Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors,

your manuscript is focused on an important topic and it is clearly written. However, I believe it can be improved by the clarification of a few points and the reorganization of the text. 

Manuscript content:

• How did water level change during the study period? Did it have any influence on vertical displacements? If possible, please include a chart of water level.
• In keywords only the earth-fill dam is refered to; please include also the concrete dam.
• Lines 77 - 84: PSInSAR and SBAS are already MTInSAR methods; please rephrase.
• Lines 267 - 272: were there common points from ascending and descending orbits? Or did the authors use nearby points? In this case, what was the distance between them?
• Figure 6: Which was the measurement interval of the GNSS data? Daily or six-hour? Did the authors apply any filters on the data?
• Figure 7: the largest differences were observed between 2019 and 2021. Is there any explanation?
• Some studies using InSAR for dam monitoring are missing. For example:
  • Milillo, Pietro, et al. "Monitoring dam structural health from space: Insights from novel InSAR techniques and multi-parametric modeling applied to the Pertusillo dam Basilicata, Italy." International journal of applied earth observation and geoinformation 52 (2016): 221-229.
  • Milillo, Pietro, et al. "Space geodetic monitoring of engineered structures: The ongoing destabilization of the Mosul dam, Iraq." Scientific reports 6.1 (2016): 37408.

Manuscript organization:

• Section 1 (Introduction) is missing a brief explanation of paper organization.
• Section 1.1 (Study area) should be in section 2 (Materials and Method).
• Lines 293 - 320 should be in section 3 (Results and Discussion).
• Lines 331 - 351 should be in section 3 (Results and Discussion).
• Lines 422 - 500 should be in section 3 (Results and Discussion).
• Lines 507 - 532 should be in section 3 (Results and Discussion).

Best regards

Author Response

We sincerely appreciate your comments, which will really help us improve our work. We provide answers to your questions with appropriate explanations.

 

Comment 1. How did water level change during the study period? Did it have any influence on vertical displacements? If possible, please include a chart of water level.

Response 1. In accordance with previous studies [Tretyak, K., Serant, O. & Bisovetskyi, Y. (2024). Modeling of temperature deformations on the Dnister HPP dam (Ukraine). Journal of Applied Geodesy, 19(2), 247-264. https://doi.org/10.1515/jag-2024-0060], no dependence was found between the spatial movements of the GNSS network points and changes in the water level in the reservoir. The graph of changes in the water level in the reservoir is added to Fig. 12 and the corresponding explanations are made before it:

Based on the findings of previous investigations [18] conducted at the Dniester Hydroelectric Power Station dam, variations in the reservoir water level (Fig. 12) have been found to exert no significant influence on the spatial displacements of the GNSS monitoring points. This conclusion is supported by the low correlation coefficients, which range from 0.17 to 0.19. In contrast, a consistent relationship has been identified between the observed displacements and temperature variations. Hence, it can be concluded that the predominant factor governing the vertical movements of the control points is the thermal expansion of the dam’s concrete structures.

 

Comment 2. In keywords only the earth-fill dam is refered to; please include also the concrete dam.

Response 2. Keyword added.

 

Comment 3. Lines 77 - 84: PSInSAR and SBAS are already MTInSAR methods; please rephrase.

Response 3. Agree. Paraphrased version:

However, the monitoring of hydropower facilities is not limited to these two interferometric approaches. As reviewed in [9], additional methods include differential radar interferometry (DInSAR), ground-based radar interferometry (GBInSAR), and quasi-persistent scatterers interferometry (QPSInSAR), and multi-temporal radar interferometry (MTInSAR).

 

Comment 4. Lines 267 - 272: were there common points from ascending and descending orbits? Or did the authors use nearby points? In this case, what was the distance between them?

Response 4. During the decomposition process, we defined points from two orbits as common if they were located at a distance of up to 22 m. A corresponding explanation has been added to the text.

“…..As a result, a map of vertical deformation velocities was generated for the common points (persistent scatterers) identified in both ascending and descending orbits (Figure 5). The maximum distance threshold used to identify persistent scatterers from ascending and descending orbits as common points was approximately 22 m.”

Comment 5. Figure 6: Which was the measurement interval of the GNSS data? Daily or six-hour? Did the authors apply any filters on the data?

Response 5. As input data, we used daily GNSS data without any filtering. Subsequently, the filtering algorithm described in 3.2. Time series processing methodology was applied.

