The effect of bedform dynamics on computing suspended sediment fluxes using optical backscatter sensors and current meters

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Coastal Engineering 55 (2008) 251 – 260 www.elsevier.com/locate/coastaleng

The effect of bedform dynamics on computing suspended sediment fluxes using optical backscatter sensors and current meters Martin J. Austin a,⁎, Gerhard Masselink b a

School of Earth, Ocean and Environmental Science, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, UK b School of Geography, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, UK Received 12 January 2007; received in revised form 4 October 2007; accepted 26 October 2007 Available online 28 November 2007

Abstract The sensitivity of the suspended sediment flux is tested with respect to rapid changes in bed-level across the surf zone of a sandy beach. The suspended flux was computed using a fixed instrument array, but bed-level changes due to ripple migration caused the instrument elevations to be significantly changed during the course of the experiment. The nominal elevations of the instruments were adjusted during data processing (using the MOBS array) to maintain a fixed elevation with respect to bed-level changes. The resultant suspended sediment concentrations and fluxes were significantly different from the unadjusted data, and for the present data set O(35%) less when averaged over the tide. The maximum difference between adjusted and unadjusted fluxes may be O(260%). The results indicate that changes in bed-level, particularly those due to bedform migration, must be accounted for when processing OBS data if reliable estimates of suspended sediment transport are to be obtained in the field. © 2007 Elsevier B.V. All rights reserved. Keywords: Suspended sediment; Surf zone; Bedforms; Optical backscatter sensor; Sediment transport

1. Introduction In studies of nearshore sediment transport, it is common for in-situ instrumentation, such as Optical Backscatter Sensors (Downing, 2006) and Electromagnetic Current Meters (ECM) or Acoustic Doppler Velocimeters (ADV), to be deployed at various near-bed elevations across the shoaling-wave, surf and swash zones of natural beaches. Instruments, particularly on meso- and macrotidal beaches, are usually deployed, maintained and recovered at low tide, and instrument elevations recorded both prior and subsequent to tidal inundation. Any net trends in bed-level change are generally subtracted as a linear function of time or are ignored. Measurements of cross-shore flow velocity and suspended sand concentration at various elevations above the bed are used to quantify the cross-shore suspended sediment flux. Suspended sediment flux is frequently separated into mean and oscillatory ⁎ Corresponding author. E-mail addresses: [email protected] (M.J. Austin), [email protected] (G. Masselink). 0378-3839/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.coastaleng.2007.10.003

components after Jaffe et al., (1984). The net flux qnet at the height of the instrument can be expressed in terms of the flux due to the mean velocity u¯ and concentration ¯c , plus the transport due to the flux coupling between the oscillatory components u′ and c′, respectively: hqnet i ¼ huci ¼ P uP c þ hu Vc Vi;

ð1Þ

where 〈〉 denote time-averaging. The approach of Jaffe et al., (1984) has been followed in many studies to estimate suspended sediment fluxes from co-located ECM and OBS sensors (Hanes and Huntley, 1986; Huntley and Hanes, 1987; Osborne and Greenwood, 1992; Aagaard et al., 1998; Masselink and Pattiaratchi, 2000; Greenwood et al., 2004). This method is strictly only valid if the velocity and sediment concentration are measured at the same elevation above the bed; however, many studies have used this approach with a vertical separation between the ECM and OBS (Osborne and Greenwood, 1992; Aagaard and Greenwood, 1995; Miles et al., 2002). The resultant error depends on the type of flow being investigated and hence the vertical variation in the velocity profile, but according to (Ogston and Sternberg, 1995) the vertical

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Fig. 1. Map showing the geographical location of Sennen Cove.

