Sheared turbulent flows and wake dynamics of an idled floating tidal turbine

Domain: earth_science
Published: 2024-09-20
Editorial outcome: accept

Abstract
Ocean energy extraction is on the rise. While tides are the most predictable amongst marine renewable resources, turbulent and complex flows still challenge reliable tidal stream energy extraction and there is also uncertainty in how devices change the natural environment. To ensure the long-term integrity of emergent floating tidal turbine technologies, advances in field measurements are required to capture multiscale, real-world flow interactions. Here we use aerial drones and acoustic profiling transects to quantify the site- and scale-dependent complexities of actual turbulent flows around an idled, utility-scale floating tidal turbine (20 m rotor diameter, D). The combined spatial resolution of our baseline measurements is sufficiently high to quantify sheared, turbulent inflow conditions (reversed shear profiles, turbulence intensity >20%, and turbulence length scales >0.4D). We also detect downstream velocity deficits (approaching 20% at 4D) and trace the far-wake propagation using acoustic backscattering techniques in excess of 30D. Addressing the energy-environment nexus, our oceanographic lens on flow characterisation will help to validate multiscale flow physics around offshore energy platforms that have thus far only been simulated.

Introduction
Ocean energy has great potential to help decarbonise global energy demands, with tides offering the most predictable marine renewable resource1. Tidal stream energy generation, which mostly uses underwater horizontal axis turbines conceptually similar to wind turbines, is estimated to meet up to 11% of the UK’s current annual electricity demand (34 TWh/year)2. However, tidal stream sites are highly energetic environments, where high loading from fast (current speeds >2 ms−1) and turbulent flows (turbulence intensities >10%) challenge reliable energy extraction, also requiring an increase in the conversion efficiency3,4. While average tidal flow velocities are largely predictable, more constrained flow passages that provide the densest tidal energy resource2, such as tidal channels, basins or headlands generate highly dynamic flow environments4. Where tidal currents accelerate in areas of complex topography and bathymetry, such sites can experience strong temporal and spatial variability in mean velocities and associated turbulence. For instance, fast currents past headlands and islands can generate back-eddies (local flow reversals) and leeward wakes bounded by strong horizontal shear (cross-stream gradient in streamwise velocity, or ‘shear layer’) hereafter referred to as 'shear lines'5–7. Vortical structures, upwelling (surface divergence), and associated downwelling are characteristic of shear lines7,8 and their often kilometre-scale streamwise extent and cross-stream location will change with underlying flow velocities, as well as wind and wave action. On finer scales, one cannot assume that vertical profiles of velocity are either spatially uniform or follow a logarithmic profile in the vertical distribution (where current velocities increase towards the surface)9–11, with highly variable vertical shear (vertical gradient in horizontal velocity). Further, the ubiquitous presence of macroscale turbulent coherent structures, known as 'boils' when their turbulent signature impinges on the free-surface, will result in high-velocity fluctuations through the water column12–15. Together, the interaction of shear (vertical and horizontal) and intermittent, yet highly dynamic turbulent motion from coherent structures can result in unsteady forcings to tidal turbine blades, rotors, support structures and foundations16,17, compromising the structural integrity and, therefore, the reliability of tidal stream turbines18–23. While turbulence intensity (TI), which relates the root-mean-square of velocity fluctuations to the mean velocity, is often considered as the key variable affecting turbine performance, TI can be a poor predictor of water column turbulence in the presence of coherent structures15. Instead, turbulence length scale, defined as the size of the largest energy-containing turbulent eddies, may have the greatest impact on turbines18,24.

Thus, detailed characterisation of highly complex tidal flows is critical to inform suitable device placing to ensure turbine loading is maintained within design limits. However, the collection of field measurements is not trivial given the diverse designs of tidal stream energy devices, especially with the emergence of floating turbine technologies. Unlike more conventional tidal turbines, which are generally fixed to the seabed and fully submerged, surface-floating devices are anchored to the seabed using mooring systems. Designed to make use of stronger current magnitudes near the sea surface, they also allow easier access for installation, operation and maintenance. Generally wider in the cross-stream dimension, some floating platforms have several horizontally-spaced rotors (2–4) with a total cross-stream extent in excess of 30 m (e.g. the PLAT-I by Sustainable Marine Power or the O2 by Orbital Marine Power). Being secured by dynamic mooring systems, floating turbines experience platform motion, excursion from their centre point during the tidal cycle in excess of tens of metres, and changes in turbine heading. Associated inflow measurements for power performance assessments, therefore, cannot be achieved using conventional acoustic Doppler current profiler (ADCP) point measurements (e.g. from a seabed-mounted ADCP) if flows vary in the cross-stream.

Apart from the inflow, it is important to assess the wake dynamics of floating platforms to quantify hydrodynamic effects on the environment (i.e. their ‘physical footprint’ on the environment including infrastructure-induced turbulence25), energy dissipation26, as well as to inform array planning27. As the flow interacts with the rotors and the floating platform itself, far-wake effects can be observed downstream, with velocity deficits and turbulence fluctuations influencing the performance of downstream turbines and energy dissipation due to wake mixing4,25,27–30. It is still unknown how wakes expand, dissipate and recover in complex natural flows, and how combined effects of free-stream turbulence, shear and waves may affect wake dynamics31,32.

To capture the fine-scale spatial heterogeneity in flows across sites, including inflow and wake dynamics, both cross-stream vessel-mounted or streamwise drifting ADCP surveys provide valuable means for flow field characterisation23,28,33–37, and it may be that such mobile measurements provide the best approach to capture the flows around dynamic floating platforms. Complementary spatial measurements of surface currents and turbulence structures can be achieved using image orthorectification38 or more novel approaches, such as radar39, satellites40, or aerial drone measurements41,42 using image-based velocity estimates. Specifically, aerial drone hovers can be used to track surface flows to extract mean velocities and turbulent coherent structures43, highly complementary to ADCP transects which omit the very near-surface. Aerial drone measurements also allow safe access to measure surface flows in the direct vicinity of platforms (e.g. upstream or even over platforms) in strong flows (>3 ms−1) where conventional mobile instrumentation would be unsafe to use.

While wake signatures are usually detected using ADCP-derived velocity quantifications, including measures of velocity deficit and turbulence intensity28, wakes can also be visualised using acoustic backscattering techniques25. Active acoustics provides a powerful tool to rapidly and remotely investigate both biological and physical properties of the water column. Whilst acoustic backscattering techniques are more commonly used for biological studies (e.g. fish detection44–46), they are also widely used in physical oceanography to map physical scattering sources47–51 and processes, specifically wave-breaking and the formation of entrained bubble clouds and subsequent advection by near-surface flows14. As surface wakes contain entrained air in the turbulent surface boundary layer (bubble entrainment)52, using acoustic scattering to trace the wake propagation of floating platforms with downstream distance may provide a high-resolution, synoptic approach to traditional velocity-related wake measures.

Field measurements still present costly and difficult endeavours, however validation at all scales is key to better understand complex flow environments and multiscale flow physics that have thus far only been simulated4,31. Inflow and wake measurements alongside advances in analysis techniques are thus vital to ensure the effective design, operation and long-term integrity of tidal stream turbines4. Our present study was motivated by the large, existing gap between theoretical predictions of flow dynamics and the fine-scale complexity of real-world flows20.

Here we set out to develop new combined survey methodologies and analytical approaches for floating platforms that can capture all three points raised above, namely, (1) the spatio-temporal variability in flows across a site, (2) the immediate inflow to the rotors and (3), downstream far-wake dynamics. Using a combination of aerial drones and acoustic sensors, we characterise the flow fields around the world’s most powerful tidal stream turbine, the floating dual-rotor O2 (Orbital Marine Power, 2 MW capacity; D = 20 m rotor diameter). The O2 is installed at Europe’s largest tidal energy test site; the European Marine Energy Centre (EMEC) in the highly energetic tidal stream of the Fall of Warness, Orkney, Scotland, UK (Fig. 1); a site which has never previously been characterised using spatial ADCP transects. Our objective is to provide a more holistic, physical oceanographic lens to multiscale flow interactions at the device-scale (excluding blade hydrodynamics), as opposed to idealised fluid dynamic conditions currently resolved in either numerical models or in the laboratory. We use aerial drone hovers to capture the surface velocity fields up- and downstream of the O2, as well as cross-stream line transects using a vessel-mounted ADCP and an EK80 echosounder to map the flows, measure wake velocity deficits and trace the far-wake propagation associated with the O2 in its idled configuration.

Results
Broad-scale spatial flow variability
Broad-scale ADCP transects across the Fall of Warness tidal channel (Fig. 1C) reveal the spatial and temporal variability across the site throughout our tidal measurements. As an example, we show vertical profiles of horizontal velocities (Fig. 2A), ADCP-derived backscatter (Fig. 2B) as well as depth-averaged horizontal velocity vectors (Fig. 2C) along each of the three lines comprising one transect during peak ebb (>3 ms−1) tidal flows (see Supplementary Fig. 1 for other tidal states and Supplementary Table 1 for environmental context). Clearly shown are the sharp horizontal gradients towards the edges of the channel (indicated by the arrows in Fig. 2C), resulting from the island wake effects of Eday to the Northeast and Muckle Green Holm to the Southwest, leading to weaker flows and flow reversals beyond the shear lines. The back-eddy system in the embayment on the Eday side is particularly prominent. The aerial drone overview image visualises the intensity and spatial extent of the surface signatures of the shear line (Fig. 2F, with matching arrow colours to Fig. 2C), dominated by upwelling boils (smooth in appearance), associated downwelling and vortical structures. Figure 2B, D capture the main areas of pronounced ADCP backscatter, a proxy for macro-turbulence entraining bubbles into the water column, mainly associated with the two shear lines.

The O2 floating tidal turbine (magenta circles in all figures) is situated at the boundary between the stronger flows in the main channel and the weaker flows influenced by the Eday shear line (note the moored location of the O2 platform was determined by available EMEC berth locations). The strong cross-stream shear in the vicinity of the O2 is further demonstrated in the gridded velocities shown in Fig. 2E. The streamlines, shown in red with start points distributed at 5D (where D = 20 m rotor diameter) intervals cross-stream of the O2, capture the behaviour of the flow cross- and downstream of the O2 with the flow direction being more variable downstream and towards Eday. There is some indication from the streamlines originating either side of the O2 overlaying increased ADCP backscatter (Fig. 2D), that a wake extends downstream of the platform. However, this is better captured during the fine-scale transects consisting of more narrowly spaced lines set out below.

On the flood tide (Supplementary Fig. 1H), the flow direction in the main channel is reversed so that the Muckle Green Holm shear line is no longer present. However, the Eday shear line and associated flow reversal, induced by the headlands and bathymetry at either end of the EMEC site, persist throughout all tidal stages except slack water.

Fine-scale flow variability
The fine-scale transects, consisting of one upstream and multiple downstream lines (5D line spacing), provide a more detailed view of the variability in the flows in the direct vicinity of the O2.

As shown in the left hand-side panels of Fig. 3 (rotated into local coordinate system with the O2 at the origin and Eday towards the positive Y-axis), the large-scale pattern of strong, more undisturbed flow towards the mid-channel (negative Y-axis) and weaker flow magnitudes towards Eday can be observed for both the ebb (Fig. 3A, B) and flood (Fig. 3C) transects during strong flows (>2 ms−1). The overlaid vectors illustrate fine-scale horizontal shear, showing the difference in local velocities relative to the upstream reference region (orange area in upstream lines). In all cases, the vectors closest to Eday point upstream of the mean flow direction, indicating relative flow reversal. The streamlines (shown in red; released 2D down- and cross-stream) appear relatively straight and parallel towards the main channel (negative Y-axis), indicating more homogenous flow, while they curve and diverge towards the Eday side (positive y-axis).

Inflow characterisation
Examining the upstream line of the fine-scale ADCP transects provides detailed insight into the inflow characteristics of the O2 at 5D (100 m). This was the closest, permissible approach that the survey vessel could make upstream of the O2 platform during peak tidal flows. An example during ebb flows is shown in Fig. 4, with other tidal states shown in Supplementary Fig. 2. The inflow region extending ±200 m cross-stream of the O2 shows a strong cross-stream gradient in streamwise velocities (Fig. 4A) with stronger flows in the freestream (negative Y values) and weaker flows towards Eday (positive Y values). Figure 4B shows the difference in streamwise velocities compared to the mean velocity experienced upstream of the rotor-swept area, highlighting the cross-stream and vertical shear upstream of the rotors in the context of the overall velocity gradients. The Eday shear line is vertically inclined (as indicated by the diagonal dashed line in 4B) and impinges directly upstream of the rotor-swept area of the O2, resulting in both vertical and cross-stream shear affecting the two rotors on either side of the platform differently, potentially even affecting different blades of a single rotor both vertically and horizontally. The depth-averaged streamwise velocity (Fig. 4C) highlights the large variability in flows across the upstream line, with flows of 3 ms−1 within the freestream of the main channel and the edge of the O2 inflow region (Y = −200 to −25 m), subsequently (Y > −25 m) dropping to below 2 ms−1 towards Eday.

When dividing the upstream ADCP transect line into three sections, as indicated by the white regions in Fig. 4C, the freestream (FS) velocity in the main channel (unaffected by the Eday shear line), the direct inflow region (O2) and the adjacent (S) region (affected by the Eday shear line), there are marked differences in the spatially averaged vertical profiles of streamwise velocity (Fig. 4D). There is strong vertical shear within the FS region, with higher velocities near the surface. In comparison, there are weaker flows near the surface (<10 m depth) in the O2 region than at depth (reversed shear, corroborated by the O2-mounted ADCP, in magenta), whilst the S region experiences weaker and more variable flows down through the water column. Overall, on the ebb tide, the impingement of the Eday shear line into the upstream area thus results in both inclined, cross-stream shear and variations in vertical shear within the inflow. During peak flood tide (>3 ms−1), the O2 inflow is unaffected by the Eday shear line, however the shear line intrudes during the decelerating flood tide (Supplementary Fig. 2M, N).

