the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-resolution water level and storage variation datasets for 338 reservoirs in China during 2010–2020
- 1State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China
- 2School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- 3DTU Space, National Space Institute, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
- 4Department of Environmental Engineering, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
- 1State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China
- 2School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- 3DTU Space, National Space Institute, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
- 4Department of Environmental Engineering, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
Abstract. Reservoirs and dams are essential infrastructures in water management, thus information of their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC), is crucial for understanding their properties and interactions on hydrological and biogeochemical cycles. However, knowledge of these reservoir characteristics is scarce or inconsistent at national scale. Here, we introduce comprehensive reservoir datasets of 338 reservoirs in China, with a total of 470.6 km3 storage capacity (50 % Chinese reservoir storage capacity). Given the scarcity of publicly available gauged observations and operational applications of satellites for hydrological cycles, we utilize multiple satellite altimetry missions (SARAL/AltiKa, Sentinel-3 A and B, and CroySat-2) and Landsat satellite data to produce a comprehensive reservoir dataset on the WSE, SWA, and RWSC during 2010–2020. Validation against gauged measurements of 93 reservoirs demonstrates the relatively high accuracy and reliability of our remotely-sensed datasets: (1) Across gauge comparisons of RWSC, the median statistics of CC, NRMSE, and RMSE are 0.76, 15 %, and 0.035 km3, with a total of 75 % validated reservoirs (70 of 93) having good RMSE from 0.002 to 0.35 km3 and NRMSE values smaller than 20 %. (2) Comparisons of WSE retracked by four satellite altimeters and gauges show good agreement. Specifically, percentages of reservoirs having good and moderate RMSE values smaller than 1.0 m for CryoSat-2 (validated in 30 reservoirs), SARAL/AltiKa (8), Sentinel-3A (25), and Sentinel-3B (25) are 90 %, 88 %, 64 %, and 76 % respectively. By taking advantages of four satellite altimetry missions, we are able to densify WSE observations across spatiotemporal scales. Statistically, around 85 % validated reservoirs (53 of 62) have RMSE values below 1.0 m, while 63 % reservoirs (39 of 62) have a good data quality with RMSE values below 0.6 m. Overall, our study fills such a data gap with regard to comprehensive reservoir information in China and provides strong support for many aspects such as hydrological processes, water resources, and other studies. The dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.5812012 (Shen et al., 2021).
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Youjiang Shen et al.
Status: final response (author comments only)
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CC1: 'Suggestions to authors', Zhaokai Wang, 30 May 2022
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2021-470/essd-2021-470-CC1-supplement.pdf
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AC1: 'Reply on CC1', Dedi Liu, 16 Jul 2022
Dear Zhaokai Wang,
We appreciate positive feedback on our work. We checked these three reservoirs you mentioned, the statistical metrics on water level and RWSC can be found in our datasets, including time-series and error reports for all reservoirs (e.g., time-series of rwsc 93/232res.pdfs). Please note that you collected in-situ data before 2016, are not available from our side. We obtained daily water level and storage data spanning 2015–May 2021 for 93 reservoirs in this study. Thus, we might fail removed some outliers for these three reservoirs. We added this issue and demonstrated our “Advantages and Limitations” in the main text. Anyway, we would conduct a data quality control again in our final dataset. Moreover, this dataset will be updated regularly, and we will introduce more general algorithms with better performance and include more satellite missions. The associated paper would be our next paper, but as a supplement of this work.
At this stage, we generated the remotely sensed datasets with highest level of confidence on the quality and novelty of datasets. We provided different levels of processed satellite datasets, which can be used for users for different purposes. We agree that some outliers may exist and can be attributed to the fact of error in satellite water level or area, or a combination. Nevertheless, over 75% reservoirs evaluated by in-situ observations fall into the good category showing NRMSE values of RWSC below 20%. More than 85% reservoirs evaluated by in-situ observations fall into the good category showing RMSE values of WSE retracked by these four altimeters below 1.0 m. We will continue to mine and optimize the algorithm as described in the article in future development, for better satellite-based water level estimation.
Kind regards.
