Spatiotemporal Variability of Aeolian Sand Transport in a Coastal Dune Environment

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1 Journal of Coastal Research West Palm Beach, Florida September 2006 Spatiotemporal Variability of Aeolian Sand Transport in a Coastal Dune Environment Andreas C.W. Baas and Douglas J. Sherman Department of Geography King s College London Strand London, WC2R 2LS, UK andreas.baas@kcl.ac.uk Department of Geography Texas A&M University College Station, TX 77843, U.S.A sherman@geog.tamu.edu ABSTRACT BAAS, A.C.W. and SHERMAN, J.S Spatiotemporal variability of aeolian sand transport in a coastal dune environment. Journal of Coastal Research, 22(5), West Palm Beach (Florida), ISSN Aeolian sand transport on beaches and in dune environments shows a great spatial and temporal variability that has important implications for modeling and monitoring of coastal systems. Yet there have been few quantifications or statistical characteristics of transport variability in natural environments. Transport variability can result from bed surface control in the form of differentiation in grain size, surface moisture, and microtopography, or can be induced by fluid forcing in the form of gusts, burst-sweep events, and streamwise vortices. A field experiment was conducted on a coastal dune near Guadalupe, California, to quantify transport variability over spatial scales of m and temporal scales of seconds. Results show that spanwise (lateral) variability increases with spatial scale and decreases with temporal scale. Minimum transport variability over the smallest distances and longest time scales is on the order of 30%, providing error margins for transport-rate measurements and model extrapolations. Variability reaches a maximum level at spatial scales larger than roughly half the boundary-layer height. In relation to shear velocity, greatest variability is found near the transport threshold and smallest variability occurs during periods of high shear velocities. ADDITIONAL INDEX WORDS: Coastal management, field measurements, sediment transport, error margins. INTRODUCTION Aeolian sediment transport in most natural environments exhibits a great spatial and temporal variability. Lack of understanding and quantification of this phenomenon poses a challenge to realistic modeling of transport rates and simulating morphological change, particularly when two-dimensional transect models are extrapolated to three-dimensional landscapes. Temporal variability has received considerable attention with respect to saltation response to fluctuations in shear velocity, U *, primarily in wind tunnels (BUTTER- FIELD, 1993; MCEWAN and WILLETTS, 1991; SPIES et al., 2000) and transport intermittency under natural winds (LEE, 1987; STOUT and ZOBECK, 1997). In modeling transport and resulting morphological change over any considerable spanwise dimension (perpendicular to the wind), however, aeolian transport is generally treated as a uniform blanket of saltating grains that respond in unison to fluctuations in shear velocity. Observations of active transport on beaches and in deserts challenge this simplification, as aeolian streamers and transport patterns introduce a great variability in both time and space (BAAS, 2003), which may be responsible for the poor correspondence between transport rates derived from predictive equations and measured with sand traps in the field (ARENS, 1996; HAFF, 1996; SHERMAN et al., 1998). A more rigorous quantification of spatiotemporal DOI: / received and accepted in revision 12 January transport variability may therefore help in adjusting transport equations for realistic two-dimensional and three-dimensional simulations, including estimates of error margins, and may provide insights into deficiencies of the conceptual transport model. This article reviews possible origins of transport variability and reports statistical characterizations from field data on a spatial scale of 10s of centimeters to meters and on a temporal scale of seconds to minutes. Within the confines of field instrumentation, several quantifications of transport variability are presented and some implications for realistic transport modeling are discussed. BACKGROUND Transport variability can be conceptualized as a result of spatial and temporal variations in both the fluid forcing on the bed and the erodibility of the surface. Possible controls on erodibility include spatial differentiation of surface conditions such as moisture content, grain size, and microtopography. Possible sources of variable fluid forcing include gusts and eddies in the wind, and possibly related turbulent flow structures such as burst-sweep events and streamwise vortices. The impact of moisture on threshold shear velocity, U *t, has been investigated using a variety of approaches and parameterizations (BELLY, 1964; CHEPIL, 1956; FECAN et al., 1999; HOTTA et al., 1984; NEUMAN and NICKLING, 1989). Resulting