displacement can result in errors in the flux estimates in the order of 50–100%. However, Miles et al. (2002) argue that the errors are much smaller since current measurements at z ≈ 0.1– 0.2 m are sufficiently low in the water column to be representative of the near-bed depth-averaged flow, while Aagaard et al. (1998) correct the mean currents for elevation displacement by assuming a logarithmic current velocity profile and an empirical bed roughness parameter. There are remarkably few studies where sediment fluxes in the surf zone have been determined at a number of elevations from the bed with multiple (N 4) co-located OBS and ECM (Beach and Sternberg, 1992; Miller, 1999; Conley and Beach, 2003; Masselink et al., 2007); however, several studies have applied this methodology to the swash zone (Butt et al., 2005; Masselink et al., 2005). More often, Acoustic Backscatter Sensors have been deployed with multiple current meters in the shoaling wave zone (Vincent et al., 1991; Osborne and Vincent, 1996; Davies and Thorne, 2005), but aeration and turbulence preclude their use in the region of breaking waves. Recent studies have highlighted that the shoaling and surf zones of most natural beaches are populated by highly dynamic orbital and anorbital scale wave ripples at low to moderate energy levels (Clifton, 1976; Osborne and Vincent, 1993; Crawford and Hay, 2003; Masselink et al., 2007) and megaripples under more energetic wave conditions (Hanes et al., 2001; Gallagher, 2003). These studies have demonstrated that ripples may be highly dynamic over short time scales, adjusting to variations in hydrodynamic forcing with changes to ripple geometry (Hay and Bowen, 1993; Osborne and Vincent, 1993; Crawford and Hay, 2001; Hanes et al., 2001) and migration rate (Vincent and Osborne, 1993; Traykovski et al., 1999; Hanes et al., 2001; Austin et al., 2007). The passage of ripples under an instrument rig results in changes in the bed-level under that rig. If instruments have been set to specific nominal elevations when exposed at low tide, the changes in bed-level move the effective elevation of these instruments with respect to the bed. Bar migration is another process that can frequently result in bed-level changes with respect to sensors and it is generally the case that only extreme burials are acknowledged in the literature. Unless the instrument

elevations are adjusted during data processing, using information such as that obtained from an acoustic altimeter or OBS sensor burial to account for this changing bed, it is unknown whether observed changes to flow or sediment dynamics are real, or the artefact of variable elevation above the bed. The complexity of concentration structures in both time and space over bedforms further highlights the difficulties in making concentration estimates using point measurements. The result is that the position of the instrumentation relative to the bedforms will be important (Vincent et al., 1999). Suspension over steep ripples is typically enhanced by vortex entrainment (Tunstall and Inman, 1975). A common sequence of events sees the growth of a lee-wake vortex at around the time of maximum flow velocity, which is subsequently ejected from the crest towards flow reversal (Thorne et al., 2003). Depending of the grain settling velocity, the sediment entrained within the vortex will either be advected with the residual flow or will settle rapidly to the bed. It is therefore obvious that measurements of suspended sediment concentration made over and around a ripple crest are likely to display some impact of vortex enrainment and, if sediments are sufficiently fine, clouds of sediment may be advected over the adjacent ripple trough regions (Osborne and Vincent, 1996). The aim of this study is to quantify the influence of not adjusting the relative elevation of co-located near-bed instruments, specifically optical backscatter sensors and electromagnetic current meters, during the calculation of suspended sediment fluxes in the nearshore zone of a sandy beach, and the potential wider significance of these findings. 2. Field site and methodology Here, we present suspended sediment and cross-shore flow velocity data collected in the surf zone at Sennen Cove, Cornwall, UK (Fig. 1). Suspended sediment and flow velocity data sampled at a nominal elevation of z = 3 cm are compared to that adjusted for changes in bed-level and thereby maintained at a constant elevation above the bed. Sennen Cove is a 2 km long embayed beach constrained by rocky headlands at either end. It faces north-west into the North

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Fig. 2. Photo showing the configuration of the sensors on the instrument rig at Sennen (PT–pressure transducers; OBS–optical backscatter sensors; ECM– electromagnetic current meters; SRP–sand ripple profiler; ADV–acoustic Doppler Velocimeter; and ALT–altimeter). The horizontal separation between the OBS sensors and the most distant ECM was 70 cm.

