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AUV-Based Observations of Rough BedHydrodynamicsSergio Jaramillo and Geno Pawlak

Abstract—For highly irregular boundaries such as coral reefs,the choice of a characteristic roughness scale for use in hy-drodynamic modeling is not straightforward. As an initial stepin relating measurable physical scales to hydrodynamic rough-ness, we explore the use of a combination of sidescan, high-resolution altimetry, and water velocity measurements collectedwith a REMUS-100 (Remote Environmental Monitoring UnitS,Hydroid, Inc) AUV in the vicinity of the Kilo Nalu Nearshore ReefObservatory (Oahu, HI). Different substrate classes (i.e. sand,coral, mixed) are identified using principal component analysisof variables derived from sidescan backscatter. Each bottom classis then connected to physical roughness measurements madewith a high-resolution altimeter. Within the sampled region,sandy areas show a spectral root mean square (RMS) heightof less than 3cm, while coral patches show RMS heights between8cm to 14cm. To gain insight into the hydrodynamic responseto broad-band roughness, we conducted a series of tests overthe reef using the AUV mounted DVLs. In order to validatethese hydrodynamic measurements, along-shore water velocitiesare sampled with spatial averaging of 50-100m and comparedwith two nearby bottom-fixed ADCPs. The results show that theREMUS DVLs can be effective in resolving steady boundary layerstructure, which integrate the effects of roughness and implicitlyreflect the response of wave motion to the boundary. Ongoingefforts are aimed to correlate bottom type, roughness RMS andhydrodynamic response at reef scales.

Index Terms—Hydrodynamic roughness, tropical reef, AUVobservations.

I. INTRODUCTION

ONE of the common features of tropical reefs is thediversity of bottom types that can be found within a

relatively small area. From a hydrodynamic perspective, coral,sand, pavement, affect wave and current velocity in differentways. For instance, steady flows over a rough boundary havea vertical structure generally characterized by a logarithmicprofile that is determined by the bed stress and a hydrodynamicroughness scale, z0. When this rough boundary consists of ho-mogeneous elements (such as sand ripples) the hydrodynamicroughness scale can be related to a physical length such asripple height, ripple wave length or sand grain diameter. Thebed stress can be parameterized in terms of a friction factorwhich also depends on the bed roughness. However, whenroughness amplitudes are large, z0 is no longer uniquely deter-mined by the roughness height alone, but is a function of otherparameters that reflect the bed geometry including horizontalelement spacing [1]. For highly irregular environments, suchas most tropical coral reefs, where dominant length scales are

Department of Ocean and Resources Engineering, University of Hawaii atManoa. Holmes Hall 402 Honolulu, HI 96822, USA

not easily identified, bed stress parameterizations are morechallenging and have not been validated.

Roughness over a stretch of coral reef around the Kilo NaluObservatory, off the south coast of Oahu, HI, was measuredby [2] between 5-20 m depths at scales between 0.3 and 10m. The bed was characterized by a broad spectral distributionof length scales over this range with a characteristic spectralslope of -3.0±0.7. The spectral energy, indicative of roughnessamplitude, also showed high spatial variability on scales of50-100 m. Since existing analytical models of wave-currentinteraction assume that the bottom roughness is homogeneous,it is not clear what roughness length scales could be used incases such as the one described above, where the roughnessis broad-banded.