PIV on aerial drone imagery
The effects of the Eday shear line impingement in the area immediately surrounding the O2 can be directly quantified using LSPIV (Large-scale Particle Image Velocimetry) techniques applied to the aerial drone imagery. This gives a unique insight into the inflow directly upstream of each rotor simultaneously. The oblique drone shot (Fig. 5A) viewing the O2 from the main channel shows the Eday shear line in close proximity beyond the platform, characterised by highly dynamic coherent flow structures (dominated by vortices and boils, defined and visualised by their vorticity and divergence, respectively, in Fig. 5G, H). In Fig. 5B, we show a single video frame presenting the drone’s vertically downward-facing field of view during an ebb tide PIV hover over the O2. Incorporating all of the instantaneous (4 Hz) velocity fields over a two-minute hover, we show the mean horizontal surface velocity magnitude and turbulence intensity (TI) in Fig. 5C, D, respectively. The flow magnitude is reduced on the side towards Eday (positive Y) as well as directly adjacent to the (masked) platform. Consistent with the ADCP transects, the flows are strongest in the freestream on the main channel side of the O2 (negative Y). The turbulence intensity (TI) increases towards Eday as well as directly downstream of the rotor arms. Looking at the cross-stream distribution of spatial mean properties, it can be seen that the velocity magnitude (Fig. 5E) decreases across the O2 from about 3.5 ms−1 in the freestream to below 3 ms−1 in the shear line, consistent with the cross-stream gradient observed in the upstream ADCP line (Fig. 4C). Turbulence intensity (Fig. 5F) increases from 10% in the freestream to more than 30% towards Eday with an associated increase in turbulence length scales (Lu/D) from 0.3 to more than 0.6. The increase in turbulence intensity and length scales in the shear line is due to the prevalence of intermittent, intense turbulent coherent structures. An example of these is shown in the instantaneous vorticity and divergence fields in Fig. 5G, H, respectively. The corresponding figure during the ebb inflow at 5.5D upstream of the O2 is shown in Supplementary Fig. 3. The variability in mean magnitude across the O2 is similarly visible during peak flood tides (Supplementary Fig. 4), however, the prevalence and intensity of turbulent coherent structures is more equal on either side of the turbine and of similar value in TI and length scales in the freestream.

Wake dynamics
The O2 far-wake dynamics are characterised using water column measurements of ADCP-derived velocity and backscatter data, EK80-derived backscatter and PIV-derived surface velocities. In what follows, all presented measurements are taken from when the O2 turbine was not generating but with the rotors submerged in their idled, operational configuration. Despite this, the wake signal from the overall platform is noticeable and quantifiable in all estimated flow and scattering metrics. The ADCP-derived backscatter data are visualised in the right-hand side panels of Fig. 3. All three transects during both the ebb and flood tides show a strong wake signature downstream of the O2, with bubble clouds entrained into the wake generally intensifying during the second or third downstream line (~200–300 m downstream distance or 10D). The propagation of the wake downstream can be traced using the overlaid streamlines, which show the behaviour of the flow cross- and downstream of the O2 location. Notably, wake propagation between the two ebb flows differs and maybe associated with flow variability at larger scales as there is no clear difference in wind speed and direction or wave height between the two transects.

Figure 6 shows a representative ebb flow transect with water-column ADCP-derived velocities in 6A and corresponding EK80-derived backscatter in 6B. While the wake is not clearly visible in the velocity data, the backscatter clearly visualises the wake propagation through bubble entrainment. The wake during this ebb transect propagates towards Eday, which is aligned with the ADCP streamlines presented in Fig. 3B. In this transect, the wake first expands and deepens (~5D–20D downstream; Fig. 6B), before starting to dissipate during the last line (>25D downstream).

We subsequently analysed the concurrent EK80 backscatter data, isolated surface-connected bubble entrainment through macro-turbulence and were thus able to trace the cross-stream dynamics in the Y-dimension (mean cross-stream location in m), the spread (cross-sectional area in m2) and backscattering strength (Sv; dB re 1m−1) of the wake as a function of downstream distance from the O2 at a higher resolution compared to the ADCP-derived backscatter (Supplementary Fig. 5). As a result, the wake dynamics for all transects where flow speeds exceeded 0.75 ms−1 (leading to generation of a wake) are summarised in Fig. 7. Downstream of the O2, there is an increase in the variation in cross-stream location of the wake with increasing downstream distance (Fig. 7A), with this ‘meandering’ being independent of the mean velocity magnitude but with more variation on the ebb than the flood tide. The area (Fig. 7B) does not notably change during the first 100–200 m downstream (5–10D), while it almost doubles in extent further downstream (>200 m downstream or >10D) when the mean velocity magnitude exceeds 1.5 ms−1 with no discernible difference between the ebb and flood tides. Further, during these higher flow speeds, the backscattering strength (Fig. 7C) of the wake is generally highest immediately downstream of the O2 (5D) and only decreases as the wake dissipates further downstream. Finally, to assess full wake recovery, we performed one longer ebb tide transect, extending the downstream lines to 1600 m (80D) downstream of the O2 (Supplementary Fig. 6). Here, the wake’s physical scattering could be traced up to 1300 m downstream (65D).

Overall, the wake forms immediately downstream of the turbine (see drone image in Fig. 1D) and can be characterised by shallow, intense bubble entrainment at the first downstream line (~100 m downstream or 5D). The bubbles then start to disperse in the cross-stream and vertically (as deep as 20 m) as they are advected downstream.

Wake velocity deficit from PIV
Following analyses of all three PIV downstream hovers (2.75, 5.5 and 8.25D) during both peak ebb and flood tidal flows, the wake’s velocity deficit (UD) is discernible across all downstream distances, despite the spatial gradient in flow velocities and in the absence of the O2 generating (Fig. 8A). However, the most notable velocity deficit (>20%) is measurable at 2.75D, extending ±15 m in the cross-stream. At larger downstream distances, the peak in UD is less pronounced but tends towards Eday (positive Y), consistent with Fig. 3. Figure 8B shows the corresponding variation of turbulence intensity (TI) and turbulence length scales (Lu/5D). On the ebb tide, TI increases towards the Eday shear line (Y > 30 m) but is relatively constant across the O2 wake area and in the freestream. Lu/5D is elevated within the downstream wake area of the O2. As previously noted, on the flood tide, the distributions of both TI and Lu/5D are more equal across the extent of the sample area, with turbulent coherent structures prevalent near the shear line, the wake and the freestream area. The corresponding spatial distributions of the PIV data at 5.5D as per Fig. 5, are provided in Supplementary Figs. 7, 8 and show that TI is relatively constant and weak (10%) within the wake, especially when compared to near the shear line where TI more than doubles. However, turbulence length scales increase in the wake area due to the larger scales of spatial coherence found here. We subsequently assessed the variation of fluctuations in the velocity components over time at three single locations: (1) within the O2 wake, (2) near the shear line and (3) the freestream area (Fig. 8C). Near the shear line (S), the streamwise component (u’) shows strong intermittent reductions in velocity (~2 ms−1 positive fluctuations of the ebb-direction of mean flow) which are indicative of large and intense turbulent coherent structures being advected through the sample area. The freestream (FS) area shows notable velocity fluctuations (~0.5 ms−1), however, the wake area (O2) is characterised by much smaller amplitude and higher frequency fluctuations. The cross-stream component (v’) shows similar scale fluctuations (~0.5 ms−1) near the shear line and freestream area, but again smaller amplitude and higher frequency fluctuations within the wake. Therefore, although the wake signature is highly discernible visually (Supplementary Figs. 7, 8), the velocity fluctuations within it are less intense and of similar amplitude in both horizontal components (resulting in small TI but higher Lu) compared to the natural turbulent coherent structures in the adjacent areas.

Wake velocity deficit from ADCP
To compare our PIV-derived surface velocity deficits to the ADCP water column data, we extracted the nearest up- and downstream lines of the corresponding ebb and flood tide transect, respectively. Due to the O2’s excursion from its mean centre point across tidal states (Fig. 1F) and the requirement to maintain a safe operational distance, the first downstream ebb tide transect line was at ~4D, and the flood tide transect line was at ~9D. Figure 9 shows a sharp decrease in along-channel velocity at rotor depth (left upper panel), even though the turbine was not generating. During the ebb tide, the velocity deficit at 4D reaches nearly 20% at rotor depth, while it is less pronounced closer to the surface (10%), which is in accordance with the PIV-derived surface velocity deficit of ~10% at 5D. The flood tide deficit (lower panels) is lower (12%), consistent with the increased downstream distance of the transect line (approx. 9D). The scattering from entrained bubbles (Fig. 9, right panels) is most intense near the surface, up to 10 m depth.

Methods
Study site
Surveys were performed in the Fall of Warness (FoW), a tidal stream situated between the islands of Eday and Muckle Green Holm, in between Westray Firth to the north and Stronsay Firth to the south, Orkney, Scotland, UK (Fig. 1). The FoW site is characterised by semidiurnal tides with current magnitudes exceeding 3.5 ms−1 during spring tides76. The mean tidal flow is orientated from the northwest (315–330°) to the southeast (150°) during the flood, with the reverse during the ebb tidal phase. We define the axis 330 to 150 degrees as 'streamwise' (X) for the purposes of our analysis, with 'cross-steam' (Y) directed towards 60 degrees (Fig. 1C). The peripheries of the FoW are dominated by large, kilometre-scale eddies most prominent to the west of Eday and to the east of Muckle Green Holm77,78. While the tidal flow is largely ebb-dominant in Westray Firth and flood-dominant in Stronsay Firth, this tidal asymmetry (variations between the flood and ebb phases of the tidal cycle) is less pronounced in the FoW76.

Floating tidal turbine
The European Marine Energy Centre (EMEC) manages the test site within the FoW and provides eight grid-connected tidal energy test berths, ranging from 12 to 50 m in overall depth. The O2 is moored at Berth 5 (59°08.712’ N 002°48.999’ W) in water depths of 40–45 m below chart datum. The O2 has a 74 m long hull with twin 1 MW power-generating nacelles (contra-rotating; cut-in current speed is 1 ms-1) at the end of retractable leg structures. The rotor diameter (D) on each nacelle is 20 m, with a maximum draft of 24 m depth during operation. When the legs and rotors are parked horizontally in operational mode, the cross-stream extent of the platform (tip to tip) is 45 m. The O2 platform experiences platform excursion exceeding 40 m in both the cross- and along-stream directions (movement during tidal cycle, see Supplementary Fig. 8) and experiences more than 5° difference in turbine heading from flood to ebb tide (Supplementary Table 1). During the time of the surveys, the O2 was not generating due to maintenance. However, the leg structures were submerged in operational mode, with the rotors left idle, but the platform itself generated a visible surface wake (see Fig. 1D). Therefore, even with the turbine not generating, surveys were designed to quantify both the inflow and wake dynamics as a function of distance to the O2 platform.

Transect design
Data were collected in the FoW during spring tides (April 16th = new moon) on five consecutive survey days between April 13th and 17th 2022 (Fig. 1E). Repeat parallel-line transects perpendicular to the dominant flow direction (cross-stream) were performed on board an 18-m-long catamaran vessel (The Green Quest, Green Marine UK Ltd). Travelling at a constant vessel speed of 5-7 knots through the water, the boat provided a stable platform for acoustic data collection and a large forward unobstructed deck area (24 m2) for aerial drone operations. Two types of transect surveys were performed, smaller-scale transects around the O2 turbine (hereafter 'fine-scale' transects), and 'broad-scale' transects across the FoW (Fig. 1C). Transects were designed to best capture variation in hydrodynamics across all tidal states.

On the more fair-weather days (April 13, 14 and 17), fine-scale parallel-line transects were run up- and downstream of the O2. On the 13 and 14, each transect started on the upstream side (1 line), followed by consecutive transect lines (4–11) downstream of the O2 to characterise both the inflow and the downstream wake and associated flow fields. On the 17th, this was reversed, and transects started downstream, with the final line upstream. Line spacing was ~100 m (5D), and each line was ~400 m in the cross-stream designated by waypoints and followed manually by the vessel skipper (see Supplementary Fig. 9 for all fine-scale vessel tracks). Transects were repeated approximately every hour to capture flow variation across tidal states. Each transect was preceded by aerial drone surveys (see section below). Broad-scale transects were conducted on the more inclement weather days (April 15 and 16) and consisted of three lines per transect (~600 m line spacing, ~2500 m in the cross-stream). With higher winds and associated sea states more than 2 on the Beaufort scale, these days were not suitable for aerial drone surveys, however conditions were less restrictive for acoustic instrument operation. These broad-scale transects provided context on the wider flow interactions with the O2 as well as the characterisation of the spatial variation in flows across the FoW. A summary of all transects is provided in Supplementary Table 1. Most tidal states were covered during the broad-scale transects, while the fine-scale transects missed the onset of the flood tide (Fig. 1E).

ADCP data collection and processing
A downward-facing Teledyne RDI Workhorse Monitor ADCP (600 kHz) operating in Mode 1 with bottom-tracking (Mode 5) enabled was mounted on a side pole (submerged 1.35 m) on the starboard side of the vessel. The ADCP was configured to sample ensembles at an interval of 4.22 s (each ensemble comprised 5 water column pings and two bottom-track pings) with 55 vertical bins of 1 m (ambiguity velocity, the maximum observable along-beam velocity, was set to 4.62 ms−1). A USB-connected GPS was linked to the incoming ADCP data stream acquired with VMDas software to provide navigational information during transects. The ADCP’s internal compass was calibrated (5.08° error) on the morning of April 13 following mobilisation in Kirkwall. As part of the standard quality control procedures, ADCP data were post-processed in WinADCP (v. 1.14; RD Instruments, Inc.) using default parameters, and data were checked for anomalous pitch and roll. True water velocities were computed by subtracting the bottom-tracked boat velocity. A threshold for good velocity data was set to 35 m depth to omit dubious velocity readings near the maximum range of the ADCP. ADCP velocities were rotated to the local coordinate system. Acoustic scattering in the water column was extracted using volume-backscattering strength (Sv, measured in decibels, dB). This was calculated over a finite volume (maximum of 40 bins) from the ADCP’s recorded raw echo intensity data using a working version of the sonar equation as described in ref. 79 and updated in ref. 80 using a Kc coefficient of 0.45 dB/count for all four beams. Sv was evaluated separately for each bin along each of the four beams of the ADCP. For each range bin, the maximum of the four beams was taken to create depth profiles of the maximum level of scattering through the water column.