Atlantic Ocean and is exposed to both swell and locally generated windsea. Sennen is classified as a macrotidal (mean spring range box = 5.3 m) low tide terrace beach with a steep upper section (tanβ ≈ 0.06) and a gently sloping lower section (tanβ ≈ 0.03). The average significant wave height is 1.4 m and the median grain diameter (D50) is 0.7 mm. 2.1. Instrumentation During the field survey, multiple instruments rigs were deployed in a cross-shore transect across the intertidal beachface. A rig measuring the vertical distribution of wave-current flows and suspended sediment concentration was deployed around mid-tide level at Sennen (Fig. 2). Subsequently, over a tidal cycle the instruments were exposed to periods of broken, breaking and shoaling waves. Water velocity was measured using six miniature (0.032 m diameter discus head) Valeport electromagnetic current meters (ECM) deployed to record flow velocities at nominal elevations of 0.03, 0.06, 0.09, 0.13, 0.19 and 0.29 m from the bed. A vertical array of in-house designed and built miniature optical backscatter sensors (MOBS) were used to measure the suspended sediment concentration at nominal elevations of − 0.02, − 0.01, 0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.09, 0.13, 0.19 and 0.29 m from the bed. These were calibrated by suspending known quantities of local sediment in glycerol using the method developed by Butt et al. (2002). A 2 MHz single-point acoustic altimeter was deployed 0.5 m above the bed to monitor the bed-level at the instrument station and a Sand Ripple Profiler (SRP) monitored bedforms along a 2 m cross-shore transect. The MOBS sensors

can also be used to record bed-level changes, because when individual sensors become buried by accretion, their output is maximum. The water depth was measured using a pressure transducer (PT) installed 0.02 m below the sand surface. Atmospheric pressure, recorded with an emerged PT, was subtracted from the calibrated data and the water depth was determined by assuming that a pressure of 0.01 Pa is equivalent to a 1 cm head of water. All instruments were cabled to shorebased computers where the hydrodynamic data were synchronously logged at 4 Hz. 2.2. Bed-level determination Time series of the bed-level were computed from the acoustic altimeter, SRP and MOBS-array. Altimeter data were processed following the method of Gallagher et al. (1996) and Saulter et al. (2003), which work on a sliding-window of 32-s of data (128 datapoints). A histogram, with class limits corresponding to the range limits of the altimeter (0.4–0.8 m) and class intervals of 0.02 m was computed, and the most populous class taken as the first estimate of the bed-level. A second histogram was then computed, with class limits corresponding to the first bed estimate ± 0.05 m and class intervals of 0.005 m. The most populated class from the second histogram was then assumed to be the bed-level. The 32-s interval bed-level time series was interpolated onto the original timebase giving a time series of the bed-level at 4 Hz. SRP data were processed following the method described by (Masselink et al., 2007) to provide a cross-shore profile of the bed immediately below the SRP each minute of the high tide. Unfortunately,

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Fig. 3. Top–comparison of bed-levels derived from the acoustic altimeter (dash) and MOBS-array (solid). Bottom–SRP-derived bed evolution over the high tide period (grey shaded region in upper plot) where the shading indicating ripple crests (light) and troughs (dark) between +5 and − 6 cm, respectively. The dashed horizontal line indicates the cross-shore position of the MOBS array.

breakers plunging directly over the instrument rig during much of the tide resulted in highly aerated water, which degraded the performance of the SRP providing only a narrow window of data. Prior to the determination of the bed-level from the MOBS array, the data required a degree of pre-processing. The MOBS data are only useful when collected at night; during the day, the ambient sunlight causes saturation of the output signal of the sensors. Data collected around dawn and dusk are only moderately affected by daylight and are characterised by a small non-zero offset that progressively increases (sun-rise) or decreases (sun-set) with time. These data were corrected by deriving a time series of the offset using 1-min data segments based on the 10% exceedence of the suspended sediment concentrations, and subtracting the offset time series from the suspended sediment data. Changes in the bed-level were determined through careful inspection of the OBS data, noting periods when the sensors at different elevations became saturated as the bed adjusted to bedforms migrating through the sensors. Fig. 3 compares the bed-level derived from the MOBS and acoustic altimeter and reveals some significant differences. During the first two hours of the tide, the MOBS bed-level time series is leading the altimeter; the signals are approximately in anti-phase. Between 23:30 and 00:30 there is poor coherence between the time series; it is only towards the end of the high tide that the altimeter and MOBS bed-levels correspond. The reason for the discrepancies is partly shown by the SRP data, where it can be observed that after 23:30, the existing ripples

divide and the organisation of the bed becomes less structured. Visual observations also indicated that the ripples were not exactly shore-parallel, but were migrating landwards at a rate of 0.6 cm min− 1 at an angle of ∼ 10°. Since the focus of this analysis is towards suspended sediment data obtained from the

Fig. 4. Illustration of the instrument positions relative to the ripple morphology, demonstrating the adjustment of MOBS elevations to burial at four time intervals caused by ripple migration. z is the initial nominal elevation of the sensor above the bed and z′ the adjusted elevation.