2 Aeolian Sand Transport Spatiotemporal Variability 1199 models relating moisture content and transport rate vary widely, however, and the variables involved are often difficult to measure in field settings (NAMIKAS and SHERMAN, 1995). In addition, these models predict a cessation of transport at relatively low moisture contents (on the order of 5% by weight), but transport is observed even during periods of intense precipitation on a humid beach (ARENS, 1996; JACKSON and NORDSTROM, 1998), and many sand traps become clogged with moist sand. The interactions between moisture and transport are not satisfactorily understood, and research in this area is hampered by operational challenges involved with measuring surface moisture contents accurately in the field. The influence of grain size is expressed in BAGNOLD s (1936) formulation for threshold shear velocity. Numerical modeling using KAWAMURA s (1951) transport equation, which incorporates U *t, shows that over the majority of the size range of natural sands, transport rates change on the order of 5 10% per 0.1 mm change in grain size (BAAS, 2003). Differences in transport rate are especially pronounced near the threshold shear velocity. A moderate spatial segregation of naturally graded dune sand can therefore result in variability of transport rates on the order of 10 20%. Small surface undulations, or microtopography, may have prominent effects on local transport rates resulting in spatial variability, especially when shear velocities are near threshold. The more exposed parts of the surface experience higher shear stresses, inducing higher local transport rates. Simulations using the coupled boundary layer flow and sand transport model SAFE (VAN DIJK et al., 1999), for example, indicate transport rates over 5 cm high, undulations that are 10 25% higher than those over neighboring flat surface areas under shear velocities just above threshold. The surface controls described above are likely correlated in natural environments and may either enhance or offset the combined effect on differentiated transport rates. It can furthermore be presumed that the distribution of these controls changes considerably over time. Indeed, the interactions between the varied surface conditions and the resulting variability in transport may shift, maintain, or even pronounce the very surface differentiation itself. Fluid forcing by the wind is far from uniform and steady under natural conditions (KAIMAL et al., 1972; WYNGAARD, 1992) and can thus be a source of spatiotemporal transport variability. Wind blowing over beaches and deserts contains large fluctuations and gusts over multiple scales, which are subsequently reflected in transport variability. Investigations of gustiness and transport have primarily been limited to time-series measurements at single, stationary points. LEE (1987) used a continuously weighing sand trap and a hot-film anemometer to measure gustiness and its effect on transport in a natural environment at a sampling rate of 2 Hz. He found that gustiness did not have a pronounced effect on temporal transport variability. In contrast, BUTTERFIELD (1993, 1999), conducting wind tunnel studies with lasers, optical sensors, and thermal anemometry, found that periodic gustiness increases induced transport rates to magnitudes significantly higher than predicted using an equivalent, but steady, average wind velocity. STOUT and ZOBECK (1997) employed standard cup anemometry and a Sensit saltation detector in the field and found that sporadic bursts in saltation account for nearly all sediment transport. The presence of burst-sweep events in the airflow and their possible effects on transport have also been investigated primarily using single, stationary point measurements. BAUER et al. (1998) used thermal anemometry and statistical timeaveraging techniques that established the presence of structural events in wind across a beach that could be interpreted as burst-sweep events, but correlations between these structures and transport events were not clearly apparent. STERK et al. (1998) measured burst-sweep events and used a highfrequency saltiphone to record transport events. They found that these coherent flow structures were associated with high saltation intensities and accounted for roughly 60% of the average shear stress, while only occurring for approximately 20% of