For each transect, a frozen-field81 approximation was made to allow the spatial visualisation of the depth-averaged data. Broad-scale transects were gridded at 5D, while the fine-scale transects were gridded at 1D in both streamwise and cross-stream directions. Gridding was undertaken using two-dimensional linear interpolation. The non-dimensional ADCP-derived wake velocity deficit (UD) is defined as:

UD = U1 − UW / U1 (1)

where U1 and UW are the upstream and within-wake velocities, respectively. U1 was extracted from the direct inflow region of the upstream line with a cross-stream extent of 45 m centred around the O2 location.

Additionally, near-continuous data were collected by ADCPs installed on the O2 platform. These were two downward-facing RDI Workhorse Monitor ADCPs (600 kHz) recording water column velocities at 1 Hz and a vertical bin size of 0.5 m with a range of 20 m (40 depth bins). These were mounted one at each end of the main O2 hull, such that data uncontaminated by the platform’s wake was recorded on both flood and ebb tidal cycles by the upstream ADCP. This uncontaminated data were merged into a single time series and bin-averaged at 15-minute intervals, to provide depth-mean velocities, as shown in Fig. 1E.

Echosounder data collection and processing
A downward-facing Simrad split-beam EK80 echosounder operating a 38/200 kHz single beam combi transducer (ES38-200-18C, 18° beamwidth) was mounted on a moonpool pole located on the aft deck (nominal depth of transducer face = 1.3 m below sea surface). The EK80 was configured for ‘normal’ operation (continuous wave single frequency pulse at full power; 250 W for 200 kHz, 500 W for 38 kHz) with a ping interval of 500 ms for both frequencies, and a pulse duration of 0.256 ms for the 200 kHz, and 1.024 ms for the 38 kHz frequency, respectively. Acoustic data were collected using the standard EK80 software (Simrad). Analyses focused on capturing near-surface scattering from surface-entrained bubble clouds by macro-turbulence, e.g. turbulent coherent structures, the wake of the O2 platform as well as island wakes. Datasets were initially processed in Esp3, an open-source software for visualising and processing active acoustics data82. Inclement weather and turbulent conditions dominating high-flow environments can cause attenuation of the transmitted and/or received acoustic backscatter due to entrained air bubbles and increased noise from a variety of sources83. After visual inspection of both frequencies, analysis was focused on the 200 kHz frequency as it provided useful data closer to the sea surface and showed less signal attenuation (due to strong scattering from surface bubbles) compared to the 38 kHz data. The in-built algorithms in Esp3 were applied for seabed removal (bottom detection), signal attenuation (bad transmits, spikes and dropouts) due to impulse or transient noise, and near-surface data were removed to exclude echosounder transmit pulse in the transducer near field (~1 m). The time varied gain (TVG) noise correction following ref. 84 was applied in Esp3 to reduce the effect of signal degradation and TVG-amplified background noise apparent below intense surface wake signatures. Factory calibration settings were used as the characterisation of physical scattering was predominantly intended to give a relative measure of bubble entrainment by macro-turbulence in this high-flow environment. Data were manually edited where needed, e.g. to re-define the seabed around abrupt changes in bathymetry or in areas of potential sediment resuspension. All data were gridded at a resolution of 5 pings along the track by 0.5 m depth bins, and integrated mean volume-backscattering strength data (hereafter Sv; dB re 1m−1; logarithmic, see ref. 85 for definitions of acoustic variables) were exported applying an Sv threshold of −70 dB re 1m−1. Data were then further processed in Matlab (Mathworks, R2021b) and the processing steps involved in wake isolation and tracing are outlined in Supplementary Fig. 5. To delineate surface bubble entrainment, image processing was applied to the Sv data, retaining only surface-connected scattering in the water column using a −50 dB threshold (Supplementary Fig. 5C). The applied de-noising and thresholding sufficiently reduced the impacts of excess attenuation and multiple scattering to isolate and trace the O2 wake (Supplementary Fig. 10). The wake propagation was then traced by calculating the mean cross-stream location in the Y-dimension (m), the cross-sectional area (spread in m2) and the maximum backscattering strength (Sv; dB re 1m-1) of the surface-connected scattering within cross-stream sections (Y = ±100 m) downstream of the O2 platform (Supplementary Fig. 5D–F).

Aerial drone surveys and processing
Aerial drone surveys allowed flow measurements close to, and over the O2, thereby complementing boat transects which had to maintain a safe distance to the platform, especially upstream during strong flows. Aerial drone surveys consisted of multiple hovers up- and downstream of the O2 to characterise the inflow and its wake signature across tidal states (Fig. 6). All surveys were performed by the same qualified (UK Civil Aviation Authority) pilot, assisted by an observer who was in control of releasing and catching the drone during take-off and landing. Drone missions were performed using a DJI Phantom 3 Advanced quadcopter recording 2K video at 30 Hz. Drone hovers (holding station at a given altitude with a vertically downward-facing camera) were performed upstream (5.5D), over (0D) and downstream (2.75, 5.5 and 8.25 D) of the O2, aligned with the mean flow direction. The hover locations were pre-planned in Matlab, using the mean O2 location, and then exported into the Litchi flight application. The hovers were performed at 65 m altitude, holding station for 130 s at each location (we refer to these as ‘two-minute hovers’ throughout). The hover duration was a compromise between reliably characterising the turbulent flow at each location and sampling all locations within a short enough total period to allow direct inter-comparison under the assumption of Taylor’s frozen-field approximation81. We assessed the convergence of statistics (mean velocity and turbulence intensity) — see Supplementary Fig. 11 — showing that they converge in the freestream regions but show less good convergence near the shear line due to the prevalence of large-scale turbulent coherent structures there. The drone’s altitude resulted in a footprint of 107.97 m × 61.40 m on the water surface with a resolution of 4.02 cm per pixel. Instantaneous velocity fields (with a spatial resolution of about 2.6 m), calibrated using the drone’s altitude, were extracted at 0.266 s (8 frames) intervals through each drone hover using the particle image velocimetry (PIV, also commonly referred to as ‘large-scale PIV'; LSPIV38) method described in ref. 41 and fully detailed in the Supplementary Methods 1. These were then used to derive the instantaneous vorticity and divergence fields using the standard MATLAB functions (curl and divergence, respectively), as well as calculate cross-stream distributions of spatial and temporal mean properties (velocity magnitude and turbulence intensity; TI). TI was calculated as

TI = sqrt(u'2) / U (2)

where u' is the turbulent fluctuations and U is the location-specific time-mean velocity. Turbulence length scales (Lu(ΔX)) were estimated by calculating the mean correlation as a function of streamwise separation (Ru,x(ΔX)) prior to integration up to the separation needed for the correlation curve to decrease to 1/e. This method follows ref. 86 and is examined in more detail in the Supplementary Methods 2.

