There has been an increasing interest in the use of Acoustic Doppler Current Profilers (ADCPs) to characterise the hydraulic conditions near river engineering structures such as dams, fish passes and groins, as part of ecological and hydromorphological assessments. However, such ADCP applications can be limited by compass errors, obstructed view to navigation satellites, frequent loss of bottom tracking and spatially heterogeneous flow leading to erroneous water velocity measurements. This study addresses these limitations by (i) developing a heading sensor integration algorithm that corrects compass errors from magnetic interference, (ii) testing a Total Station based technique for spatial ADCP data referencing and (iii) evaluating a recently proposed data processing technique that reduces bias from spatial flow heterogeneity. The integration of these techniques on a radio control ADCP platform is illustrated downstream of a weir with fish pass on the River Severn, UK. The results show that each of the techniques can have a statistically significant effect on the estimated total water velocities and can strongly affect measures of vorticity. The obtained three-dimensional flow maps are suitable to describe the magnitude and orientation of the fish pass attraction flow in relation to competing flows and to highlight areas of increased vorticity.

INTRODUCTION

Acoustic Doppler Current Profilers (ADCPs) have evolved as a useful tool to characterise the flow distribution of river reaches (e.g. Dinehart & Burau 2005; Rennie & Church 2010). A number of studies (Gaeuman & Jacobson 2005; Johnson et al. 2009; Jamieson et al. 2011, 2013) have illustrated the potential of ADCPs to quantify the flow field near river engineering structures as part of ecological and hydromorphological assessments. These studies have highlighted a range of ADCP data quality issues, including: (i) errors in the ADCP-internal compass data caused by changes in the local magnetic field (e.g. from steel reinforcements), (ii) limited line of sight to navigation satellites when using ADCPs in conjunction with Global Navigation Satellite Systems, (iii) discontinuous water velocity measurements caused by the loss of the ADCP bottom tracking (BT) signal, and (iv) lack of accurate three-dimensional (3D) water velocity measurements in spatially heterogeneous flows. These limitations reduce the applicability of ADCPs to characterise the hydrodynamics near engineered flow obstacles. For example, Jamieson et al. (2013) found spatial ADCP data referencing based on the global positioning system (GPS) to be insufficiently reliable when monitoring the hydraulics induced by stream barbs on a river in a heavily wooded and deep valley. Jamieson et al. (2011) experienced BT loss near a wing dike and attributed this problem to high water turbidity and turbulence and Johnson et al. (2009) found the ADCP data collected near surface flow outlets at dams to be biased because of large spatial flow heterogeneity.

This study introduces novel techniques of ADCP data collection and assesses a recently developed method of data post-processing to address these data quality issues. The proposed methods are integrated on a radio control ADCP platform and illustrated by quantifying the 3D distribution of water velocities immediately downstream of a weir with fish pass. The installation of fish passes at engineering structures designed to regulate discharge has been a wide-spread approach to restore the longitudinal connectivity of freshwater ecosystems (Katopodis & Williams 2012). Policy efforts towards restoring the ecological integrity of rivers (EC 2000, 2007) and the increasing evidence on the low efficiencies of existing fish passes (Bunt et al. 2012; Noonan et al. 2012) have led to a strong need for more post-construction assessment to gain a better understanding of the various factors determining the biological effectiveness of fish passes. The hydrodynamic conditions near fish pass entrances have been recognised as a key factor influencing the ability of fish to locate and enter these facilities (Piper et al. 2012; Williams et al. 2012; Lindberg et al. 2013). Yet, there is a lack of methods for the spatially continuous in-field quantification of near-pass hydrodynamics. To the authors’ knowledge, this paper presents the first in-field solution to rapidly quantify the spatially continuous distribution of water velocities near fish pass entrances using an ADCP.

ADCPs are mono-static sensors that measure water velocities and depths by transmitting and receiving acoustic pulses with 3–4 transducers along beams spread at an angle of usually 20–30° relative to the vertical direction. The arrangement allows for the use of a single acoustic signal to obtain measurements in multiple depths along the vertical water column (termed ‘ensemble’; Mueller & Wagner 2009). The water velocities measured in the directions parallel to each acoustic beam are processed to resolve a 3D vector describing the flow in the x, y and z directions of a coordinate system aligned with the instrument (Mueller & Wagner 2009). ADCPs have an internal fluxgate compass to determine the transformation angle required to reference these velocities to the local ambient magnetic field (magnetic north) and, after correcting for the site-specific magnetic declination, to true north. When the boat velocity is determined from ADCP-external sensors (e.g. because of BT loss), the effect of moderate errors in on the velocity components referenced to north can be large as it depends on the magnitude and direction of the actual water velocity and the ADCP boat velocity . For a ratio of 1, an error in of 10° can lead to a 17% error in the measured water velocity magnitude and an error of up to 20° in the water velocity direction (computed based on Gaeuman & Jacobson (2005)). A potential practical and low-cost solution to this limitation is the correction of ADCP compass errors with an inertial measurement unit (IMU) consisting of micro-electromechanical gyroscopes and accelerometers. Some IMUs fuse the inertial sensor data to provide orientation measurements relative to the direction of gravity, which are constrained neither in motion nor to any specific environment or location (Madgwick et al. 2011).

