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Keywords:

  • Aeolian transport;
  • field study;
  • transport rate

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Observations
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgment
  8. References
  9. Supporting Information

[1] Aeolian transport represents the result of wind–surface interactions, and therefore depends strongly on variations in the characteristics of the sediment surface. We conducted field observations of aeolian transport of typical dune sand in three 80 m × 80 m plots with different surface treatments: gravel-covered sand, enclosed shifting sand, and open (unprotected) shifting sand. The study was performed at the Shapotou Aeolian Experiment Site in the southeastern part of China's Tengger Desert to compare the effects of these different surface treatments on aeolian transport. To do so, we analyzed the flux density profiles and transport rates above each surface. The flux density profiles for all three treatments followed the exponential decay law that was proposed by most previous researchers to describe the saltation flux density profiles. Coefficients of the exponential decay function were defined as a function of the surface and the wind velocity. The enclosed and open plots with shifting sand had similar flux density profiles, but the flux density above gravel-covered plots showed that transport decayed more slowly with increasing height, producing flux density profiles with a higher average saltation height. The transport rate above the three treatment plots tended to increase proportionally with the cube of the mean wind velocity and with the maximum wind velocity during the observation period, but was more strongly correlated with the square of drift potential. Transport rates above the plot with open shifting sand were greater than those above the plots with enclosed shifting sand and the gravel-covered plot.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Observations
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgment
  8. References
  9. Supporting Information

[2] Aeolian transport is a central issue in aeolian research because it is an important geomorphological process in many desert and coastal regions, as well as on Mars and possibly on Venus and Titan [Bagnold, 1941; Greeley and Iversen, 1985; Pye and Tsoar, 1990; Sherman and Lyons, 1994; Sherman et al., 1998; Dong et al., 2003a, 2011a]. In addition, aeolian transport creates various problems, such as obscuring the sun, impeding traffic, damaging crops and electrical switches, abrading paint, and forcing humans and animals to seek shelter, in addition to degrading valuable and nonrenewable soil resources [Fryrear and Saleh, 1993]. Attempts to study aeolian transport include studies of the transport rate, of the variation of sediment flux or the flux density profile with height, and of the underlying physical principles and microscopic mechanisms [Anderson and Haff, 1988; Bagnold, 1941; Greeley and Iversen, 1985; Pye and Tsoar, 1990; Livingstone and Warren, 1996; Dong and Qian, 2007]. Most of these issues have been studied extensively through field observations and wind tunnel simulations, but the underlying physical principles and microscopic mechanisms have mainly been subjected to theoretical research to encode the basic physical mechanisms responsible for the empirical phenomena observed in the field and in laboratory experiments.

[3] A thorough understanding of aeolian transport requires a dynamic integration of laboratory experiments, field observations, and theoretical models. However, our current knowledge of aeolian transport does not yet permit such an ideal integration. Consequently, field observations, laboratory experiments, and theoretical studies are being conducted relatively independently. In particular, field observations remain an essential aspect of aeolian transport research because they confirm the results of laboratory experiments and theoretical analyses, and also reveal issues that should be examined through simulations in laboratory experiments and thereby provide the basic information required to support theoretical analysis.

[4] To support field observations, the Shapotou Aeolian Experiment Site was established in 2005 in the southeastern part of China's Tengger Desert by the Key Laboratory of Desert and Desertification, Chinese Academy of Sciences. Field studies of aeolian transport are the primary goal of this station, with an emphasis on two central issues: the aeolian transport rate and the main factors that influence this rate, and the corresponding flux density profiles, which describe the variation of the aeolian sediment flux with height. Although considerable effort has been devoted since the 1930s to estimating the amount of sediment transported by the wind, and although various useful models have been published, it remains difficult to estimate aeolian transport rates, especially under field conditions [Dong et al., 2003a, 2011a]. The ratio of predicted to field-observed transport rates in the literature ranges from 0.26 to 300 [Svasek and Terwint, 1974; Berg, 1983; Bauer et al., 1990; Sherman, 1990; Nordstrom and Jackson, 1992; Sherman et al., 1998]. Similarly, considerable effort, including wind-tunnel tests [e.g., Butterfield, 1999], field observations [e.g., Greeley et al., 1996; Namikas, 2003; Ellis et al., 2009], numerical simulations, and theoretical analyses [e.g., Anderson and Haff, 1988; Kang et al., 2008], has been devoted to characterizing the variation in the mass flux density of windblown sediment as a function of height, and various equations have been proposed [e.g., Sherman and Farrell, 2008; Dong et al., 2011b], but the determination of flux density profiles remains imprecise, and is often complicated by variations in field conditions.