An assessment of drone stability is given in Supplementary Methods 3 and Supplementary Table 2 which gives a conservative upper bound for a mean error in PIV-derived velocity measurements of 0.149 ms−1 which for our typical flow speeds (order 2 ms−1) results in a TI sensitivity threshold of 0.075. Finally, the PIV-derived wake velocity deficit was derived in a similar manner to the ADCP UD, using the immediate inflow region (45 m cross-stream extent) of the upstream hover.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of “Troubled waters: Revealing sheared turbulent flows and wake dynamics of a floating tidal turbine”\nThis investigation presents new flow measurements around the Orbital O2 tidal turbine located in the Fall of Warness, UK. The paper presents measurements collected with a mobile acoustic current Doppler profiler, echo sounder, and with a drone following a PIV technique. Results show new information about the turbine’s inflow conditions and about the wake generated by the floating tidal energy platform and its turbines.\nField measurements around tidal turbines are scarce, mainly because there are only a few operating full-scale tidal turbines in the field and because collecting data in energetic tidal sites is difficult and expensive. I find the data presented in the paper to be relevant to the tidal energy industry and necessary for evaluating the hydrodynamic environmental effects of tidal energy extraction. However, there are aspects of the paper that need to be greatly improved before publication.\nTo ease this review, I will answer the following questions provided in this journal review guidelines.\nWhat are the noteworthy results?\nThe data itself is relevant and unique, especially the acoustic wake measurements and PIV velocity estimations from drone images, which is a novel technique barely applied in the field.\nThe wake description, although simple, is noteworthy because most wake velocity deficit estimates come from numerical simulations, and there are not many field data sets about it.\nWill the work be of significance to the field and related fields? How does it compare to the established literature? If the work is not original, please provide relevant references.\nYes, it will be relevant for the tidal energy field and for the field of fluid dynamics.\nThe work is original because it is new data in a different site, and around a turbine that to the best of my knowledge very few studies have been published. Prior work regarding spatial flow measurements has been cited. However, the paper does not include in the introduction nor in the discussion comparisons with other sites and with other turbine wakes either from models or from the field data.\nThere is vast literature about turbulence measurements at tidal energy sites that is not included in the paper, and I think is important to include it to provide context and to better discuss the obtained results.\nDoes the work support the conclusions and claims, or is additional evidence needed?\nThe flow description is mostly qualitative and includes snapshots of what is happening in this variable unsteady environment. Perhaps authors could include or summarize the results for different tidal conditions.\nAnother main concern is that the authors describe the turbine wake and claim to have measured or detected wake meandering. Turbine wake meandering has been previously observed in laboratory experiments and in detailed computer fluid dynamic simulations of flow passing through a rotating turbine. This feature has been linked to the interaction between the rotor shed vortex and the counter-rotating the tip-vortex shed by the rotating turbine blades.\nIn this paper, the authors indicate that the turbines were not operating, meaning the turbine blades were not rotating, so the authors need to justify how they observed the wake meandering feature and what mechanism is producing the meandering.\nAre there any flaws in the data analysis, interpretation and conclusions? Do these prohibit publication or require revision?\nYes, the publication requires revision.\nAnother important concern is about the estimation of turbulence metrics from the PIV data.\nAuthors use only 2 minutes of data to obtain turbulence statistics, in previous works it has been established that to get robust turbulence statistics in unsteady tidal flows, at least 5 minutes of data are needed, in order to have statistical stationarity and to capture a wide range of turbulence length-scales. The authors need to justify their choice of time-series length and discuss errors in their calculations.\nIs the methodology sound? Does the work meet the expected standards in your field?\nThe field measurements techniques are sound and are state of the art. ADCPs are the instrument of choice in oceanography for measuring velocities, and post processing seems to be PIV is a widely used technique in the lab, not common in the field.\nAuthors should specify in the methods how is the PIV data processed, include sources of error and mention encountered difficulties. Also, need to specify how other quantities such as divergence and vorticity were estimated.\nADCP processing seems to be standard, but authors should specify how the gridded products were obtained.\nIs there enough detail provided in the methods for the work to be reproduced?\nAs mentioned before, more details need to be included in the methods section to reproduce all the data post processing.\nSpecific comments:\nThe draft does not have page numbers nor line numbers, which would be helpful for conducting the review. Here I detailed some questions and suggestions regarding figures, results, and methods.\nFigure 2:\nAre the top panels instantaneous data? It is very hard to see the details in Panels A and B\nFigure 3:\nHow were these data gridded and interpolated? Is not fully specified in the methods section. Is the data collected along the black lines used here for interpolation? How many transects are included in the interpolation?\nAuthors need to specify at which depth were these data collected or if it is depth averaged.\nArrows in the left-hand panels are misleading, arrows typically show flow direction rather than a difference with upstream velocity. Perhaps the authors can include a velocity deficit color plot.\nAlso, there should not be data over the platform location, since the platform is supposed to be there, hence no data can be collected on top of it.\nFigure 4:\nPanel D, figure S: how do you explain the reversed shear near the bottom?\nAlso in the text: what do you mean by “the shear line intrudes” and by inclined later shear?\nSupplemental materials Figures S2 are impossible to read.\nFigure 5:\nVorticity and divergence: Authors should explain how those quantities were estimated in the methods. Vorticity is a vector, I assume this is vertical vorticity. There is only one snapshot of vorticity in Fig. 5, but there is no description or explanation on the sign of vorticity and if the distribution of vorticity makes sense in the text. Same with divergence, why is divergence relevant for wake dynamics?\nFigure 6: It would be really helpful if these data were presented in space rather than in time, as are the rest of the data presented in the paper.\nFigure 7: In panel A is very hard to distinguish the reduced wake velocities.\nWhy does the acoustic wake signal deepen if bubbles are buoyant? What could be driving the bubbles to move downward?\nFigure 9 and 10:\nI find this to be one of the most important or more relevant result of the paper.\nRegarding, TI, what is the error of TI estimation with PIV?\nDiscussion\nThe discussion section would be greatly improved if comparisons were made with other available datasets and models. Discussion mostly focuses on their own data and how their data is relevant.\nFor example: How do the wake velocities compared to previously estimated wake velocities obtained from models (if available). How does turbulence intensity compare to other estimates? How does the wake vary with different tidal conditions? What can you expect for a operating turbine wake?\nSupplemental Figures S1 and S2 are not readable.", "reviewer_2": "The authors present measurements and discussions about ocean currents at a floating tidal turbine in the Fall of Warness, UK. The measurements were conducted via multiple instruments, e.g., ADCP, PIV, beam combi transducer, and aerial drone surveys, and different data were recorded, such as ocean current speed and turbulence fluctuation.\nI. Intellectual merit\n1) I would say such work., e.g., ocean measurements, is interesting and beneficial to the community, given that it is lacking and desired for ocean energy development, especially their multiscale features (flows at and away from the turbine). Also, the work may promote understanding and modeling (in its validation) of ocean flow problems other than floating tidal turbines, such as floating offshore wind energy. Additionally, the submission is well-written, although a little too long.\n2) Basically, this submission is a case study, and its results are location-dependent. As a result, the understanding presented in this paper is mainly restricted to the location of this study. The work may be further enhanced so its results may bear insight for other sites, if more analysis (as mentioned in the following paragraph) is provided, such as info to show the complete evolution/swinging of the wake over full cycles of tides. Others are comparison of the results to those without turbine/floating device and those at turbine operation conditions, via modeling if not measurements.\n3) The work primarily describes the measurement results and corresponding flows, and it lacks insight and analysis on the flows patterns and mechanisms. For instance, there is lack of discussion on correlations between speeds/turbulence/flows at different locations from the turbine, to reflect multiscale features of the flows. Additionally, it would be interesting if results/measurement are provided for flows at the turbine and those further away, e.g., further away in the southeast and northwest directions.\nII. Presentation problems\n4) Fig. 3 --- for the velocity field on the left, the color bars shows that dark and light color mean low and high speed, respectively. But the length of vectors in them seem to be in the opposite way. For instance, the color shows high speed in the lower right corner of the left figure in Fig. 3, but the vector length there indicates low speed there. Do I miss something?\n5) Some results/analysis may be better presented. For instance, the discussion about Fig. 8, i.e., the paragraph right before the figure is unclear. I don't see an explanation of each of the lines in the figure.\n6) From Fig. 9, visually, it seems that U_d and U have certain correlation at 5D and 7.5D. Moreover, V has such a relation at 2.5D and 7.5D, and it is about zero, exhibiting no correlation at 5D. Are those true, and if yes, why?\n7) This work is valuable for ocean modeling modeling (e.g., its validation). Therefore, it helps if more details are provided with this regard, such as adding time and date of the measurements in figure captions.\n8) I cannot judge much on measurement techniques but wonder if the measured data by ADCP mounted on the vessel are affected by the blocking/wake from the vessel itself. An discussion on this helps.\nOverall, I think this work has impact on understanding of the influence of the turbine on the flows and engineering problems at its location. However, it lacks intellectual merit, particularly science impact/insight on sciences and flow mechanisms. After revision, the submission may fit a domain science journal, such as Ocean Eng., Coastal Eng.", "reviewer_3": "Key Results\nThe Authors present results of an experimental campaign at the Orbital Marine Power tidal turbine platform in Scotland UK. Through acoustic profiling and analyzing aerial drone imagery, they attempt to reconstruct the flow conditions around and in the wake of the turbine. To the best of my knowledge, this is the first experiment of flow characterization around a tidal turbine.\nExperimental results show that the presence of areas of high turbulence intensity in the inflow region of the turbine may reduce the current speed at the rotors. This may have implications for the turbine’s structural stability and power generation. Such high flow variability is attributed to the particular configuration of the site (presence of islands).\nValidity\nThe Authors do not support experimental findings with data on the power generated at the turbine. Even if the turbine was not functioning during the experiments, power data generated during similar tidal events are not included/mentioned in the paper. It is then impossible to ascertain whether flow variability has an effect on the turbine performance.\nData gathered during aerial surveys is not provided and a few details are included on their processing pipeline. It is difficult to assess whether aerial imagery was of enough quality to be processed. Given the extremely challenging environmental settings during the experiment (I expect wind to be rather severe at the Fall of Warness), the drone vibrations may have significantly impacted images and velocity estimations. Also, velocities estimated from PIV tend to be rather variable. Typically, longer time intervals help to mitigate this aspect. However, I could not find any reference on the velocity temporal standard deviations in the paper.\nSignificance\nSince experimental results are very much connected to the specific site where the turbine is installed, I do not expect the paper’s conclusions to highly impact the field of ocean energy extraction. The paper may suggest that the performance of the O2 turbine is not optimal, but it does not introduce any new findings on power generation from tides.\nThe Authors claim that they develop new survey methodologies, however, I do not find their observational techniques particularly novel. Acoustic current profiling is a well-established technique. PIV was first introduced in the 1990s and aerial PIV from drones has also been adopted in many settings, mostly in riverine but also in coastal areas.\nData and methodology\nRegarding data acquisition, I think the Authors should have better commented on how they ensured that transects were parallel and, therefore, data profiles were consistently parallel. I expect navigation at the experimental site not to be easy and, thus, some corrections to the data could have been made.\nADCP data were collected from an instrument mounted on a pole submerged 1.35m. Did the Authors apply any correction to get data up to the sea surface?\nMore details should have been provided regarding aerial drone surveys and processing:\n• Wind conditions during the hovers, wave conditions, and seasonality at the site should be included.\n• Appearance of the sea surface: the presence of features is fundamental to acceptable PIV data\n• PIV parameters: the Authors mention Ref. Lieber et al., 2021. However, experimental settings may have been diverse and PIV parameters should have been properly adapted.\n• Drone stability is not discussed. The Authors used a commercial drone platform, which is not capable of compensating for high wind conditions. In Lieber et al., 2021, the Authors mention that “There were no static reference points within the camera’s field of view so that whole-field contamination from the relative motion of the UAV cannot be removed”. However, this is not the case for the O2 experiment, where the turbine (or part of it) could have been regarded as static during the 2 minutes-long hovers.\n• PIV only provides a velocity characterization of the sea surface. I believe the Authors should have better clarified and discussed this.\n• Standard deviations of the PIV velocity fields are not provided.\nSuggested improvements\nI suggest the Authors to take into account information on the power generation performance of the turbine. Such data could help in truly assessing whether the O2 is performing well or is negatively affected by the island wakes.\nPIV data should be stabilized before processing.\nClarity and context\nGenerally, I had difficulties in identifying the mean streamwise and cross-stream direction in Figures. I think such pieces of information should be properly included in all Figures.\nFigure 3 is of poor quality.\nI found it difficult to identify the O2 in Figure 6.\nVisualization of the ebb and flood tides is also not easy. For instance, this sentence: “The ebb and flood tide upstream distance to the O2 is 103 m and 108 m, respectively, while the ebb and flood tide downstream distance to the O2 is 86 m and 189 m, respectively.” would be clearer through a Figure sketch.\nReferences\nI think the Authors should consider similar experimental studies that make use of aerial drone surveys for non-invasive flow characterization.", "reviewer_4": "Review of the manuscript: “Troubled waters: revealing sheared turbulent flows and wake dynamics of a floating tidal turbine” by Lieber et al. submitted to Nature Communications.\nOverview:\nThis manuscript presents the results of a resource characterisation at one of the berths at the EMEC site in Scotland where an idled floating tidal stream turbine is deployed, using vessel-mounted ADCP and drone imagery to measure the velocity field. Results capture the three dimensional nature of the tidal flow at this location which is highly influenced by the local bathymetry and nearby headland, which induced flow curvature and large levels of shear. Such findings will be of use for the tidal turbine developer to characterise loading when device is in operation.\nGeneral comments:\nWhilst the analysis of the time-averaged velocity field from ADCP and drone is good, there are not enough insights into the turbulence characteristics that are developed during the different tidal phases measured. Unfortunately, the turbine is in parked position which does not generate a significant wake throughout the water column as the results evidence. Moreover, the use of free-surface velocity measurements through drone imagery is very recent as is the need for further understanding and/or correlation of such measurements with the velocities through the water column, which the authors did not analyse. In many instances the authors link ADCP signal to bubbles as a signature of turbulence, but this seems quite speculative, e.g. the idled turbine blades would not undergo cavitation and thus they will not generate air bubbles but fluctuations to the velocity field.\nAnother key fluid-mechanics aspect that has received little attention is the wave field, acknowledging this would be difficult but aiming for a publication in Nature Communications would require such extensive analysis. Authors claim that during two ebb cycles the results differ notably. Thus, the question is what drove those changes, where waves very different in period, direction or height?\nThe manuscript has several flaws regarding the description of the flow dynamics:\n♣ In figure 5 you present “divergence” field, but you have not defined what that parameter is and how is calculated.\n♣ Figure 9 B. This plots the time evolution of the velocity series in absolute magnitude whilst authors indicate these are velocity fluctuation, which is an erroneous concept.\n♣ Authors refer many times to “Shear line”, but this is not a concept used in fluid mechanics. Perhaps you aimed to refer to the shear layer but this is unclear.