ADCP-measured water velocities have to be corrected for boat velocities, which are typically determined from BT (Gordon 1996). Common ADCP software flags ensembles without a valid BT signal as bad, indicating that the obtained measurements are unusable. These measurements can be recovered through the integration of external positioning systems such as GPS, based on which the boat velocity is estimated. However, fish passes and other engineered river structures are frequently installed close to river banks and these areas are particularly affected by degradation in GPS accuracy (Rennie & Rainville 2006). The problem may increase in small rivers, where the sky view can be obstructed over a large proportion of the water surface. This limitation can be addressed through the integration of ADCPs with alternative, local positioning systems such as tracking Total Stations (TS), which achieve 3D positioning precision of sub-cm level without relying on navigation satellites (Kirschner & Stempfhuber 2008).

Repeated ADCP measurements are necessary to capture the temporally averaged flow field in rivers (Muste et al. 2004). The conventional method of repeated ADCP measurements involves the averaging of multiple 3D water velocity vectors, each of which is resolved independently from the 3–4 along-beam velocities measured at the same time. This method assumes that the water velocities in the areas insonified by the beams are spatially homogeneous. The diameter of a circle enclosing the four beam footprints increases at a ratio of 0.76 m per 1 m increase in depth (calculated based on Rennie et al. (2002), for a 1,200 kHz WorkHorse RioGrande ADCP). Nystrom et al. (2002) argued that the distance between the beam footprints is comparable to the size of large-scale turbulence, so that the assumption of homogeneous flow can easily be violated in spatially complex hydraulic conditions. The data post-processing method suggested by Vermeulen et al. (2014) can avoid this bias by reducing the velocity sampling volume assumed to be homogeneous. The method uses a least squares procedure to estimate the 3D velocity vector that fits best to a set of along-beam velocities measured in similar locations during repeated cross-sectional measurements. However, the approach has not been tested in ADCP applications near flow obstacles.

The aim of this study was to integrate ADCPs with external sensors and novel data processing techniques for the accurate, in-field and rapid quantification of the spatially continuous 3D water velocity distribution near fish pass entrances. This was achieved through three core objectives:

  1. develop an IMU-based heading sensor integration algorithm that corrects ADCP compass data biased by magnetic interference;

  2. test a TS-based technique that provides spatially referenced ADCP data in areas of limited sky view and determines boat velocities in areas of BT loss; and

  3. evaluate the derivation of 3D water velocities as suggested in Vermeulen et al. (2014) to address the ADCP data bias caused by spatial flow heterogeneity.

METHODS

Case study site

The study site was a 55 m reach immediately downstream of Shrewsbury Weir on the River Severn (Figure 1). The River Severn is the longest river in the UK and one of its main salmon rivers (NASCO 2009). It flows from Plynlimon, Ceredigion, in the Welsh mountains to Gloucestershire, where it discharges into the British Channel. A total of 41 obstructions, with nine of them being considered significant barriers to upstream fish migration, can be identified along the course of the river. Shrewsbury Weir is the last major migration barrier to Atlantic Salmon (Salmo salar) before spawning grounds in the upper catchments. This study focused on the fish pass installed on the right river bank, constructed in 1976 as a pool and weir pass and then refurbished in 2006 as a deep vertical slot pass. Throughout the study, the streamwise direction was defined to be orthogonal to the weir crest (Figure 1).

Figure 1

Study location; the white dash-point line depicts the extent of the study area and the white dashed lines show cross sections referred to throughout the main text; the arrow pointing to the location of the fish pass entrance is orientated perpendicular to the front wall of the fish pass; and stand for the streamwise and cross-stream directions; the images on the bottom right show the study site on the day of the data collection looking in the upstream direction (20 August 2014).

Figure 1

Study location; the white dash-point line depicts the extent of the study area and the white dashed lines show cross sections referred to throughout the main text; the arrow pointing to the location of the fish pass entrance is orientated perpendicular to the front wall of the fish pass; and stand for the streamwise and cross-stream directions; the images on the bottom right show the study site on the day of the data collection looking in the upstream direction (20 August 2014).

Data collection

Velocity and depth data were collected using a 1,200 kHz WorkHorse RioGrande ADCP (Teledyne RDI 2007) deployed from an ARC-Boat radio control platform (HR Wallingford 2014). The data were collected along 13 cross-sectional and eight longitudinal profiles spaced approximately four meters apart (Figure 2). Each profile was repeatedly sampled to capture the mean 3D velocity patterns. Hereafter, each cross-sectional repetition is referred to as transect. Cross sections within 28 m from the weir foot as well as longitudinal profiles were sampled six times to account for larger turbulence. Cross sections further than 28 m were sampled four times. The ADCP recorded velocity and depth data at an average frequency of 1.5 Hz and with a mean boat speed of 0.42 ms–1. The vertical measurement resolution was set to 0.12 m. The discharge was assumed to be constant and equal to 7.1 m3 s–1 based on records from the nearest gauging station (Figure 1).

Figure 2

Sampling strategy and technical survey setup (not to scale).