the time. The existence of streamwise vortices in the airflow is largely confined to wind-tunnel studies. Some measurements have been gathered in a natural environment (e.g., KLEWICKI et al., 1995), but their interactions with sediment transport have not been established. Investigating coherent flow structures in natural boundary layers is challenging because of the inherent need for multiple measurements in a three-dimensional framework, using high-frequency sensors, to establish the precise nature of coherent flow structures and their impact on sediment transport. Independent from the above framework of bed surface control and fluid forcing is the concept of self-organized criticality in aeolian transport (MCMENAMIN et al., 2002), where high-frequency observations of transport events under steady winds in a wind tunnel display a power-law frequency-magnitude distribution, or a 1/f signature characteristic of selforganized criticality. Thus, the aeolian transport system may organize itself to a critical state where transport events of all sizes can occur spontaneously, unrelated to proportional fluid forcing or surface-control mechanisms. If this mechanism is indeed operating in aeolian environments, transport variability can only be treated in a statistical manner and direct cause-and-effect relationships with turbulence or the bed surface may not be discernable. Field investigations that quantify spanwise transport variability are few, hampered by several instrumentation issues. Manual sand traps are laborious to deploy and operate in large quantities and are often limited to a low spatial and temporal resolution. Automatically weighing traps with electronic signal acquisition, on the other hand, are usually available only in limited numbers, so that a large array of traps is not feasible. In addition, sand-trapping efficiencies and the effects of slightly different trap specifications and positioning are often not accurately known, and trap results cannot be adjusted appropriately. One of the few comprehensive field investigations of spanwise transport variability is by GARES et al. (1996). Measuring alongshore transport on a beach, they found that, over 15-minute intervals, variability on the order of 25% of the mean can be found on a scale of 1 m and on the order of 50% on spatial scales of 1 50 m. Recently, JACKSON et al. (in press) compared sediment transport rates measured with five sand traps spaced at 1-m intervals and

3 1200 Baas and Sherman Figure 1. Instrument array deployed in the Guadalupe dune area. Line up of black tubes is Safire array. Dominant winds blow from bottom-left corner to top right. found that transport variability commonly exceeded 150% within the array. Field Deployment METHODS A field experiment was conducted on June 28, 2001, on the top of a large coastal dune in the Guadalupe dune field, approximately 5 km west of the town of Guadalupe, California. The windward side of this dune is dominated by a large, saucer-shaped, concave stoss slope, rising to a relatively sharp crest line. The top of the dune beyond the crest line is bare and flat with a slope of less than 1 degree from horizontal over a distance of more than 50 m, and an integrated instrument array (described below) was deployed at the downwind end (Figure 1). Due to the abrupt crest line at the upwind edge, there were no obstructions or interferences to the development of a uniform boundary layer over the fetch of 50 m. Based on ELLIOTT s (1958) empirical formula incorporating fetch and surface roughness, the height of the internal boundary layer (IBL) at the instrument array is estimated at 2 m, where the surface roughness, z 0, is modeled according to NIKURADSE (1933). The bed was prepared by flattening and homogenizing of the surface sediment, with rakes, shovels, and brooms, over the fetch of 50 m and a width of approximately 15 m in order to minimize differentiation in terms of grain size, moisture content, and microtopography. Surface sand samples indicated a mean grain size of 0.47 mm, a median grain size of 0.50 mm, and a standard deviation of 0.58 mm, indicating a medium-sized, moderately sorted surface sand on the Udden Wentworth scale at this site (UDDEN, 1914; WENTWORTH, 1922). The instrument array consisted of three anemometer towers placed along a line perpendicular to the dominant westerly winds with a lateral spacing of 1.75 m (Figure 1). Each tower contained five Gill-type three-cup anemometers, at heights of 0.1, 0.35, 0.6, 0.85, and 1.10 m above the bed, and a wind vane at a height of 2 m. Cup anemometers and the wind vanes were sampled at 5 Hz. Between these towers and extending beyond