Figure name changed: Figure 6. Time series of vertical displacements at the earth-fill dam, comparing InSAR-derived (12-day interval) and GNSS-measured (daily) data.

 

Comment 6. Figure 7: the largest differences were observed between 2019 and 2021. Is there any explanation?

Response 6. This is a very valid observation. We cannot fully state the reason for the deviation of the time series in the period from 2019-2021. It is obvious that the differences are related to the time series of the InSAR data. Most likely, the data after 2021 received more accurate orbital corrections of the satellite.

 

Comment 7. Some studies using InSAR for dam monitoring are missing. For example:

• Milillo, Pietro, et al. "Monitoring dam structural health from space: Insights from novel InSAR techniques and multi-parametric modeling applied to the Pertusillo dam Basilicata, Italy." International journal of applied earth observation and geoinformation 52 (2016): 221-229.
• Milillo, Pietro, et al. "Space geodetic monitoring of engineered structures: The ongoing destabilization of the Mosul dam, Iraq." Scientific reports 6.1 (2016): 37408.

Response 7. Added to references.

 

Comment 8. Manuscript organization:

• Section 1 (Introduction) is missing a brief explanation of paper organization.

Added at the end of Intro.

• Section 1.1 (Study area) should be in section 2 (Materials and Method).
• Lines 293 - 320 should be in section 3 (Results and Discussion).
• Lines 331 - 351 should be in section 3 (Results and Discussion).
• Lines 422 - 500 should be in section 3 (Results and Discussion).
• Lines 507 - 532 should be in section 3 (Results and Discussion).

Corresponding changes have been made to the manuscript.

 

Best regards

 

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript presents a comprehensive and technically sound study on the integration of multi-source geodetic data (GNSS, RTS, and InSAR) to monitor the vertical, thermally-induced displacements of the concrete dam at the Dniester Hydroelectric Power Plant. The research is highly relevant, addressing a critical topic of infrastructure health monitoring under challenging conditions. The key strength of the paper lies in its innovative fusion of datasets and the development of a novel Fourier-based optimization algorithm for time-series analysis. The methodology is robust, and the results are clearly presented. The references are appropriate. However, the manuscript exhibits the following weaknesses that need to be addressed or improved before it can be considered for publication:

1. The authors conclude that the PS-InSAR and GNSS time series exhibit good agreement in consistent seasonal patterns and a common long-term trend (e.g., “The vertical displacement time series obtained from GNSS and InSAR (Figure 6) show good agreement…”). However, this conclusion appears to rely mainly on visual interpretation rather than rigorous quantitative evidence. From Figures 6 and 7, the consistency between the two datasets is not particularly prominent—differences in peak values, phase shifts, and offsets of several millimeters are visible. Therefore, the claim that they differ by only “a few pixels” may be overly subjective. To strengthen this conclusion, I recommend adding quantitative analyses such as correlation coefficients (R² or Pearson), RMSE, cross-correlation for phase lag evaluation, or significance tests. Additional metrics such as peak-to-peak difference or linear regression (or Bland–Altman) analysis would further substantiate the claimed consistency.
2. The manuscript correctly identifies that only one GNSS point (ACP2) has a direct spatial coincidence with an InSAR persistent scatterer (PS37), and the trends at other locations appear consistent. I recommend enhancing the Results and Discussion section by explicitly acknowledging this limitation. This can be reframed constructively as a rationale for the future installation of corner reflectors at all GNSS sites, thereby turning a limitation into a forward-looking recommendation.
3. The parameters for the maximum number of filtered points (N) and harmonics (K) are stated to be determined empirically. The manuscript does not explain the rationale or criteria used to select the ranges of N and K. Please provide justification or sensitivity analysis for these empirical choices.
4. The resolution of several figures is low, and many annotations are difficult to read. For example, in Figure 2, the labels WP4, CP40, and WP3 are unclear; in Figure 4a, nearly all labels are hard to distinguish; and in Figure 5, PS37 appears to be placed next to a colored circle rather than the red triangle, which may cause confusion. It is recommended to improve figure resolution and adjust label placement for better readability.
5. The title of Figure 13 (“Spatial distribution of the amplitudes of vertical displacements (mm) of control points...”) appears inaccurate, as the figure currently shows only the locations of GNSS, InSAR, and RTS points, without any visible encoding (e.g., color gradient or scaled symbols) representing amplitude values. Please either modify the figure to visualize amplitude information or revise the title accordingly.