Figure 2

Sampling strategy and technical survey setup (not to scale).

A Leica Nova MS50 (Leica Geosystems 2015) with TS capability was used to automatically track a 360 ° prism installed directly above the centre of the ADCP (Figure 2). To support the accurate implementation of the sampling strategy a software application was developed in Matlab to display the real-time boat positions against the planned cross-sectional path. This ensured that the spatial variation of the individual transects of a measurement section and the resulting loss in spatially dependent flow features (Jamieson et al. 2011) were minimised. On average, 81.0% of all ensembles were at distances below 1 m to a straight line fitted through the ensemble locations of the respective measurement section.

All data were recorded on a laptop mounted on the ARC-Boat and controlled on shore from another laptop via Windows Remote Desktop Connection (Figure 2). The TS data were transmitted wirelessly to the on-board laptop using a MOXA NPort W2150 wireless device server. Bespoke software was developed in C ++ to record the data from the MS50 and an x-IMU inertial measurement unit (x-io Technologies 2012). The ADCP data were recorded using the ADCP software WinRiver II by Teledyne RD Instruments Inc. To enable temporal synchronisation of the sensors, their data were time stamped with the Windows PC time of the logging computer (for TS and IMU) and the ADCP-internal real-time clock (for the ADCP). To keep the accumulated drift of the real-time clock below 0.05 s, the absolute time of the clock was set by the Windows PC time of the logging computer at least every 30 minutes in WinRiver II. The error of the time synchronisation depends on the recording frequencies of the sensors, which were 1.5, 5.4 and 64 Hz on average for the ADCP, TS and IMU, respectively. In total, 0.56% of all ensembles had a temporal offset to the nearest TS sample above 0.15 s. These were excluded from the analysis to limit the error in spatial data referencing.

Compass correction

Temporary compass errors were corrected by integrating the absolute heading data from the ADCP-internal fluxgate compass with relative heading data from the ADCP-external IMU. The ADCP-IMU integration algorithm detects biased ADCP compass data through cross-correlation analysis of the time synchronised compass and IMU data within a shifting window of length w and for lags of –1, 0 and 1 (Figure 3). The data within a window were considered biased if none of the three cross-correlation coefficients was positive and significant . The window was shifted by until the end of the data series was reached. Data detected as biased were then replaced by corrected heading values , which were computed as shown in Equation (1): 
formula
1
where are the ADCP compass heading data, are the IMU heading data, i is the ADCP ensemble index and d is the distance in the data series from i to the centre position of the closest previous window with unbiased ADCP compass data. If the beginning of the compass data series was biased, was computed as follows: 
formula
2
with d becoming the distance from i to the centre position of the closest subsequent window with unbiased compass data. Figure 3 illustrates the implementation of the algorithm using data collected at a river cross section with a steel hulled narrowboat moored on one of the river banks and affecting the local magnetic field.
Figure 3

ADCP-IMU integration illustrated using data collected on a cross section of the River Thames at Eynsham, Oxfordshire (UK), with a moored steel hulled vessel acting as source of magnetic interference. (a) Time synchronised data of ADCP compass and x-IMU. (b) Detection of biased ADCP compass data; the two inlay plots show the results of the cross-correlation analysis for unbiased (left plot) and biased (right plot) compass data, where stands for cross-correlation function. (c) Results of the compass error detection. (d) Results of the compass error correction.

Figure 3

ADCP-IMU integration illustrated using data collected on a cross section of the River Thames at Eynsham, Oxfordshire (UK), with a moored steel hulled vessel acting as source of magnetic interference. (a) Time synchronised data of ADCP compass and x-IMU. (b) Detection of biased ADCP compass data; the two inlay plots show the results of the cross-correlation analysis for unbiased (left plot) and biased (right plot) compass data, where stands for cross-correlation function. (c) Results of the compass error detection. (d) Results of the compass error correction.

The error in the compass heading was defined as shown in Equation (3): 
formula
3
The effects of the circular nature of degrees were accounted for (e.g. if and , ).

Spatial data referencing

The ADCP data were spatially referenced using the tracking TS. The ADCP positions were transformed to global positions in the UTM coordinate system based on reference measurements with a differentially corrected GPS (Trimble GeoExplorer 6000 GeoXH). The positioning error caused by the temporal offset between ADCP and TS data was estimated as follows: 
formula
4
where is the total BT-based boat velocity for ensembles with a valid BT signal. The TS positions were used to estimate the boat velocity for ensembles (i) affected by BT signal loss or (ii) with unrealistically high BT-based total boat velocity magnitudes (>1.4 ms–1). The error in the total TS-based boat velocity was estimated as shown in Equation (5): 
formula
5
To assess whether was directionally biased by a non-stationary channel bed, moving bed tests were performed in three locations of the study area (Figure 2) for durations of at least 400 s each.

