Thank you for the interesting manuscript. In my review below, I have tried to answer the review criteria mentioned on the journal website.

1. Does the paper address relevant scientific questions within the scope of HESS?

Yes. The paper offers a new approach to measure river water levels using cameras and deep learning, and its concrete implementation for a use case. This could be considered as the development of a new source of data to better observe river water levels.

1. Does the paper present novel concepts, ideas, tools, or data?

Yes. While the technology they use is not new (but state of the art), the idea to merge iPhone LiDAR data with water segmentation, and propose a concrete implementation with Arduino & Raspberry Pi has not been tested until now, to my knowledge.

1. Are substantial conclusions reached?

The scope of this study is limited to a single site and there is no discussion as to how easy the methodology employed in this work could generalize, but the results obtained show good promise.

1. Are the scientific methods and assumptions valid and clearly outlined?

Yes. The authors provide a detailed presentation of their methodology and technologies. I would be interested to see more experiments to assert the genericity and ease of use of the method, but it might go beyond the scope of this current work. 

The main one I have in mind would be to create a water segmentation network that does not rely on the images of the same camera and compare with the current results. Such datasets exist (see [1] where some of them are cited). In practice, having to annotate hundreds of images at each site to make the method work seems a hard limitation on the scope of this work.

1. Are the results sufficient to support the interpretations and conclusions?

Yes, although the conclusions made by the authors remain limited in scope.

1. Is the description of experiments and calculations sufficiently complete and precise to allow their reproduction by fellow scientists (traceability of results)?

Yes. The authors also provide a github link with their code. The research is dependent on the proprietary Apple iPhone 13 LiDAR sensor, but it seems reasonable.

1. Do the authors give proper credit to related work and clearly indicate their own new/original contribution?

Yes.

1. Does the title clearly reflect the contents of the paper?

Yes.

1. Does the abstract provide a concise and complete summary?

Yes.

1. Is the overall presentation well structured and clear?

Yes.

1. Is the language fluent and precise?

Yes.

1. Are mathematical formulae, symbols, abbreviations, and units correctly defined and used?

- Equation 1: "camrea" should be "camera"

- The efficiency criteria used in the work (R², NSE, RMSE, PBIAS) should be defined with a formula for more clarity.

1. Should any parts of the paper (text, formulae, figures, tables) be clarified, reduced, combined, or eliminated?

- L161-170. This part should be clarified. I am not sure how the GCPs are used to register the LiDAR sub-regions that were captured. An explanation of the AruCo marker would help. Maybe simplify here and refer+merge with Section 4.3?

- Figure 1 should be better explained, especially Fig 1b with, I think, the AruCo markers & the white numbers in black background.

- L298-301/Table 2. This should be better motivated or explained. As I understand, I am not sure why non river ground truth pixels should be ignored.

- L287-292. There is no mention of how the KNN K parameter was validated. I also wonder if that parameter played a role in the results (L376-385).

- L316-331. Isn't there a possibility that the Transformer networks are "overfitting" the single camera training set? 

1. Are the number and quality of references appropriate?

Yes, although I was a bit surprised to not see any mention of [1, 2] as the work is quite related.

[1] Vandaele et al., https://doi.org/10.1007/978-3-030-71278-5_17

[2] Vandaele et al., https://doi.org/10.5194/hess-25-4435-2021

This manuscript presents an original vision-based water level measurement approach applicable to real-time flood conditions. While I'm not able to evaluate many technical aspects relating to computer vision, AI and electronics involved in this technique, I found that the approach is rigorous, the methods are sound and openly available, and the research work is very well presented. I therefore feel that this contribution is significant for the water-related research community. Hereafter are a few comments for discussion and improvement of the paper.

It should be noted somewhere (e.g. in the abstract) that the system cannot work at night.

L142 The geographical coordinates of the site would be useful.

4.3 L266 Is the focal length fixed or variable?

Why don’t you estimate the DLT parameters directly? What is the advantage of estimating the intrinsic parameters beforehand?

Editorial:

Eq. 1: camera (camrea)