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MARCH 2014
A Robust Estimation Method for Correcting Dynamic Draft Error in
PPK GPS Elevation Using ADCP Tilt Data
Eau Terre Environnement, Institut National de la Recherche Scientifique, Quebec, Quebec, Canada
Hydrology and Ecohydraulic Section, Meteorological Service of Canada, Environment Canada, Quebec, Quebec, Canada
(Manuscript received 11 June 2013, in final form 2 December 2013)
Measuring temporal and spatial variations in water level with high resolution and accuracy can provide
fundamental insights into the hydrodynamics of marine and riverine systems. Real-time kinematic global
positioning systems (RTK GPS), and by extension postprocessed kinematic (PPK) positioning, have provided
the opportunity to achieve this goal, by allowing fast and straightforward measurements with subdecimeter
accuracy. However, boat-mounted GPS are subject to movements of the water surface (e.g., waves, longperiod heaves) as well as to the effects of dynamic draft. The latter contaminate the records and need to be
separated and removed from the data. A method is proposed to postcorrect the elevation data using tilt
information measured by an attitude sensor—in this case, an acoustic Doppler current profiler (ADCP)
equipped with internal pitch and roll sensors. The technique uses iteratively reweighted least squares (IRLS)
regressions to determine the position of the center of rotation (COR) of the boat that leads to optimal
corrections. The COR is also allowed to change in time by performing the IRLS analyses on data subsamples,
thus accounting for changes in weight distribution, for example, due to personnel movements. An example of
application is presented using data collected in the Saint Lawrence fluvial estuary. The corrections exhibit
significant reductions associated with the boat motion while keeping subtle variations in water levels likely
related to local hydrodynamics.
1. Introduction
In recent years, real-time kinematic global positioning
systems (RTK GPS), and by extension postprocessed kinematic (PPK) positioning, have played an increasingly
important role in the study of marine and riverine environments. Aboard survey vessels or mounted on buoys,
kinematic GPS record any motion of the water surface
and measurement platform across the whole frequency
spectrum. Relying on differential carrier-phase measurements, they are capable of subdecimeter accuracy in both
horizontal and vertical positioning (Awange 2012; Ghilani
and Wolf 2012). Such vertical accuracy is required in order to measure variations in water level associated with
waves, long-period heave, tides, and nonperiodic motions
Corresponding author address: Pascal Matte, Eau Terre Environnement, Institut National de la Recherche Scientifique, 490 rue
de la Couronne, Qu
ebec QC G1K 9A9, Canada.
E-mail: [email protected]
DOI: 10.1175/JTECH-D-13-00133.1
Ó 2014 American Meteorological Society
such as those due to morphological features, currents,
and dynamic draft effects (Bisnath et al. 2004b). When
acquired with precision, temporal and spatial variations
in water level can provide new insights into dynamical
processes (e.g., Sime et al. 2007) and can be very helpful
in the calibration of numerical models (e.g., Church et al.
2008; Capra et al. 2010) or satellite altimetric measurements (e.g., Watson et al. 2003).
More specifically, RTK and PPK GPS have proven
very beneficial in hydrographic surveys for the determination of chart and tidal datum (e.g., Riley et al.
2003; Bisnath et al. 2004a; Moegling et al. 2009) and for
monitoring changes in seabed morphology when combined with multibeam echosounders (e.g., Church et al.
2009). In bathymetric soundings, they represent an advantage over traditional tidal and heave compensation
methods typically restricted only to long- and shortperiod motions, respectively (Work et al. 1998; Blake
2007). Kinematic GPS have also been used for the
measurement of tides as a complement to shore-based
gauges or in offshore regions where stations are remote
or nonexistent (e.g., Hess 2003; Zhao et al. 2004; Hughes
Clarke et al. 2005). Notably, centimeter-level differences were obtained by Rocken et al. (1990) and Bisnath
et al. (2003) between GPS and tide gauge water levels.