them on one side, 35 Safires were deployed with a spacing of 0.1 m and with their sensitive element centered at 4 cm above the bed. This transverse array contained two 0.4-m breaks at two of the anemometer towers to accommodate the lowest cup anemometers. The spanwise distance covered by the Safire array was 4.0 m. Safires detect the impacts of saltating grains on a sensitive element of 2 2cm 2 at a sampling rate of 20 Hz and generate an analogue voltage signal that can be interpreted as a measure of relative transport rate (BAAS, 2004). The 35 Safires were not identical in sensitivity, necessitating a normalization of the collected data. Differences in momentum threshold for the detection of grains meant that highly sensitive sensors measured transport during a larger proportion of the time and generated larger analogue signals under active transport. These differences were rectified by downgrading the data from all Safires to match the least sensitive sensor with respect to the temporal percentage of measured transport activity. The minimized data were then normalized, assuming total transport over the full period of a measurement run was equal across the array. The least sensitive sensor had a momentum threshold of roughly kgms 1 (Baas, 2004), which implies that all data represent detection only of grains with a diameter greater than approximately 0.4 mm, assuming an average horizontal flight speed of saltating grains on the order of 2 m s 1. Seven measurement runs of about 15 minutes were conducted, during which synchronized time series of wind speed, wind direction, and relative transport rates were collected. Figure 2 shows an example of normalized Safire data obtained during a run. The wind-vane data indicate that airflow direction was largely confined to within 15 degrees from array normal. Under these angles, the effective spacing between instruments (relative to the flow) is changed by less than 3.5%, and the impact of such deviations on data-analysis procedures can be considered negligible. Data Analysis Procedures Time series of relative transport rate from individual Safires were block averaged over periods of 1, 5, 10, 30, 60, and 120 seconds. Larger temporal scales were not considered because of the normalization of Safire data at the 15-minute scale. Following GARES et al. (1996), spanwise variability was investigated by assessing coefficients of variation of relative transport, measured by the Safire array, as a function of spanwise distance on each of the temporal scales. The coefficient of variation, CoV, was determined over all Safires contained within a lateral distance, y. The lateral distance was varied from 0.1 (i.e., containing two Safires) to 4.0 m (i.e., covering the entire array), with increments of 0.1 m. For each y, a coefficient of variation was determined for all possible permutations of the lateral distance fitted across the 4-m long array. Thus a lateral distance of y 3.5 m can be fitted six times across the array (i.e., between y 0 and y 3.5, between y 0.1 and y 3.6, between y 0.2 and y 3.7, etc.), whereas a lateral distance of y 0.2 m can be fitted

4 Aeolian Sand Transport Spatiotemporal Variability 1201 Figure 2. An example of a collection of 20-Hz time series obtained from the Safire array, covering a period of 100 seconds. Data shown is normalized. 39 times. The coefficients were averaged over all permutations to yield a mean coefficient of variation for that spanwise scale, and these averages were determined for each block period in a measurement run. The procedure was repeated for each of the temporal scales and for all seven runs. A second stage of analysis focused on possible relationships between spanwise variability in transport and shear velocity at different temporal scales. It was necessary to determine temporal lags between shear velocity and transport intensity prior to this, as it may be expected that transport exhibits a delayed response to fluctuations in shear velocity. Time series of shear velocity at 1 Hz were obtained from leastsquares regression of the law-of-the-wall through wind-speed data from the different heights above the bed. The time series of U * from the three towers were subsequently averaged to generate a single 1-Hz time series of average shear velocity across the instrument array. The 20-Hz data of transport intensity from the 35 Safires were reduced to a single 1-Hz time series by block averaging over periods of 1 second and across all sensors. The generated shear velocity and transport intensity time series were then used to determine cross-covariance coefficients as a function of temporal lag at a 1-second resolution. For