We present a series of observations of high-resolutionroughness measurements and of the flow over the coral reefin the vicinity of Kilo Nalu, obtained using a REMUS-100 (Remote Environmental Monitoring UnitS, Hydroid, Inc)autonomous underwater vehicle (AUV) with a customizednarrow beam altimeter (Imagenex Technology Corp). Thehydrodynamic measurements focus on the steady structureof the currents. These currents integrate the effects of thebottom roughness and implicitly reflect the response of wavemotion to the boundary (i.e. [3]). The immediate goals are tofirst use the AUV sidescan sonar to identify different bottomtypes. We then link these bottom types to measurements ofbottom roughness obtained with the narrow beam altimeter.We explore the use of the AUV Doppler velocity logs (DVLs)as means for obtaining a large scale view of the hydro-dynamic response to irregular, broad-band roughness. SinceAUV-based hydrodynamic measurements have been reportedto show biases in the direction of the vehicle motion [4],we perform a series of tests over the reef with the purposeof characterizing our particular instrument bias and find themost appropriate settings for the DVLs. Using bottom-fixedADCPs we evaluate the performance of the DVLs and weshow that the DVL vertical current profiles, with a spatialaverage between 50-100m, successfully capture the evolutionof the current boundary layer on several transects over therough reef.

II. STUDY SITE

The study area is located offshore of Hon-olulu, Hawaii, at the Kilo Nalu Observatory(www.soest.hawaii.edu/OE/KiloNalu, [5]) on the southshore of Oahu. Figure 1 shows the location of the studysite including a close-up of the survey region showing the

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AUV track. Markers KN1 and KN2 denote the positionof two upward-looking 1.2MHz acoustic Doppler currentprofilers (ADCP, RD Instruments) that are part of the KiloNalu Observatory. The survey domain, covers approximately800x500m ranging in depth from 8 to 35m. The domain wasrotated by 30 degrees so that it is aligned with the bathymetriccontours. The seabed at Kilo Nalu is comprised of sand andcoral reef, exhibiting a wide range of roughness scales [2].The wave environment at the study site is seasonal, althoughwave energy is present year round [5]. During wintertime,wave heights are small (1 m significant wave height)associated with weak south swells. Short-period energyresults from wrapping swells generated by northeasterlytrade winds. During summer, significant wave heights arehigher, rarely larger than 3 m, resulting from very long periodswells generated by distant southern hemisphere storms.Wave breaking generally occurs only inshore of the 5-misobath. The offshore slope of the study area is relativelysteep, ranging from 0 to 40 m deep within 1 km. This steepslope suggests that local bathymetry accounts for most of thewave transformation processes, in contrast with broad-shelfcontinental regions.

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Figure 1. Place holder: Study area. The blue line shows the track followedby the AUV during the sidescan and roughness survey. Markers KN1 andKN2 denote the location of two moored ADCPs that are part of the KiloNalu Nearshore Reef Observatory.

III. INSTRUMENTATION

A REMUS-100 AUV, equipped with a 900kHz acousticsidescan system (Marine Sonic Technology, Ltd), was used tosurvey the study area. The sidescan sonar recorded acousticintensity reflected from the seabed on either side of thevehicle. The AUV was programmed to maintain an altitudeof 3m above the bottom at a cruise velocity of 1.3 m/s,which rendered a sonar coverage of about 60m in the cross-track direction. The resulting spatial resolution of the acousticimages was approximately 6cm in the cross-track directionand 12cm in the along-track direction. As the sidescan imagesrecorded only backscatter intensity, they do not provide directinformation regarding bottom range. Therefore, the AUV wasoutfitted with a narrow beam (2.5◦ beam width) altimeter witha sampling frequency between 9 and 18Hz, which results inalong-track resolution between 7 and 14cm.

Profiles of current velocity were collected using the AUV’supward and downward looking 1.2MHz DVLs (RD Instru-ments). The DVLs where configured to sample in 1m binswith a blanking distance of 1m, and sampling frequency of1 Hz. Measurements obtained with AUV and vessel mountedADCPs have been reported to show biases in the direction ofthe vehicle motion [6], [4]. Through a series of preliminarytests, discussed later, we found that the bias for our instrumentwas reduced by choosing an ambiguity velocity [7] close tothe sum of the vehicle velocity and the maximum expectedcurrent velocity. However, since there is no bias observed inthe cross-track velocity measurements, we focus our obser-vations on the boundary layer structure of the along-shorevelocity measured using cross-shore tracks. The two bottom-fixed 1.2Mhz ADCPs (KN1 and KN2 in Figure 1) are mooredat approximately 12m and 23m depths. These instrumentsprovided profiles of current velocity to be used for comparisonpurposes. Both ADCPs sampled velocity over most of thewater column continuously at 2Hz in 25cm bin.

