 1
Figure 1. Comparison of four different extrapolation procedures to determine the dynamic threshold Shields number Θt . Nondimensionalized transport rate Q∗ ≡ Q/  ρp q (ρp/ρa − 1)gd 3 50 versus Shields number Θ ≡ ρau 2 ∗ /[(ρp − ρa)gd50]. Symbols and error bars correspond to the wind tunnel measurements by Creyssels et al. [2009, ρp = 2500 kg/m3 , d50 = 242 µm] and Ho et al. [2011, ρp = 2470 kg/m3 , d50 = 232 µm]. Note that … view at source ↗
Figure 2
Figure 2. (a) Sketch of the wind tunnel at Lanzhou University. (b) Particle size distributions of sand beds used in the experiments, where Sample 6 refers to the original sand from Tengger Desert. sure saturated transport at its downstream end [Selmani et al., 2018]. The wind velocity was measured near the end of the working section at four elevations z above the sand bed (z = [4.2, 10.2, 15, 30.1] cm) using I-type hot-wire p… view at source ↗
Figure 3
Figure 3. Exemplary mean wind velocity profiles for (a) sand Sample 1, (b) sand Sample 5, and (c) sand Sample 6. Squares and error bars correspond to the measurements by the hot-wire probes. Lines correspond to equation (3) for conditions with absent (blue) and present (red) saltation transport. 3.2 Threshold Measurement Methods 3.2.1 Visual Method The visual method is a standard method to determine the dynamic threshold [Bag… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Relationship between wind shear velocity u∗ and free stream wind velocity U∞ for a represen￾tative run for each sand sample: (a) for sand Samples 1 and 2, (b) for sand Samples 3 and 4, and (c) for sand Samples 5 and 6. The lines correspond to fits to the measurements (…
Figure 5
Figure 5. Figure 5: Measurements of the threshold parameters (symbols) and 95% confidence intervals (if known). (a) Threshold parameter A50 = ut / p (ρp/ρa − 1)gd50 measured in the present (WS = well-sorted sand samples, PS = poorly sorted sand samples) and previous studies [Bagnold, 1937…

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