[5] Aeolian transport represents the result of wind–surface interaction, so characteristics of both the wind and the surface strongly influence both the aeolian transport rate and the flux density profile. For example, aeolian transport differs greatly over shifting sand, gravel, and vegetated surfaces [Wu, 1987]. In the present field study, we studied the aeolian transport characteristics above three experimental plots: gravel-covered sand, open (unprotected) shifting sand, and enclosed shifting sand, to compare the effect of fetch and surface cover on aeolian transport.

2. Field Observations

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Observations
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgment
  8. References
  9. Supporting Information

[6] The Shapotou Aeolian Experiment Site (37°32′N, 105°02′E) is located in the southeastern part of China's Tengger Desert. This area is a typical shifting dune field free of any vegetation and dominated by reticulate dunes (Figure 1). The primary ridges of the reticulate dunes are 3 to 20 m tall, spaced at 30 to 170 m, and aligned in a NE-SW direction. The subsidiary ridges are 1 to 6 m tall, spaced at 20 to 70 m, and aligned in a NW-SE direction [Hasi et al., 1999]. Annual precipitation is about 180 mm, most of which falls in the summer and autumn.

image

Figure 1. Typical landscape around the Shapotou Aeolian Experiment Site.

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[7] A 600 m × 800 m area was flattened to permit observations of aeolian transport in 2005. Three 80 m × 80 m plots were then established within the area. First, an area of open shifting sand was flattened without further treatment to ensure that sediment transport came from both outside and inside the plot. Second, an enclosed plot of shifting sand was surrounded by 20 m wide straw checkerboard barriers on all four sides. This plot proved to ensure that sediment transport came primarily from inside the plot. Third, a gravel-covered plot was established by creating a 30-mm-thick layer of gravel with a mean diameter of 3 mm that completely covered the sand surface to ensure that sediment transport came exclusively from outside the plot. The three plots were connected to form a 240 m × 80 m experiment area, aligned perpendicular to the primary NW wind (Figure 2). The mean diameter (−log2d) of the dune sand was 2.42 φ (= 0.19 mm), with 90% ranging between 0.125 and 0.250 mm (Figure 3).

image

Figure 2. Layout of the three plots with different surfaces (checkboard barriers are used to enclose the sand).

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image

Figure 3. Grain-size distribution of the sediments in the experiment plots; φ = −log2d.

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[8] Wind data (the free-stream wind velocity) was acquired from a meteorological tower at the center of the site at eight heights (1, 2, 4, 8, 16, 24, 32, and 48 m above the surface) using FC-1 wind sensors connected to a data logger produced by the Changchun Meteorological Instrument Research Institute (Changchun, Jilin Province, China). The wind data acquisition system was set to record wind velocity averaged at 1-min intervals. The aeolian transport observation plots were 100 to 300 m upwind from the central meteorological tower (Figure 2).

[9] We observed sediment transport in the three plots in April and May 2008. Sediment transport was measured using vertical segmented sediment samplers (LDDSEG samplers) designed by the Key Laboratory of Desert and Desertification, Chinese Academy of Sciences [Dong et al., 2011b]. This sampler is about 1 m tall, and is divided into 50 openings (each 20 mm × 20 mm) to collect the horizontally transported wind-eroded sediment at 50 heights at 20-mm intervals. Each opening is connected to a sediment chamber that is inclined downward at an angle of 30° with respect to the horizontal, and the chamber is removed after each observation period whose length depends on the wind speed to weigh the collected sediment. Weight measurements were obtained using an electronic balance with 0.001-g precision. The sampler was evaluated in a wind tunnel before using it at the study site. Sampling efficiency of the samplers defined in wind tunnel tests for dune sand from this area ranged from 72 to 87%. An overall sampling efficiency of 80% (the average sampling efficiency) was adopted in the present study to correct the transport data. Simultaneous sediment transport observations were conducted in the three plots using one LDDSEG sampler per plot, installed at the downwind edge of each plot.

3. Results and Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Observations
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgment
  8. References
  9. Supporting Information

[10] We observed a total of 23 aeolian transport events. The duration of these events varied greatly, depending on the strength of the wind. Flux density profiles and transport rate will be discussed based on the observed results corrected by the overall sampling efficiency of 0.8.