3D water velocity estimation

3D water velocities were estimated from the along-beam velocities using the Matlab ADCP tools implementing the method by Vermeulen et al. (2014). The method combines the along-beam velocity samples located within the same 3D cell of a fitted mesh. For the study at hand, the longitudinal , lateral and vertical mesh cell dimensions were chosen to be 2.00 m, 0.40 m, and 0.15 m, respectively (Figure 4(a)). The cell size selection determines the volume for which spatially homogeneous flow is assumed, which is in contrast to conventional repeated transect ADCP data processing, where the minimum size of this volume is fixed and determined by the ADCP beam spread and measurement depth (Figure 4(b)). The sensitivity of the 3D velocity estimates to the mesh cell size was quantified by comparing the average number of along-beam velocity samples per cell and the average of the total water velocity magnitudes of the mesh cells of a cross section for 36 different mesh cell sizes. The effect of the number of transects taken along a cross section on was quantified by calculating the mean change in caused by including another transect.

Figure 4

(a) Plane view of the 3D velocity projection mesh on the ADCP track (shown for section b in Figure 1); and stand for the longitudinal and the lateral mesh cell dimensions. (b) Volume for which spatially homogeneous flow is assumed in the processing method by Vermeulen et al. (2014) with the cell dimensions used in this study and the minimum W in conventional processing of data from a 1,200 kHz WorkHorse RioGrande ADCP with a vertical measurement resolution of 0.12 m; an instrument draft of 0.11 m was assumed for both methods.

Figure 4

(a) Plane view of the 3D velocity projection mesh on the ADCP track (shown for section b in Figure 1); and stand for the longitudinal and the lateral mesh cell dimensions. (b) Volume for which spatially homogeneous flow is assumed in the processing method by Vermeulen et al. (2014) with the cell dimensions used in this study and the minimum W in conventional processing of data from a 1,200 kHz WorkHorse RioGrande ADCP with a vertical measurement resolution of 0.12 m; an instrument draft of 0.11 m was assumed for both methods.

Depths and water velocities in unmeasured locations of the study area were estimated through ordinary kriging as suggested by Jamieson et al. (2011, 2013) and Rennie & Church (2010), using a 0.25 × 0.25 m2 grid for depths and a 0.50 × 0.50 × 0.15 m3 grid for velocities. The error in the water velocities introduced by the spatial interpolation was quantified through cross-validation (Webster & Oliver 2007) and defined as follows: 
formula
6
where and are the water velocities obtained through the method by Vermeulen et al. (2014) and predicted through kriging in the same location, and , which are the velocity directions in the stream coordinate system. The cross-validation was carried out for a sample of 1,000 points randomly selected out of the 10,371 measurements.

Effect of data correction techniques

To assess the effects of the suggested techniques, namely (i) IMU-based compass correction, (ii) TS-based recovery of ensembles with BT loss and (iii) the water velocity estimation by Vermeulen et al. (2014), the 3D distribution of water velocities in the case study reach was quantified with and without the application of each of these techniques. As counterpart to the 3D water velocity estimation by Vermeulen et al. (2014), the 3D velocities were resolved using the conventional repeated transect processing method as implemented in the Matlab application VMT (Parsons et al. 2012). For each processing variant, the total water velocity magnitudes and the absolute area-weighted vorticity measure (Crowder & Diplas 2002) were computed from the 3D flow distribution obtained after kriging interpolation and compared through descriptive statistics. Wilcoxon signed-rank tests were used to test whether the differences in caused by each of the techniques were statistically significant. The vorticity measure was computed as suggested by Shields & Rigby (2005): 
formula
7
 
formula
8
where , and are the changes in the streamwise, cross-stream and vertical water velocities in the streamwise, cross-stream and vertical directions , and .

These hydrodynamic measures were chosen because they reflect the absolute water velocity magnitudes and the strength and abundance of spatial velocity gradients , both of which are known to affect fish swimming behaviour near fish passes (e.g. Larinier 2002; Enders et al. 2009). To explore spatial variations in the effects of the techniques, the analysis was carried out for the cross sections b, d and f shown in Figure 1 and for the horizontal planes at depths of 0.35 and 1.10 m.

RESULTS

Compass correction

The ADCP-IMU integration algorithm corrected 836 ensembles (4.8% of the total number of ensembles) potentially affected by compass errors. Table 1 and Figure 5 show the statistical and spatial distribution of the detected errors. The differences in obtained with and without compass correction (all other processing steps held constant) were significant in statistical terms , but subtle in physical terms for all cross sections and horizontal planes analysed (Table 2).

Table 1

Errors in ADCP compass heading , Total Station based positioning , boat velocity estimation and water velocity error introduced by kriging

  Mean Median Standard deviation Sample size 
Compass correction 
 (deg) 2.59 1.68 3.47 836 
Spatial data referencing 
 (m) 0.021 0.016 0.018 13,543 
 (ms–1–0.001 –0.001 0.075 
 (ms–10.047 0.028 0.058 
Kriging cross validation 
 (ms–1–0.001 –0.001 0.075 1,000 
 (ms–10.057 0.045 0.049 
 (ms–1–0.002 0.002 0.080 
 (ms–10.058 0.044 0.055 
 (ms–10.000 0.000 0.022 
 (ms–10.016 0.013 0.015 
  Mean Median Standard deviation Sample size 
Compass correction 
 (deg) 2.59 1.68 3.47 836 
Spatial data referencing 
 (m) 0.021 0.016 0.018 13,543 
 (ms–1–0.001 –0.001 0.075 
 (ms–10.047 0.028 0.058 
Kriging cross validation 
 (ms–1–0.001 –0.001 0.075 1,000 
 (ms–10.057 0.045 0.049 
 (ms–1–0.002 0.002 0.080 
 (ms–10.058 0.044 0.055 
 (ms–10.000 0.000 0.022 
 (ms–10.016 0.013 0.015 
Table 2