Furthermore, reliable estimates of the vertical motion of
the sea surface have been achieved with buoy-mounted
GPS for mean sea level and ocean wave measurements
(e.g., Rocken et al. 1990; Kelecy et al. 1994). Similarly,
estimates of wave heights made by Bender et al. (2010a,
b) in the context of Hurricane Katrina were shown to be
as accurate as data obtained from a strapped-down, oneaxis accelerometer, if properly corrected for tilting.
In rivers and estuaries, local variations in water-level
profiles are important to characterize. For example,
Bauer et al. (2007) were able to identify pools with potential sand deposition in the Sandy River, by comparing
water surface profiles taken at different years with
a RTK GPS. Similarly, Sime et al. (2007) surveyed the
main thalweg and branch channels of the lower Fraser
River using RTK GPS and observed strong streamwise
fluctuations in water surface slope, reflecting the pronounced riffle-pool variations as well as the backwater
and acceleration effects induced by diagonal bar complexes. Likewise, Rennie and Church (2010) identified
local variations in water surface slope over a succession
of riffles and pools in the Fraser River, using RTK GPS.
During vessel surveys, pitch and roll motions modify
the reported water levels, as they both rotate and translate the GPS antenna. The measured vertical positions
can fluctuate by a few decimeters as the boat shifts
position due to changes in boat loading, weight distribution, heading, speed, wave, wind, and current action.
These effects are referred to as dynamic draft, which is
the sum of static draft, settlement, and squat. Static draft
is the draft of the vessel at rest, when fully loaded (with
equipment, fuel, and personnel). Settlement is the vertical lowering of the moving vessel, relative to what its
level would be if it were motionless; it is measured at the
vessel’s center of rotation (COR). Squat refers to the
sinking of the vessel’s stern into the water as speed
increases; it acts as a lever arm from the COR to the
mounted instrument, thus changing its angle and draft
(CHS 2008; NOAA 2010).
Random oscillations, for example, due to waves, can
easily be removed from the records by smoothing. However, nonperiodic and low-frequency vertical displacements can either be attributed to variations of the water
surface elevation or to dynamic draft effects, and need to
be accounted for. While it is crucial to keep the actual
displacements of the water surface, shifts arising from
the boat movements have to be identified and removed
from the records.
In many of the above-mentioned studies (Kelecy et al.
1994; Work et al. 1998; Bisnath et al. 2003; Hess 2003;
Riley et al. 2003; Zhao et al. 2004; Hughes Clarke et al.
2005; Church et al. 2009; Moegling et al. 2009; Bender
et al. 2010a,b), the effects of pitch and roll and/or dynamic draft on the measured water levels have been
examined, by use of either squat models or sensors for
attitude determination. Because squat depends on several factors, such as channel depth and cross section,
shape of the ship’s hull, and ship speed (Barrass 2004),
estimating the vessel squat characteristics as a function
of speed through the water is not a trivial task (e.g.,
Beaulieu et al. 2012). On the other hand, using attitude
sensors implicitly means that the position of GPS antennas relative to COR of the measurement platform is
known and constant over time, two conditions not easily
met in many surveys. In fact, the position of the COR, if
not a priori known, must first be determined to calculate
the vertical displacements of the antennas induced by
pitch and roll motions. For this purpose, Alkan and
Baykal (2001), for example, lifted their survey boat from
the sea to the shore and mapped it in three dimensions
with all the equipment in place. However, this technique
is unpractical and most often impossible to achieve.
Furthermore, the weight distribution, and thereby the
position of the COR, may change in time due to fuel
consumption and personnel movements on board, which
limits the applicability of the method. As an alternative to
attitude sensors, Beaulieu et al. (2009) applied the on-thefly (OTF) GPS technology, which is a class of RTK surveys, using the Canadian Coast Guard’s GPS network to
measure ship squat in the Saint Lawrence waterway. The
ship and escort boat were each equipped with two OTF
GPS antennas on the longitudinal axis (bow and stern)
and two others on the starboard and port sides to ensure
simultaneous measurements of all vessel movements
(rolling, sinkage, trim, etc.). Although the vertical accuracy can be high (65 cm in 95% of all cases, as confirmed
by validation pretests), the required number of antennas
(four per boat) makes this technique less attractive.