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3. Results 3.1. Bed level and suspended sediment concentration The dynamic evolution of the bed, the water depth h and the significant wave height Hs (determined as 4 times the standard deviation of the water surface elevation time series) is illustrated in Fig. 5. Under symmetrical rising and falling phases of the tide and relatively constant wave forcing (Hs = 0.35 m) regular and distinct ripples with a height of ∼0.05 m are clearly evident migrating through the instruments (Fig. 5c; refer to Masselink et al., 2007 for a detailed description of the bedform dynamics during the experiment). Visual observation of the time series of c, for both the uncorrected and adjusted data, reveal some clear differences (Fig. 6). Over the ripple crests, the reported values of c for the uncorrected data (Fig. 6a) are significantly elevated above those reported by the height-corrected sensor (Fig. 6b). This variation is quantified in panel (c), which reveals that whilst the mean Δc over the tide is small (0.8 g l− 1), during suspension events it frequently exceeds 30 g l− 1. It is useful to quantify the implication of erroneous measurements of c when modelling suspended sediment concentration by computing the reference concentration at the bed C0. Fig. 5. Summary of the forcing conditions, bed-level evolution and vertical suspended sediment profile during the tide. (a) water depth h; (b) significant wave height Hs; (c) OBS-derived bed-level.

MOBS array it was decided to use the MOBS-derived bed-level time series for all subsequent analysis, thereby minimising bedlevel variations due to the spatial separation of sensors. 2.3. Time series correction The cross-shore flow velocity u and suspended sediment c data were calibrated and de-spiked with the application of a moving temporal filter operating on 1-min long sections of data. Data lying greater than 3 standard deviations from the segment mean were removed and the missing values linearly interpolated. Visual inspection of the time series before and after despiking indicated that this process only removed spikes and that good data were not incorrectly eliminated from further analysis. The data were then linearly interpolated at 0.01 m intervals between the nominal elevations of − 0.02 and 0.17 m and adjusted for bed-level changes using the MOBS-derived bedlevel time series. For each sample interval, the elevation of the interpolated u and c were compared to the bed-level, and any measurements at elevations below the bed-level removed (Fig. 4). The first observation above the bed was set to an elevation of 0.01 m and the number of vertical u and c observations maintained by extrapolating the vertical profile to an elevation of 0.17 m. The product of this adjustment are time series of u and c, at 1 cm intervals, in a layer with a constant elevation of 1–17 cm above the bed, regardless of the orbitalscale ripples migrating under the instruments.

Fig. 6. Time series of suspended sediment concentration (−) and reference concentration at the bed C0 (o). (a) c recorded at a fixed elevation above the bed, nominally 3 cm; (b) c recorded at a constant elevation of 3 cm above the bed by adjusting sensors for bed-level changes; (c) difference between corrected and uncorrected c; and (d) bed-level.

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Fig. 7. Ensemble-averaged sediment concentration over a ripple. Left panels–ensemble-averaged wave velocity (solid line) and ±twice the standard deviation (dotted line). Right panels–vertical distribution of suspended sediment over the average wave cycle, where high concentration is indicated by the darkest shading. The vertical dashed line indicates the time of flow reversal and the stoss and lee ripple faces are defined as being seaward and landward of the crest, respectively.

The convection–diffusion equation (Nielsen, 1986) was fitted to the raw and adjusted MOBS data cð zÞ ¼ C0 ez=ls ;

ð2Þ

and time-averaged over ∼8.5-min segments (Fig. 6). c is the timeaveraged concentration at height z, C0 is the reference sediment concentration and ls is the sediment mixing length scale (ls =es/ws, where es is the sediment mixing coefficient and ws is the sediment fall velocity of the sediment in suspension). Care was taken to ensure that for the raw data, data from buried MOBS sensors were not included. This reveals similar trends to above, with a significant over-estimation of the reference concentration C0 computed from the raw data of 7 g l− 1 when averaged over the whole tide. The mixing length scale for the tide, computed using adjusted and unadjusted data, was ls ≈ 6–9 cm and ls ≈ 2.5–3.7 cm, respectively. As expected, ls was of a similar magnitude to ripple height. In addition to bed-level changes, the presence of a rippled bed results in an increase in the spatial and temporal complexity of concentration structures in the water column. Horizontal gradients in suspended sediment concentration over the rippled bed were investigated by ensemble averaging multiple waves from different spatial locations over ripples. A total of 137 individual wave events were extracted from the adjusted time series when the sensors were above the stoss, crest, lee and trough sections of ripples. Each wave was required to have a peak velocity (onshore or offshore) of at least 0.35 m s− 1 and the individual waves were re-sampled to a normalised time scale