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AC1: 'Reply on CC1', Dedi Liu, 16 Jul 2022
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RC1: 'Comment on essd-2021-470 (Stefano Galelli)', Stefano Galelli, 11 Jul 2022
General comments
Manuscript essd-2021-470 describes a novel dataset providing water surface, level, and storage information for 338 reservoirs in China. In my opinion, this is a much-needed dataset that fills in an important gap, since data on water reservoirs are typically not available to the international community. I believe many studies and downstream applications will thus benefit from these data.
Overall, both manuscript and dataset are well organized, although a few important probably deserve more attention. In particular:
1. I am not entirely convinced about the approach used to estimate the hypsometric relationships, which, if I understand correctly, are based on water level and surface data estimated from satellite data. In general, water level data are rather reliable, while it is always a challenge to get the right water surface data (a matter that explains the use of image enhancing techniques), a problem that might affect the quality of the curves. So, why not using a DEM to get the right curves? This could be done for many reservoirs. Estimating the hypsometric relationships from a DEM would also limit the need for water surface data.
2. It looks like many reservoirs have a negative value of storage (Figure 7). What further confuses me is that the gauged data have also negative values. How do you explain this matter (for both estimated and gauged data)? Shouldn’t this problem be corrected? And wouldn’t a more precise hypsometric relationship help?
3. The quality of the presentation (including figures) could be enhanced. Please refer to my comments below.
4. Are the water level and storage data retrieved from http://xxfb.mwr.cn/index.html available in the repository? Please correct me if I am wrong, but I couldn’t find them. If that’s true, I would encourage to authors to share those—it is not possible to download them from the aforementioned website.
Specific comments
- Line 60 (“It is obvious that …”). This sentence is not clear. Are you referring to China? If yes, I would state it clearly.
- Line 61-62. I suggest being more precise here. What are the reservoirs for which data are already available? Are the data public? And, importantly, what type of data are available?
- Line 74. What do you mean with “difficult to be accessed”? Can they be accessed?
- Line 64-85. Vu et al. (2022) has just released a water level, surface, and storage dataset for 10 reservoirs in the Lancang Basin, China, for the period 2008-2020. This dataset was created using satellite data and modelling techniques similar to the ones reported here, so this is why I’m mentioning that study. Please note I’m a co-author of that paper, so please feel free to discard my comment.
- Table 1 is very informative (and I would leave it as is); however, it somewhat mixes studies and datasets that have different geographical foci and intents (e.g., global v. regional). I would therefore suggest including another table specifically focussed on China. It will help readers understand what is currently available—and how this study complements the state-of-the-art.
- Line 111. “Testbed”?
- Line 112-113. This sentence is not clear.
- Figure 1. I suggest improving / re-drawing Figure 1. It’s very hard to visualize the reservoirs (pink squares). Also, the colour-bar for the elevation is missing.
- Section 2.1. How about the Repeat cycle of SARAL/AltiKa?
- Equations (1) and (2). Which technique did you use to estimate the various corrections? Were these corrections applied uniformly to all reservoirs or were they site-specific?
- Line 178. I would say a few words about the algorithm developed by Zhao and Gao (2018). Also, is the code available?
- Line 179. What do you exactly mean with “reservoir shapefiles”?
- Line 180-186. I found this part to be not that clear.
- Line 190-203. I’m a bit confused by this approach: why not estimating the hypsometric relationships from the DEM? The SRTM mission, for instance, was carried out in 2000, so the SRTM-DEM could provide detailed hypsometric relationships for all reservoirs built after the year 2000.
- Line 2010-211 (“especially the regions where the reservoir storage are dynamic”). What does this mean?
- Line 231-239. I have nothing against qualitative assessments (and in fact think it’s useful in this case), but I suggest being precise about how the letter grades were assigned. Ideally, the assessment should be reproducible.
- Figure 4. Please consider the option of using the same symbol (with different size or different colour) to provide information about RMSE. I found the combination of symbols and colours to be confusing.
- Figure 6. What do the different colours (red, blue) represent?
- Line 295. Do you mean Figure 6?
- Line 301. Do you mean Figure 7?
- Figure 7. Shouldn’t you correct for negative values?
- Section 3.3. The content of this sub-section does not qualify as Result (Section 3). Why not placing it in a stand-alone section? Perhaps, it could be moved to the repository.
- Line 390-394. Not clear.