\nFurther analysis of the PIV field would be required, e.g. authors refer to those shear lines but without quantifying Reynolds stresses, time-averaged vorticity, etc.\nFinally, authors claim that IEC standards do not consider deploying two measuring devices on the lateral sides of the turbine. Please review the standards because they do present this very clearly in Figure 4 of the IEC 62600-200 2013.\nRecommendation:\nThe reviewer considers that the manuscript does not have a rigorous analysis of the fluid dynamics of the flow at this site, instead it provides a good resource characterisation in terms of mean velocity distribution and time series. Unfortunately, the turbine is not operating, reducing the impact of this study in the community. The recommendation is to reject this paper. Hopefully the comments provided here are useful for the authors to improve this paper and resubmit it to another journal.\nMinor comments:\nPlease provide page number and line numbers as it is good practice that eases reviewers’ task.\n♣ Abstract: “utility-scale floating...” need to indicate that the turbine was not in operation during the measurements.\n♣ Abstract: “sufficiently high to provide… and downstream velocity deficit”. what are the key elements that you have unveiled of such flows?\n♣ Introduction, first sentence: OE is capable of partly contributing to the decarbonisation but not to absorb all of it. Needs update.\n♣ Remove “underwater” as it is intrinsic of tidal turbines.\n♣ “shear lines”, what do you mean by shear lines? is this referring to the developed flow?\n♣ “spread laterally”, flow would spread normal to the coastline rather than laterally.\n♣ “km-scale spatial extent of shear lines”. still unclear what this is.\n♣ “horizontal gradient”, do you mean in the transverse direction? horizontal involves x and y.\n♣ Boils. Boils are only one type of coherent flow structure depending on the bathymetry features.\n♣ “floating multi-rotor turbines”. Floating turbines can also be with a single rotor\n♣ Introduction, second page. The standard actually recommends that two ADCPs are deployed on the sides of the turbines to capture this spatial variability, which has even been discussed in previous studies looking at tidal turbine loading.\n♣ “It is still unknown… various spatial scales”. It is known that turbulence intensity and lengthscale will drive the wake recovery, they just need to be characterised at the site. This statement needs to be corrected.\n♣ Introduction, page 3, last paragraph. Need to highlight that the device was not in operation.\n♣ Figure 1. It would be helpful to include flow directions of ebb and flood tides.\n♣ Results, page 6. “and vortices”. What kind of vortices and how did you determined these?\n♣ Page 11. “divergence distributions”. Can you define this quantity?\n♣ Page 13. “Notably…”. would this be attributed to wind-induced shear or waves? have you measured the wave conditions at the site?\n♣ Page 16. “the wake’s intensity and area”. This is not clear. What do you mean by wake area and how is this quantified?\n♣ Page 16. “of flow speed and downstream distance”, and more parameters such as TI or lengthscales now only speed here.\n♣ Page 16. “The bubbles then start to disperse laterally…”. This read very speculative. It is very hard to characterise the flow based on these \"bubbles\". Why did you not look into the analysis of turbulence intensities in their vertical components? what about vertical velocity distribution? Bubbles are an idealisation as they would only be introduced by the blades when cavitating and as the device is not operating they are not in the flow.\n♣ Page 18. “velocity fluctuations of the U and V components over time”. These are not fluctuations but instantaneous variation.\n♣ Page 18. “In the shear line area… through the sample area”. Again, this is speculative because if this is a shear layer there would be vortices induced due to lateral shear that travel intermittently downstream.\n♣ Page 18, “more isotropic compared”. More isotropic based on what criterion? To establish this you would need to look into the three components of turbulence intensity and is not the case.\n♣ Page 18. “The negative velocity deficit… leading to vertical blockage effects”. this are likely to be due to bathymetry effects as an idled turbine will not have a large thrust that can block the flow. However, changes in bathymetry (both roughness and slope) can contribute to this, as shown in your figure 2.\n♣ Page 20. “tank experiments.”. Do you mean water tanks?\n♣ Page 20. “there are no studies to date… shear during the inflow”. This is the very similar to studying a device with a given yaw angle, as it encounters a given shear across the rotor.\n♣ Page 20. “in the cross-stream ‘meandering’ is… idealised wake modelling.”. This is going to be confusing to the readers. Wake meandering occurs only if the turbine is operating as the rotor rotates, it induces wake meandering downstream. However, here the turbine is not operating, thus there is no wake meandering as such. Flow can be oscillating laterally as it goes through a porous body. This needs to be amended.\n♣ Page 21. “Current power curve standards… both tidal states”. See previous comment. Figure 5 of the IEC 62600:200 show how to locate two ADCPs.\n♣ Page 21. “There is currently a lack of measurements… turbine-turbine interaction from being developed”. That is right. More needs to be done here."}, "author_response": "Responses to Reviewers\nBelow is a point-by-point response to all four reviewers. Reviewer comments are highlighted in bold using indentation with responses provided below. We sincerely apologise for erroneously omitting the line numbers during first submission. This has now been amended and we refer to Line numbers in the new (‘clean’) document version. We also show all changes in the manuscript text file with track changes.\nReviewer #1 (Remarks to the Author):\nReview of “Troubled waters: Revealing sheared turbulent flows and wake dynamics of a floating tidal turbine”\nThis investigation presents new flow measurements around the Orbital O2 tidal turbine located in the Fall of Warness, UK. The paper presents measurements collected with a mobile acoustic current Doppler profiler, echo sounder, and with a drone following a PIV technique. Results show new information about the turbine’s inflow conditions and about the wake generated by the floating tidal energy platform and its turbines.\nField measurements around tidal turbines are scarce, mainly because there are only a few operating full-scale tidal turbines in the field and because collecting data in energetic tidal sites is difficult and expensive. I find the data presented in the paper to be relevant to the tidal energy industry and necessary for evaluating the hydrodynamic environmental effects of tidal energy extraction. However, there are aspects of the paper that need to be greatly improved before publication.\nTo ease this review, I will answer the following questions provided in this journal review guidelines.\nWhat are the noteworthy results?\nThe data itself is relevant and unique, especially the acoustic wake measurements and PIV velocity estimations from drone images, which is a novel technique barely applied in the field. The wake description, although simple, is noteworthy because most wake velocity deficit estimates come from numerical simulations, and there are not many field data sets about it.\nWe thank the reviewer for their thoughtful consideration of our manuscript and positive evaluation of our novel data collection approaches. The PIV analyses in this context is indeed relatively novel, and in fact, we have pioneered this technique in our 2021 study for tidal, highly dynamic environments. There has since been one further study by Fairley et al who have assessed aerial drones and PIV techniques for mapping tidal flows which we originally cited in the introduction (L103) and which we now also refer to in the discussion (L 486) and the methods (L714). As part of the revision, we have further developed the inflow and wake analyses incorporating turbulent length scales extracted from the aerial drone imagery of surface flows, please refer to updated Figures 5F and 8B, the added section in the Methods (L711 -714) and the accompanying figure in the Supplementary Information (Fig. S10).\nFinally, we would go further to say that this is the first study that presents wake data obtained downstream of a multi-MW, floating tidal stream turbine platform.\nLieber, L., Langrock, R. & Nimmo-Smith, A. A bird’s eye view on turbulence: Seabird foraging associations with evolving surface flow features. Proceedings of the Royal Society B (2021).\nFairley, I. et al. Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment. Renew Energy 196, 839–855 (2022).\nWill the work be of significance to the field and related fields? How does it compare to the established literature? If the work is not original, please provide relevant references.\nYes, it will be relevant for the tidal energy field and for the field of fluid dynamics.\nThe work is original because it is new data in a different site, and around a turbine that to the best of my knowledge very few studies have been published. Prior work regarding spatial flow measurements has been cited. However, the paper does not include in the introduction nor in the discussion comparisons with other sites and with other turbine wakes either from models or from the field data.\nThere is vast literature about turbulence measurements at tidal energy sites that is not included in the paper, and I think is important to include it to provide context and to better discuss the obtained results.\nWe thank the reviewer for their positive feedback and appreciate the comments regarding comparison with other sites and turbulence measurements. We have originally cited other field and numerical studies mostly in the introduction, however, we have now provided more direct comparisons and concrete examples in the Discussion, including comparisons of vertical shear profiles from other sites, such as the Alderney Race (English Channel), Ramsey Sound (Wales, UK), the Grand Passage in the Bay of Fundy (Canada) as well as from moored ADCP deployments in the Fall of Warness (L467-469), as well as turbulence intensities (L481) and wake measurements (L532), where applicable. Unfortunately, no other study on the O2 platform has been published to date and the PLAT-I platform (assessed by Guerra et al 2021) is the only other floating platform considered to-date. However, we have now added new measurements on turbulence length scales (see updated Figures 5 and 8) and discuss our results with theoretical predictions of inflow conditions, wake propagation and recovery.\nWe have also added a section in the Introduction to define measures of turbulence (turbulence intensity and length scales; L69-73), thereby expanding our previous text which was more focused on turbulent coherent structures.\nGuerra, M., Hay, A. E., Karsten, R., Trowse, G. & Cheel, R. A. Turbulent flow mapping in a high-flow tidal channel using mobile acoustic Doppler current profilers. Renew Energy 177, 759–772 (2021).\nDoes the work support the conclusions and claims, or is additional evidence needed?\nThe flow description is mostly qualitative and includes snapshots of what is happening in this variable unsteady environment. Perhaps authors could include or summarize the results for different tidal conditions.\nWe have originally provided figures focussing mainly on the stronger flows during the stronger flows during the ebb and flood throughout, but we now provide a comprehensive list of additional figures during other tidal states in the Supplementary Information as well as comparisons in the main text, e.g. in L193-L196, L280-282. The Supplementary Information now includes comprehensive flow visualisations of all broad-scale transects (Fig. S1) as well as detailed inflow transect lines for every fine-scale transect (Fig. S2). We now also provide a Summary Table (Supplementary Table S1) on mean tidal velocities for the duration of each transect (as measured by the O2 ADCPs) and concurrently measured environmental data (e.g. wind and waves) as well as information on O2 platform heading and displacement, drone flight times and in-flight wind conditions. We’d like to note that we have originally summarised the behaviour of the wake as a function of flow velocity in a summary plot (originally Figure 8, now Figure 7) and have now added labels to distinguish the flood and ebb tidal cycle which we trust helps to interpret the variable nature of wake propagation.\nAnother main concern is that the authors describe the turbine wake and claim to have measured or detected wake meandering. Turbine wake meandering has been previously observed in laboratory experiments and in detailed computer fluid dynamic simulations of flow passing through a rotating turbine. This feature has been linked to the interaction between the rotor shed vortex and the counter-rotating the tip-vortex shed by the rotating turbine blades.\nIn this paper, the authors indicate that the turbines were not operating, meaning the turbine blades were not rotating, so the authors need to justify how they observed the wake meandering feature and what mechanism is producing the meandering.\nWe have originally used the term “meandering” to refer to the variation in cross-stream (Y-dimension) location as the wake is advected downstream, best visualised in our wake summary plot (now Figure 7). However, we appreciate the reviewer’s concern that this might be confusing in light of the traditional use of the team “meandering”. We have amended the manuscript throughout and now discuss the variation as the mean “cross-stream” location of the wake in the Y-dimension which shows large-scale cross-stream variation (±50 m in the mean cross-stream location during a single transect of order 30 min duration), please refer to L513. It should also be noted that the O2 platform is not static but is fixed to a dynamic mooring. It is thereby in constant motion (e.g. side-to-side movement) which may influence the behaviour of the wake. We have now added the cross-stream movement (in meters) of the O2 platform in the new Summary Table (Supplementary Table S1). We have also referred to this in the Discussion (L518) and draw attention to new findings from the offshore floating wind literature which suggest that side-to-side motion of a floating offshore wind platform can be a novel origin for the onset of wind wake meandering (L515):\nLi, Z., Dong, G., & Yang, X. (2022). Onset of wake meandering for a floating offshore wind turbine under side-to-side motion. Journal of Fluid Mechanics, 934. https://doi.org/10.1017/jfm.2021.1147\nAre there any flaws in the data analysis, interpretation and conclusions? Do these prohibit publication or require revision?\nYes, the publication requires revision.\nAnother important concern is about the estimation of turbulence metrics from the PIV data. Authors use only 2 minutes of data to obtain turbulence statistics, in previous works it has been established that to get robust turbulence statistics in unsteady tidal flows, at least 5 minutes of data are needed, in order to have statistical stationarity and to capture a wide range of turbulence length-scales. The authors need to justify their choice of time-series length and discuss errors in their calculations.\nYes, this is always a challenge but in reality, there are compromises between overall flight time and being able to capture various sites (upstream and downstream hovers) under the assumption of Talyor’s frozen field. We have expanded on this in the Methods in L695-L697. We have now assessed convergence given our hover time (please refer to Supplementary Fig. S9). In short, TI and velocity magnitude measurements show convergence over time in the freestream, however, do not fully converge in the shear line region due to the presence of large intermittent, turbulent coherent structures. This has now been added to the Methods section in L697-L700. Further we have included an assessment of the expected error in TI due to drone stability (L714-717).\nIs the methodology sound? Does the work meet the expected standards in your field?\nThe field measurements techniques are sound and are state of the art. ADCPs are the instrument of choice in oceanography for measuring velocities, and post processing seems to be PIV is a widely used technique in the lab, not common in the field.\nAuthors should specify in the methods how is the PIV data processed, include sources of error and mention encountered difficulties. Also, need to specify how other quantities such as divergence and vorticity were estimated.\nADCP processing seems to be standard, but authors should specify how the gridded products were obtained.\nWe thank the reviewer for this comment and have now updated our Methods section to include these details. The gridding is now specified in L634 and the calculation of divergence and vorticity is specified in L706 in the Methods. Further we have added an assessment of the expected error in TI due to drone stability (L714-717).\nIs there enough detail provided in the methods for the work to be reproduced?\nAs mentioned before, more details need to be included in the methods section to reproduce all the data post processing.\nWe have now updated our Methods section to include these details.\nSpecific comments:\nThe draft does not have page numbers nor line numbers, which would be helpful for conducting the review. Here I detailed some questions and suggestions regarding figures, results, and methods.\nWe sincerely apologise for this and have now included line numbers.\nFigure 2:\nAre the top panels instantaneous data? It is very hard to see the details in Panels A and B\nYes, these are instantons. We agree and have now amended the figure caption and adjusted the layout for Figure 2 to improve clarity and readability.\nFigure 3:\nHow were these data gridded and interpolated? Is not fully specified in the methods section. Is the data collected along the black lines used here for interpolation? How many transects are included in the interpolation?\nWe have further clarified the gridding and interpolation process in the Methods sections in L634, specifying that we used 2-dimensional linear interpolation.\nAuthors need to specify at which depth were these data collected or if it is depth averaged.\nWe have amended the caption to specify that these data (Fig. 3) were depth-averaged.\nArrows in the left-hand panels are misleading, arrows typically show flow direction rather than a difference with upstream velocity. Perhaps the authors can include a velocity deficit color plot.\nThis plot is aimed to visualise the complex flows around the O2 platform, including relative variation in flows, the shear line impingement as well as the behaviour of the flows (using streamlines). We have clarified the display by reducing the density and increasing the size of the vectors.\nAlso, there should not be data over the platform location, since the platform is supposed to be there, hence no data can be collected on top of it.\nWe acknowledge that there would be no data at this point and we have now amended this (however, it is in the nature of the interpolation that they cover the O2 location).