Effects of the suggested ADCP data correction techniques on the water velocity magnitude and the area-weighted vorticity ; p-values <0.05 show statistically significant effects of the respective techniques on

Section/plane  (ms–1)
 
ΓABSATOT (s–1
Min Max Mean Standard deviation Sample size p-value 
All corrections applied 
 Cross 0.012 0.917 0.130 0.116 1,133 – 0.061 
0.019 0.487 0.285 0.107 526 – 0.052 
0.006 0.543 0.203 0.159 433 – 0.034 
 Horizontal at depth (m) 0.35 0.003 0.938 0.217 0.149 8,104 – 0.066 
1.10 0.003 0.598 0.195 0.129 6,030 – 0.073 
No compass correction (all other corrections applied) 
 Cross 0.012 0.870 0.130 0.114 1,133 0.000 0.061 
0.020 0.488 0.287 0.107 526 0.000 0.051 
0.006 0.543 0.200 0.160 433 0.002 0.034 
 Horizontal at depth (m) 0.35 0.002 0.904 0.217 0.150 8,104 0.000 0.066 
1.10 0.003 0.611 0.195 0.129 6,030 0.000 0.073 
No BT replacement (all other corrections applied) 
 Cross 0.013 0.799 0.131 0.111 1,133 0.428 0.061 
0.018 0.523 0.291 0.113 526 0.007 0.068 
0.004 0.499 0.198 0.156 433 0.000 0.032 
 Horizontal depth (m) 0.35 0.002 0.881 0.211 0.144 8,104 0.000 0.066 
1.10 0.007 0.592 0.193 0.126 6,030 0.243 0.073 
Conventional 3D velocity estimation instead of Vermeulen et al. (2014) (all other corrections applied) 
 Cross 0.003 0.607 0.119 0.093 1,133 0.000 0.057 
0.065 0.574 0.289 0.112 526 0.050 0.057 
0.008 0.444 0.203 0.156 433 0.808 0.027 
 Horizontal at depth (m) 0.35 0.003 0.864 0.219 0.146 8,104 0.000 0.061 
1.10 0.003 0.825 0.200 0.135 6,030 0.000 0.067 
No corrections applied 
 Cross 0.009 0.716 0.122 0.097 1,133 0.001 0.061 
0.027 0.584 0.301 0.109 526 0.000 0.079 
0.010 0.466 0.204 0.159 433 0.001 0.029 
 Horizontal at depth (m) 0.35 0.003 0.868 0.219 0.148 8,104 0.019 0.067 
1.10 0.002 0.735 0.199 0.137 6,030 0.000 0.072 
Section/plane  (ms–1)
 
ΓABSATOT (s–1
Min Max Mean Standard deviation Sample size p-value 
All corrections applied 
 Cross 0.012 0.917 0.130 0.116 1,133 – 0.061 
0.019 0.487 0.285 0.107 526 – 0.052 
0.006 0.543 0.203 0.159 433 – 0.034 
 Horizontal at depth (m) 0.35 0.003 0.938 0.217 0.149 8,104 – 0.066 
1.10 0.003 0.598 0.195 0.129 6,030 – 0.073 
No compass correction (all other corrections applied) 
 Cross 0.012 0.870 0.130 0.114 1,133 0.000 0.061 
0.020 0.488 0.287 0.107 526 0.000 0.051 
0.006 0.543 0.200 0.160 433 0.002 0.034 
 Horizontal at depth (m) 0.35 0.002 0.904 0.217 0.150 8,104 0.000 0.066 
1.10 0.003 0.611 0.195 0.129 6,030 0.000 0.073 
No BT replacement (all other corrections applied) 
 Cross 0.013 0.799 0.131 0.111 1,133 0.428 0.061 
0.018 0.523 0.291 0.113 526 0.007 0.068 
0.004 0.499 0.198 0.156 433 0.000 0.032 
 Horizontal depth (m) 0.35 0.002 0.881 0.211 0.144 8,104 0.000 0.066 
1.10 0.007 0.592 0.193 0.126 6,030 0.243 0.073 
Conventional 3D velocity estimation instead of Vermeulen et al. (2014) (all other corrections applied) 
 Cross 0.003 0.607 0.119 0.093 1,133 0.000 0.057 
0.065 0.574 0.289 0.112 526 0.050 0.057 
0.008 0.444 0.203 0.156 433 0.808 0.027 
 Horizontal at depth (m) 0.35 0.003 0.864 0.219 0.146 8,104 0.000 0.061 
1.10 0.003 0.825 0.200 0.135 6,030 0.000 0.067 
No corrections applied 
 Cross 0.009 0.716 0.122 0.097 1,133 0.001 0.061 
0.027 0.584 0.301 0.109 526 0.000 0.079 
0.010 0.466 0.204 0.159 433 0.001 0.029 
 Horizontal at depth (m) 0.35 0.003 0.868 0.219 0.148 8,104 0.019 0.067 
1.10 0.002 0.735 0.199 0.137 6,030 0.000 0.072 
Figure 5

(a) Spatial distribution and magnitude of the detected absolute ADCP compass error ; the dash-point line denotes the extent of the study area. (b) Statistical distribution of (n = 836).