IV. BOTTOM CLASSIFICATION

The classification of different seabed substrates using thesidescan data collected during our survey is described in detailin [8] and follows a similar process as that used by [9] usingship-mounted multibeam data. Sidescan backscatter data wascollected with the vehicle navigating at constant altitude of 3meters above the bed (mab) at an average speed of 2.3m/s.Acoustic images were subsequently filtered to minimize theeffects of instrument and survey variations. They were thenre-sampled to a 5x5cm resolution grid and georeferencedusing software developed by the Hawaii Mapping ResearchGroup (HMRG). A mosaic of the processed sidescan datais shown in Figure 2A. Areas covered by coral reef can beidentified as darker areas with coarser texture in the middleand in the shallower parts of the domain, with areas coveredby sand or pavement appearing brighter and with smoothertexture. An abandoned sewer pipe is also visible near theeastern boundary of the surveyed area. A full close-up of thismosaic is sufficiently well-resolved to enable identificationidentification of the moored instrumentation from the KiloNalu Observatory.

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To reduce the dimensionality of the data set, we dividedeach image into 6x6m boxes in which various statistical quan-tities were calculated. The variables that proved to be mostuseful in capturing the variability of the dataset were: vari-ance, skewness, entropy, power spectral slope, and anisotropy(Figure 2). Higher values of variance, and entropy are well-correlated with coral patches (Figures 2B and D, respectively),while low values of each were associated with sandy areas.Higher anisotropy values are representative of areas covered bysand ripples (Figure 2F). The empirical orthogonal functions(EOFs) calculated using these normalized variables showedthat 90% of the variability of the dataset was captured by thefirst three modes of the EOFs (53, 19, and 18% of the variance,respectively). To interpret the results from the EOF analysis,several individual images were visually classified into areasof either, sand, coral, rippled sand, or mixed sand and coral.Comparisons between the visual classifications and the EOFdistributions indicated that the coral and sand regions weredistinguished by the magnitude of the first EOF, with coralareas represented by EOF#1 coefficients greater than 0.2, andsandy areas by EOF#1 coefficients lower than -0.1. EOF#2coefficients greater than about 0.4 were correlated to sandripples. The 6x6m boxes that contain mixed sand and coralwere not clearly identified using these EOFs.

V. ROUGHNESS MEASUREMENTS

A key parameter for wave transformation modeling is themeasured fixed bed roughness value [10]. An AUV providesan improved sampling platform for this fixed bed roughnessmeasurements relative to the boat-mounted approach used by[2] due to the stability of the platform and proximity to the bed.Using the narrow beam altimeter mounted on our REMUS-100 AUV, we are able to directly measure range to the bottomwith a horizontal resolution of approximately 10cm. Figure 4Ashows the spectral RMS roughness calculated from the spatialseries of bottom range. Each RMS value is representative of a6m segment of the bottom range series. It must be noted thatthe positions where roughness measurements are obtained liewithin the vehicle’s nadir, where valid sidescan data is notavailable. Therefore, in order to compare the results from thesidescan EOF analysis and the roughness measurements, thesidescan EOF coefficients are averaged over an 18m radiusaround the vehicle altimeter track. Figures 4B and C show theaveraged coefficients of EOF#1 and EOF#2. Even with the ex-pected errors introduced by averaging and uncertainties relatedto the georeferencing of the sidescan images, a least squaresfit of EOF#1 to the RMS roughness produced satisfactoryresults with an r2 = 0.53 [8]. In general, there is significantvariability in bed roughness throughout the study area, withmaximum roughness values found near the shallower borderand in the middle of the domain corresponding to coral reefpatches (coarser areas in Figure2A) with RMS between 14cmand 8cm respectively. In the areas covered by sand, the RMSroughness values are less than 3cm (brighter, smoother areasin Figure 2A).