Author Response

We would like to sincerely thank you for the thorough evaluation and constructive feedback. We greatly appreciate the positive assessment of our study’s relevance, methodological robustness, and data integration approach. The comments provided are very helpful, and we have carefully addressed each of the suggested improvements in the revised version of the manuscript.

 

Comment 1. The authors conclude that the PS-InSAR and GNSS time series exhibit good agreement in consistent seasonal patterns and a common long-term trend (e.g., “The vertical displacement time series obtained from GNSS and InSAR (Figure 6) show good agreement…”). However, this conclusion appears to rely mainly on visual interpretation rather than rigorous quantitative evidence. From Figures 6 and 7, the consistency between the two datasets is not particularly prominent—differences in peak values, phase shifts, and offsets of several millimeters are visible. Therefore, the claim that they differ by only “a few pixels” may be overly subjective. To strengthen this conclusion, I recommend adding quantitative analyses such as correlation coefficients (R² or Pearson), RMSE, cross-correlation for phase lag evaluation, or significance tests. Additional metrics such as peak-to-peak difference or linear regression (or Bland–Altman) analysis would further substantiate the claimed consistency.

Response 1. We have removed information from the text regarding consistency in the phase of seasonal fluctuations and focus on common long-term trends. To this end, changes have been made to Fig. 6 - linear trend lines for both data sets have been added.

 

Comment 2. The manuscript correctly identifies that only one GNSS point (ACP2) has a direct spatial coincidence with an InSAR persistent scatterer (PS37), and the trends at other locations appear consistent. I recommend enhancing the Results and Discussion section by explicitly acknowledging this limitation. This can be reframed constructively as a rationale for the future installation of corner reflectors at all GNSS sites, thereby turning a limitation into a forward-looking recommendation.

Response 2. Thank you for your comment! Indeed, at this facility and other hydroelectric power plants in Ukraine, we are carrying out work on installing comprehensive geodetic stations that combine GNSS antennas with corner reflectors for radar monitoring.

This information was described in the text!

“…The integration of radar interferometry into the system of geodetic observations at this and other hydropower facilities requires a larger number of points with collocated ground-based and remote sensing measurements. For the combined application of InSAR and GNSS methods, this can be achieved by installing ground-based corner reflectors for radar monitoring at GNSS network sites [27]. In this way, GNSS network points will appear as persistent scatterers on the deformation velocity map derived from radar images, thereby enabling reliable validation of the InSAR results. … ”

To emphasize this information, we have highlighted it as a separate paragraph in section 3 Results. In addition, this is summarized in the Conclusions (statement 2):

“2. The reliability of InSAR monitoring was confirmed by comparing the results of ground-based observations and remote sensing at common points of the geodetic network. Therefore, it is recommended to expand the network of comprehensive geodetic points, which structurally integrate corner reflectors for radar signal reflection, a GNSS antenna, and a prism reflector for linear-angular measurements.”

Comment 3. The parameters for the maximum number of filtered points (N) and harmonics (K) are stated to be determined empirically. The manuscript does not explain the rationale or criteria used to select the ranges of N and K. Please provide justification or sensitivity analysis for these empirical choices.

Response 3. Relevant explanations were added in the paragraph after formula (2):

To determine the value of N, the application of the 3σ rule (the standard 99.7% rule) is recommended. This is an a priori estimate that corresponds to a realistic level of noise and gross errors in field GNSS measurements or sensor-based time series. Typical ranges of the “acceptable” percentage of rejected data under the 3σ criterion are as follows:

• approximately 0.3% under ideal conditions,
• but in practice (due to multipath effects, signal disruptions, or atmospheric influences) – 1-5%.

However, studies have shown that for GNSS measurements, these ranges can be extended. Based on an analysis of eight years of GNSS satellite clock correction data, the percentage of discarded observations was reported as approximately 3.4% for GPS, 3.7% for GLONASS, 2.7% for BeiDou, and about 20% for Galileo [35]. Based on our experience in processing GNSS measurements obtained under normal conditions, we recommend a maximum data rejection threshold of 5%. Under challenging conditions, such as limited satellite visibility or the presence of electromagnetic interference, this threshold may be increased up to 10%. For InSAR PSI/SBAS data, a filtering level of 20–40% is typical, while values exceeding 50% are considered critical [36, 37]. From our experience, the maximum number of harmonics (K) depends on the amount of data and the presence of noise. In general, the optimal number of harmonics does not exceed 12, while the upper limit of 20 is rarely justified.