References
Vu, D. T., Dang, T. D., Galelli, S., and Hossain, F.: Satellite observations reveal 13 years of reservoir filling strategies, operating rules, and hydrological alterations in the Upper Mekong River basin, Hydrol. Earth Syst. Sci., 26, 2345–2364, https://doi.org/10.5194/hess-26-2345-2022, 2022.
- AC2: 'Reply on RC1', Dedi Liu, 23 Jul 2022
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CC2: 'Comment on essd-2021-470', Xingguo Mo, 12 Oct 2022
This study constructed the monthly water level and storage variation datasets for 338 reservoirs in China using multi-source satellites, and also validated the results by massive in-situ datasets. The workload of this article is impressive, and the dataset is complete and consistent with the article. However, there are also many issues unsolved (see details below). Therefore, I could not be more positive in this regard unless the following comments have been addressed with more convincing results presented in this manuscript.
Moderate to major issues:
Line 165-168: It's unreasonable that you put all reservoirs with different baseline together to talk about their performance under the same metric. According to the reason you select Sentinel-3 as the first baseline, do you think CryoSat-2 has a higher temporal resolution than SARAL/AltiKa? How did you calculate the systematic biases of the reservoirs with no overlap period of different altimeters (such as GRAND_ID 5405 and 5861)?
Line 173-175: Did you consider that the reservoir might freeze in winter when you derive the WSE with algorithm for water surface from altimeters?
Line 233-234: Please show the quantitative metrics for different grades.
Line 288-289: The validation for SWA is not convincing enough. Try to illustrate the accuracy of the SWA using high-resolution images, such as Sentinel-2.
Line 295-296: In my opinion, a reliable A–E curve is a prerequisite for calculating the RWSC of the reservoirs, so I don't think it makes any sense to calculate the RWSC of the reservoirs with poor R2 values.
Line 370: This part should not be included in this article because it's off topic.
Minor comments：
Line 20: Write the full name on the first occurrence of the abbreviation.
Line 71-72: Where is the problem? You also did this in section 3.2.2.
Line 90-92: The word "highest" is too strong here. Why the novelty of datasets can be illustrated by validation data?
Line 138-139: Please add relevant references.
Line 144: Why didn't you show the repeat cycle of SARAL/AltiKa?
Line 182-184: This sentence is not clear.
Line 190-191: Monthly fluctuations of reservoir SWA and WSE can be quite large. This processing may introduce errors because SWA and WSA do not correspond in time.
Line 280: Please add the legend for surface water extent.
Line 317-319: This sentence is not clear.
Datasets: Please name the GRAND_ID of the reservoirs according to certain rules.
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AC3: 'Reply on CC2', Dedi Liu, 12 Oct 2022
Dear Xingguo Mo,
Thank you for your time and efforts in reviewing our manuscript. This was a prompt reponse to your comments and detailed responses are expected to be here in the coming days.
Firstly, we are very happy to hear your positive feedback on our complete and consistent datasets and your recognition of impressive wordload of our work, which are an important contribution to the large-scale studies and could provide strong support for many aspects.
Secondly, we agree with some of the issues you raised, and all of them have been addressed well in our revised datasets as we modified our datasets in the past four months of manuscript review. Data version-2 and the revised paper will be uploaed once we recieve all comments from reviewers.
Here is a brief summary of the issues you raised, which have been already addressed from our side.
Issue 1 - systematic biases of satellite altimetry-derived water level.
Please note we provide two modes of satellite water level, standard rate (single satellite) and high rate (multiple satellites). For High rate, we need to remove systematic biases among satellites. The biases are removed using two approaches, (1) directly removing the mean differences for thoese with enough overlapping periods. (2) for thoese without overlapping periods, remotely sensed surface area time series are used to act as an anchor of biased time series, allowing for estimation of the relevant biases. Here, a 2-D cost function in surface-area–water-level coordinates is minimized within a Gauss–Helmert adjustment scheme.
Issue 2 - quantitative metrics for different letter grades assigned to reservoirs without in situ observations.
This issue is solved and replied in previous reviewer comment.
Issue 3 - The validation for SWA is not convincing enough.
We have already added a cross-validation against other similar existing water area datasets such as RealSAT and a SR paper in our revised datasets. Please note that validation againts our remotely-sensed area has been done using 139 reservoirs with in situ water level, and our altimetry-derived water level in our manuscript.
