The manuscript deals with image processing aimed at obtaining reliable flow discharge estimates under continuous unsupervised operation. The utility of this approach is acknowledged by the WMO, recently issuing an update to WMO-No. 168, section 5.3.7.3 – Image-based methods for discharge measurements. The authors add to the general discussion on the applicability, accuracy and associated uncertainties by presenting the results from nearly one-year continuous observations on the River Dart, UK. The data were collected at Austins Bridge, where available hydrometric records span back to 1958, and treated in compliance with the WMO-No. 168 requirements.

The River Dart at Austins Bridge yields the longest hydrometric record on the River Dart, but its hydrologic presentation in subsection 2.1 is apparently scarce. It seems however important to put the experimentation in the proper context, in terms of both average and extreme flows. The notion on ‘reference observations’ appears as early as in Subsection 2.5.1, where they are briefly mentioned in L172 as a source of the conversion factor, but the details are lacking. What is their distribution across the velocity/discharge range? What are the standard data collection protocols? Even the number of observations is uncertain. The total number of reference observations (303) is presented in the Abstract, but never discussed elsewhere. Were some of the, or all aDcp observations (at least some were, as stated in L182, but clearly not all, since the record starts is 1981)? Subsection 2.5.1 is vague on the subject, L177 has no notions on the Ua protocol, and in the rest of the subsection only aDcp data are discussed.

The manuscript is generally well-written, and most issues that can be reported are related to an overall quality of presentation and text flow. While the manuscript grosso modo has most of the data needed, some of these data are scattered across the manuscript making it hard to follow. Several by-line comments are also given below:

L31: are these mentioned below algorithms, in the sense of Kanade-Lukas algorithm, or rather methods and approaches, as per WMO-No. 168?

L41: reference the Lukas-Tomasi algorithm, and probably mention Kanade here also, since KLT in KLT-IV might presumably stand for ‘Kanade-Lukas-Tomasi’ (indeed, references from L132 can be moved up here).

L43: PIV is not presented above, but is apparently the same as “PIV” in LSPIV, for the latter is the same PIV in the narrower context (WMO-No.168).

L43-44: in referencing (Tauro et al., 2018b) it can be noted that at least some of the results of that paper were obtained using “woodchips … continuously deployed” from the upstream so not fully in the context of a “continuous, automated, and unsupervised workflow”.

L94-95: this phrase evokes the question on what were the hydrological conditions during the experiment, of which there is no notion in Section 2.1. How they are compared to long-term averages and extremes? It is only later in the manuscript (L178) that we will know that the reference flow measurements range from 1.7 to 145 m3s-1; no info is provided on the flow discharge ranges during the experiment.

L170: “20 segments of equal size”; from Figure 2, these are segments of equal width, not equal area, though for polygons the latter is most commonly assumed. This needs to be stated explicitly. Am I right to understand this so that while the flow width increases with the increasing discharge, the width of each segment increases accordingly?

Also, this first paragraph of the Subsection 2.5.1 is somewhat confusing. First, while the basic references are provided, I would be interested in having more details on how exactly the observed data were extrapolated over the regions out of the camera sight using these three techniques; why there are 20 segments, and not more/less – how is this justified? In L170, was it so that “average velocities for 20 segments of equal sizes are established” stands for the water surface velocities averaged across the segment width? What is the number of pixels with data in each segment surface? How exactly was alpha derived? The (Perks, 2024b) reference is misguiding in that might presumably lead to the supportive information while it simply references a figure derived from unexposed data. Were the alpha values derived segment-wise for all 60 aDcp measurements? Were the segments similar or close in size (width?) in aDcp and extrapolation exercises? Further on, is the “cell” in L173 equivalent to “segment” in the lines above? If not, the difference must be explained otherwise the text is ambiguous.

L180: In “calculate the (1-D) velocities derived from reference observations”, can these be replaced simply by Ua, as given in L177? Similar to this, can “1-D” be omitted from most instances where its presence is misleading or redundant, e.g., L184, L187, L191-192, L198 and almost each and every instance below. Once the definition is given, there is no need to reiterate, and it makes sense to substitute. As in L180, same in L196-197, same in L198-199, and so on.

In this Subsection 2.5.2, again, the statistics for reference stage observation can be highly utile. Were these reference observations mentioned in L196 the same as presented elsewhere including the Abstract? Can the authors consider providing the details either here or in the Section 3?

L210: It is probably more correct to not start the “Velocity reconstruction” section with the overview of the EA data. Also, in Figure 3, velocity reconstruction is applicable to both procedures employed, see Subsections 2.5.1 and 2.5.2 accordingly. Thus Section 3.1 must be renamed accordingly.

L220: more justification is needed here or above on claiming the constant Froude as a best-performing model, since the Figure 4 is indecisive on this matter.

Figure 5: is it correct that the distances are given from the left bank while the camera is on the right bank?