each run, the temporal lag with the highest covariance coefficient was identified. Coefficients at optimum lag were generally on the order of 0.6, compared with magnitudes of at zero lag, and optimum lags ranged from 1 to 6 seconds for the seven measurement runs. Five temporal scales were considered: 5, 10, 30, 60, and 120 seconds. After applying the temporal lags to wind-speed time series to maximize correlations, the original wind-speed and Safire data were reduced to each of the five temporal scales by block averaging. Shear velocities were determined for each period together with the corresponding CoV in transport intensity on the spanwise scale of the entire array ( y 4.0 m). RESULTS Spatiotemporal Characteristics of Transport Variability The effect of spanwise scale on transport variability at temporal scales from 1 to 120 seconds is shown in Figure 3. This graph presents the mean coefficient of variation (averaged over all block periods in all runs) as a function of spanwise distance for the six temporal scales selected. At the largest scale considered here, 120 seconds, each run contains at least six independent block periods of data, and this was deemed an acceptable minimum. Next to each curve in Figure 3 is reported the total number of block periods, N, from which results were obtained. For smaller time scales, N is not equal to the maximum possible over seven runs because block periods with no measured transport in the array were not included in the analysis. For comparison, the analysis procedure was applied to a synthetic series of comparable Gaussian random data on the 10-second time scale, and the resulting curve for the coefficient of variation as a function of lateral distance is included in Figure 3. The Gaussian data

5 1202 Baas and Sherman Table 1. Results of least-squares regression of a power-law relationship between the coefficient of variation, CoV, and the spanwise scale, y, according to CoV a y b, for six averaging periods. Temporal Scale (s) Coefficient, a Exponent, b R GARES et al. (1996), who also found a CoV on the order of 25 50% on a spanwise scale of 1 50 m. Figure 3b shows that the relationships between variability and spanwise scale follow an approximately linear log-log relationship. Thus, transport variability appears to follow a simple power-law relationship with lateral scale. Table 1 shows the coefficients and regression coefficients of a powerlaw regression of the form CoV a y b. Although the absolute magnitudes of variability, controlled by the coefficient a, vary greatly between temporal scales, the nature of the relationship with regard to spanwise scale, the exponent b, differs less so. The exponent varies from 0.27 at the 1-second scale down to 0.14 at the 120-seconds period. The results deviate most significantly from the regression line at the largest spanwise scale, beyond approximately 3.0 m. Spanwise Variability in Relation to Shear Velocity Figure 3. (a) Relationships between average coefficient of variation and spanwise scale for six averaging periods; (b) idem, plotted in log-log space. yields a low variability that remains constant with respect to spanwise distance, while the field data for the 10-second scale shows an increase in variation with larger spanwise distances. This comparison indicates that the results are not simply an artifact of the analysis procedure. Figure 3a reveals that average transport variability increases steadily with greater spanwise distance, and the trend is steeper for smaller temporal scales. Variability is significantly greater at shorter temporal scales: over the smallest spanwise distance of 0.1 m, the coefficient of variation is 107% over 1-second periods, compared with 29% over 120-second periods. Over the largest spanwise distance of 4.0 m, these magnitudes are 266% over 1-second periods and 48% over 120-second periods. The variability at the largest temporal scale compares favorably with the results reported by The effects of shear velocity on spanwise transport variability over a span of 4 m are shown in Figure 4. At each time scale, spanwise variability is greatest at low shear velocities, and the spread in this variability is also greater. At high shear velocities, the coefficient of variation is smaller and more consistent. The tapering of large spread at low shear velocities toward narrow spread at high shear velocities appears to occur at slightly lower shear velocities for larger time scales. Variability remains below 50% at shear velocities above roughly 0.72 m s 1 at the 5-second scale, for example, whereas it remains below 50% at shear velocities above roughly 0.57 m s 1 on the 60-second scale. The