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VI. CURRENT MEASUREMENTS

We can expect that such a wide range of roughness scalesand bottom substrates will be reflected in the structure ofwaves and currents within our domain. The AUV’s DVLs canbe useful in resolving the spatial variability of the currentsvertical structure which integrates the effects of bottom rough-ness and implicitly reflect the response of wave motion to theboundary. The DVLs mounted on the AUV were not conceivedwith the purpose of obtaining hydrodynamic measurementsfor scientific use, but were designed to improve the accuratenavigation of the vehicle. The subsurface AUV navigationfor our REMUS-100 is achieved through a combination ofa Long Base Line (LBL), Ultra Short Baseline (USBL), andDVLs. Whenever an acoustic position (by LBL and USBL)is not available, the vehicle uses dead reckoning navigationtechniques to estimate its location. The vehicle speed isestimated using DVL bottom-tracking velocity measurements,or propeller revolutions (when DVL bottom-tracking is un-available), and the heading is obtained by a combination ofinputs from the vehicle’s magnetic compass/heading sensorand the yaw-rate sensor. For the REMUS-100 AUV, theRDI manufactured DVLs are very similar to the broadbandRDI Workhorse acoustic Doppler profiler (ADCP) so that, in

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Figure 2. Normalized demeaned variables used in the EOF analysis. A) Mosaic of sidescan backscatter collected over the Kilo Nalu observatory; B) Variance;C) Skewness; D) Entropy; E) Power spectral slope; F) Anisotropy.

addition to serving as a navigation aid for the vehicle, one canalso obtain velocity profiles in the water column.

Before the DVL data can be used for hydrodynamic mea-surements, the accuracy and limitations of the DVLs needsto be assessed. In this section we describe some of the testsdesigned to investigate these capabilities. We then present aset of current observations collected over the reef and comparewith stationary measurements made with moored ADCPs.

A. Characterization of velocity bias

Although the velocity bias in the direction of the vehicle’smotion reported by [6], [4] has been primarily associatedwith low scatter environments, observations include at least

one high-scatter case [4]. Some possible sources for this biasinclude ringing, and errors related to bottom tracking andbeam geometry. Track related errors in ship-mounted ADCPswere shown to be negligible by [6], but neither kind of errorshave been addressed for AUV mounted ADCPs. Calibrationtechniques that correct for these type of errors have beendescribed by [11], [12], however they were not used by [6],[4].

In order to find the DVL settings that would minimize thevelocity bias in the direction of the vehicle motion, an initialexperiment was conducted in the vicinity of the 12m node atthe Kilo Nalu Observatory (Figure 5). Velocity measurementsfrom the AUV DVLs are are then compared with the Kilo Nalu

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bottom mounted ADCP (KN1). With the vehicle cruising at3m depth, three transects (shoreward and seaward) in the cross-shore direction and 3 transects in the along-shore directionwere run. Each transect was repeated 3 times at differentvehicle speeds (1.5, 2, and 2.5m/s) and each time a differentambiguity velocity (2.5, 3, and 3.5m/s) was set for the DVLs.The downward facing DVL was used in the comparison withthe stationary ADCP. Only the first 6-7 bins are used from theDVL data, since the lowest bins are affected by variations inbottom depth, especially for cross-shore transects.

As reported by [6] and [4], the cross-track velocities didnot show any appreciable bias and coincide well with the sta-tionary ADCP (see next section), but the along-track velocityshow a clear bias in the direction of the vehicle motion. Figure6 shows a comparison between the moored ADCP at KN1 andthe AUV measured velocities for a run made with an ambiguity

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velocity setting of 3.5 m/s. The velocity in each bin of the DVLwas horizontally averaged for each cross-shore leg, while thefixed ADCP velocities were interpolated to match the depthsof the DVL bins. A 10-min moving average was applied tothe KN1 data to eliminate surface waves.