35. Maciuk, K., Varna, I., & Krzykowska-Piotrowska, K. (2024). A Study of Outliers in GNSS Clock Products. Sensors, 24(3), 799. https://doi.org/10.3390/s24030799
36. Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., Crippa, B. (2016). Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing,Volume 115. 78-89. https://doi.org/10.1016/j.isprsjprs.2015.10.011.
37. Manunta M. et al. (2019). The Parallel SBAS approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation. Remote Sensing of Environment, 234. 111457. 1109/TGRS.2019.2904912

 

Comment 4. The resolution of several figures is low, and many annotations are difficult to read. For example, in Figure 2, the labels WP4, CP40, and WP3 are unclear; in Figure 4a, nearly all labels are hard to distinguish; and in Figure 5, PS37 appears to be placed next to a colored circle rather than the red triangle, which may cause confusion. It is recommended to improve figure resolution and adjust label placement for better readability.

Response 4. Comments taken into account. Appropriate changes have been made.

 

Comment 5. The title of Figure 13 (“Spatial distribution of the amplitudes of vertical displacements (mm) of control points...”) appears inaccurate, as the figure currently shows only the locations of GNSS, InSAR, and RTS points, without any visible encoding (e.g., color gradient or scaled symbols) representing amplitude values. Please either modify the figure to visualize amplitude information or revise the title accordingly.

Response 5. Fig. 13 shows the position of the control points on the dam. The number next to each point indicates the maximum amplitude value. For better understanding, an additional explanation has been added before the figure:

 

«….The number next to the point position determines the amplitude value in mm.»

 

 

Best regards

Reviewer 3 Report

Comments and Suggestions for Authors

The comments are in attached file.

Comments for author File: Comments.pdf

Author Response

 

This paper monitored the Dniester HPP dam by GNSS, InSAR and total station. It is good idea to combine all those data together for DAM deformation monitoring. However, there are already lost of literature in DAM deformation monitoring by InSAR. The author should take care about the InSAR processing method in this field, as well as the GNSS data processing.

In most of the case, the concrete dam may not perform ±10mm deformation seasonally. That GNSS seasonal signal are tropospheric delay consistent with the precipitation and the temperature. My suggestion is to add leveling data on the dams to compare with the InSAR LOS results.

 

Thank you for your comment! We would like to draw your attention to the fact that we are talking about the thermal expansion of the concrete structures of the dam, and with them - the control points of the geodetic network.

The fact of seasonal vertical movements of GNSS antennas within +-10 mm is present not only at the GNSS network points on the dam, but also at other permanent stations, including the points of the IGS network.

Unfortunately, we do not have a sufficiently long time series of geometric leveling results. However, we believe that they are successfully replaced by high-frequency data of ground observations of the linear-angular network.

 

For the theory part:

How to combine the InSAR results with GNSS need to derive the LOS deformation to 3D geometry coordinates. The corresponding literature and equations should be added.

The GNSS results in vertical is not the dam deformation but the tropospheric time series that can not be regarded as the dam deformation. The InSAR LOS deformation can not be compared with the GNSS vertical results without considering the horizontal deformation.

Equation 1 and 2 are good for FFT, but it is not the key for fusing the InSAR, GNSS and total station. My suggestion is to add the SAR LOS – 3D positioning equations form the literature.

 

When processing the InSAR data, two data sets were used: for the ascending and descending orbits. And Fig. 5 presents the vertical component of the deformation obtained after the decomposition process. Relevant literature and equations have been added to the text.

 

Based on the results of the joint processing of ground survey, GNSS, and remote sensing data, we obtained consistent results that confirm seasonal vertical movements of the dam within a few millimeters. It is the use of different data acquisition methods that allows us to confirm the fact of temperature-induced deformations of the concrete part of the dam. Regarding the InSAR results, the study utilized vertical displacement data of permanent scatterers derived from the decomposition of deformation vectors obtained from ascending and descending Sentinel-1 orbits.

The presented equations (1) and (2) are part of an algorithm that does not combine GNSS, InSAR, and linear-angular measurements, but is used to process time series data by applying filtering procedures that reduce the impact of gross errors and increase the accuracy of approximation by the harmonic model.

Relevant literature and equations (SAR LOS – 3D positioning) have been added to the text.

 

 

For the analysis part:

It is important to find the PS points that can be referred with the GNSS station or the total station.

The PS points on the dam should be clearly analyzed by the averaged SAR images. As the PS 13, 18,20,22, 23, 27 are listed in a neat row. The intensity of those point on the dam should also have the graphic feature.