coefficient of variation attains similar magnitudes, on the order of 20 30%, at high shear velocities on all temporal scales. These qualitative observations are restricted, however, by the smaller number of samples at high shear velocities. DISCUSSION Interpretation of the results is restricted primarily by the varying sensitivity thresholds of the Safires. Because these sensors are limited to detecting saltating grains with a diameter greater than 0.4 mm, only a portion of the total amount of sand in transport is measured. The fine fraction may or may not exhibit spatiotemporal trends in transport variability different from those presented here. The scatter plots in Figure 4 show a wide spread in individual coefficients of variation, particularly at low shear velocities. The average CoV values used in Figure 3 are therefore associated with a similar wide spread around the mean. The largest spanwise

6 Aeolian Sand Transport Spatiotemporal Variability 1203 Figure 4. scales. Scatter plots of determinations of spanwise transport variability on a spanwise scale of 4 m and associated shear velocity for five temporal scale considered is 4.0 m, so extrapolation to larger scales is cautioned. Despite these limitations, the statistical characterizations have significant implications. Variability in transport depends on the spanwise scale considered, with higher variability on larger scales. The results underscore the problem of measuring transport rates in the field. Even at the smallest spanwise distance and longest temporal scale (Figure 3) or at the highest shear velocities (Figure 4), the lowest coefficient of variation remains at roughly 25%. This value may be interpreted as a minimum margin of error for transport rate measurements in the field. Variability is minimized by conducting measurements over longer periods. This corroborates the usual practice of deploying sand traps over periods of 10 minutes or longer (PYE and TSOAR, 1990), but also highlights the problems of obtaining representative transport rates at high frequencies. Unless a large number of traps or sensors are deployed, individual measurements over periods of less than 30 seconds may be subject to an error of %. Figure 3 further shows that, at this field site, spanwise variability reaches a relative constant upper limit at a minimum y that depends on the temporal scale. At the three largest periods (30, 60, and 120 seconds), variability reaches a stable level at a spanwise distance of roughly 2 m, whereas at the shortest time scales, a stable level is reached at spanwise scales greater than 3 m. The good fit of a power-law relationship between variability and y may be indicative of a fractal spatiotemporal nature of transport below a scale of 3 m. This observation is particularly compatible with the power-law relationship found by MCMENAMIN et al. (2002) between the frequency and the magnitude of transport events and with their proposed self-organized criticality of initiation of saltation. The deviation from the power-law at spanwise distances greater than 3.0 m may be related to the size of the internal boundary layer. The height of the IBL was estimated at 2 m, but it may have been higher because of enhanced surface roughness under transport conditions (BLUMBERG and GREE- LEY, 1993; HSU, 1971; SHERMAN, 1992). Using estimates of roughness length, z 0, derived from law-of-the-wall regressions on the wind data, yields an IBL height on the order of 6 m. The largest spanwise variability in transport is expected to be introduced by the largest sized eddies in the IBL. The spanwise spacing of these eddies scales with the height of the IBL multiplied by Von Karman s constant (PANOFSKY and DUTTON, 1984; STULL, 1988). The largest eddies may therefore be estimated on the order of 2.5 m, which compares fairly well with the 3-m scale on which variability reaches a relatively stable level. If the preceding argument is accepted, it implies that the power-law relationship may be extrapolated to larger spanwise distances under internal boundary layers developed over longer fetches, and hence reaching to greater heights. In this study, the sand surface at the field site was smoothed and homogenized to minimize the effects of bedsurface control on spatiotemporal transport variability, so the resulting variability transport should be directly related to the turbulence characteristics of the airflow. The relationships between spanwise variability and shear velocity can also be placed in the context of turbulence in the IBL. The higher variability under low shear velocities on the smaller temporal scales can be attributed to the slower passage of