t/T, where t is time and T is the wave duration, using a sampling interval of 0.01 s. According to their spatial location over the bedform, the re-sampled events were combined into ensemblewave events by averaging u and c. This provided four ensemble events, each representing a cross-shore position over a ripple. The ensemble-averaged waves for each of the four spatial locations are shown in Fig. 7. It is clear that the waves extracted from each location were of similar magnitude and skewness; therefore differences in suspended sediment distribution should be due to the processes operating at each sample location. The slightly lower velocities in the trough can be explained by the adjusted data being deeper into the boundary layer in this region. Over the stoss face of the ripple, at around the point of maximum onshore flow velocity (t/T ≈ 0.1) sediment is suspended to an elevation of around z = 15 cm. This sediment then rapidly begins to settle to the bed and by the time of flow reversal, and during the offshore stroke of the wave, there is little suspension until the influence of the subsequent wave becomes apparent at t/T = 0.9. A similar pattern of events is observed over the ripple crest; however, the suspension is more intense and is slightly lagged compared to the stoss face. Some suspension is also evident during the offshore phase of the flow. The lee face displays a double onshore suspension peak, which is likely related to the double onshore peak in the averaged wave velocities. The ripple trough displays very different behavior: throughout the wave cycle there is a high concentration of sediment in a layer very low in the water column and no intense suspension peak during the peak onshore flow phase.

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Table 1 Mean and maximum error in cross-shore sediment flux Δq between combinations of u and c that are corrected or uncorrected for bed-level changes Experiment (−)

u (m s− 1)

c (g l− 1)

Δq(x¯) (%)

Δq (max) (%)

SEN23

Uncorrected Corrected Uncorrected

Corrected Uncorrected Uncorrected

− 6.3 42.2 34.8

24.8 260.9 261.4

Error is expressed as: Δq = [(qun / qcor) × 100] − 100.

Fig. 8. Burst-averaged, net suspended sediment flux at 3 cm computed using uncorrected u and c (dashed line) and corrected u and c (solid line). Error bars plot one standard error associated with the burst-averages.

3.2. Influence on suspended sediment fluxes A significant impact of not adjusting for bed-level changes is observed in the calculation of the suspended sediment flux. The net instantaneous suspended sediment flux uc was computed following Eq. (1) and averaged over bursts of 2048 data points (∼ 8.5 min). The results, together with the standard error, are plotted in Fig. 8. A visual inspection reveals noticeable differences between the flux calculated with corrected and uncorrected u and c and a Student's t-test reveals that the fluxes are significantly different with α = 0.01. Integrating the flux

over the high tide indicates that by not correcting for bed-level changes the total flux is over-estimated by 35%; however, the maximum difference can be a factor 2. The influence of adjusting for bed-level changes when computing the flux is also shown in the frequency domain (Fig. 9). The spectra of c are dominated by peaks at 0.03, 0.12 and 0.24 Hz, indicative of suspension due to wave groups (33 s),incident waves (8 s) and the first harmonic of the incident waves (4 s). The spectra of u display peaks at the incident and harmonic frequencies and the uc co-spectra are also dominated by the incident frequency energy. The total suspended sediment flux was onshore and transport occurred mainly at the incident wave frequency. Further inspection of the co-spectra and the ensemble averaged suspension events (Fig. 7) indicate that sediment is ‘pumped’ onshore because maximum suspended sediment concentrations coincide with onshore wave velocities, i.e., under the wave crests. The difference in the high tideintegrated raw and adjusted sediment fluxes computed from the co-spectra is 22%, very similar to that computed in the temporal domain, and furthermore the discrepancies due to bed-level changes do not appear to be frequency dependent; qualitatively the suspended sediment fluxes are not really affected by bedlevel changes. The variable bottom boundary should be considered with respect to both the flow velocity and suspended sediment sensors to determine upon which it has greatest influence. The analysis so far, has shown that, for the present data set, the cross-shore flux is over-estimated by 35% over the whole tide if both the uncorrected flow velocity and suspended sediment data are used. The maximum error for an individual data segment is ∼260%. The analysis was repeated with various combinations of corrected/uncorrected data to quantify this difference, and the results are presented in Table 1. The findings indicate that the greatest error in suspended flux calculations arises through the use of uncorrected suspended sediment data. Failure to correct u for changing bed-level results in a mean error O(6%), whereas uncorrected c induces an error O(40%). It should be noted that the sign of the errors between u and c is different. This can be explained by considering that if the current meters are too close to the bed they will be deeper in the velocity boundary layer and hence the reported flow velocity will be reduced compared to higher elevations. 4. Discussion and conclusions