A method is presented here for postcorrecting systematic errors in GPS elevations associated with dynamic draft effects, using tilt information measured by
an attitude sensor—in this case, an acoustic Doppler
current profiler (ADCP) equipped with internal pitch
and roll sensors. It is assumed that the low-frequency
motions of the water surface do not induce changes in
the pitch and roll angles of the boat and that these rotations are exclusively related to dynamic draft effects.
Hence, high-frequency oscillations are first removed
from the records by smoothing, and the resulting lowpassed PPK GPS and ADCP time series are resampled
to a common time vector and lagged to eliminate any
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synchronization issues. The implemented procedure
applies iteratively reweighted least squares (IRLS) regressions to determine the position of the COR of the
boat that leads to optimal tilt corrections. The COR is
also allowed to change in time, by performing the IRLS
analyses on smaller segments of the time series, thus accounting for changes in weight distribution over time.
The method is tested using data collected in the Saint
Lawrence fluvial estuary along repeated transects, aimed
at documenting the lateral and intratidal variations in
water levels and currents, at cross sections characterized
by complex geometries (e.g., river bends, tidal flats) and
in regions of contrasting tidal ranges and/or degrees of
ebb–flood asymmetry.
The paper is divided as follows: section 2 details the
implemented method, section 3 applies the procedure to
the Saint Lawrence River data, section 4 discusses the
results, and section 5 follows with concluding remarks.
2. Methods
To calculate the vertical corrections, a coordinate
system is defined with the origin located at the COR of
the boat (Fig. 1). The x axis is relative to the centerline
of the boat and is positive to the bow. The y axis is
perpendicular to the x and is positive to port. The z axis
points to the nadir direction and is positive upward.
Pitch is described as the forward and backward rotation
of the boat about the transverse y axis and is positive
when the bow of the boat goes up. Roll is described as
the side-to-side rotation about the longitudinal x axis
and is positive when the port of the boat goes up.
The corrections applied to the data are aimed at reducing systematic errors in the recorded GPS elevations
by minimizing the variations in water levels that correlate the most with the long-period movements induced
by pitch and roll. The input time series of observations
(i.e., water surface elevations, and pitch and roll angles)
are thus filtered versions of the original time series. The
GPS elevations are detrended to remove the effects of
tides, by subtracting a cubic smoothing spline function
from the original time series. The cubic smoothing spline
s is constructed for the specified smoothing parameter
p and weights wi so that it minimizes (Reinsch 1967; de
Boor 1978)
p å wi [yi 2 s(ti )] 1 (1 2 p) (d2 s/dt2 )2 dt ,
where yi represents the observed water levels measured
at times ti. Here, p 5 0 would produce a straight-line
fit to the data, while p 5 1 corresponds to the cubic
spline (exact) interpolant. The csaps Matlab function
FIG. 1. Configuration of the GPS and ADCP on the boat and its
associated coordinate system, with the origin located at the COR.
(MathWorks 2012) is used with a very low smoothing
parameter (p 5 1027), well suited to the slowly varying
character of tides. The wi are set to 1 for all data points.
Similarly, mean values in the pitch and roll data are
subtracted to correct for sensor misalignment, using the
detrend Matlab function (MathWorks 2012). The input
time series are then smoothed to remove random oscillations due to waves, using cubic smoothing spline functions
[cf. Eq. (1)] with a smoothing parameter of p 5 0.05, thus
keeping enough variations in the records to allow corrections for systematic errors. As a result of filtering, the
computed corrections only become effective when the
low-passed pitch and roll signals are departing from zero.