3.1. Flux Density Profiles

[11] Sediment flux density is generally expressed as the mass of sediment passing through a unit area perpendicular to the transport direction within a unit time. To establish the sediment flux density profiles, we converted the observed sediment masses in each observation into units of kg m−2 h−1. Figure 4 shows some representative results for the variation in observed sediment flux density as a function of height (i.e., geometric mean height at the center of each sample chamber) in the three plots at different mean wind speeds.

image

Figure 4. Observed flux density profiles in the three plots.

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[12] Regression analyses showed that for all three plots and all wind speeds, the flux density decays exponentially with height:

  • display math

where q(z) is the flux density at height z, and a and b are regression coefficients. Table 1 indicates that all the correlations are statistically significant, in more than 85% of the cases, the correlation coefficient (r2) was greater than 0.98. In only four cases, such as those at 8.72 ms−1 for the plot with enclosed shifting sand (non-fitted) and at 8.47, 8.72 and 9.10 ms−1 for the plot open shifting sand, the flux density profiles deviate significantly from the exponential decay law (with r2 < 0.8) because rippled sand accumulation about 30–40 mm tall formed about 0.5 m upwind the sampler.

Table 1. Results of the Regression Analyses for the Flux Density Profiles Above the Three Surfacesa
PlotV (m s−1)QO (kg m−1 h−1)abzar2
  • a

    Values represent the regression parameters for the function q(z) = aez/b, where Qo is the observed transport rate and z is the height above the surface. V represents the free-stream wind velocity measured at 16 m above the surface, and za is the average saltation height; r2 is the correlation coefficient at significance level p < 0.05.

Gravel-covered plot5.293.6132.60.0280.0280.999
5.310.928.50.0310.0310.996
5.333.9136.80.0290.0290.994
5.894.1119.20.0370.0370.958
5.934.6189.10.0260.0260.999
6.232.963.00.0430.0430.990
6.554.7199.20.0240.0241.000
7.0757.51250.70.0450.0450.996
7.113.0110.90.0260.0260.999
7.1867.61756.00.0400.0400.981
7.193.762.10.0580.0580.989
7.3349.0605.50.0840.0840.956
7.7012.6428.20.0290.0290.999
7.8313.3278.00.0450.0450.996
8.3428.7486.40.0540.0540.973
8.4714.5390.40.0300.0300.990
8.5438.9486.10.0780.0780.994
8.5635.8505.80.0720.0720.999
8.7214.5419.20.0320.0320.997
8.9613.3288.20.0400.0400.994
9.1030.6599.40.0380.0380.979
9.1724.9542.30.0440.0440.992
11.374.8889.60.0770.0770.934
 
Plot with enclosed shifting sand5.290.432.10.0140.0141.000
5.311.591.30.0170.0171.000
5.332.077.60.0230.0230.996
5.894.5229.80.0200.0200.999
5.931.349.60.0220.0220.995
6.234.0197.60.0190.0190.997
6.550.03339.90.0240.0240.999
7.0755.02036.90.0250.0250.999
7.114.6163.10.0280.0281.000
7.1840.7150.20.2610.2400.859
7.1912.0410.70.0290.0290.999
7.3351.71578.90.0320.0321.000
7.7018.9496.20.0270.0270.934
7.8318.6542.50.0330.0330.992
8.3426.0631.80.0360.0360.995
8.4722.2702.80.0290.0290.998
8.5419.2349.90.0500.0500.996
8.5622.4414.70.0560.0560.991
8.7227.8
8.9618.3641.60.0260.0260.998
9.1015.3438.60.0270.0270.993
9.1745.11171.00.0330.0330.995
11.3097.51494.20.0640.0640.954
 
Plot with open shifting sand5.294.2189.90.0230.0231.000
5.311.382.00.0160.0161.000
5.332.5113.90.0220.0221.000
5.894.4176.10.0230.0230.998
5.932.6151.20.0180.0180.999
6.233.5143.80.0250.0251.000
6.555.9258.30.0220.0220.999
7.0774.02913.50.0240.0240.999
7.115.6194.00.0270.0270.997
7.1889.61403.90.0600.0600.985
7.197.8312.90.0240.0240.999
7.3367.42355.80.0290.0290.993
7.7012.8401.70.0230.0230.983
7.8314.5484.20.0300.0300.999
8.3417.4458.90.0350.0350.989
8.4718.5105.10.0930.0930.629
8.5424.2792.10.0240.0240.989
8.5623.1806.70.0250.0250.997
8.7233.5602.20.0220.0220.725
8.9617.5450.00.0310.0310.991
9.1038.2390.00.0940.0940.798
9.1790.31106.50.0680.0680.964
11.30110.8391.10.2940.2600.951

[13] Integration of the flux density profile function for height yields the calculated transport rate (Qc) that is defined as the mass passing through a unit width perpendicular to the transport direction within a unit time. An ideal flux density profile function should also be a good predictor of the transport rate.