\nFigure 4:\nPanel D, figure S: how do you explain the reversed shear near the bottom?\nAlso in the text: what do you mean by “the shear line intrudes” and by inclined later shear?\nThe Eday shear line is vertically inclined, and we have now indicated this by the diagonal dashed line in Figure 4B. The shear line impinges directly upstream of the rotor-swept area of the O2, resulting in both vertical and cross-stream shear affecting the two rotors on either side of the platform differently, potentially even affecting different blades of a single rotor both vertically and horizontally. We have modified the text to describe this as ‘impingement’ rather than an intrusion, e.g. in L264, L279 and L283, L469 and L473.\nSupplemental materials Figures S2 are impossible to read.\nWe agree and have improved all figures in the Supplementary information.\nFigure 5:\nVorticity and divergence: Authors should explain how those quantities were estimated in the methods. Vorticity is a vector, I assume this is vertical vorticity. There is only one snapshot of vorticity in Fig. 5, but there is no description or explanation on the sign of vorticity and if the distribution of vorticity makes sense in the text. Same with divergence, why is divergence relevant for wake dynamics?\nWe have now clarified how these are calculated and utilised in the Methods section (L706) and have clarified the sign in the caption for Figure 5. Surface divergence is a useful measure of turbulent coherent structures that lead to surface upwelling (“boils” – positive divergence) and associated downwelling . This has also been clarified in the Introduction (L63-L65).\nFigure 6: It would be really helpful if these data were presented in space rather than in time, as are the rest of the data presented in the paper.\nPlease note, this plot has now been moved to the Supplementary Information (Fig. S5). Time was deliberately retained on the x-axis to emphasise the duration of a single sampling transect and the corresponding data with meters on the X axis is displayed in the curtain plot (now Figure 6).\nFigure 7: In panel A is very hard to distinguish the reduced wake velocities.\nYes – which is why we then include the acoustic backscatter that acts as a clearer indicator of the location of the wake in this complex and variable natural flow environment and in the absence of the O2 generating. Please note, ADCP-derived velocity deficits are specifically addressed in what is now Figure 9.\nWhy does the acoustic wake signal deepen if bubbles are buoyant? What could be driving the bubbles to move downward?\nSmall bubbles have a very low rise-velocity (order mm/s) and so are readily advected vertically downwards when vertical velocities exceed this. Hence, they have been widely used to map the distribution of near-surface physical flow features. We have updated the Introduction (L114-L119) to explain this phenomenon and included some helpful example references, e.g.:\nNimmo-Smith, W. A. M., Thorpe, S. A. & Graham, A. Surface effects of bottom-generated turbulence in a shallow tidal sea. Nature 400, 251–254 (1999).\nThorpe, S. A., Green, J. A. M., Simpson, J. H., Osborn, T. R. & Nimmo Smith, W. A. M. Boils and turbulence in a weakly stratified shallow tidal sea. J Phys Oceanogr 38, 1711–1730 (2008).\nWe have also included references on how active acoustics are used in physical oceanography to map physical processes (L114), including turbulence and microstructure, gas seeps, or mixed layer depth.\nFigure 9 and 10:\nI find this to be one of the most important or more relevant result of the paper.\nThank you. Please note, we have further enhanced these plots with additional analyses on turbulence intensity and length scales within the O2 wake area, the shear line and the freestream in Figure 8B (previously Fig. 9) and shaded errors in Figure 9 (previously Fig. 10).\nRegarding, TI, what is the error of TI estimation with PIV?\nWe have now included as assessment of the error of TI estimation with PIV in our Method section (L714-L717).\nDiscussion\nThe discussion section would be greatly improved if comparisons were made with other available datasets and models. Discussion mostly focuses on their own data and how their data is relevant.\nFor example: How do the wake velocities compared to previously estimated wake velocities obtained from models (if available). How does turbulence intensity compare to other estimates? How does the wake vary with different tidal conditions? What can you expect for a operating turbine wake?\nSupplemental Figures S1 and S2 are not readable.\nWe thank the reviewer for this helpful observation and have now entirely restructured our Discussion to include comparison with other studies (submerged turbines and another floating turbine) and simulations, e.g.:\nBlackmore, T., Batten, W. M. J. & Bahaj, A. S. Influence of turbulence on the wake of a marine current turbine simulator. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 470, (2014).\nWe now also compare our turbulence intensities and wake deficits with other studies previously only mentioned in the introduction. Wake propagation as a function of tidal state is provided in Figure 7. We have also improved all figures in the Supplementary Information.\nReviewer #2 (Remarks to the Author):\nThe authors present measurements and discussions about ocean currents at a floating tidal turbine in the Fall of Warness, UK. The measurements were conducted via multiple instruments, e.g., ADCP, PIV, beam combi transducer, and aerial drone surveys, and different data were recorded, such as ocean current speed and turbulence fluctuation.\nI. Intellectual merit\n1) I would say such work., e.g., ocean measurements, is interesting and beneficial to the community, given that it is lacking and desired for ocean energy development, especially their multiscale features (flows at and away from the turbine). Also, the work may promote understanding and modeling (in its validation) of ocean flow problems other than floating tidal turbines, such as floating offshore wind energy. Additionally, the submission is well-written, although a little too long.\nWe thank the referee for their support and agree that our rare field observations bridge a gap in transferring idealised numerical and laboratory findings to the real world.\n2) Basically, this submission is a case study, and its results are location-dependent. As a result, the understanding presented in this paper is mainly restricted to the location of this study. The work may be further enhanced so its results may bear insight for other sites, if more analysis (as mentioned in the following paragraph) is provided, such as info to show the complete evolution/swinging of the wake over full cycles of tides. Others are comparison of the results to those without turbine/floating device and those at turbine operation conditions, via modeling if not measurements.\nWithin the scope of the data that were feasible to collect, we have further developed our analysis and related discussion to broaden the relevance of our work beyond the specific site – highlighting the large-scale variability that will be encountered as these technologies are deployed in equally complex natural environments that are not readily simulated numerically or at laboratory scales. As part of the revision, we have further developed the inflow and wake analyses incorporating turbulent length scales extracted from the aerial drone imagery of surface flows, please refer to updated Figures 5F and 8B, the added section in the Methods (L711 -714) and the accompanying figure in the Supplementary Information (Fig. S10). We have extensively reworked the Discussion to address these points.\n3) The work primarily describes the measurement results and corresponding flows, and it lacks insight and analysis on the flows patterns and mechanisms. For instance, there is lack of discussion on correlations between speeds/turbulence/flows at different locations from the turbine, to reflect multiscale features of the flows. Additionally, it would be interesting if results/measurement are provided for flows at the turbine and those further away, e.g., further away in the southeast and northwest directions.\nFigure 3 specifically shows the spatial variability in the speeds and relative directions of the flows in the vicinity of the O2, while the mechanisms leading to the observed patterns are described and discussed within the relevant sections. Our sampling was determined by the as-deployed siting of the O2 platform, that following our measurements presented here, might have been better placed elsewhere on the EMEC site had our insights be available before berth locations were set. We now discuss our new analyses on turbulence intensities and length scales in relation to inflow measurements as well as wake propagation and recovery.\nII. Presentation problems\n4) Fig. 3 --- for the velocity field on the left, the color bars shows that dark and light color mean low and high speed, respectively. But the length of vectors in them seem to be in the opposite way. For instance, the color shows high speed in the lower right corner of the left figure in Fig. 3, but the vector length there indicates low speed there. Do I miss something?\nAs explained in both the caption and accompanying Results text, the vectors show the distribution of the flows relative to the O2 inflow region. The specific intention is to highlight the local scale flow variations as suggested above (in direct vicinity and away from the turbine). We have now modified the figure by reducing the density of displayed vectors to improve the clarity in presentation.\n5) Some results/analysis may be better presented. For instance, the discussion about Fig. 8, i.e., the paragraph right before the figure is unclear. I don't see an explanation of each of the lines in the figure.\nWe have modified this figure (now Figure 7) to separate out the Ebb and Flood for each of the transects represented by the individual lines and have updated the caption and associated text accordingly. We have also modified the figure to only include downstream wake characteristics.\n6) From Fig. 9, visually, it seems that U_d and U have certain correlation at 5D and 7.5D. Moreover, V has such a relation at 2.5D and 7.5D, and it is about zero, exhibiting no correlation at 5D. Are those true, and if yes, why?\nWe apologise for the poor choice of colour-coding within the original plot that led to confusion between the panels, we have now amended the plot to show the distinction between the Ud (A) and U & V (C) parts. We have also included turbulence intensity and length scales to this figure in (B).\n7) This work is valuable for ocean modeling (e.g., its validation). Therefore, it helps if more details are provided with this regard, such as adding time and date of the measurements in figure captions.\nWe thank the reviewer for this comment and have now provided a detailed Summary table in the Supplementary Information (Table S1) of all measurement transects and associated mean tidal velocities for the duration of each transect (as measured by the O2 ADCPs) and concurrently measured environmental data (e.g. wind and waves) as well as information on O2 platform heading and displacement, drone flight times and in-flight wind conditions. We have also updated all figure captions to include the date and transect number.\n8) I cannot judge much on measurement techniques but wonder if the measured data by ADCP mounted on the vessel are affected by the blocking/wake from the vessel itself. An discussion on this helps.\nWe have amended the Methods section to highlight that the ADCP was mounted below the hull depth and thus be unaffected by the vessel.\nOverall, I think this work has impact on understanding of the influence of the turbine on the flows and engineering problems at its location. However, it lacks intellectual merit, particularly science impact/insight on sciences and flow mechanisms. After revision, the submission may fit a domain science journal, such as Ocean Eng., Coastal Eng.\nOur work provides a bridge between simulations and reality that is fundamental to allow the wider adoption of this (and similar) technology and analyses approaches to measure flow interactions with floating infrastructure, which includes their impact on the natural environment. The latter is not only important for realised energy capacity but also for environmental and ecological considerations – we have now expanded on this highly topical issue in the Discussion (in L539 onwards, and L549 onwards). We have extensively modified the manuscript to better highlight these points and emphasize our objective on providing a more holistic, oceanographic lens on flow characterisation (L134 in the Introduction).\nOur baseline data are highly valuable for a number of reasons as they will: 1) inform the developer in its current location, 2) help EMEC to re-consider berth locations, 3) inform site characterization approaches at the nearby planned tidal array site (Westray Firth; refer to L538) and further afield and 4) increase the understanding of complex, natural tidal stream sites by providing data for modelling data sets that have yet to be validated (e.g. the RealTide project).\nFinally, we have substantially advanced conventional and novel instrument-specific analysis approaches, including ADCP-related wake tracing and PIV-derived turbulence intensity and length scales (please refer to comments below on newly added analyses) as well as velocity deficits. Therefore, we believe that this is a highly valuable contribution, with methods that can be extended to other offshore platforms, including floating wind (please refer to changes in the Discussion in L539 onwards).\nReviewer #3 (Remarks to the Author):\nKey Results\nThe Authors present results of an experimental campaign at the Orbital Marine Power tidal turbine platform in Scotland UK. Through acoustic profiling and analyzing aerial drone imagery, they attempt to reconstruct the flow conditions around and in the wake of the turbine. To the best of my knowledge, this is the first experiment of flow characterization around a tidal turbine.\nExperimental results show that the presence of areas of high turbulence intensity in the inflow region of the turbine may reduce the current speed at the rotors. This may have implications for the turbine’s structural stability and power generation. Such high flow variability is attributed to the particular configuration of the site (presence of islands).\nWe thank the reviewer for the consideration of our manuscript.\nValidity\nThe Authors do not support experimental findings with data on the power generated at the turbine. Even if the turbine was not functioning during the experiments, power data generated during similar tidal events are not included/mentioned in the paper. It is then impossible to ascertain whether flow variability has an effect on the turbine performance.\nWe thank the reviewer for this comment and can confirm that the turbine was not functioning during the time of our measurements. However, our study was not aimed to assess power performance of the turbine. Instead, our measures provide a baseline for the non-operational status and characterise the highly variable inflow region as well as the variable wake propagation which was still measurable despite the turbine not operating. Additionally, obtaining power performance data from tidal energy developers is not easy as such data is commercially sensitive– particularly in cases where turbines are prone to functional difficulties for prolonged periods.\nData gathered during aerial surveys is not provided and a few details are included on their processing pipeline. It is difficult to assess whether aerial imagery was of enough quality to be processed. Given the extremely challenging environmental settings during the experiment (I expect wind to be rather severe at the Fall of Warness), the drone vibrations may have significantly impacted images and velocity estimations. Also, velocities estimated from PIV tend to be rather variable. Typically, longer time intervals help to mitigate this aspect. However, I could not find any reference on the velocity temporal standard deviations in the paper.\nWe appreciate the reviewer’s concerns. To clarify, each flight consisting of multiple hovers up- and downstream of the platform approaches 4 GB in file size and will thus not be deposited online at this stage but the aerial drone data can be made available upon request.\nWe have now expanded the detail in the Methods section with emphasis on the processing. We have also provided a summary table in the Supplementary information (Table S1) with information extracted from the O2 weather station, including wind speed and direction and wave height. This table also provides in-flight wind readings at altitude which show that wind conditions were benign (averaging 6.53±2.00 ms-1) on the days the fine-scale transects and associated drone hovers were performed. During the days with higher wind speeds (15th & 16th), we undertook the broad-scale transects only and did not attempt to conduct drone hovers given higher winds. Based on Fairley et al. 2022 who assessed drone stability using a similar sized drone and wind speed range (up to 5.3 ms-1), frame to frame movement was converted to error in ms-1, giving a mean value of 0.07 ms-1 with an average geo-referencing error of 2.1 meters (reference provided below). For our flow speeds (order 2 ms-1), this equates to a contamination of turbulence intensity of order 0.035 – this has now been added to the Methods in L714-717. In addition to providing further details on our PIV analyses that reduces the inherent variability in these data (including the use of correlation thresholds, see L705), we have now assessed convergence of statistics given our hover time (please refer to Supplementary Fig. S9). In short, TI and velocity magnitude measurements show convergence over time in the freestream, however, do not fully converge in the shear line region due to the presence of large intermittent, turbulent coherent structures. This has now been added to the Methods section in L697-L700. Further we have added an assessment of the expected error in TI due to drone stability (L714-717). Extending the sampling duration to fully account for these would have then limited our spatial coverage under the assumption of Taylor’s frozen field.\nFairley, I. et al. Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment. Renew Energy 196, 839–855 (2022).\nSignificance\nSince experimental results are very much connected to the specific site where the turbine is installed, I do not expect the paper’s conclusions to highly impact the field of ocean energy extraction.\nThank you for your comment. Our work provides a bridge between simulations and reality that is fundamental to allow the wider adoption of this (and similar) technology and analyses approaches to measure flow interactions with floating infrastructure, which includes their impact on the natural environment. The latter is not only important for realised energy capacity but also for environmental and ecological considerations (energy-environment nexus) – we have now expanded on this highly topical issue in the Discussion (in L539 onwards, and L549 onwards). We have extensively modified the manuscript to better highlight these points and emphasize our objective on providing a more holistic, oceanographic lens on flow characterisation (L134 in the Introduction).