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A quality‐controlled data set comprising measurements of aeolian sand transport rates obtained at three disparate field sites is used to evaluate six commonly employed transport rate models (those of Bagnold, Kawamura, Zingg, Owen, Hsu, and Lettau and Lettau) and to recalibrate the empirical constants in those models. Shear velocity estimates were obtained using the von Kármán constant and an apparent von Kármán parameter. Models were recalibrated using non‐linear regression and non‐linear regression with least‐squares lines forced through axes origins. Recalibration using the apparent von Kármán parameter and forced regression reduced the empirical constants for all models. The disparity between the predictions from the different models is reduced from about an order of magnitude to about a quarter of an order of magnitude. The recalibrated Lettau and Lettau model provided the greatest statistical agreement between observed and predicted transport rates, with a coefficient of determination of 0·77. Evaluation of the results suggests that our estimations of threshold shear velocity may be too slow, causing errors in predicted transport rates. Copyright © 2012 John Wiley & Sons, Ltd.

Aeolian transport of sand and dust is driven by turbulent winds that fluctuate over a broad range of temporal and spatial scales. However, commonly used aeolian transport models do not explicitly account for such fluctuations, likely contributing to substantial discrepancies between models and measurements. Underlying this problem is the absence of accurate sand flux measurements at the short time scales at which wind speed fluctuates. Here, we draw on extensive field measurements of aeolian saltation to develop a methodology for generating highfrequency (25 Hz) time series of total (vertically-integrated) saltation flux, namely by calibrating high-frequency (HF) particle counts to low-frequency (LF) flux measurements. The methodology follows four steps: (1) fit exponential curves to vertical profiles of saltation flux from LF saltation traps, (2) determine empirical calibration factors through comparison of LF exponential fits to HF number counts over concurrent time intervals, (3) apply these calibration factors to subsamples of the saltation count time series to obtain HF height-specific saltation fluxes, and (4) aggregate the calibrated HF height-specific saltation fluxes into estimates of total saltation fluxes. When coupled to high-frequency measurements of wind velocity, this methodology offers new opportunities for understanding how aeolian saltation dynamics respond to variability in driving winds over time scales from tens of milliseconds to days.

SHERMAN, D.J.; LI, B.; FERRELL E.We report the results of fteld experiments designed to cotnpare four types of aeolian saltation setisors: the Saftre; the Wenglor® Particle Counter; the Miniphone; and the Buzzer Disc. Sets of sensors were deployed in tight spatial an-ays and sampled at rates as fast as 20 kHz. In two of the three trials, the data from the sensors are compared to data obtained from sand traps. The Miniphone and the Buzzer Disc, based on microphone and piezoelectric technologies, respectively, produced grain itnpact counts cotiiparable to those derived from the trap data. The Satire and the Wenglor® Particle Cottnter produce count rates that were an order of magnitude too slow. Satires undercount because of their large moinentum thteshold and because its signal is saturated at relatively slow transport rates. We conclude that the Miniphone and the Buzzer Disc are appropriate for deployment as grain counters because their stnall size allows them to be installed in closely-spaced sets.

[1] The variability of the apparent von Kármán parameter a during aeolian saltation was evaluated in a field experiment at Jericoacoara, Brazil. To test this variability, velocity profiles, Reynolds stress and sand transport data were gathered and analyzed. We used 15 data sets with sand-laden airflow and 2 runs with clear airflow. Results indicated an inverse linear relationship between a and bulk, gravimetric sand concentration (R 2 = 0.746) and sand transport rate (R 2 = 0.577). We found values of a as low as 0.26. This means that using the von Kármán constant, (0.40) rather than the variable a to estimate shear velocity based on the slope of the velocity profile will lead to over estimation of shear velocity, averaging about 20% in this study. This, in turn, produces an over estimation of wind-blown sand transport rates based on models that have terms with shear velocity cubed. Citation:

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