  • display math

[14] To confirm the goodness of fit of equation (1), we compared the transport rate calculated using equation (2) with the actual observed transport rate obtained by summing up the observed mass flux at all heights (Figure 5). The fit was strong and statistically significant (r2 = 0.98, significance level < 0.05). Therefore, equation (1) provides an adequate description of the aeolian flux density profiles in the three plots. This may be because the particle-size distribution (Figure 3) in the study plots is predominantly within the range for saltation [Pye and Tsoar, 1990]. It is now widely accepted that the mass flux density of suspended sediment decays with increasing height following a power function [Zingg, 1953; Liu, 1960; Nickling, 1978; Takeuchi, 1980; Fryrear, 1987; Ni et al., 2003], while the mass flux density of saltating sediments decays with increasing height following an exponential decay function such as equation (1) [Kawamura, 1951; Horikawa and Shen, 1960; Williams, 1964; Nalpanis, 1985; Nalpanis et al., 1993; Dong et al., 2003b; Ellis et al., 2009]. Our results confirm this belief.

image

Figure 5. Relationship between the observed transport rate (Qo) and the transport rate (Qc) calculated using equation (2) (both Qo and Qc are corrected by the sampling efficiency).

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[15] The regression coefficients in equation (1) can be defined as a function of the surface treatment, wind speed, and aeolian transport rate. Equation (2) indicates that coefficient a should be proportional to the transport rate. This is supported by the relationship between coefficient a and observed transport rate Qo in Figure 6. Although regression analysis can yield various empirical relationship between coefficient a and Qo, equation (3) in which coefficient a is proportional to Qo is also proven to be a reasonable good descriptor with a little reduced correlation coefficient (Figure 6).

  • display math

where Qo is the observed transport rate (kg m−1 h−1), k is a regression coefficient that equaled 18.0 for the gravel surface (r2 = 0.77, < 0.05), 23.4 for the enclosed shifting sand (r2 = 0.73, < 0.05), and 25.0 for the open shifting sand (r2 = 0.64, < 0.05). The relationships for the enclosed shifting sand and open shifting sand were similar, but both differed greatly from that for the gravel surface. Difference in coefficient k implies the influence of surface treatment on aeolian transport, which will be discussed in section 3.2.

image

Figure 6. Relationship between regression coefficient a in equation (1) and the observed aeolian transport rate. Regression equations are presented in section 3.1 of the results.

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[16] Equation (4) can be derived from equation (1):

  • display math

[17] Thus, regression coefficient b in equation (1) characterizes the relative decay rate of flux density with increasing height. The greater the b value, the more gently the flux density decays with height. Figure 7 shows that the relative decay rate depends on both the surface treatment and the wind speed, and that it increases with increasing wind speed. The enclosed shifting sand and open shifting sand plots had similar relative decay rates, but the relative decay rate above the gravel-covered plot was much larger (i.e., the rate of decay was smaller). This can be explained if saltation is more intense over the gravel-covered plot (i.e., because the collisions between saltating particles and the gravel are more elastic) and if saltation height increases as the wind speed increases. Previous wind tunnel tests reached the same conclusions [Dong et al., 2003b, 2004].

image

Figure 7. Relationship between regression coefficient b in equation (1) with wind speed. Regression equations are presented in section 3.1 of the results.

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[18] The relative intensity of saltation can be represented by the average saltation height:

  • display math

where za (m) is the average saltation height. In the present study, we calculated the average saltation height using the following equation:

  • display math

[19] Table 1 indicates that the average saltation height equaled the corresponding value of b. This is because the value of b in the present study was so small that the value of e−1/b in equation (6) was almost negligible. In that case:

  • display math

[20] It is worth noting that b will become significantly greater when saltation increases in intensity, and that za will then differ increasingly from coefficient b. This analysis suggests that the relative decay rate and the average saltation height are two aspects of the same physical phenomenon, and that a greater saltation height corresponds to a lower relative decay rate.