The ADCP time series are also reinterpolated to
a common time vector (that of the GPS), and lagged in
such a way that the correlation between observed water
levels and vertical corrections for pitch and roll is maximal, thus eliminating any synchronization issues. Moreover, to account for temporal variations in the position
of the COR—for example, due to changes in the weight
distribution of the boat—the time series are divided
into subsets, or transects, each of which being analyzed
Mathematically, the observed water levels can be represented by a linear model of the form
h 5 Ax 1 e ,
where h is an n 3 1 vector of the filtered GPS elevations,
A is an n 3 m tilt correction matrix (here m 5 3), x is an
m 3 1 vector of unknown COR parameters, and e is an
n 3 1 vector of observational error. Corrections for the
motion of a measurement platform can be performed by
use of rotation matrices (e.g., Edson et al. 1998; Miller
sinP(t1 )
Dz(t) [ Ax 5 4
sinP(tn )
et al. 2008). To transform the GPS elevations from ship
coordinates to earth coordinates, a first rotation about
the x axis (roll), followed by a rotation about the intermediate y axis (pitch), are calculated (cf. Teledyne
RD Instruments 2010). A translation along the z axis is
then applied to transform GPS-rotated heights into true
GPS orthometric heights. The resulting tilt corrections
Dz applied to the data are defined as
3 2 3
sinR(t1 ) cosP(t1 ) 1 2 cosR(t1 ) cosP(t1 )
7 4 5
sinR(tn ) cosP(tn ) 1 2 cosR(tn ) cosP(tn )
where P and R are the low-passed pitch and roll time
series of length n, respectively; t is an n 3 1 time vector;
and a, b, and c are the x–y–z coordinates of the GPS
antenna relative to the COR, respectively (Fig. 1). Hence,
the farther the antenna is from the COR, the larger the
displacements Dz are for given angles of rotation. The
first two columns of A correspond to vertical translations
of the antenna due to the pitch and roll movements, while
the third column is the vertical shift associated with its
rotation. The latter correction is either strictly positive or
strictly negative, depending on the sign of c, and is up to
two orders of magnitude smaller than the translations, for
small P and R angles.
Robust parameter estimation models can be used to
solve Eq. (2) in a way to reduce the influence of variations in water levels other than those associated with
dynamic draft effects. A number of techniques with various levels of efficiency and effectiveness have been proposed, some of which were described by Huber (1996),
Awange and Aduol (1999), and Goncalves et al. (2012).
Among them, IRLS regression analysis (Holland and
Welsch 1977; Huber 1996) has successfully been applied
to geophysical problems (see, e.g., Bube and Langan 1997;
Leffler and Jay 2009). The IRLS algorithm reduces the
influence of high-leverage data points that increase residual variance by downweighting the outliers. The level of
confidence in the computed parameters is therefore increased compared to ordinary least squares (OLS) analyses.
The IRLS solution to Eq. (2) is given by
convergence of the residual. At each iteration j, the
following steps are repeated:
x 5 (AT WA)21 AT Wh ,
where ri 5 R0i /st.
5) A new solution is obtained by application of Eq. (4),
with wi 5 diag(W).
where W is an n 3 n diagonal weight matrix. The initial
solution is obtained from OLS regression by setting the
weight matrix to the identity matrix, that is, W 5 I. Iterations are then performed on x and W until there is
1) The residual R is computed from previous fit [i.e.,
Eq. (4)]:
Rj 5 Wj21 (h 2 Ax)j21 .
2) The residuals are adjusted using leverage li, as advised
by DuMouchel and O’Brien (1990), which is a measure of the influence of each point i on the least
squares fit:
R0i 5 Ri /
1 2 li ,
where Ri are the ith elements of vector R, R0i is
the adjusted residuals, and li 5 diag[A(AT A)21 AT ].
(Weisberg 2005).
3) A standard deviation estimate s is computed using
the median absolute deviation (MAD) of adjusted
residuals from zero:
s 5 MAD(R0i )/0:6745,
where the constant 0.6745 makes the estimate unbiased for the normal distribution.