\nOur baseline data are highly valuable for a number of reasons as they will: 1) inform the developer in its current location, 2) help EMEC to re-consider berth locations, 3) inform site characterization approaches at the nearby planned tidal array site (Westray Firth; refer to L538) and further afield and 4) increase the understanding of complex, natural tidal stream sites by providing data for modelling data sets that have yet to be validated (e.g. the RealTide project). Finally, our study provides important baseline data to address changes in our most dynamic coastal marine habitats.\nWe have substantially advanced conventional and novel instrument-specific analysis approaches, including ADCP-related wake tracing and PIV-derived turbulence intensity and length scales (please refer to comments below on newly added analyses) as well as velocity deficits. Therefore, we believe that this is a highly valuable contribution, with methods that can be extended to other offshore platforms, including floating wind (please refer to changes in the Discussion in L539 onwards).\nThe paper may suggest that the performance of the O2 turbine is not optimal, but it does not introduce any new findings on power generation from tides.\nThe Authors claim that they develop new survey methodologies, however, I do not find their observational techniques particularly novel. Acoustic current profiling is a well-established technique. PIV was first introduced in the 1990s and aerial PIV from drones has also been adopted in many settings, mostly in riverine but also in coastal areas.\nThank you for your comment. Although ADCPs are a standard oceanographic tool, they have not until this study been applied to map out the fine-scale details of flows in the vicinity of a tidal turbine structure drawing together both the standard velocity information as well as our novel adoption of wake tracing using acoustic backscattering. Yes, PIV is also a standard tool and we and other authors have previously applied it to coastal environments, however, we have demonstrated for the first time, the extraction of turbulence metrics (including turbulent coherent structures, turbulence intensity and length scale), particularly with regard to a utility-scale platform and associated inflow and wake dynamics (including wake velocity fluctuations, wake velocity deficits and wake recovery). We are not aware of any other work that has combined and reported on a combination of these two complementary approaches around a utility-scale turbine.\nData and methodology\nRegarding data acquisition, I think the Authors should have better commented on how they ensured that transects were parallel and, therefore, data profiles were consistently parallel. I expect navigation at the experimental site not to be easy and, thus, some corrections to the data could have been made.\nWe thank the reviewer for this comment as yes, undertaking boat-based transects in tidal flows that approach 8 knots whilst maintaining safe navigation is challenging. No corrections to the data have been applied as it is not possible to do so. After initial scoping of the site (first transect), the boat skippers were provided with waypoints setting out parallel-line transects aligned perpendicular with the dominant flow direction. They then manually navigated the survey vessel to the best of their abilities given the highly variable flows across the site as well as wind and wave conditions. We always included our boat transects within our data and plots prior to any gridding to demonstrate the minor variability that is inherent with this kind of sampling. We have now included all boat tracks in the Supplementary Information (Fig. S8) to show the consistency in our transect lines (with the exception of our very first scoping transects). The skippers have extensive experience in navigating tidal steam environments, further improved by the suitability of the survey vessel in terms of power (top speed of 27kn) and stability (catamaran). We have now expanded on this in the Methods (L601).\nADCP data were collected from an instrument mounted on a pole submerged 1.35m. Did the Authors apply any correction to get data up to the sea surface?\nNo, wherever profiles have been used, they have been shown with the separation from the surface (including the blanking distance and mounting depth). Where we show spatial plots, these are representing depth-means, we have included this now in all captions as well.\nMore details should have been provided regarding aerial drone surveys and processing:\n• Wind conditions during the hovers, wave conditions, and seasonality at the site should be included.\n• Appearance of the sea surface: the presence of features is fundamental to acceptable PIV data\n• PIV parameters: the Authors mention Ref. Lieber et al., 2021. However, experimental settings may have been diverse and PIV parameters should have been properly adapted.\n• Drone stability is not discussed. The Authors used a commercial drone platform, which is not capable of compensating for high wind conditions. In Lieber et al., 2021, the Authors mention that “There were no static reference points within the camera’s field of view so that whole-field contamination from the relative motion of the UAV cannot be removed”. However, this is not the case for the O2 experiment, where the turbine (or part of it) could have been regarded as static during the 2 minutes-long hovers.\n• PIV only provides a velocity characterization of the sea surface. I believe the Authors should have better clarified and discussed this.\n• Standard deviations of the PIV velocity fields are not provided.\nThank you for this suggestion. We have now included a full summary table of environmental conditions covering the entire survey periods (Supplementary Table S1). This includes mean tidal velocities for the duration of each transect (as measured by the O2 ADCPs) and concurrently measured environmental data (e.g. wind and waves) as well as information on the O2 platform heading and displacement, drone flight times and in-flight wind conditions. We have also extended our Methods to include more details on the PIV processing including correlation thresholds, a key indicator of data quality (L705 onwards). We have also assessed convergence of statistics (mean velocity and turbulence intensity) in L697-L700 with additional plots provided in the Supplementary Fig. S9.\nBased on Fairley et al. 2022 who assessed drone stability using a similar sized drone and wind speed range (up to 5.3 ms-1), frame to frame movement was converted to error in ms-1, giving a mean value of 0.07 ms-1 with an average geo-referencing error of 2.1 meters. For our flow speeds (order 2 ms-1), this equates to a contamination of turbulence intensity of order 0.035 – this has now been added to the Methods in L714-717. Please also refer to our Summary Table (Supplementary Table S1) where we now provide in-flight wind readings at altitude which show that wind conditions were benign (averaging 6.53±2.00 ms-1) on the days the fine-scale transects (and associated drone hovers) were performed.\nFurther, the O2 platform is not static but is anchored using a dynamic mooring system, which means that it does move over time scales of minutes and seconds. We have now included a metric for the O2 platform movement as part of the above-mentioned Summary Table (Supplementary Table S1) where it can be seen that the variance in the movement is of order several meters - therefore it cannot be treated as a static reference point.\nFairley, I. et al. Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment. Renew Energy 196, 839–855 (2022).\nThank you, we refer to these measures as surface measurements throughout and we have now also included a discussion point on this in L484. In short, given our findings on vertical shear variability in the cross-stream, it is rather unlikely that PIV-derived surface currents at this site could be reliably translated to currents at depth as suggested elsewhere. However, aerial imagery is useful in highlighting regions of flow variability that require further measurement campaigns.\nAlthough the SD of the velocity field is not provided, we provide the spatial distributions of both the velocity magnitude and turbulence intensity; the latter reflecting SD, as it is calculated using the variance divided by the mean. We therefore feel it is unnecessary to provide the SDs separately.\nSuggested improvements\nI suggest the Authors to take into account information on the power generation performance of the turbine. Such data could help in truly assessing whether the O2 is performing well or is negatively affected by the island wakes.\nPIV data should be stabilized before processing.\nOur study was not aimed to assess power performance of the turbine. Instead, our measures provide a baseline for the non-operational status and characterise the highly variable inflow region as well as the variable wake propagation which was still measurable despite the turbine not operating. Additionally, obtaining power performance data from tidal energy developers is not easy as such data is commercially sensitive– particularly in cases where turbines are prone to functional difficulties for prolonged periods.\nRegarding drone stability, for our flow speeds in the order 2 ms-1, a contamination of turbulence intensity of order 0.035 has now been added to the Methods in L714-717.\nClarity and context\nGenerally, I had difficulties in identifying the mean streamwise and cross-stream direction in Figures. I think such pieces of information should be properly included in all Figures.\nFigure 3 is of poor quality.\nI found it difficult to identify the O2 in Figure 6.\nVisualization of the ebb and flood tides is also not easy. For instance, this sentence: “The ebb and flood tide upstream distance to the O2 is 103 m and 108 m, respectively, while the ebb and flood tide downstream distance to the O2 is 86 m and 189 m, respectively.” would be clearer through a Figure sketch.\nThank you for this feedback. To aid clarity we have added the terms “streamwise” and “cross-stream” to this figure and caption (Figure 1C). Throughout we have orientated our Y-axis with the cross-stream direction.\nWe have now improved the clarity of this figure, also increasing the size of the vectors and reducing the density of the vectors. We have also improved the resolution of the figure.\nPlease note, this figure has now been moved to the Supplementary Information (Fig. S5) but we have added clarity to the caption to indicate when the O2 was passed (between the upstream and the first downstream line, ‘D1’).\nWe agree that our previous explanation was unclear and have therefore added annotations to the figure as suggested to clarify the distances. Please also note that we have added shaded errors to show variability in these measurements.\nReferences\nI think the Authors should consider similar experimental studies that make use of aerial drone surveys for non-invasive flow characterization.\nWe have originally referred to Fariley et al. in the Introduction and have now referred back to this study in the Methods (L714) and Discussion (L484) as well. We have not referenced studies that did not provide quantitative measures directly from the drone imagery.\nHowever, in the Introduction we also highlight other surface flow measurement techniques as obtained from radar and satellites, such as:\nLopez, G., Bennis, A., Barbin, Y. & Sentchev, A. Surface currents in the Alderney Race from high-frequency radar measurements and three-dimensional modelling. Phil.Trans.R. Soc. A 387, 20190494 (2020).\nMarmorino, G. Investigation of Turbulent Tidal Flow in a Coral Reef Channel Using Multi-Look WorldView-2 Satellite Imagery. Remote Sens (Basel) 14, (2022).\nFairley, I. et al. Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment. Renew Energy 196, 839–855 (2022).\nReviewer #4 (Remarks to the Author):\nReview of the manuscript: “Troubled waters: revealing sheared turbulent flows and wake dynamics of a floating tidal turbine” by Lieber et al. submitted to Nature Communications.\nOverview:\nThis manuscript presents the results of a resource characterisation at one of the berths at the EMEC site in Scotland where an idled floating tidal stream turbine is deployed, using vessel-mounted ADCP and drone imagery to measure the velocity field. Results capture the three dimensional nature of the tidal flow at this location which is highly influenced by the local bathymetry and nearby headland, which induced flow curvature and large levels of shear. Such findings will be of use for the tidal turbine developer to characterise loading when device is in operation.\nWe thank the reviewer for the consideration of our manuscript.\nGeneral comments:\nWhilst the analysis of the time-averaged velocity field from ADCP and drone is good, there are not enough insights into the turbulence characteristics that are developed during the different tidal phases measured. Unfortunately, the turbine is in parked position which does not generate a significant wake throughout the water column as the results evidence. Moreover, the use of free-surface velocity measurements through drone imagery is very recent as is the need for further understanding and/or correlation of such measurements with the velocities through the water column, which the authors did not analyse. In many instances the authors link ADCP signal to bubbles as a signature of turbulence, but this seems quite speculative, e.g. the idled turbine blades would not undergo cavitation and thus they will not generate air bubbles but fluctuations to the velocity field.\nWe thank the reviewer for their recognition of the novelty of our measurement techniques and we draw attention to our expanded Methods, Results and Discussion sections where we have expanded upon our analysis of turbulence (specifically turbulence intensity and length scales in both the inflow as well as the wake regions of the O2, and adjacent regions, such as the shear line and the freestream). Please refer to updated Figures 5F and 8B, the added section in the Methods (L711 -714) and the accompanying figure in the Supplementary Information (Fig. S10).\nWe have now also included a discussion point on the transferability of surface currents to depth in L484. In short, given our findings on vertical shear variability in the cross-stream, it is rather unlikely that PIV-derived surface currents at this site could be reliably translated to currents at depth as suggested elsewhere.\nSmall bubbles generated by surface wave breaking and entrainment of air at the water surface by turbulent features have a very low rise-velocity (order mm/s) and so are readily advected vertically downwards when vertical velocities exceed this. Hence they have been widely used in physical oceanography to map the distribution of near-surface physical flow features.\nWe have updated the Introduction (L114-L119) to explain this phenomenon and included some helpful example references, e.g.:\nNimmo-Smith, W. A. M., Thorpe, S. A. & Graham, A. Surface effects of bottom-generated turbulence in a shallow tidal sea. Nature 400, 251–254 (1999).\nThorpe, S. A., Green, J. A. M., Simpson, J. H., Osborn, T. R. & Nimmo Smith, W. A. M. Boils and turbulence in a weakly stratified shallow tidal sea. J Phys Oceanogr 38, 1711–1730 (2008).\nWe have also included references on how active acoustics are used in physical oceanography to map physical processes (L114), including turbulence and microstructure, gas seeps, or mixed layer depth.\nAnother key fluid-mechanics aspect that has received little attention is the wave field, acknowledging this would be difficult but aiming for a publication in Nature Communications would require such extensive analysis. Authors claim that during two ebb cycles the results differ notably. Thus, the question is what drove those changes, where waves very different in period, direction or height?\nWe have now included a full Summary table of the environmental conditions covering our data collection campaign. This comprehensive summary table (Supplementary Table S1) provides information on mean tidal velocities for the duration of each transect (as measured by the O2 ADCPs) and concurrently measured environmental data (e.g. wind and waves) as well as information on O2 platform heading and displacement, drone flight times and in-flight wind conditions. While not considered here, as waves were negligible during the small-scale transects, the inclusion of high waves would likely further influence wake propagation and recovery (L523). We also refer to waves later in the Discussion (L495), stating that platform motion under the combined effects of currents and waves can further amplify rotor load fluctuations potentially leading to damaging effects as found here:\nArcos, F. Z. De, Vogel, C. R. & Willden, R. H. J. A numerical study on the hydrodynamics of a floating tidal rotor under the combined effects of currents and waves. Elsevier Preprint, (2023).\nHowever, there is no clear difference in wind speed, direction and wave height between the two ebb cycle transects and hence we do not have a clear explanation for the observed variability and it may therefore be associated with flow variability at larger-scales than our present measurements capture.\nRegarding wake propagation, we have also expanded our Discussion on the variation in cross-stream location of the wake. The cross-stream movement (in meters) of the O2 platform (Supplementary Table S1) is now referred to in the Discussion (L518) and we also draw attention to new findings from the offshore floating wind literature which suggest that side-to-side motion of a floating offshore wind platform can be a novel origin for the onset of wind wake meandering (L515):\nLi, Z., Dong, G., & Yang, X. (2022). Onset of wake meandering for a floating offshore wind turbine under side-to-side motion. Journal of Fluid Mechanics, 934. https://doi.org/10.1017/jfm.2021.1147\nThe manuscript has several flaws regarding the description of the flow dynamics:\n In figure 5 you present “divergence” field, but you have not defined what that parameter is and how is calculated.\nWe have now clarified how all turbulence parameters are calculated in our Methods in L706 onwards. Specifically for divergence, we used the standard MATLAB function divergence (L707).\n Figure 9 B. This plots the time evolution of the velocity series in absolute magnitude whilst authors indicate these are velocity fluctuation, which is an erroneous concept.\nWe agree that there was a lack of clarity between the figure 9B and its caption that we have now rectified and we have also modified the corresponding Results section, referring to ‘fluctuations in velocity‘ in L409-L413. Please also note, we have modified this Figure (now Figure 8) to show additional analyses on turbulence intensity and length scales in the O2 wake and adjacent regions.\n Authors refer many times to “Shear line”, but this is not a concept used in fluid mechanics. Perhaps you aimed to refer to the shear layer but this is unclear.