Figure 5

(a) Spatial distribution and magnitude of the detected absolute ADCP compass error ; the dash-point line denotes the extent of the study area. (b) Statistical distribution of (n = 836).

Spatial data referencing

The temporal offset between the ADCP and the TS data translated to an average positioning error of 0.021 m and the TS-based and BT-based boat velocities showed a mean difference of 0.047 ms–1 (Table 1 and Figure 6). None of the three moving bed tests suggested a non-stationary channel bed based on the criterion provided in Mueller & Wagner (2009) for stationary moving bed tests with external boat position reference. In total, 22.3% of the ensembles (3,880) had invalid BT signals. Ensembles collected in very shallow areas near the edges of the study area as well as those located closer to the weir were more prone to loss of BT (Figure 7). The TS-based recovery of ensembles with BT loss led to statistically significant changes in only for three of the five studied sections (Table 2). For cross section d, the uncorrected loss of BT led to an increase in the area-weighted vorticity by more than 30%.

Figure 6

(a) Error in spatial data referencing caused by the temporal offset between ADCP and Total Station data (n = 13,543). (b) Absolute error in the Total Station based boat velocity estimates (n = 13,543).

Figure 6

(a) Error in spatial data referencing caused by the temporal offset between ADCP and Total Station data (n = 13,543). (b) Absolute error in the Total Station based boat velocity estimates (n = 13,543).

Figure 7

Spatial distribution of ensembles with invalid BT signals – the dash-point line denotes the extent of the study area.

Figure 7

Spatial distribution of ensembles with invalid BT signals – the dash-point line denotes the extent of the study area.

3D water velocity estimation

Figure 8 shows the results of the sensitivity analyses for the water velocity estimation method by Vermeulen et al. (2014). The total number of cells for which 3D velocities could be estimated and the average number of along-beam velocity samples per cell were highly sensitive to changes in the mesh cell size dimensions (Figure 8(a) and 8(b)). showed little sensitivity to the lateral and vertical cell dimensions, but strongly decreased with an increase in the longitudinal dimension up to around 1.5 m (Figure 8(c)). The change in caused by including more transects approached zero as the total number of transects increased (Figure 8(d)). For the tested section (section b in Figure 1) the effect of including the 6th and 7th transect were below 0.03 ms–1, respectively. Similar sensitivities were found for the other measurement sections.

Figure 8

Sensitivity analysis for the 3D velocity estimation by Vermeulen et al. (2014). (a), (b) Sensitivity of the total number of cells with 3D velocity estimates and the average number of along-beam velocity samples per cell to the mesh cell dimensions (shown for the data of all sections processed). (c) Sensitivity of the estimated average water velocity magnitude in the mesh cells of a section to the mesh cell dimensions (shown for the data of section b in Figure 1). (d) Change in the estimated average water velocity magnitude caused by including another transect, calculated for mesh cell dimensions of , and (shown for the data of section b in Figure 1).

Figure 8

Sensitivity analysis for the 3D velocity estimation by Vermeulen et al. (2014). (a), (b) Sensitivity of the total number of cells with 3D velocity estimates and the average number of along-beam velocity samples per cell to the mesh cell dimensions (shown for the data of all sections processed). (c) Sensitivity of the estimated average water velocity magnitude in the mesh cells of a section to the mesh cell dimensions (shown for the data of section b in Figure 1). (d) Change in the estimated average water velocity magnitude caused by including another transect, calculated for mesh cell dimensions of , and (shown for the data of section b in Figure 1).

The results of the cross-validation for the spatial water velocity interpolation are shown in Table 1 and Figure 9. The use of the 3D velocity estimation by Vermeulen et al. (2014) instead of the conventional repeated transect processing method led to statistically significant changes in for three of the five sections analysed (Table 2). Moreover, using the method by Vermeulen et al. (2014) highlighted a decrease in the area-weighted absolute vorticity from cross sections b–d by 15%, whereas the conventional procedure resulted in the same vorticity estimates for both cross sections.

Figure 9

Cross-validation of spatial water velocity interpolation through ordinary kriging; , , and r stand for streamwise, cross-stream and vertical and for linear correlation coefficient, respectively. The diagonal line in each subplot denotes the slope of 1.

Figure 9

Cross-validation of spatial water velocity interpolation through ordinary kriging; , , and r stand for streamwise, cross-stream and vertical and for linear correlation coefficient, respectively. The diagonal line in each subplot denotes the slope of 1.