4) New weights are calculated using the specified
weight function f and tuning parameter t:
wi 5 f (ri ) ,
IRLS regression analyses are performed using the
robustfit Matlab function (MathWorks 2012). A bisquare weighting function is used, defined as
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wi 5
(1 2 ri2 )2 ,
jri j , 1
jri j $ 1
with a (default) tuning constant t of 4.685. The latter can
be adjusted to penalize the outliers more or less heavily,
depending on the level of filtering needed. In the present
application, the default Matlab value is used, yielding
coefficient estimates that are approximately 95% as
statistically efficient as the OLS estimates, assuming that
the response has a normal distribution with no outliers
(MathWorks 2012). In general, for small pitch and roll
angles, departure from the default function and parameter suggested by Matlab has little effect on the
corrections (i.e., of the order of a few millimeters). A list
of available weight functions can be found in Matlab
documentation (MathWorks 2012); also summarized in
Leffler and Jay (2009).
To assess the significance of the corrections Dz, the
p values are computed for the relative standard errors
of coefficient estimates a, b, and c. They represent the
probability of obtaining a test statistic at least as extreme as the observed error. For p values less than
0.001, the null hypothesis is rejected—that is, the
correlation between the calculated corrections and the
observations is highly unlikely to be the result of random chance alone. Otherwise, for p values superior to
0.001, the correlation is not significant and the coefficients are rejected (i.e., set to zero), in which case
the solution is recalculated using only the columns of
A corresponding to nonzero coefficients x. Threshold
values on the coefficients can also be used to ensure
that realistic distances between the antenna and the
COR are obtained.
3. Data analysis
Data were collected at 13 cross sections of 1–4-km
width on the Saint Lawrence fluvial estuary, Quebec,
Canada, during the summer of 2009. Each cross section
was surveyed repeatedly over a period of approximately
12 h, corresponding to the semidiurnal tidal period.
Boat speed was maintained at 1–2 m s21 on average to
ensure data of good quality. Two Trimble R6 RTK GPS
receivers—the base, located on the shore, and the rover,
mounted at the rear of the boat (Fig. 1)—were used for
positioning and water-level measurements along the
transects (Trimble 2003). They were operated simultaneously, collecting data at a frequency of 1 Hz. Tilt information was obtained from the internal pitch and roll
sensors of a 600-kHz RD Instruments Rio Grande ADCP,
mounted on the side of the boat (Fig. 1). The frequency of
acquisition was set to 2.5 Hz.
For the purpose of testing, the elevation data measured during one crossing at Portneuf were used, which
present systematic shifts typically encountered during
the campaign due to dynamic draft effects. As shown in
Fig. 2a, measured water levels are indeed highly correlated with the variations in pitch and roll angles.
Before using the data, the input time series were detrended and/or demeaned to remove variations associated with tides or sensor misalignment and then
smoothed to remove random oscillations due to waves.
Note that GPS elevations are detrended solely for the
sake of regression analysis; once the correction Dz is
made, the trend is reapplied to the elevation time series. The resulting filtered time series are shown in
Fig. 2b. It can be seen that even the sharpest variations
appearing in the original records (Fig. 2a) are preserved in the filtered time series.
The first entry of Table 1 shows the regression coefficients [a, b, c] obtained for the chosen transect,
representing the position of the antenna that leads
to the optimal corrections. Because the pitch and roll
angles are relatively small (cf. Fig. 2b), vertical corrections from the third column of A in Eq. (3) were very
small too. Consequently, the value of c was not significant according to its p value, which was much higher
than 0.001; it was thus set to zero. The zero time lag
indicates that the elevation and pitch and roll time series were synchronous. Furthermore, the correlation
coefficient (0.790) highlights the strong relation that
exists between the computed correction and the elevation time series.
The impact of dividing the time series into smaller
subsets and performing successive IRLS analyses was
assessed. In Table 1, results are presented from consecutive 5-min intervals of the same transect as in the
first entry. The optimized coefficients slightly differ from
each other, highlighting the respective influence of pitch
and roll on each subsample. The corresponding standard
deviations are thus smaller than for the total transect
(24.6-min interval), with the exception of the fourth
5-min subset, because coefficients are adjusted to the
local conditions prevailing during each time interval.