less energetic eddies across the bed surface. The scatter plot at the smallest time scale of 5 seconds in Figure 4 shows that no transport was detected below a shear velocity of roughly 0.3 m s 1. This compares well with the theoretical fluid threshold shear velocity (BAGNOLD, 1936) of 0.32 m s 1 for the median grain size of the surface sediment (0.5 mm), or 0.29 m s 1 for the minimum grain size detectable by the Safires (0.4 mm). The finding that transport variability is greatest near this threshold agrees with NICKLING s (1988) observation that saltation is initiated over a range of shear velocities around threshold. Transport occurs in small and intermittent events, resulting in a high spanwise variability rather than an instant activation of the entire sand bed at threshold. The gradual decrease in spanwise variability (and its spread) with higher shear velocities supports the characterization of transport patterns at Windy Point, California, according to streamer families at low shear velocities, nested streamers at medium, and clouds with embedded streamers at high shear velocities (BAAS, 2003). Those observations showed that differences in relative transport rates

7 1204 Baas and Sherman inside vs. outside streamers decrease at higher shear velocities. CONCLUSIONS This study shows average transport variability ranging from 29% over a spanwise distance of 0.1 m and periods of 120 seconds to 266% over a spanwise distance of 4.0 m and periods of 1 second. Variability increases at greater spanwise scales and decreases over longer measurement periods and at higher shear velocities. The field experiment indicates that even homogenous bed surfaces experience a great spatial variability in sand transport that can be attributed to turbulence in the airflow. The observed trends of transport variability in relation to spanwise scale and shear velocity may have important implications for the modeling and monitoring of aeolian sand transport. Lateral extrapolations of numerical transport simulations along a transect may be subject to a minimum error margin on the order of 30%. Specific error margins may be estimated from results presented here based on the temporal resolution of the simulation, the modeled shear velocity, and the scale of lateral extrapolation. The findings suggest that measurements of transport rates for management and monitoring purposes should be conducted with multiple sand traps or sensors deployed over lateral distances similar to the height of the internal boundary layer. Such an approach allows for an assessment of maximum spanwise variability and thus for gauging confidence intervals around bulk transport rates. Transport measurements should be integrated over long time periods to minimize spanwise variability. A lateral array of at least three logarithmically spaced sand traps may be used to evaluate the linear log-log trend in variability as a function of spanwise scale for the purpose of extrapolation and/or error estimation. ACKNOWLEDGMENTS The authors would like to thank Bernard Bauer, Jean Ellis, Eugene Farrell, David Hansen, Jim McDermott, and Mark Lange for valuable help in the field and Unocal 76 and Cannon Associates for permission to work in the Guadalupe Dune area. Norb Psuty is thanked for securing sponsorship for the presentation of results at the IGU Coastal Sessions & Meeting: Coasts Under Stress II in New Orleans. Two anonymous reviewers are thanked for comments and suggestions that helped improved the manuscript. Critical financial support was provided by a dissertation improvement award from the National Science Foundation, Geography and Regional Sciences Program (# ). LITERATURE CITED ARENS, S.M., Rates of aeolian transport on a beach in a temperate humid climate. Geomorphology, 17, BAAS, A.C.W., The Formation and Behavior of Aeolian Streamers. Los Angeles, California: University of Southern California, PhD dissertation, 412p. BAAS, A.C.W., Evaluation of saltation flux impact responders (Safires) for measuring instantaneous aeolian sand transport intensity. Geomorphology, 59, BAGNOLD, R.A., The Physics of Blown Sand and Desert Dunes. London: Chapman and Hall, 265p. BAUER, B.O.; YI, J.; NAMIKAS, S.L., and SHERMAN, D.J., Event detection and conditional averaging in unsteady aeolian systems. Journal of Arid Environments, 39, BELLY, P.Y., Sand Movement by Wind. U.S. Army Corps of Engineers, Coastal Engineering Research Center, Technical Memorandum 1. BLUMBERG, D.G. and GREELEY, R., Field studies of aerodynamic roughness length. Journal of Arid Environments, 25, BUTTERFIELD, G.R., Sand transport response to fluctuating wind velocity. In: CLIFFORD, N.J.; FRENCH, J.R., and