Issue 4 - makes any sense to calculate the RWSC of the reservoirs with poor R2 values.
Partly agree, we will divide our RWSC data into two parts for users to further selections, i.e., with higher R2 values and with R2 values.
Issue 5 - the reservoir might freeze in winter when you derive the WSE with algorithm for water surface from altimeters?
No, we just use the official and commonly used algorithms to derive the water level. This issue have minor influences on our derived water level considering the locations and climates of our provided reservoirs, and we will add some explanations and discussions in our paper.
All other minor comments and detailed responses to above moderate issues will appear here in the coming days.
Kind regards.
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RC2: 'Reply on AC3', Anonymous Referee #2, 15 Oct 2022
This study constructed the monthly water level and storage variation datasets for 338 reservoirs in China using multi-source satellites, and also validated the results by massive in-situ datasets. The workload of this article is impressive, and the dataset is complete and consistent with the article. However, there are also many issues unsolved (see details below). Therefore, I could not be more positive in this regard unless the following comments have been addressed with more convincing results presented in this manuscript.
Moderate to major issues:
Line 165-168: It's unreasonable that you put all reservoirs with different baseline together to talk about their performance under the same metric. According to the reason you select Sentinel-3 as the first baseline, do you think CryoSat-2 has a higher temporal resolution than SARAL/AltiKa? How did you calculate the systematic biases of the reservoirs with no overlap period of different altimeters (such as GRAND_ID 5405 and 5861)?
Line 173-175: Did you consider that the reservoir might freeze in winter when you derive the WSE with algorithm for water surface from altimeters?
Line 233-234: Please show the quantitative metrics for different grades.
Line 288-289: The validation for SWA is not convincing enough. Try to illustrate the accuracy of the SWA using high-resolution images, such as Sentinel-2.
Line 295-296: In my opinion, a reliable A–E curve is a prerequisite for calculating the RWSC of the reservoirs, so I don't think it makes any sense to calculate the RWSC of the reservoirs with poor R2 values.
Line 370: This part should not be included in this article because it's off topic.
Minor commentsï¼
Line 20: Write the full name on the first occurrence of the abbreviation.
Line 71-72: Where is the problem? You also did this in section 3.2.2.
Line 90-92: The word "highest" is too strong here. Why the novelty of datasets can be illustrated by validation data?
Line 138-139: Please add relevant references.
Line 144: Why didn't you show the repeat cycle of SARAL/AltiKa?
Line 182-184: This sentence is not clear.
Line 190-191: Monthly fluctuations of reservoir SWA and WSE can be quite large. This processing may introduce errors because SWA and WSA do not correspond in time.
Line 280: Please add the legend for surface water extent.
Line 317-319: This sentence is not clear.
Datasets: Please name the GRAND_ID of the reservoirs according to certain rules.
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RC2: 'Reply on AC3', Anonymous Referee #2, 15 Oct 2022
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AC3: 'Reply on CC2', Dedi Liu, 12 Oct 2022
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AC4: 'Comment on essd-2021-470', Dedi Liu, 16 Oct 2022
We have carefully studied all comments and made corrections/changes as suggested. We performed strict data quality control to eliminate any potentially problematic data points, and data version 2 is expected to be released as the final dataset within the next three days.
Here, we briefly summarize the improvements to our dataset, taking into account the minor and moderate issues raised by the reviewers.
- For water levels, we added six satellite altimeters (previsous one with four satellite altimeters) in this version and provided two modes, standard rate and high rate. For the high rate, we need to eliminate the systematic bias between satellites. The biases are removed using two methods, as detailed in comment AC3, to address the issues raised by Reviewer 2.
- For water area, we added a section to describe the accuracy assessment to evaluate our area dataset based on in situ water levels in 93 reservoirs and our generated altimetric water levels (standard rate and high rate) to address the issue raised by reviewer 2.
- For water storage variation, we added DEM-based estimates, so that we provide two types of storage change, i.e., using satellite-based water levels and water area, and using satellite-based water area and DEM-based curves. The storage changes are validated against in situ storage changes for 93 reservoirs, addressing the issues raised by reviewer 1. (Please note the major issue raised by reviewer 1 why we have negative values for storage variation dataset, because he incorrectly assumes that we provide storage data.)