Correlation coefficients are also higher in subsets where
water-level variations induced by dynamic draft movements are the strongest. Overall, the displacement of the
COR is within 61 m in all directions, reflecting the
combined action of external factors (such as currents,
winds, weight distribution, etc.) on the position of the
COR. Despite these dissimilarities, maximum absolute
differences in water levels between the analyses performed using the whole transect (first entry) and using
5-min intervals were less than 1 cm (given maximum
absolute corrections of ;3 cm; cf. Fig. 3).
FIG. 2. (a) Elevation data for a transect at Portneuf measured with a kinematic GPS, compared to pitch and roll data from an ADCP. (b) Filtered time series of elevations, and pitch and
roll angles.
The corrections obtained by IRLS analyses for the
transect at Portneuf are shown in Fig. 3. The computed
correction is shown in the top panel, while the signals
before and after correction are presented in middle
panel. Variations associated with pitch and roll motions are significantly reduced, although not completely eliminated in some instances. The fluctuations,
however, remain within an interval of 61 cm once
corrected, which is comparable to the data vertical
accuracy at Portneuf, as generally expected for kinematic GPS surveys (Ghilani and Wolf 2012). The
weight time series appearing in the bottom panel of
Fig. 3 follows the variations in the residual and shows
how the IRLS regression analysis reduces the influence of nonzero residuals on calculated coefficients.
Lower weights are attributed to portions of the signal
where variations caused by pitch and roll motions still
remain as well as where variations with no apparent
correlation with the boat motion appear, possibly related to local hydrodynamics.
4. Discussion
GPS elevations are subject to errors other than those
related to dynamic draft effects. These include data
latency associated with the motion of the rover during
data transmission from the base receiver, reception,
and processing at the rover. In the present application,
positioning errors caused by this time difference tend
to be small, since boat speed was maintained around
1–2 m s21 on average. Other factors that limit the positioning accuracy of kinematic surveys are errors associated with spikes in positional dilution of precision
(PDOP), tropospheric and ionospheric refraction, weak
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TABLE 1. Regression coefficients for a transect at Portneuf,
along with their associated statistics. First entry shows results for
the entire crossing (24.6-min interval), while the other entries
correspond to 5-min subsets of the transect.
Time interval
[a, b, c] (m)
Entire transect
[20.854, 0.468, 0.000]
5-min subsets
[0.000, 0.673, 0.000]
[20.865, 0.630,0.000]
[0.394, 0.409, 0.000]
[20.629, 0.000, 0.000]
[0.782, 0.782, 0.000]
Std dev
lag (s)
satellite geometry, ephemeris error, multipathing, obstructions to satellite signals due to topography or infrastructure, base station coordinate errors, firmware
algorithms, and weather (Blake 2007; Ghilani and Wolf
2012). These factors should be taken into account when
possible in the planning of a field campaign. During the
Saint Lawrence campaign, the number and configuration of available satellites was not considered due to
time and resources constraints—only the weather was,
mainly for security reasons. However, the number of
available satellites was always high (.10 in the example
shown, with PDOP , 2) due to a very open survey environment, thus limiting the associated errors. Moreover, errors associated with tropospheric delay were
limited by short-baseline surveys (,4 km). Some of the
remaining errors were directly filtered out in the receivers (e.g., multipath effects), the rest being partly
cancelled by data smoothing. In the robust model, only
the variations in measured heights that are significantly
correlated with pitch and roll (through the p value criterion) were corrected. Also, the weights attributed to
uncorrelated variations were reduced, leaving them almost unchanged. This allows a separation between dynamic draft and GPS-related errors. The latter perturb
the robust model only if they are synchronized with the
boat movements, which is improbable.
Because of the strategy of repeated transects used in
the Saint Lawrence fluvial estuary, data from each river
crossing was analyzed and corrected separately. Although smaller regression intervals than the transect
length might be desired (e.g., Table 1), care must be taken
at the junctions of neighboring subsets because of discontinuities appearing between successive corrections.