The comparison shows that positive DVL measured veloci-ties (crosses) occur for the shoreward legs, and negative DVLvelocities (circles) occur during the seaward legs, oscillatingroughly between 2cm/s and −4cm/s, respectively, while thestationary ADCP measurements show velocities very closeto zero (solid line, rarely reaching 1cm/s). Notice that thedifference in DVL measured velocity for each consecutiveshoreward and seaward legs varies with AUV speed and dis-tance to the instrument. Similar results were observed for therest of the experiment. Due to the small time lapsed betweentransects in opposite directions (6 minutes on average) we canneglect the natural variability of the currents during this periodand define the bias velocity as the difference between the meanvelocities in two consecutive legs in opposite directions.

Figure 7 shows vertical profiles of bias velocity for dif-ferent ambiguity velocities and AUV speeds (calculated frombottom track velocities). The magnitude of the bias velocityvaried significantly for the different transects depending on thecombination of DVL ambiguity velocity and vehicle speed.The best results (minimum bias) are obtained with an AUVspeed of 2.5m/s and ambiguity velocity of 2.5m/s (Figure 7a,black line). These settings result in a mean bias of about1.5cm/s, which is ∼3.7 times less bias than using an ambiguityvelocity setting of 3.5m/s. This shows that the choice ofambiguity velocity settings can lead to differences in theobserved velocities that are of the same order of magnitudeas the bias. The comparison of the DVL velocities from thelong-shore transects with the stationary ADCP measurementsat KN1 produced similar results.

Errors in the bottom tracking of the vehicle velocities canaffect the accuracy of the water velocities recorded. Fong andMonismith [6] argue that, because the differences in their GPSspeed estimates and those obtained using bottom track are onaverage low (∼0.5 cm/s per transect), the bias induced by anerror in boat speed is two orders of magnitude smaller thanthat observed in the reported water velocities. These velocity

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Figure 6. Comparison between stationary cross-shore velocity at KiloNalu (solid line) and AUV based along-track velocity measurements at thefollowing depths: A) 5m, B) 6m, C) 7m, D) 8m, and E) 9m. The AUV speedfor the first pair of measurements (red crosses and circles) is 2.5m/s, forthe second pair (blue) is 2m/s, and for the third pair (black) is 1.5m/s. Theambiguity velocity for this run is 3.5m/s.

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differences are not calculated by [4].Integrating in time the vehicle position, obtained from the

acoustic transponders, we can get an estimated vehicle speedthat we can compare to the DVL bottom tracking veloc-ity (assuming that the acoustic positions represent accuratepositional estimates). For the transects done with ambiguity

velocity settings of 3.5m/s, the mean difference between thetwo estimates is 4.13cm/s, for the transects with ambiguityvelocity settings of 3.0m/s the mean difference is 1.87cm/s,and for the transects with ambiguity velocity settings of 2.5m/sthe mean difference in the vehicle speeds is 0.82cm/s. Sincethese values are not as small as in [6], and for some transectseven an order of magnitude higher, it seems that errors due toinaccurate bottom track or misalignment in the ADCP coordi-nate frame with respect to the vehicle reference frame couldbe partly responsible for the bias observed. However, usingthe correction method developed by [11] for ship mountedADCPs, which, in theory, corrects for systematic errors inbottom tracking and beam configuration, only brings a verysmall improvement in our observed bias of O(10−3m/s).