The InSAR data that used in this paper should be introduce as well as the InSAR data processing should be added.

Using FFT to analyzed the GNSS vertical deformation is a very tradition work. That trend are mainly caused by atmosphere that should not be regarded as the dam deformation.

 

 

Only one point was detected at this research site, as is common with GNSS and InSAR (ACP2-PS37). Figure 6. Shows the time series of vertical displacements of both datasets. All other points do not have a common location. Therefore, we emphasize the importance of creating complex geodetic points at the site, which will combine GNSS and corner reflectors for radar monitoring of the territory (see Conclusions, paragraph 2).

In Fig. 5, an averaged SAR image is added. It can be seen that the dam has a sufficiently large number of pixels with a high value of the intensity of the reflected radar signal. Unfortunately, the StaMPS method, which was used during processing, there is no possibility to analyze and present the intensity characteristics of points that are defined as permanent scatterers. As far as I know, such an opportunity is available when using the GECORIS package, the results of which I am familiar with.

The combination of satellite and ground-based geodetic observations confirms the presence of temperature-induced vertical surface movements of the concrete part of the dam. This is evidenced by the results of determining seasonal vertical amplitudes based on GNSS, InSAR data, and most importantly, ground-based linear-angular measurements, which are devoid of the influence of tropospheric signal delay.

 

 

For the concrete and earth-filled dam part. The horizontal deformation cause by water level changes are significant most of time. My suggestion is to add some analysis on the horizontal deformation monitoring between InSAR and total staions. Following the LOS – 3D decomposition method.

In accordance with previous studies [Tretyak, K., Serant, O. & Bisovetskyi, Y. (2024). Modeling of temperature deformations on the Dnister HPP dam (Ukraine). Journal of Applied Geodesy, 19(2), 247-264. https://doi.org/10.1515/jag-2024-0060], no dependence was found between the spatial movements (including horizontal movement)of the GNSS network points and changes in the water level in the reservoir. The graph of changes in the water level in the reservoir is added to Fig. 12 and the corresponding explanations are made before it:

Based on the findings of previous investigations [18] conducted at the Dniester Hydroelectric Power Station dam, variations in the reservoir water level (Fig. 12) have been found to exert no significant influence on the spatial displacements of the GNSS monitoring points. This conclusion is supported by the low correlation coefficients, which range from 0.17 to 0.19. In contrast, a consistent relationship has been identified between the observed displacements and temperature variations. Hence, it can be concluded that the predominant factor governing the vertical movements of the control points is the thermal expansion of the dam’s concrete structures.

 

Some specific revision comments:

1. Figure 5 should add the averaged SAR image of the dam in SAR The readers can understand the PS condition.

Сompletely agree with you! A SAR image added.

2. 8 is not so reasonable. The concrete part and the earth-filled part may have different deformation in vertical but it may not be in that condition. The leveling data in this field should be added for analysis.

Fig. 8 is intended to demonstrate that the results of InSAR monitoring can be used to differentiate sites based on different surface types or reactions, as observed in vertical deformation. Thus, concrete surfaces exhibit a clear seasonal pattern, which is approximated by the minimum number of harmonics, whereas the earthen dam does not yield such clear results. The main conclusion is the possibility of using InSAR for the investigation of seasonal temperature deformations of engineering structures.

Unfortunately, there is no reliable leveling data set for this object. Therefore, in our studies, we used the available linear-angular measurement data, which are in good agreement with the GNSS and InSAR results.

 

 

Following Reference need to be added：

1. Czikhardt, H. Van Der Marel, and J. Papco, “GECORIS: An open-source toolbox for analyzing time series of corner reflectors in InSAR geodesy,” Remote Sensing, vol. 13, no. 5, p. 926, Mar. 2021, doi: 10.3390/rs13050926.
2. Brouwer, S.; Hanssen, R.F. A treatise on InSAR geometry and 3-D displacement estimation. IEEE Trans. Geosci. Remote Sens. 2023, 61, 1–11. https://doi.org/10.1109/TGRS.2023.3322595
3. Ziemer, J.; Janichen, J.; Stein, G.; Liedel, N.;Wicker, C.; Last, K.; Denzler, J.; Schmullius, C.; Shadaydeh, ; Dubois, C. Identifying Deformation Drivers in Dam Segments Using Combined X- and C-Band PS Time Series. Remote Sens. 2025, 17,2629. https://doi.org/10.3390/rs17152629

Added to references.