FIG. 3. (top) Computed correction for a transect at Portneuf, (middle) original (filtered) and
corrected elevations, and (bottom) bisquare weights from the IRLS analysis.
More tests are needed to determine the optimal time
interval to apply to each subset at a given location.
Although the GPS antenna is fixed on the boat, the
position of the COR is generally unknown and can move
due to changes in weight distribution, for example,
caused by personnel movements on board. The use of
IRLS weighting functions allows for obtaining robust
coefficients, corresponding to the optimal distances between the antenna and the COR in terms of residual
variance reduction. More work, however, is necessary to
assess the performance of other weighting functions and
to define practical limits for the tuning parameters,
which may vary from place to place and as a function of
the conditions that prevail. In the example shown, pitch
and roll angles were relatively small, so that the impact
of the choice of the weighting function and tuning constant on the corrected water levels was minor (of the
order of a few millimeters). This may not be the case
under more extreme dynamic draft conditions.
The regression approach put forward here is intended
to be used in a context where the position of the COR is
unknown and/or changes in time. Its position can only be
determined when time variations in tilt angles occur and
when these variations are significantly correlated with
changes in the measured GPS elevations. Therefore, the
presented method does not apply in contexts where the
boat is tilted by a constant angle during the whole survey, unless the ADCP is perfectly aligned in the vertical.
In this case, the average pitch and roll can be used to
shift the GPS data, using the known or iteratively determined COR.
5. Conclusions
ADCPs are used in a wide variety of applications,
from discharge monitoring to the investigation of sediment transport, turbulence, or habitat quality (e.g., Lu
and Lueck 1999; Yorke and Oberg 2002; Shields and
Rigby 2005; Rennie and Church 2010). In the present
application, the tilt information provided by an ADCP
was used in place of traditional attitude sensors, thus
broadening its range of applicability. In contrast, the
use of kinematic GPS technology in the study of riverine and marine systems is relatively new. As experience is gained in the field, measurement techniques
and data analysis procedures are refined. As argued
by Work et al. (1998), extreme care and detail must be
maintained during data collection and processing to
yield useful data.
A simple method was presented to postcorrect elevation data obtained from a boat-mounted GPS. Using
data collected in the St. Lawrence fluvial estuary, contamination of the measured elevations was shown, arising
from dynamic draft effects undergone by the boat as it
followed its survey path. The observed systematic errors
were correlated with pitch and roll data obtained from
an ADCP, which were used to correct the PPK GPS
data, thereby reducing the error to within instrumentation accuracy. This improved accuracy demonstrates
the potential of using boat-mounted PPK GPS in
a variety of environments and conditions, as long as
there is no signal obstruction and sufficient information is available to compensate for boat movements.
Even when the measured tilt angles are relatively
small, there is no reason not to perform the corrections if tilt information is available. Obviously, under
more extreme dynamic draft conditions, as the tilt angles
increase, the need for such a correction becomes increasingly important.
The combination of kinematic GPS and ADCP technologies allows simultaneous acquisition of both water
level and velocity data, which is crucial for the calculation of accurate discharges in rivers. This becomes especially useful when there is no water-level gauge close
to the study site, or when cross-sectional variations
cannot be adequately captured by shore-based gauges.
Likewise, local variations in water levels either due to
local geographic features or transient processes can only
be precisely measured using instruments capable of high
resolution and accuracy, such as PPK GPS.
Acknowledgments. This work was supported by
scholarships from the Natural Sciences and Engineering Research Council of Canada and the Fonds
de recherche du Qu
ebec—Nature et technologies. The
field campaign was funded by Environment Canada
(Meteorological Service of Canada). Special thanks go
to Guy Morin, Jean-Franc¸ois Cantin, Patrice Fortin,
Olivier Champoux, and Catherine Leblanc for their
contribution to the field work as well as to two anonymous reviewers for their constructive comments on the
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