At a later stage, a revised manuscript with adjustments based on all comments will be provided, pending editorial decision.
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RC3: 'Comment on essd-2021-470', Anonymous Referee #3, 23 Oct 2022
The authors used altimetry data and Landsat-derived water extent product to estimate water storage changes between 2010-2020 for hundreds of reservoirs in China. They validated satellite-derived water level and storage time series against in situ data. I think the authors did great work validating altimetry data while the extent and storage validation parts are relatively weak. As some recent studies on reservoir water dynamics have already covered the same reservoirs in this study, I would not suggest this dataset can be valuable in this research area. In addition, I have several major concerns about this study. Please see my comments below.
Major Comments:
- Monthly surface water extent and storage time series have been estimated for nearly all reservoirs from the GRanD in Zhao and Gao (2018) and Hou et al. (2022). As these global studies have covered the same Chinese reservoirs in this study, I was wondering what it is the novelty of this data product. I think it may be worthwhile if you could extend to more Chinese reservoirs, e.g., delineated by Song et al (2022). It provides nearly 100 000 reservoir polygons in China, which has not yet been achieved in the GRanD. You will find more overlapped altimetry data for these reservoirs.
- I do not think you can estimate reservoir storage when the correlation between elevation and extent is poor. Please refer to Busker et al. (2019). Therefore, the total number of reservoirs whose storage can be estimated using remote sensing should be lower than 338. Could you also provide statistics on the robustness of the elevation-extent relationships for all reservoirs you analyzed?
- Jason 1/2/3 and Topex/Poseidon together provide higher temporal (10-day) surface water elevation observations over the past three decades. Why did the authors choose low-frequency SARAL/AltiKa, Sentinel-3 A and B, and CroySat-2? Especially CroySat-2 has only one observation per year. And Sentinel-3 and SARAL/AltiKa do not fully cover your study period (2010-2022).
- The structure of the Data and Methods section is very poor. It is very difficult to distinguish the methods you developed for your study from the methods embedded in the published dataset you used. I would recommend separating data and method description. And include another subsection to describe in-situ data that you highlight in the introduction. I think you also need to introduce more about JRC-GSWD data, which is one of the main datasets you used for your study.
- Zhao and Gao (2018) have produced monthly surface water extent time series for all reservoirs from the GRanD using JRC-GSWD. Why do you need to use their method again to derive reservoir water extents using the same input data source JRC-GSWD? It does not make sense to me. The reservoir monthly time series can be accessed via https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/DF80WG.
- Please include the reservoir name for all your analysis, which would be very important information in this m/s. For example, you only show GRanD ID in Figure 5 but I am keen to know which reservoir it is in China.
- I did not find any validation on your reservoir water extent data. As there are many published satellite-derived surface water extent products, I think you can compare yours against some of them.
- As you mentioned that the accuracy of altimetry data is poor in some large reservoirs (P12L255-256), you should provide more analysis on the influence of reservoir size on the accuracy of altimetry data.
Specific Comments:
- P2L54-56: The hypsometry relationship is developed by the overlapped measurements of elevation and extent. Are these two steps to calculate storage or two approaches? Please clarify.
- P2L61-P3L1: Can you provide any references about the limited number (approx. 30) of available Chinese reservoir data from Hydroweb, G-REALM and DAHITI?
- P3L68: Please clarify “large observations”
- P3L78: Can you introduce the Hydrostat as well and add a reference to it?
- Table1: It was a great work to summarize relevant studies in Table 1 but it looks quite messy. I would suggest to simply this summary, highlight important points and divide information (e.g., time, temporal resolution, altimetry data, validation data, etc.) into a few more columns. Does “/” mean unknown or not applicable? Please name satellite sources for altimetry data rather than say “online databases”.
- P6L112: “these missions” is not relevant to the context here.
- P6L112-113: Please explain why altimetry data show highest values in ungauged or poorly gauged areas?
- P6L116-119: It is not clear here. Please put the cover period and capacity information into a table.
- Section 2.1: Please carefully check this section about the description of different satellite instruments. I am not 100% sure if all the information here is correct or not. For example, “Hz” or “GHz”; Sentinel-3 SRAL have Ku-band and C-band with different frequencies, two radar modes and two tracking modes, I am not sure which mode or band do you refer to.