Best regards

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors,

thank you for changing the paper following my recommendations. I still think the separation between Methods and Results can be improved. Not all "Time series processing methodology" must go to Results section. Lines 377 to 467 from the manuscript new version must remain in section "Materials and method".

Best regards

Author Response

 

Comment 1. Dear authors,

thank you for changing the paper following my recommendations. I still think the separation between Methods and Results can be improved. Not all "Time series processing methodology" must go to Results section. Lines 377 to 467 from the manuscript new version must remain in section "Materials and method".

 

Response 1. Dear Reviewer, we are sincerely grateful for your constructive comments, which have helped us to improve our manuscript. Your current remark appeared clear and reasonable at first glance. However, during the revision process, we realized that implementing this change would significantly affect the logical flow of the material. It seems to us that it will be difficult to perceive information about the data filtering and optimization methodology separated from the results.

Therefore, we decided to keep the text unchanged. To emphasize the results more clearly, we have modified the subsection title to “3.2. Filtering and Optimization of Time Series Data.”

 

Best regards

 

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have addressed all points raised during the review process. No further revisions are required except for one minor issue regarding Figure 13. The explanation provided in the main text should be incorporated directly into the figure caption to ensure it can be understood without reference to the text. Once this is corrected, I recommend acceptance for publication.

Author Response

 

Comment 1. The authors have addressed all points raised during the review process. No further revisions are required except for one minor issue regarding Figure 13. The explanation provided in the main text should be incorporated directly into the figure caption to ensure it can be understood without reference to the text. Once this is corrected, I recommend acceptance for publication.

Response 1. The proposed changes have been made.

 

Figure 13. Spatial distribution of the amplitudes of vertical displacements of control points on a concrete dam, determined by the GNSS, RTS, and InSAR methods. The number next to the point position indicates the amplitude value in mm.

 

We sincerely thank you for your constructive comments that helped improve our work.

Best regards

Reviewer 3 Report

Comments and Suggestions for Authors

This version improved  a lot both in the content but also in the English quality. 

I have only suggestion on the conclusion part. As the author believe that the temperature drive the vertical deformation of the concrete dam. It should also be reasonable that the dam will move horizontal annually. The author should check the horizontal deformation trend between temperature .

One of the explanation of this annually vertical deformation are the hydrological loading. The hydrological loading is  a common sense  and with traditional method to remove it from the long data sets. I hope the author could discussion this topic in the results or conclusion part. 

Author Response

 

Comment 1. This version improved  a lot both in the content but also in the English quality. 

I have only suggestion on the conclusion part. As the author believe that the temperature drive the vertical deformation of the concrete dam. It should also be reasonable that the dam will move horizontal annually. The author should check the horizontal deformation trend between temperature.

One of the explanation of this annually vertical deformation are the hydrological loading. The hydrological loading is  a common sense  and with traditional method to remove it from the long data sets. I hope the author could discussion this topic in the results or conclusion part. 

 

Response 1. In accordance with previous studies [Tretyak, K., Serant, O. & Bisovetskyi, Y. (2024). Modeling of temperature deformations on the Dnister HPP dam (Ukraine). Journal of Applied Geodesy, 19(2), 247-264. https://doi.org/10.1515/jag-2024-0060], no dependence was found between the spatial movements of the GNSS network points and changes in the water level in the reservoir at Dniester HPP Dam. In the first revised version of the manuscript, the graph of changes in the water level in the reservoir is added to Fig. 12 and the corresponding explanations are made before it:

Based on the findings of previous investigations [18] conducted at the Dniester Hydroelectric Power Station dam, variations in the reservoir water level (Fig. 12) have been found to exert no significant influence on the spatial displacements of the GNSS monitoring points. This conclusion is supported by the low correlation coefficients, which range from 0.17 to 0.19. In contrast, a consistent relationship has been identified between the observed displacements and temperature variations. Hence, it can be concluded that the predominant factor governing the vertical movements of the control points is the thermal expansion of the dam’s concrete structures.

We completely agree with you on the importance of considering the hydrological load factor when analyzing dam deformations. Even though this factor does not create a significant impact on this facility, we agree with you on the need to highlight this information in the Conclusions:

“Conclusion 4.…….At this facility, temperature is the dominant factor influencing the observed vertical displacements. The effect of hydrological loading is evidently present; however, due to the low correlation between the spatial displacements of the control points and the variations in the reservoir water level, it cannot be distinguished from the overall deformation trend.”

 

We sincerely thank you for your comments that helped improve our work.

Best regards