Fig. 9. Spectral domain investigation of suspended sediment flux. (Top) spectra of raw (dash) and adjusted (solid) 〈c〉; (centre) spectra of raw and adjusted 〈u〉; (bottom) co-spectra of raw and adjusted 〈uc〉, where 〈〉 indicates time-averaging over the whole high tide period.

Comparisons between suspended sediment concentrations and cross-shore flow velocities recorded at a nominal fixed elevation above the bed, and those adjusted for changes in bed-

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Fig. 10. Vertical suspended sediment concentration profile computed over the whole tide (circles) fitted with the vertically-invariant, convection–diffusion model (solid line).

level, indicate that suspended sediment fluxes may be significantly in error. This is significant in two respects: firstly, many previously reported measurements of nearshore sediment fluxes are likely to be over-or under-estimated; and secondly, models that have been tested and calibrated against field data will also erroneously predict the actual fluxes. Suspended sediment fluxes measured in the nearshore are often used to explain morphological development and change (Jaffe et al., 1984; Doucette, 2002). There is frequently poor quantitative agreement between the measured net morphological change and the volume of sediment predicted to have been transported as suspended flux (Aagaard et al., 1998) and other processes are often invoked in an attempt to explain the disparity (bedload flux; longshore flux, wave skewness; infiltration). However, the presence of wave-formed ripples in the nearshore, and inappropriate measurement/analysis practices, can have a highly significant influence. For example, the reference concentration C0 at the bed (Nielsen, 1986), which is frequently used when modelling the vertical suspended sediment profile is seen to be significantly in error if computed using uncorrected data (Fig. 6). Over both plane and rippled beds, sediment concentration decreases exponentially above the bed. Therefore, if accretion (erosion) reduces (increases) the distance between the bed and OBS sensor, the measured sediment concentration will increase (decrease) coincidentlly; this can be illustrated by the vertical suspended sediment profile. The vertical suspended sediment profile was computed using the height-adjusted data collected over the whole tide (Fig. 10)

and fitted with the convection–diffusion model (Eq. 2). The exponential nature of the decay in sediment concentration above the bed results in a bed-level change having a disproportionate effect on the observed concentration. Taking the example of the suspended concentration z = 3, 6 and 9 cm above the bed (c = 3, 1.3 and 0.55 g l− 1, respectively), the difference in c from 3 to 6 cm is 1.7 g l− 1, and between 6 and 9 cm 0.75 g l− 1. So over a 6 cm layer of the water column, there is a ∼ 60% variation in c. The impact of bed-level changes on suspended sediment measurements depends on the steepness of the exponential profile, i. e., the mixing length scale. The latter is generally related to the ripple height and is therefore rather small for planar beds. Thus, the effect of a, say, 5 cm bed-level change over flat bed is likely to have a greater impact on the suspended sediment measurements than over a rippled bed (Nielsen, 1992). The mixing length scale is also smaller for coarser sediments; hence, here bed-level changes are also expected to have a greater effect. Of secondary importance, a rippled bed provides additional complications due to the possibility of vortex entrainment enhancing the suspension of (mainly fine) sediments. However, although the vortex forms in the lee of the ripple crest, once ejected it can be advected across distances of several ripple wavelengths so, to some degree, its influence becomes naturally spatially averaged. The sharp suspension peak associated with the crestal region remains a process capable of enhancing suspension at a particular spatial location, but the migration of the bedforms, which is typically in the region of 0.5–2 cm min− 1 (Vincent and Osborne, 1993; Doucette, 2002; Austin et al., 2007), provides some degree of natural averaging since during a 10-min period a ripple of 20 cm wavelength λ is likely to have migrated a distance of 0.25λ to λ. The analysis of the suspended sediment data has identified two complimentary processes which should be accounted for whenever bed-level changes are occurring; the question that remains is which is of most importance? The tide-averaged vertical suspended sediment profile (Fig. 10) illustrates that variations in concentration with height are significant, regardless of the presence of bedforms, the effects of which are averaged-out in that analysis. It is therefore of first-order importance to obtain measurements of suspended sediments within a constant frame of reference (i.e., at constant elevation above the bed). Once that constant reference frame is obtained, analysis of further near-bed processes can proceed with confidence. While the present analysis of suspended sediment fluxes has identified that it is critical to account for bed-level changes when processing OBS data, it has also indicated that the error in not correcting the velocity measurements is small (6–7%). This is significant since it suggests that current measurements obtained in the lower ∼0.2 m of the water column are indeed representative of the near-bed depth-averaged flow velocity, supporting the findings of Miles et al. (2002). Hence, the error associated with pairing velocity and concentration measurements from moderately different elevations is insignificant given the extent of other potential experimental errors such as air bubbles (Puleo et al., 2006). This provides some confidence