HARDISTY, J. (eds.), Turbulence: Perspectives on Flow and Sediment Transport. New York: Wiley, pp BUTTERFIELD, G.R., Near-bed mass flux profiles in aeolian sand transport: high-resolution measurements in a wind tunnel. Earth Surface Processes and Landforms, 24, CHEPIL, W.S., Influence of moisture on erodibility of soil by wind. Soil Science Society of America Proceedings, 20, ELLIOTT, W.P., The growth of the atmospheric internal boundary layer. Transactions of the American Geophysical Union, 39, FECAN, F.; MARTICORENA, B., and BERGAMETTI, G., Parameterization of the increase of the aeolian erosion threshold wind friction velocity due to soil moisture for arid and semi-arid areas. Annales Geophysicae, 17, GARES, P.A.; DAVIDSON-ARNOTT, R.G.D.; BAUER, B.O.; SHERMAN, D.J.; CARTER, R.W.G.; JACKSON, D.W.T., and NORDSTROM, K.F., Alongshore variations in aeolian sediment transport: Carrick Finn Strand, Ireland. Journal of Coastal Research, 12, HAFF, P.K., Limitations on predictive modeling in geomorphology. In: RHOADS, B.L. andthorn, C.E. (eds.), The Scientific Nature of Geomorphology. New York: Wiley, pp HOTTA, S.; KUBOTA, S.; KATORI, S., and HORIKAWA, K., Sand transport by wind on a wet sand surface. In: Proceedings of the 19th Coastal Engineering Conference of the ASCE. New York: ASCE, pp HSU, S.-A., Wind stress criteria in eolian sand transport. Journal of Geophysical Research, 76, JACKSON, N.L. and NORDSTROM, K.F., Aeolian transport of sediment on a beach during and after rainfall, Wildwood, NJ, USA. Geomorphology, 22, JACKSON, N.L.; SHERMAN, D.L.; HESP, P.A.; KLEIN, A.H.F.; BAL- LASTEROS, F. Jr., and NORDSTROM K.F., in press. Small-scale spatial variations in aeolian sediment transport on a fine-sand beach. Journal of Coastal Research, Special Issue No. 39, in press. KAIMAL, J.C.; WYNGAARD, J.C.; IZUMI, Y., and COTE, O.R., Spectral characteristics of surface-layer turbulence. Quarterly Journal of the Royal Meteorological Society, 98, KAWAMURA, R., Study of sand movement by wind. University of Tokyo, Report of the Institute of Science and Technology, 5, KLEWICKI, J.C.; METZGER, M.M.; KELNER, E., and THURLOW, E.M., Viscous sublayer flow visualization at R PhysicsofFluids,7, LEE, J.A., A field experiment on the role of small scale wind gustiness in aeolian sand transport. Earth Surface Processes and Landforms, 12, MCEWAN, I.K. and WILLETTS, B.B., Numerical model of the saltation cloud. Acta Mechanica, 1, MCMENAMIN, R.; CASSIDY, R., and MCCLOSKEY, J., Self-organised criticality at the onset of aeolian sediment transport. Journal of Coastal Research, Special Issue No. 36, pp NAMIKAS, S.L. and SHERMAN, D.J., A review of the effects of surface moisture content on aeolian sand transport. In: TCHAK- ERIAN, V. (ed.), Desert Aeolian Processes. New York: Chapman and Hall, NEUMAN, C.M. and NICKLING, W.G., A theoretical and wind tunnel investigation of the effect of capillary water on the entrainment of sediment by wind. Canadian Journal of Soil Science, 69,

8 Aeolian Sand Transport Spatiotemporal Variability 1205 NICKLING, W.G., The initiation of particle movement by wind. Sedimentology, 35, NIKURADSE, J., Strömungsgesetze in rauhen Rohren. VDI-Forschungsheft, beilage zu Forschung auf dem Gebiete der Ingenieurswesens, 361B. PANOFSKY, H.A.andDUTTON, J.A., Atmospheric Turbulence. New York: Wiley, 397p. PYE, K. and TSOAR, H., Aeolian Sand and Sand Dunes. London: Unwin Hyman, 396p. SHERMAN, D.J., An equilibrium relationship for shear velocity and apparent roughness length in aeolian saltation. Geomorphology, 5, SHERMAN, D.J.; JACKSON, D.W.T.; NAMIKAS, S.L., and WANG, J., Wind-blown sand on beaches: an evaluation of models. Geomorphology, 22, SPIES, P.J.; MCEWAN, I.K., and BUTTERFIELD, G.R., One-dimensional transitional behaviour in saltation. Earth Surface Processes and Landforms, 25, STERK, G.; JACOBS, A.F.G., and VAN BOXEL, J.H., The effect of turbulent flow structures on saltation sand transport in the atmospheric boundary layer. Earth Surface Processes and Landforms, 23, STOUT, J.E. and ZOBECK, T.M., Intermittent saltation. Sedimentology, 44, STULL, R.B., An Introduction to Boundary Layer Meteorology. Dordrecht: Kluwer, 666p. UDDEN, J.A., Mechanical composition of some clastic sediments. Bulletin of the Geological Society of America, 25, VAN DIJK, P.M.; ARENS, S.M., and VAN BOXEL, J.H., Aeolian processes across transverse dunes. II: modelling the sediment transport and profile development. Earth Surface Processes and Landforms, 24, WENTWORTH, C.K., A scale of grade and class terms for clastic sediment. Journal of Geology, 30, WYNGAARD, J.C., Atmospheric turbulence. Annual Review of Fluid Mechanics, 24,