L244-245: how exactly the conclusion that “as few as eight flow gauging measurements” suffice to successfully quantify the cross-section averaged velocity was reached? Does it imply that the uncertainty (variability) level is acceptable at this n.

L266: “autonomously analysed in an unsupervised workflow”. This said, it must be noted however that while the imagery was indeed collected in that way, its further treatment and the final output weren’t possible without the prior work on-site, including aDcp surveys under challenging conditions of the below 1% flood. Without this, based solely on the a priori knowledge of the site, what the potential errors of the standalone application could have been?

L278-279: Top and bottom blind zones are present in aDcp measurements, along with right and left margins, so the reported Q is corrected for these in RiverSurveyor software. The wetted cross-section area has some effects on the aDcp-derived flows, but it is not to be overestimated.

Overall, I concur with the authors in that the major utility of the described approach is in the “development or refinement of stage-discharge rating curves”, where the OTV and OTV-like approaches can be highly utile in increasing the accuracy of high flow estimations. For the River Dart at Austins Bridge, the highest observed peak flow is 550 m3s-1 (1979-1980), almost 400 m3s-1 over the observed highest peak flow. At this part of the flow duration curve, as far as I understand, the OTV can substantially improve the flow estimates in the straight single-thread non-deformable channel.

This paper presents an interesting study on the use of a camera gauge for long-term measurement of flow velocity and discharge. Its structured and clear approach, coupled with the evaluation of an automatic system over a period of approximately one year, is a noteworthy contribution. Also, the discussion of interpolating and extrapolating surface velocities demonstrates the potential of camera gauges as a viable supplement or maybe even alternative for continuous monitoring in hydrology.

However, I think, there are several areas where further elaboration or clarification is needed. The paper focuses primarily on the methodological aspects, and therefore, I think it would be more suitable to be considered as a technical note rather than a research paper. This reclassification would better reflect its contribution.

The authors assume stable intrinsic and extrinsic parameters throughout the year-long observation period. This assumption warrants deeper discussion, particularly in light of potential influences like temperature changes (e.g. as shown by Elias et al., 2020) or environmental factors like wind causing camera movement. Would a frequent update of the position/orientation not be needed? What are the expected error ranges if no update occurs? Addressing these issues in more detail is necessary to increase the robustness of the conclusions.

The assumption of stable flow conditions during the calibration period also needs further exploration. I think, it would be beneficial to discuss how this impacts measurement accuracy, particularly over extended periods with potential varying riverbed or cross-sectional conditions. The authors could include further metrics such as standard deviations and deviations from the reference measurements temporally resolved during the observation period to provide more insight of such influences.

This study highlights the great potential of camera gauges for long-term, nearly continuous monitoring of hydrometric parameters. However, further discussion of the methodological assumptions and calibration limitations would greatly enhance its applicability.

Further smaller comments:

L38-41: The description of the Lucas-Tomasi method should clarify that it is not a stand-alone approach to Particle Tracking Velocimetry (PTV) or Particle Image Velocimetry (PIV). Rather, it is another matching algorithm besides, e.g., NCC or FFT based approaches – however with the advantage of being a least square approach allowing for particle rotation and deformation during tracking, that complements particle detection methods or grid-based strategies.

L104: Clarify what is meant by a "distorted camera model." Do the authors mean a camera model that describes the intrinsic camera parameters including distortion parameters?

L107: The role of location and orientation (iii) in the workflow needs clearer distinction. Are these parameters derived from ground control points (GCPs) via spatial resection? This should be elaborated for better understanding. Is the location/orientation not the consequence from using the GCPs measured in the image and in the object space and then estimating the location/orientation using, e.g., spatial resection?

L120: Confirm whether the projection error refers to deviations in object space or image space, i.e., do you indeed mean projection error or rather reprojection error?

L163: There is already a study showing that one surface velocity from image velocimetry is enough to get discharge with entropy approach (Bahmanpouri et al., 2022)

L228-232: What is the reason for the deviation?

L245: The distribution of the eight reference measurements across different water levels should be clarified. It is critical to evaluate whether these measurements adequately represent the range of conditions encountered at the specific study site. Might it be more important to consider range of water level at which reference available and how well this represents most gauge situations?

L304: The authors can estimate the error influence theoretically by varying the water level and flow velocities and see how strong changes of estimated velocities are.

References – Note, please do not consider the references as a suggestion to be implemented in the manuscript rather than a proposal to more literature to underline my comments made:

Elias, M., et al. (2020): Assessing the influence of temperature changes at the geometric stability of low-cost chip cameras. Sensors, (https://www.mdpi.com/1424-8220/20/3/643)

Bahmanpouri, F., et al. (2022): Estimating the Average River Cross-Section Velocity by Observing Only One Surface Velocity Value and Calibrating the Entropic Parameter. Water Resources Research, (https://doi.org/10.1029/2021WR031821)