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that the flux estimates from earlier studies, which used singlepoint measurements of velocity, paired with a small array of OBS sensors (Osborne and Greenwood, 1992; Aagaard et al., 1998), are likely to be qualitatively correct and the errors small. Observational and anecdotal evidence suggests that waveformed ripples are ubiquitous features of the surf and shoalingwave zones of natural beaches (Nielsen, 1981; Clifton and Dingler, 1984; Vincent and Osborne, 1993; Thornton et al., 1998; Soulsby and Whitehouse, 2005; Austin et al., 2007; Masselink et al., 2007) and in concert with sandbar migration and periodic accretion/erosion result in significant bed-level changes. It is therefore suggested that the methodology outlined above is applied particularly with respect to adjusting OBS elevations in response to changes in bed-levels when computing suspended sediment fluxes. Failure to account for bed-level changes caused by ripples migrating through the measurement area may result in errors in the reported suspended sediment flux. In the present case, with 5 cm ripples, it is O(35%) over a tidal cycle, but this is expected to increase with ripple height. To put this finding into context, in a recent investigation (Puleo et al., 2006) suggest that the presence of air bubbles in the water around OBS's may cause a measurement error of ∼25%; we therefore suggest that the error due to bedform dynamics can also be highly significant. Acknowledgements We would like to thank Peter Ganderton, Tony Butt, Jon Tinker, Daniel Buscombe, Tamsin Watt, Louise Hockley and Nigel Auger for invaluable assistance in the field. Many thanks to Nicholas King and the King family for letting us use their holiday home as our field station. This research was funded by NERC grant NER/A/S/2003/00553 (Cross-shore sediment transport and profile evolution on natural beaches—the XShore project) awarded to Paul Russell (PI), Gerd Masselink (CI) and Tim O’Hare (CI). References Aagaard, T., Greenwood, B., 1995. Suspended sediment transport and morphological response on a dissipative beach. Continental Shelf Research 15 (9), 1061–1086. Aagaard, T., Nielsen, J., Greenwood, B., 1998. Suspended sediment transport and nearshore bar formation on a shallow intermediate-stae beach. Marine Geology 148, 203–225. Austin, M.J., Masselink, G., O’Hare, T., Russell, P.E., 2007. Relaxation time effects of wave ripples on tidal beaches. Geophysical Research Letters 34, (L16606). doi:10.1029/2007GL030696. Beach, R.A., Sternberg, R.W., 1992. Suspended sediment transport in the surf zone: response to incident wave and longshore current interaction. Marine Geology 108, 275–294. Butt, T., Miles, J.R., Ganderton, P., Russell, P.E., 2002. A simple method for calibrating optical backscatter sensors in high concentrations of noncohesive sediments. Marine Geology 192, 419–424. Butt, T., Russell, P.E., Puleo, J., Masselink, G., 2005. The application of bagnold-type sediment transport in the swash zone. Journal of Coastal Research 21, 169–189. Clifton, H.E., 1976. Wave-formed sedimentary structures: a conceptual model, in Beach and Nearshore Sedimentation. In: Davis Jr., R.A., Ethington, R.L. (Eds.), SEPM special publication, 24, pp. 126–148.

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