11 There has been an increasing interest in the use of Acoustic Doppler Current Profilers 12 (ADCPs) to characterise the hydraulic conditions near river engineering structures such as 13 dams, fish passes and groins, as part of ecological and hydromorphological assessments. 14 However, such ADCP applications can be limited by compass errors, obstructed view to 15 navigation satellites, frequent loss of bottom tracking and spatially heterogeneous flow 16 leading to erroneous water velocity

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... ements. This study addresses these limitations by 17 (i) developing a heading sensor integration algorithm that corrects compass errors from 18 magnetic interference, (ii) testing a Total Station based technique for spatial ADCP data 19 referencing and (iii) evaluating a recently proposed data processing technique that reduces 20 bias from spatial flow heterogeneity. The integration of these techniques on a radio control 21 ADCP platform is illustrated downstream of a weir with fish pass on the River Severn, UK. 22 The results show that each of the techniques can have a statistically significant effect on the 23 estimated total water velocities and can strongly affect measures of vorticity. The obtained 3-24 dimensional flow maps are suitable to describe the magnitude and orientation of the fish pass 25 attraction flow in relation to competing flows and to highlight areas of increased vorticity. 29 30 3 31 Acoustic Doppler Current Profilers (ADCPs) have evolved as a useful tool to characterise the 32 flow distribution of river reaches (e.g. Dinehart and Burau, 2005; Rennie and Church, 2010). 33 A number of studies (Gaeuman and Jacobson, 2005; Jamieson et al., 2011, 2013; Johnson et 34 al., 2009) have illustrated the potential of ADCPs to quantify the flow field near river 35 engineering structures as part of ecological and hydromorphological assessments. These 36 studies have highlighted a range of ADCP data quality issues, including: (i) errors in the 37 ADCP-internal compass data caused by changes in the local magnetic field (e.g. from steel 38 reinforcements), (ii) limited line of sight to navigation satellites when using ADCPs in 39 conjunction with Global Navigation Satellite Systems (GNSS), (iii) discontinuous water 40 velocity measurements caused by the loss of the ADCP Bottom Tracking (BT) signal and (iv) 41 lack of accurate 3-dimensional (3D) water velocity measurements in spatially heterogeneous 42 flows. These limitations reduce the applicability of ADCPs to characterise the hydrodynamics 43 near engineered flow obstacles. For example, Jamieson et al. (2013) found spatial ADCP data 44 referencing based on the Global Positioning System (GPS) to be insufficiently reliable when 45 monitoring the hydraulics induced by stream barbs on a river in a heavily wooded and deep 46 valley. Jamieson et al. (2011) experienced BT loss near a wing dike and attributed this 47 problem to high water turbidity and turbulence and Johnson et al. (2009) found the ADCP 48 data collected near surface flow outlets at dams to be biased because of large spatial flow 49 heterogeneity. 50 51 This study introduces novel techniques of ADCP data collection and assesses a recently 52 developed method of data post-processing to address these data quality issues. The proposed 53 methods are integrated on a radio control ADCP platform and illustrated by quantifying the 54 3D distribution of water velocities immediately downstream of a weir with fish pass. The 55 installation of fish passes at engineering structures designed to regulate discharge has been a 56 4 wide-spread approach to restore the longitudinal connectivity of freshwater ecosystems 57 (Katopodis and Williams, 2012). Policy efforts towards restoring the ecological integrity of 58 rivers (EC, 2007, 2000) and the increasing evidence on the low efficiencies of existing fish 59 passes (Bunt et al., 2012; Noonan et al., 2012) have led to a strong need for more post-60 construction assessment to gain a better understanding of the various factors determining the 61 biological effectiveness of fish passes. The hydrodynamic conditions near fish pass entrances 62 have been recognised as a key factor influencing the ability of fish to locate and enter these 63 facilities (Lindberg et al., 2013; Piper et al., 2012; Williams et al., 2012). Yet, there is a lack 64 of methods for the spatially continuous in-field quantification of near-pass hydrodynamics. To 65 the authors' knowledge, this paper presents the first in-field solution to rapidly quantify the 66 spatially continuous distribution of water velocities near fish pass entrances using an ADCP. 67 68 ADCPs are mono-static sensors that measure water velocities and depths by transmitting and 69 receiving acoustic pulses with three to four transducers along beams spread at an angle of 70 usually 20 to 30 degrees relative to the vertical direction. The arrangement allows for the use 71 of a single acoustic signal to obtain measurements in multiple depths along the vertical water 72 column (termed 'ensemble'; Mueller and Wagner, 2009). The water velocities measured in 73 the directions parallel to each acoustic beam are processed to resolve a 3D vector describing 74 the flow in the x, y and z directions of a coordinate system aligned with the instrument 75 (Mueller and Wagner, 2009). ADCPs have an internal fluxgate