[21] These results indicate that the flux density profiles for the three surface treatments are adequately described by the coefficients a and b in equation (1). Figures 6 and 7 indicate that the flux density profiles for the enclosed and open shifting sand plots were very similar, but that both differed greatly from that for the gravel-covered plot. The characteristics of surface cover that influences saltation of particles is therefore an important variable that influence the flux density profile.

3.2. Transport Rate

[22] Most previous research revealed that aeolian transport rate was proportional to the cube of wind speed (Dong et al., 2003a). Although the field observed data in Figure 8 is scattered, trend lines that relate transport rate linearly with the cube of both the mean wind speed (Figure 8a) and the maximum wind speed (Figure 8b) can be found. This is because the wind speed fluctuated during the observation period, but the stronger winds, including the maximum wind speed, were more significant in determining the transport rate.

image

Figure 8. Relationship between transport rate and (a) mean wind speed, (b) maximum wind speed, and (c) drift potential.

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[23] The drift potential parameter proposed by Fryberger [1979] that is calculated by equation (8) is commonly used by aeolian researchers to characterize the transport potential of a wind. We used our data to describe the relationship between the observed transport rate (Qo) and the drift potential (DP). We found that the transport rate was more strongly correlated with drift potential (Figure 8c) than with the mean and maximum wind speeds.

  • display math

where Dp is drift potential, V is wind velocity at 10 m height, Vt is the threshold wind velocity at 10 m height, t is time wind blew, expressed as a percentage on N summary.

[24] We obtained the following empirical relationship between transport rate and drift potential by regression analysis:

  • display math

where Dp is the drift potential (in vector units, VU) and A is a regression coefficient, which equaled 2967.6 for the gravel-covered plot (r2 = 0.93, < 0.05), 2891.4 for the plot with enclosed shifting sand (r2 = 0.76, < 0.05), and 3996.5 for the plot with open shifting sand (r2 = 0.80, < 0.05). The transport rates for the gravel-covered plot and the plot with enclosed shifting sand were similar, and both were much smaller than the value for the plot with open shifting sand. This is because the gravel-covered plot tends to trap passing sand particles, whereas the aeolian transport above the plot with enclosed shifting sand does not become saturated because of the limited fetch length.

4. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Observations
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgment
  8. References
  9. Supporting Information

[25] Although the study of aeolian transport has continued for decades, field studies remain necessary to provide a thorough understanding of sand transport. The Shapotou Aeolian Experiment Site was established for this purpose. In the present study, we designed a field experiment that let us compare the effects of the surface on sand transport. Simultaneous observations of aeolian transport in three plots with different surfaces confirmed our hypothesis that the surface treatments would influence both the flux density profile and the aeolian transport rate. The plots with enclosed and open shifting sand had similar flux density profiles that differed greatly from the profile above the gravel-covered plot. However, the plot with enclosed shifting sand and the gravel-covered plot had similar transport rates that were much lower than that above the plot with open shifting sand. These differences suggest that sediment availability is an important factor in determining the transport rate. However, surface characteristics that influence saltation intensity and the mean saltation height are important in determining the flux density profile.

[26] Flux density profiles above all three plots were expressed well by an exponential decay function, as was proposed by most previous authors to describe the saltation flux density profiles. This indicates that the exponential decay law does a good job of describing the saltation flux density profiles. We also found that the influence of the wind and of the surface properties could be characterized well by the regression coefficients in this function, and that the coefficients represent the transport rate, relative decay rate, and average saltation height.

[27] A limitation of the present study is that aeolian transport over the gravel-covered plot did not reach an equilibrium state that would have eliminated the surface's ability to trap saltating particles. Consequently, the transport rate in this plot was lower than that above the plot with open shifting sand. Despite this limitation, the present results suggest some interesting issues for future study, such as the trapping effect, saturation and equilibrium processes involved in aeolian transport over gravel surface.

Acknowledgment

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Observations
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgment
  8. References
  9. Supporting Information

[28] We gratefully acknowledge funding from the National Natural Science Foundation of China (41130533, 41171010).

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Observations
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgment
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Field Observations
  5. 3. Results and Discussion
  6. 4. Conclusions
  7. Acknowledgment
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
jgrd17938-sup-0001-t01.txtplain text document3KTab-delimited Table 1.

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