DISCUSSION

Performance of heading sensor integration

At the case study site, only few ensembles were affected by compass errors. The largest errors (up to 35°) occurred close to the left river bank and near the right bank immediately downstream of the fish pass (Figure 5). It is not straightforward to attribute the detected compass errors to distinct error sources. The presence of steel sheet pilings along the entire left bank suggests that the errors there were caused by magnetic interference. Compass errors detected further away from the banks were considerably smaller in magnitude and errors >3° typically persisted over only a few ensembles. These errors might have been caused by instrument dynamics as observed by Gaeuman & Jacobson (2005), who reported compass errors up to 9° caused by manually rattling the ADCP mount. To the authors’ knowledge, this is the first study that quantifies the magnitude of ADCP compass errors in the field. Further use of the suggested ADCP-IMU integration will provide additional evidence on the significance of this error in ADCP-based flow mapping applications. The only prerequisite for using the suggested algorithm is that the compass errors are temporary rather than persistent throughout the survey. Unless the ADCP vessel itself causes permanent magnetic interference (e.g. steel hulled vessels), this assumption will hold for many sites, where significant magnetic interference is likely to occur only in the immediate vicinity of modified river banks or engineering structures. The sensor integration approach can be superior to the replacement of all ADCP compass data with those of another absolute heading source such as a GPS compass (Zhao et al. 2014) because: (i) it does not involve problems of heading misalignment between the ADCP and the external heading source; and (ii) it does not depend on environmental factors such as clear sky view to GPS satellites.

Performance of Total Station based ADCP positioning

This study illustrated that tracking TS can be integrated with ADCPs using WIFI and bespoke data logging software to achieve cm-level 3D positioning accuracy independent from navigation satellites. The major limitation of tracking TS in ADCP applications is the requirement of line of sight to the tracked reflector. Permanent loss of line of sight requires the operator to regain lock to the prism. In this study, this was complicated by permanent boat motion and increased the overall time for data collection. Given the high precision of tracking TS and the relatively low measurement distances to the prism (maximum of 95.37 m), it can be assumed that errors in time synchronisation contributed by far the most to the total error in spatial ADCP data referencing. ADCPs commonly used in river research are limited in their capabilities of low-latency external triggering, so that the integration of the TS relies on temporal alignment of ADCP and TS data during post-processing, which is not an optimal solution. Time synchronisation errors may also largely explain the discrepancy between TS and BT in measuring boat velocities. The mean difference was larger than the 0.031 ms–1 reported by Rennie & Rainville (2006) for Real Time Kinematic (RTK) GPS with 10 Hz recording frequency.

Performance of 3D velocity estimation by Vermeulen et al. (2014) 

The method by Vermeulen et al. (2014) allows the user to determine the spatial resolution of the estimated 3D velocities by setting the mesh cell dimensions. In the complex flow conditions near flow obstacles and in the context of fish ecology, small cell sizes are desirable because: (i) they increase the reliability of ADCP measurements by decreasing the volume for which spatially homogeneous flow is assumed; and (ii) they provide velocities at resolutions closer to ecologically meaningful spatial scales (Shields & Rigby 2005). The sensitivity analysis in this study showed that the estimated velocity magnitudes can be highly sensitive to the selected mesh cell dimensions, so that a further decrease in the cell size relies on a sufficiently large number of along-beam velocity samples per cell. This might be achieved by further decreasing the boat track variability, which, in this study, could have potentially led to an increase in the number of along-beam samples per mesh cell of approximately one third (Figure 8(b)). However, the distinct surface flow patterns near the weir made it difficult to follow straight transect lines with the radio control boat, but relatively easy to follow previous (curved) boat tracks. The current implementation of the 3D velocity derivation by Vermeulen et al. (2014) supports the estimation of a straight mesh. Future research should look into the estimation of a non-linear mesh to enable a further increase in the spatial resolution of the estimated 3D velocities and raise the usefulness of ADCPs in fish-ecological studies.

A larger number of along-beam velocity samples per cell could also be achieved by increasing the number of repeated transects per section or the measurement duration per transect. There is little guidance to a priori determine the number of repeated ADCP transects required to capture the cross-sectional distribution of temporally averaged water velocities. Petrie et al. (2013) found four transects to be suitable to identify general trends in the streamwise velocity component but insufficient to describe the temporally averaged cross-stream velocities in bends of the lower Roanoke River (USA). However, the findings by Vermeulen et al. (2014) indicate that their data processing approach requires considerably less repeated transects to obtain a robust estimate of the mean velocity vector than the conventional processing approach. Although this finding remains yet to be confirmed by comparison to reference measurements, e.g. from a fixed vessel, it would make the technique by Vermeulen et al. (2014) particularly suitable for studies mapping the spatial flow distribution of river reaches. In practice, such studies are often carried out under time constraint so that an increase in the number of transects per section comes at the cost of a decrease in the spatial density of the sampled sections. The latter can increase the error introduced by spatial velocity interpolation, particularly in spatially complex flow conditions (e.g. Jamieson et al. 2011).

In this study, the 3D velocity components in unmeasured locations were predicted by applying kriging separately to the streamwise, cross-stream and vertical velocity components, the direction of which was defined based on channel geometry (the weir orientation). The definition of the stream coordinate system has been shown to significantly affect the interpretation of velocity components, particularly the cross-stream component (Lane et al. 2000; Petrie et al. 2013). While not investigated here, it may also impact the spatial correlation of the respective velocity components identified in kriging and the resulting interpolation.

