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: open (until 22 Aug 2022)
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CC1: 'Suggestions to authors', Zhaokai Wang, 30 May 2022
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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
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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
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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.
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AC2: 'Reply on RC1', Dedi Liu, 23 Jul 2022
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We appreciate the reviewer' insightful and helpful comments on our manuscript. We have carefully addressed all the comments (please find our responses in the Supplement).
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AC2: 'Reply on RC1', Dedi Liu, 23 Jul 2022
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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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