compass to determine the 76 transformation angle (β) required to reference these velocities to the local ambient magnetic 77 field (magnetic north) and, after correcting for the site-specific magnetic declination, to true 78 north. When the boat velocity is determined from ADCP-external sensors (e.g. because of BT 79 loss), the effect of moderate errors in β on the velocity components referenced to north can be 80 large as it depends on the magnitude and direction of the actual water velocity (V) and the 81 ADCP boat velocity (B). For a ratio B V ⁄ of 1, an error in of 10°can lead to a 17% error in 82 5 the measured water velocity magnitude and an error of up to 20°in the water velocity 83 direction (computed based on Gaeuman and Jacobson, 2005). A potential practical and low-84 cost solution to this limitation is the correction of ADCP compass errors with an inertial 85 measurement unit (IMU) consisting of micro-electromechanical gyroscopes and 86 accelerometers. Some IMUs fuse the inertial sensor data to provide orientation measurements 87 relative to the direction of gravity, which are constrained neither in motion nor to any specific 88 environment or location (Madgwick et al., 2011). 89 90 ADCP-measured water velocities have to be corrected for boat velocities, which are typically 91 determined from BT (Gordon, 1996). Common ADCP software flags ensembles without a 92 valid BT signal as bad, indicating that the obtained measurements are unusable. These 93 measurements can be recovered through the integration of external positioning systems such 94 as GPS, based on which the boat velocity is estimated. However, fish passes and other 95 engineered river structures are frequently installed close to river banks and these areas are 96 particularly affected by degradation in GPS accuracy (Rennie and Rainville, 2006). The 97 problem may increase in small rivers, where the sky view can be obstructed over a large 98 proportion of the water surface. This limitation can be addressed through the integration of 99 ADCPs with alternative, local positioning systems such as tracking Total Stations (TS), which 100 achieve 3D positioning precision of sub-cm level without relying on navigation satellites 101 (Kirschner and Stempfhuber, 2008). 102 103 Repeated ADCP measurements are necessary to capture the temporally averaged flow field in 104 rivers (Muste et al., 2004). The conventional method of repeated ADCP measurements 105 involves the averaging of multiple 3D water velocity vectors, each of which is resolved 106 independently from the three to four along-beam velocities measured at the same time. This 107 method assumes that the water velocities in the areas insonified by the beams are spatially 108 6 homogeneous. The diameter of a circle enclosing the four beam footprints increases at a ratio 109 of 0.76m per 1m increase in depth (calculated based on Rennie et al., 2002, for a 1200kHz 110 WorkHorse RioGrande ADCP). Nystrom et al. (2002) argued that the distance between the 111 beam footprints is comparable to the size of large-scale turbulence, so that the assumption of 112 homogeneous flow can easily be violated in spatially complex hydraulic conditions. The data 113 post-processing method suggested by Vermeulen et al. (2014) can avoid this bias by reducing 114 the velocity sampling volume assumed to be homogeneous. The method uses a least squares 115 procedure to estimate the 3D velocity vector that fits best to a set of along-beam velocities 116 measured in similar locations during repeated cross-sectional measurements. However, the 117 approach has not been tested in ADCP applications near flow obstacles. 118 119 129 130 METHODS 131 Case study site 132 The study site was a 55m reach immediately downstream of Shrewsbury Weir on the River 133 Severn (Figure 1). The River Severn is the longest river in the United Kingdom (UK) and one 134 7 of its main salmon rivers (NASCO, 2009). It flows from Plynlimon, Ceredigion, in the Welsh 135 mountains to Gloucestershire, where it discharges into the British Channel. A total of 41 136 obstructions, with nine of them being considered significant barriers to upstream fish 430 and (ii) assess the eco-hydrological implications of the statistically significant differences 431 they cause in near-pass hydrodynamic quantifications. 432 433 CONCLUSIONS 434 The integration of external sensors and sophisticated data post-processing were shown to 435 overcome known limitations to ADCP-based 3D flow quantifications in the complex flow 436 environments encountered near river engineering structures forming flow obstacles. The 437 ADCP-IMU integration introduced in this paper can be useful in any ADCP application at 438 sites potentially affected by magnetic interference and improves the current understanding of 439 the relevance of compass errors in ADCP measurements. The suggested approach to flow 440 19 quantification near fish pass entrances can be used complementary to fish tagging and 441 tracking studies and thereby improve the current understanding of fish passage and fish 442 response to near-pass hydrodynamics. 443 444 ACKNOWLEDGEMENTS 445 The authors gratefully acknowledge the financial support of the Environment Agency, 446 particularly Ros Wright, and the Engineering and Physical Sciences Research Council 447 (EPSRC) through which this work was undertaken. They are also grateful to Rob Davies, 448