\nWe have reviewed and clarified our use of the term “shear line”, see L55-60 in the Introduction as we acknowledge that this type and scale of oceanographic flow feature may be unfamiliar. Specifically, we have added the following:\n“For instance, fast currents past headlands and islands can generate back-eddies (local flow reversals) and leeward wakes bounded by regions of strong horizontal shear (cross-stream gradient in streamwise velocity) hereafter referred to as “shear lines”5 6. Vortical structures, upwelling (surface divergence) and associated downwelling are characteristic of shear lines7 and their often kilometre-scale streamwise extent and cross-stream location will change with underlying flow velocities, as well as wind and wave action.\n5. Neill, S. P. & Elliott, A. J. Observations and simulations of an unsteady island wake in the Firth of Forth, Scotland. Ocean Dyn 54, 324–332 (2004).\n6. Wolanski, E., Asaeda, T., Tanaka, A. & Deleersnijder, E. Three-dimensional island wakes in the field, laboratory experiments and numerical models. Cont Shelf Res 16, 1437–1452 (1996).\n7. Horwitz, R. & Hay, A. E. Turbulence dissipation rates from horizontal velocity profiles at mid-depth in fast tidal flows. Renewable and Sustainable Energy Reviews 114, 283–296 (2017).\nFurther analysis of the PIV field would be required, e.g. authors refer to those shear lines but without quantifying Reynolds stresses, time-averaged vorticity, etc.\nThank you for this comment. We have now extended our analysis of the PIV data to include estimates of turbulence length scale for three different regions during the inflow hover upstream (Supplementary Fig. S3), over the O2 (see updated Figure 5 and Supplementary Fig. S4) as well as downstream of the O2 (Supplementary Fig. S6 and S7). The three different regions that length scales are calculated for include the shear line, the area covered by the O2 and the freestream, using cross-stream averaging (~20 m).\nWe refer to this in the Results (L299):\n“Turbulence intensity (Fig. 5F) increases from 10% in the freestream to more than 30% towards Eday with an associated increase in turbulence length scales (L /D) from 0.26 to more than 0.5. The increase in turbulence intensity and length scales in the shear line is due to the prevalence of intermittent, intense turbulent coherent structures.”\nWe also refer to our new analysis and results in the Discussion (L482).\nFinally, authors claim that IEC standards do not consider deploying two measuring devices on the lateral sides of the turbine. Please review the standards because they do present this very clearly in Figure 4 of the IEC 62600-200 2013.\nWe thank the reviewer for this comment and have now outlined both deployment configurations in our Discussion, the ‘in-line’ preferred configuration as well as the ‘adjacent’ configuration. However, we have now extended our Discussion to argue that neither method would capture the immediate inflow with platform excursion extending 40 m in both the cross-stream and alongstream directions – these measurements are now available in the Summary Tabe (Supplementary Table S1) and an additional Figure in the Supplementary Information ((Supplementary Fig. S8).\nPlease see our changes in the Discussion (L497):\n“The variability in onset conditions combined with extensive platform excursion (exceeding 40 m in both the cross- and along stream directions (Supplementary Fig. S8) also has implications for power curve testing. Power curve standards following the International Electrotechnical Commission Technical Standard 62600-20063 currently recommend two independently located ADCPs deployed up- and downstream of the device (‘in-line’, preferred option) or else ‘adjacent’ to the device. This approach is designed for mono-rotor, seabed mounted (i.e. static) devices, which is understandable given their prevalence to date. We show that this approach is incapable of capturing the spatially varying flow conditions across a dual rotor floating platform (cross-stream extent of 45 m) within the bounds of the varying device position.”\nRecommendation:\nThe reviewer considers that the manuscript does not have a rigorous analysis of the fluid dynamics of the flow at this site, instead it provides a good resource characterisation in terms of mean velocity distribution and time series. Unfortunately, the turbine is not operating, reducing the impact of this study in the community. The recommendation is to reject this paper. Hopefully the comments provided here are useful for the authors to improve this paper and resubmit it to another journal.\nThank you for your consideration. We have modified the entire manuscript to better express the aim of this contribution, namely that our work provides a bridge between simulations and reality that is fundamental to allow the wider adoption of this (and similar) technology and analysis approaches to measure real-world flow interactions with floating infrastructure, which includes their impact on the physical environment. The latter is not only important for realised energy capacity but also for environmental and ecological considerations (energy-environment nexus) – we have now expanded on this highly topical theme in the Discussion (in L539 onwards, and L549 onwards). We have extensively modified the manuscript to better highlight this and also emphasize our objective on providing a more holistic, oceanographic lens on flow characterisation and interactions in the abstract (L24) and the Introduction (L134), as opposed to providing a power performance assessment.\nOur baseline data are highly valuable for a number of reasons as they will: 1) inform the developer in its current location, 2) help EMEC to re-consider berth locations, 3) inform site characterization approaches at the nearby planned tidal array site (Westray Firth; refer to L538) and further afield and 4) increase the understanding of complex, natural tidal stream sites by providing data for modelling data sets that have yet to be validated (e.g. the RealTide project). Finally, there is significant value in understanding the flow dynamics in the absence of power generation, so that they can be separated from those with power generation and inform floating structure design.\nWe have substantially advanced conventional and novel instrument-specific analysis approaches, including ADCP-related wake tracing and PIV-derived turbulence intensity and length scales (please refer to comments above on newly added analyses) as well as velocity deficits. Our combined measurement approach delivers on surface and water column complexities (rather than mean velocity distributions) which have yet to be considered in simplified numerical simulations.\nTherefore, we believe that this is a highly valuable contribution, with methods that can be extended to other offshore platforms, including floating wind (please refer to changes in the Discussion in L539 onwards).\nThis is the first study to characteries the inflow and wake of a utility scale, multi-rotor floating platform. The method is highly repeatable (regardless of site), which can help to better understand turbine performance that ultimately informs future device design as devices scale up and encounter potentially larger loading challenges as a result of spatially variable flow (refer to L489 onwards).\nMinor comments:\nPlease provide page number and line numbers as it is good practice that eases reviewers’ task.\nYes, we have done this now and apologies for the inadvertent omission during the first submission.\n Abstract: “utility-scale floating...” need to indicate that the turbine was not in operation during the measurements.\nWe have now amended this in the modified abstract (L18):\n“Here we use aerial drones and acoustic profiling transects to quantify the site- and scale-dependent complexities of actual turbulent flows around an idled, utility-scale floating tidal turbine (20 m rotor diameter, D).”\n Abstract: “sufficiently high to provide… and downstream velocity deficit”. what are the key elements that you have unveiled of such flows?\nWe have now amended this in the modified abstract (L20):\n“The combined spatial resolution of our baseline measurements is sufficiently high to quantify sheared, turbulent inflow conditions (reversed shear profiles, turbulence intensity >20%, and turbulence length scales > 0.5D). We also detect downstream velocity deficits (approaching 20% at 4D in its idled configuration) and trace the far-wake propagation downstream using acoustic scattering techniques in excess of 30D.”\n Introduction, first sentence: OE is capable of partly contributing to the decarbonisation but not to absorb all of it. Needs update.\nThis has now been modified (L44).\n Remove “underwater” as it is intrinsic of tidal turbines.\nWe keep this to be inclusive of an intended broad readership.\n “shear lines”, what do you mean by shear lines? is this referring to the developed flow?\nPlease see our comment above, we have explained this in L55-L60 of the Introduction.\n “spread laterally”, flow would spread normal to the coastline rather than laterally.\nWe have amended our terminology throughout the manuscript and provide explanation in the Introduction, now referring only to it as “cross-stream”. The orientation relative to the local coastline (embayment, headlands) is not directly relevant.\n “km-scale spatial extent of shear lines”. still unclear what this is.\nThis has been clarified see in L55-L60.\n “horizontal gradient”, do you mean in the transverse direction? horizontal involves x and y.\nThis has been clarified throughout by the appropriate use of “cross-stream and along-stream”.\n Boils. Boils are only one type of coherent flow structure depending on the bathymetry features.\nWe have amended the wording at L63.\n “floating multi-rotor turbines”. Floating turbines can also be with a single rotor\nThat’s correct and we have amended this.\n Introduction, second page. The standard actually recommends that two ADCPs are deployed on the sides of the turbines to capture this spatial variability, which has even been discussed in previous studies looking at tidal turbine loading.\nPlease see our changes in the Discussion (L497):\n“The variability in onset conditions combined with extensive platform excursion (exceeding 40 m in both the cross- and along stream directions (Supplementary Fig. S8) also has implications for power curve testing. Power curve standards following the International Electrotechnical Commission Technical Standard 62600-20063 currently recommend two independently located ADCPs deployed up- and downstream of the device (‘in-line’, preferred option) or else ‘adjacent’ to the device. This approach is designed for mono-rotor, seabed mounted (i.e. static) devices, which is understandable given their prevalence to date. We show that this approach is incapable of capturing the spatially varying flow conditions across a dual rotor floating platform (cross-stream extent of 45 m) within the bounds of the varying device position.”\n “It is still unknown… various spatial scales”. It is known that turbulence intensity and lengthscale will drive the wake recovery, they just need to be characterised at the site. This statement needs to be corrected.\nWe have added parameters to discuss this (turbulence intensity and length scales). Please also refer to the amended discussion to address this (L520):\n“…enhanced recovery likely associated with turbulent coherent structures and associated increases in TI and L /D in the shear line.”\nu\n Introduction, page 3, last paragraph. Need to highlight that the device was not in operation.\nWe have included this now in the Introduction (L139) as well as throughout all other sections (Abstract Results, Discussion, Methods).\n Figure 1. It would be helpful to include flow directions of ebb and flood tides.\nWe have now modified the figure to include this information.\n Results, page 6. “and vortices”. What kind of vortices and how did you determined these?\nWe added clarification on this in Line 180 (‘vortical structures’).\n Page 11. “divergence distributions”. Can you define this quantity?\nWe have now called these fields as they describe the spatial distributions of this parameter (L303).\n Page 13. “Notably…”. would this be attributed to wind-induced shear or waves? have you measured the wave conditions at the site?\nWe have now included a Summary table of all environmental conditions and have added a comment on this point in our discussion. We have now included a full Summary table of the environmental conditions covering our data collection campaign. This comprehensive summary table (Supplementary Table S1) provides information on mean tidal velocities for the duration of each transect (as measured by the O2 ADCPs) and concurrently measured environmental data (e.g. wind and waves) as well as information on O2 platform heading and displacement, drone flight times and in-flight wind conditions. There is no clear difference in wind speed, direction and wave height between the two ebb cycle transects and hence we do not have a clear explanation for the observed variability and it may therefore be associated with flow variability at larger-scales than our present measurements capture.\n Page 16. “the wake’s intensity and area”. This is not clear. What do you mean by wake area and how is this quantified?\nWe have expanded on our Methods to provide better clarity on how these were calculated. Detail on this can be found in the methods in L675 onwards and in the Supplementary Fig. S5. Briefly, retaining only surface-connected scattering (bubble entrainment) in the water column, the cross-sectional area (spread in both the vertical and cross-stream, in m2) was calculated. We’ve changed the word ‘intensity’ throughout to the more appropriate term ‘backscattering strength’.\n Page 16. “of flow speed and downstream distance”, and more parameters such as TI or lengthscales now only speed here.\nYes, we recognise that wake recovery is influenced by TI and length scales and have expanded on this in the Discussion (L521). Please note, we have also modified the section mentioned in the comment to:\n“Overall, the turbine wake forms immediately downstream of the turbine (see drone image in Fig. 1D) and can be characterised by shallow, intense bubble entrainment at the first downstream line (~100 m downstream or 5D). The bubbles then start to disperse in the cross-stream and vertically (as deep as 20 m) as they are advected downstream.”\n Page 16. “The bubbles then start to disperse laterally…”. This read very speculative. It is very hard to characterise the flow based on these \"bubbles\".\nPlease see earlier comment on surface-entrained bubbles and their wide-scale use as oceanographic tracers.\nWhy did you not look into the analysis of turbulence intensities in their vertical components? what about vertical velocity distribution? Bubbles are an idealisation as they would only be introduced by the blades when cavitating and as the device is not operating they are not in the flow.\nDue to the nature of our mobile transects, the vertical velocity is highly variable and also subject to vessel heave due to surface waves.\n Page 18. “velocity fluctuations of the U and V components over time”. These are not fluctuations but instantaneous variation.\nThank you, we have now amended this. When assessing the variation of the U and V velocity components over time as done in Figure 8, we now refer to “fluctuations in velocity”.\n Page 18. “In the shear line area… through the sample area”. Again, this is speculative because if this is a shear layer there would be vortices induced due to lateral shear that travel intermittently downstream.\nWe have now reviewed and amended this section in L407.\n Page 18, “more isotropic compared”. More isotropic based on what criterion? To establish this you would need to look into the three components of turbulence intensity and is not the case.\nAmended in L414 to: “of similar amplitude in both horizontal components”.\n Page 18. “The negative velocity deficit… leading to vertical blockage effects”. this are likely to be due to bathymetry effects as an idled turbine will not have a large thrust that can block the flow. However, changes in bathymetry (both roughness and slope) can contribute to this, as shown in your figure 2.\nWe agree with the Reviewer and have now lost this statement all together as we agree that it was too speculative.\n Page 20. “tank experiments.”. Do you mean water tanks?\nYes, including flumes, but we have now re-structure the Discussion and have lost this particular statement.\n Page 20. “there are no studies to date… shear during the inflow”. This is the very similar to studying a device with a given yaw angle, as it encounters a given shear across the rotor.\nYes, there are similarities and we thank the reviewer for this comment. As noted above, we have now modified the Discussion and this particular phrase is no longer in the manuscript. Please refer to our revised section in L458-476.\n Page 20. “in the cross-stream ‘meandering’ is… idealised wake modelling.”. This is going to be confusing to the readers. Wake meandering occurs only if the turbine is operating as the rotor rotates, it induces wake meandering downstream. However, here the turbine is not operating, thus there is no wake meandering as such. Flow can be oscillating laterally as it goes through a porous body. This needs to be amended.\nWe have originally used the term “meandering” to refer to the variation in cross-stream (Y-dimension) location as the wake is advected downstream, best visualised in our wake summary plot (now Figure 7). However, we appreciate the reviewer’s concern that this might be confusing in light of the traditional use of the team “meandering”. We have amended the manuscript throughout and now discuss the variation as the mean “cross-stream” location of the wake in the Y-dimension which shows large-scale cross-stream variation (±50 m in the mean cross-stream location during a single transect of order 30 min duration), please refer to L513.\n Page 21. “Current power curve standards… both tidal states”. See previous comment. Figure 5 of the IEC 62600:200 show how to locate two ADCPs.\nPlease see our changes in the Discussion (L497):\n“The variability in onset conditions combined with extensive platform excursion (exceeding 40 m in both the cross- and along stream directions (Supplementary Fig. S8) also has implications for power curve testing. Power curve standards following the International Electrotechnical Commission Technical Standard 62600-20063 currently recommend two independently located ADCPs deployed up- and downstream of the device (‘in-line’, preferred option) or else ‘adjacent’ to the device. This approach is designed for mono-rotor, seabed mounted (i.e. static) devices, which is understandable given their prevalence to date. We show that this approach is incapable of capturing the spatially varying flow conditions across a dual rotor floating platform (cross-stream extent of 45 m) within the bounds of the varying device position.”\n Page 21. “There is currently a lack of measurements… turbine-turbine interaction from being developed”. That is right. More needs to be done here.\nWe concur and we believe at this stage (prior to arrays and the built-out of turbines), there is significant value in understanding the flow dynamics in the absence of power generation, so that they can be separated from those with power generation and inform floating structure design. We thank the reviewer for their helpful comments that again highlight the importance of our work demonstrating the real-world complexities of tidal flow environments."}]

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