It is expected that errors related to platform ringing shoulddecay with distance to the instrument. To analyze this possibil-ity, the AUV was set to run 8 transects (16 legs) in the cross-shore configuration cruising at 3m depth between KN1 andKN2, and turning at approximately the 40m isobath (Figure8). The AUV then was set to navigate 10 transects at roughlythe 20m isobath navigating at a depth of 3m. The DVLs wereconfigured to sample in 1m cells, with a 1m blanking distance.Since the best results in the previous experiment were obtainedusing ambiguity velocities of 2.5 and 3 m/s, for the cross-shore and along-shore tests respectively, and a vehicle speedof 2.5m/s, the ambiguity velocity for this new test was set to2.75 m/s and a vehicle speed of 2.5 m/s (5 knots).

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The bias is clearly observed for the along-track velocities,with the velocity clearly stronger during the shoreward leg(Figure 9A) than during the seaward leg (Figure 9B), showingeven some reversals in the current direction in the bins furtheraway from the vehicle. A similar pattern is repeated for therest of the legs. For the cross-track velocities, however, thereis no bias observed.

The vertical structure of the bias appears invariant to eitherlocation or transect during the experiment. Figure 10 showsthe bias averaged over all the transects, where the red linerepresents an average over the nearshore region (∼ 0.38 - 0.6km from shore in Figure 9) and the blue is averaged over theregion where the measurements do not reach the bottom (∼ 0.6- 1.1 km from shore in Figure 9). A possible explanation for

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the ’C-like’ vertical structure of the bias velocity could be thatnear the instrument ringing effects bias the data toward thespeed of the AUV. This ringing usually decreases with depth,however, in the low scatter environment of coral reef waters,when the distance to the instrument increases, the signal-to-noise ratio decreases and ringing becomes again relevant. Moreinvestigation is still needed in order to verify this hypothesis.

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Figure 10. Left panel: Bias defined as the mean difference between theshoreward and seaward along-track velocities measured by the AUV duringthe cross-shore transects. Right panels: number of measurements averaged.

The bias is evident again in the vertical velocity mea-surements. The mean bias velocity near KN1 and KN2 isbetween 1-1.5cm/s respectively, which is of the same orderof magnitude as the vertical velocities measured (not shownhere). Again, the vertical structure of the bias follows a similar

pattern as the bias in the along-track velocities, decreasing inthe first bins and then increasing towards the bottom.

B. Vertical profiles of along-shore velocity

Steady currents in the vicinity of the Kilo Nalu Observatoryare normally along-shore oriented [5]. To avoid complicationsintroduced by the velocity bias in the direction of the vehiclemotion, we collected a series of along-shore velocity profileswhile the vehicle navigated in the cross-shore direction. TheAUV was set to run 8 transects (16 legs) in the cross-shore configuration between Kilo Nalu’s 12m node and the23m node (approximately located at 0.39 and 0.69 km fromshore in Figure 11). The vehicle was set to constant depthmode, cruising at 3m depth, and three acoustic transponderswere moored to increase navigation accuracy. The DVLs wereconfigured to sample 1m cells, with 1m blanking distance, andan ambiguity velocity of 2.7 m/s. The vehicle’s speed was setto 4 knots (∼2 m/s).

Figure 11A shows alongshore velocity contours from oneof the cross-shore legs. The data has been filtered spatiallyover 100m (50s) to eliminate surface waves. Significant spatialvariability is observed in the steady alongshore and cross-shore currents (the latter not shown). In Figure 11B, the DVLdata is compared with measurements from the 23m Kilo Nalubottom-mounted ADCP. DVL data is averaged over 100m inthe cross-shore direction (centered at the ADCP location), withthe fixed ADCP velocities averaged for the corresponding time(approximately 50s). Both instruments capture the temporalvariation and vertical structure in the current. While someportion of the scatter in the DVL data can be attributedto instrument error (the fixed ADCP used a high resolutionsampling mode with lower single ping error), the data in Figure11A suggests that much of the scatter is associated with realspatial variability.

This agreement between the moored ADCP and the DVLmeasurements of along-shore velocity suggest that AUV basedmeasurements of cross-track velocities are accurate enough toresolve the spatial current boundary layer structure over thestudy site.