3D flow and bathymetry at the study site

Figures 1012 show the bathymetry and 3D flow downstream of Shrewsbury Weir obtained using all of the ADCP data correction techniques suggested. The bathymetric map shows a large scour hole (≈4 m deep) near the weir foot towards the left river bank, coinciding with the area of the fastest water flow from the weir ( up to 0.9 ms–1). This jet may act as a competing flow that guides fish away from the pass entrance potentially leading to severe delays in upstream migration, a phenomenon observed previously in tailraces of hydroelectric plants (e.g. Scruton et al. 2007). On the measurement day, the discharge was sufficiently low for this main jet to be diverted towards the centre of the channel as it approached an area of increased material accumulation and bed elevation approximately 20 m downstream of the scour hole centre. Figure 11 shows the magnitude and orientation of the fish pass attraction flow on the right river bank. Figure 12(d) reveals a large vortex close to the fish pass, presumably induced by the plunging flow issued from the fish pass entrance. The jet from the pass entrance developed to a more uniformly directed attraction flow further downstream (Figure 12(f)) where it joined the water jet from the left bank to form a 15 m wide field of water velocity with similar magnitudes and directions.

Figure 10

Bed elevation downstream of Shrewsbury weir; the elevation is referenced to the mean sea level as obtained from GPS; the grey arrow points to the location of the fish pass entrance and is orientated perpendicular to the front wall of the fish pass.

Figure 10

Bed elevation downstream of Shrewsbury weir; the elevation is referenced to the mean sea level as obtained from GPS; the grey arrow points to the location of the fish pass entrance and is orientated perpendicular to the front wall of the fish pass.

Figure 11

Spatial water velocity distribution downstream of Shrewsbury Weir. (a) Magnitude of depth-averaged velocities . (b) Streamwise and cross-stream velocities (depicted as arrows) and vertical velocities at an elevation of 44.21 m above the sea level, corresponding to a distance of 0.35 m below the mean water surface elevation of the study area; the grey arrow in both plots (a) and (b) points to the location of the fish pass entrance and is orientated perpendicular to the front wall of the fish pass.

Figure 11

Spatial water velocity distribution downstream of Shrewsbury Weir. (a) Magnitude of depth-averaged velocities . (b) Streamwise and cross-stream velocities (depicted as arrows) and vertical velocities at an elevation of 44.21 m above the sea level, corresponding to a distance of 0.35 m below the mean water surface elevation of the study area; the grey arrow in both plots (a) and (b) points to the location of the fish pass entrance and is orientated perpendicular to the front wall of the fish pass.

Figure 12

Spatial water velocity distribution at selected cross sections downstream of Shrewsbury Weir; the top right plot shows the location of the cross sections on a 3D bathymetric display (see also Figure 1) and plots (a) to (f) show the streamwise velocities as well as the cross-stream and vertical velocities (depicted as arrows) of these sections in detail.

Figure 12

Spatial water velocity distribution at selected cross sections downstream of Shrewsbury Weir; the top right plot shows the location of the cross sections on a 3D bathymetric display (see also Figure 1) and plots (a) to (f) show the streamwise velocities as well as the cross-stream and vertical velocities (depicted as arrows) of these sections in detail.

Overall, the integration of the suggested ADCP data correction techniques had a statistically significant effect on the estimated velocity magnitudes and, for some cross sections, strongly affected the estimated area-weighted vorticity (Table 2). At the particular case study site in Shrewsbury, the correction of errors in the ADCP-internal compass was the only measure with a statistically significant effect on the total velocity estimates of all tested cross sections and horizontal depth planes. The TS-based recovery of ensembles affected by BT loss and the methodologies implemented to reduce bias from spatial flow heterogeneity, on the other hand, resulted in larger changes in the estimated area-weighted vorticity. Further studies are required to (i) determine the effects of the suggested technical solutions at other river sites and (ii) assess the eco-hydrological implications of the statistically significant differences they cause in near-pass hydrodynamic quantifications.

CONCLUSIONS

The integration of external sensors and sophisticated data post-processing were shown to overcome known limitations to ADCP-based 3D flow quantifications in the complex flow environments encountered near river engineering structures forming flow obstacles. The ADCP-IMU integration introduced in this paper can be useful in any ADCP application at sites potentially affected by magnetic interference and improves the current understanding of the relevance of compass errors in ADCP measurements. The suggested approach to flow quantification near fish pass entrances can be used complementary to fish tagging and tracking studies and thereby improve the current understanding of fish passage and fish response to near-pass hydrodynamics.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the financial support of the Environment Agency, particularly Ros Wright, and the Engineering and Physical Sciences Research Council (EPSRC) through which this work was undertaken. They are also grateful to Rob Davies, Gary Bywater and Chris Bainger from the Environment Agency and Simon Stranks from Cranfield University for their support during data collection as well as Shane O'Regan from Leica Geosystems for his technical support with the MS50 MultiStation. The authors thank two anonymous reviewers for their constructive comments that helped improving the manuscript.

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