- P7L133: please clarify “higher data availability” and why Ka-band leads to that?
- P8L175-177: Did you do the analysis on counting no-valid data between Jan and Dec by yourself or cited from previous studies?
- P8L177-178: what do you refer to by “over 12% of contamination pixels”? Do you mean all JRC-GSWD data or in Zhao and Gao (2018) study?
- P8L180-181: Please check. I am not sure if this statement is correct or not. As far as I know, the GRanD reservoir polygon is derived not only from SRTM Water Body Database, but also from Global Lakes and Wetlands Database and some other studies.
- P9L187-188: What does this sentence try to explain?
- Figure 2: All these evaluation metrics should be mentioned and explained in the data and method sections.
- P11L245-248: Please explain the criteria to filter out any reservoir in the method section.
- Figure 7: It is difficult to see observed variations (red line) in the figure.
- Sections 4.1 and 4.2 are all your results (data comparison and application in modelling). Please move these to the Results section.
References:
[1] Busker, T., de Roo, A., Gelati, E., Schwatke, C., Adamovic, M., Bisselink, B., ... & Cottam, A. (2019). A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry. Hydrology and Earth System Sciences, 23(2), 669-690.
[2] Hou, J., van Dijk, A. I., Beck, H. E., Renzullo, L. J., & Wada, Y. (2022). Remotely sensed reservoir water storage dynamics (1984–2015) and the influence of climate variability and management at a global scale. Hydrology and Earth System Sciences, 26(14), 3785-3803.
[3] Song, C., Fan, C., Zhu, J., Wang, J., Sheng, Y., Liu, K., ... & Ke, L. (2022). A comprehensive geospatial database of nearly 100 000 reservoirs in China. Earth System Science Data, 14(9), 4017-4034.
[4] Zhao, G., & Gao, H. (2018). Automatic correction of contaminated images for assessment of reservoir surface area dynamics. Geophysical Research Letters, 45(12), 6092-6099.
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AC5: 'Reply on RC3', Dedi Liu, 23 Oct 2022
Thank you for your time and efforts in reviewing our manuscript. This was a prompt reponse to your comments and detailed responses and a revised manuscript are expected to be here in the coming days.
Firstly, we have overhauled the article to significantly improve the presentation of data sources, methods, and results, and will carefully consider your suggestions.
Secondly, with respect to the originality of our data, thank you for alerting us to recent studies on reservoir hydrodynamics that may have covered the same reservoirs in this study. In our revised manuscript, we have included them in Table 1 and compared our dataset with them.
For the reservoir water areas developed by Zhao and Gao (2018), in our revised manuscript we used the new area dataset of Donchyts et al. (2022) and cross-validated it with water level time series (in situ water levels of 93 reservoirs and our altimetry data) and two other similar area datasets of water levels (GRSAD and RealSAT). This is lacking in the current studies.
For the water storage developed by Hou et al. (2022), this research work was also added to Table 1 of our revised manuscript. I am not 100% sure if the data from Hou et al. are publicly available, as we can see many similar studies that developed some datasets that are not publicly available. This issue is mentioned in our introduction and summarized in Table 1. In the second version of the data, we proposed two storage anomalies using satellite water level-area and DEM-based method using imagery area, and evaluated them using in situ observations of 93 reservoirs.
For water levels, we added two satellite altimeters from Jason-3 and ICESat-2 to version 2 of the data.
Although some studies are in progress, there are still data gaps in the reservoir water level and storage anomalies of Chinese reservoirs. For example, for the reservoir shapefile paper, in addition to Song et al. (2022), which you mentioned, there are two similar papers published in ESSD this year. We will carefully examine your comments and suggestions in the revised manuscript (which was prepared based on the comments of two previous reviewers) and clarify the authenticity and uniqueness of our dataset.
Best.
Youjiang Shen et al.
Data sets
High-resolution water level and storage variation datasets for 338 reservoirs in China during 2010–2020 Youjiang Shen, Dedi Liu, Liguang Jiang, Karina Nielsen, Jiabo Yin, Jun Liu, Peter Bauer-Gottwein https://doi.org/10.5281/zenodo.5812012
Youjiang Shen et al.
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