VII. SUMMARY

Using a REMUS-100 AUV we collected a series of ob-servations of sidescan backscatter, high-resolution altimetry,and current measurements in the proximity of the Kilo NaluNearshore Reef Observatory in the south shore of Oahu,Hawaii. The idea behind these observations is to gain insightinto the relations between bottom types, physical roughness,and the hydrodynamic response to this roughness in the highlyinhomogeneous environment typical of tropical reefs.

Using an EOF analysis of statistical variables derived fromthe sidescan backscatter images, we were able to identifydifferent type of bottoms. The first EOF clearly separates areascovered by sand and coral, while the second EOF capturesareas covered by sand ripples. Areas of mixed sand andcoral are not robustly identified using this procedure. Withinthe surveyed area, altimeter observations show a significant

8

0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75

0

5

10

15

20

Distance from shore (km)

Dep

th (

m)

(A)

alo

ng

−sho

re v

elo

city (

m/s

)

0

0.05

0.1

0.15

0.2

0 0.05 0.1 0.150

2

4

6

8

10

12

14

16

18

Dis

tan

ce

fro

m t

he

bo

tto

m

t = 12:05

Along−shore velocity (m/s)

(B)

0 0.05 0.1 0.150

2

4

6

8

10

12

14

16

18t = 12:29

0 0.05 0.1 0.150

2

4

6

8

10

12

14

16

18t = 12:47

0 0.05 0.1 0.150

2

4

6

8

10

12

14

16

18t = 13:24

DVL

ADCP

Figure 11. AUV-based current profiling. A) Cross-shore section of along-shore velocity from AUV cruising at 3m depth. The vertical dashed line indicateslocation of Kilo Nalu 23m ADCP. b) Comparison of velocity profiles from AUV DVL (100m average) with Kilo Nalu 23m ADCP (50s average). Error bandsrepresent one standard deviation of DVL data including both instrument error and spatial variability.

variability of RMS roughness. These measurements of phys-ical roughness are correlated to the different types of bottomclassified using the sidescan analysis. Areas covered by coralreef show roughness values that vary between 8cm to 14cm,while areas covered by sand have roughness values of lessthan 3cm.

We conducted a series of tests designed to analyze theaccuracy and limitations of the hydrodynamic measurementsobtained by the AUV DVLs. We defined a bias velocity asthe velocity difference between two consecutive transects inopposite directions. Results from these tests show significantbiases for the velocity components measured in the directionof the vehicle motion. We found that appropriate ambiguityvelocity settings for the AUV DVL’s can significantly reducethe bias velocities. One of the possible sources for these biasesare errors in bottom tracking and misalignment of the AUVDVL’s. Corrections for these type of errors were performedfollowing [11] but the improvement in the corrected velocitieswas negligible. Tests conducted over deeper water seem toindicate evidence of ringing affecting the measurements closeto the instrument. An increase of bias velocity in the bins,further away form the instrument could be explained by adecrease in the DVL signal-to-noise ratio with distance to theinstrument.

As the velocity component perpendicular to the vehiclemotion show no biases, we collected profiles of along-shorecurrents while the vehicle navigated in the cross-shore di-

rection. These velocity profiles were sampled with spatialaveraging of 50-100m and compared with two nearby bottom-fixed ADCPs with very good agreement. These results showthat the REMUS DVLs can be effective in resolving the currentboundary layer structure in the area of our study.

Ongoing efforts are aimed to develop quantitative com-parisons between bottom type and physical roughness, withthe spatial variation of boundary layer structure in the areasurrounding the Kilo Nalu Observatory and other tropicalreefs.

REFERENCES

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[2] V. Nunes and G. Pawlak, “Observations of physical roughness over acoral reef,” J. Coast. Res., vol. 24, pp. 39–50, 2008.

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[9] J. Preston, “Automated acoustic seabed classification of multibeamimages of Stanton Banks,” Applied Acoustics, vol. 70, no. 10, pp. 1277–1287, 2009.

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