the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Long-term water clarity patterns of lakes across China using Landsat series imagery from 1985 to 2020
- 1North China University of Water Resources and Electric Power, Zhengzhou 450046, China
- 2Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- 3University of Chinese Academy of Sciences, Beijing 100049, China
- 4Beijing Normal University, Beijing 100091, China
- 1North China University of Water Resources and Electric Power, Zhengzhou 450046, China
- 2Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- 3University of Chinese Academy of Sciences, Beijing 100049, China
- 4Beijing Normal University, Beijing 100091, China
Abstract. Monitoring the water clarity of lakes is essential for the sustainable development of human society. However, existing water clarity assessments in China have mostly focused on lakes with areas > 1 km2, and the monitoring periods were mainly in the 21st century. In order to improve the understanding of spatiotemporal variations in lake clarity across China, based on the Google Earth Engine cloud platform, a 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) was first developed using Landsat series imagery and a robust water-color-parameter-based SD model. The LAWSD30 dataset exhibited a good performance compared with concurrent in situ SD datasets, with an R2 of 0.86 and a root-mean-square error of 0.225 m. Then, based on our LAWSD30 dataset, long-term spatiotemporal variations in SD for lakes > 0.01 km2 (N = 40,973) across China were evaluated. The results show that the SD of lakes with areas ≤ 1 km2 exhibited a significant downward trend in the period 1985–2020, but the decline rate began to slow down and stabilized after 2001. In addition, the SD of lakes with an area > 1 km2 showed a significant downward trend before 2001, and began to increase significantly afterwards. Moreover, in terms of the spatial patterns, the proportion of small lakes (area ≤ 1 km2) showing a decreasing SD trend was the largest in the Mongolian–Xinjiang Plateau Region (MXR) (about 30.0 %), and the smallest in the Eastern Plain Region (EPR) (2.6 %). In contrast, for lakes > 1 km2, this proportion was the highest in MXR (about 23.0 %), and the lowest in the Northeast Mountain Plain Region (NER) (16.1 %). The LAWSD30 dataset and the spatiotemporal patterns of lake water clarity in our research can provide effective guidance for the protection and management of lake environment in China.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(3734 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Journal article(s) based on this preprint
Xidong Chen et al.
Interactive discussion
Status: closed
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RC1: 'Comment on hess-2021-630', Anonymous Referee #1, 17 Mar 2022
This manuscript represents a significant contribution in the form of an extensive application ready lake clarity data product covering small and large lakes of China over a period of 35 years. This data product, LAWSD30 (and associated derivations), will be a valuable asset to future endeavours that seek to understand the drivers of lake clarity change in China.
Specific comments:
1. Include the normalised RMSE in addition to the RMSE; the normalised RMSE summarises the explanatory power of a model in a manner that is independent of scale, and so enables a ready comparison across modelling efforts.
2. Line 118 : replace 'product' with 'application ready data product'.
3. Lines 147 - 48: it is not clear how 'user' and 'producer' map to the terminology used in Peckel et al 2016. Please clarify or re-word.
4. Line 211: replace 'Following him' with 'Following Chen et al. (2021)', assuming that is what is meant?
5. Line 215: for the convenience of the reader, please briefly describe the constituent BAP criteria and how the BAP was calculated, rather than pointing to Chen et al. 2021 (which then on-refers the reader to White et al. 2014 for further details).
6. Line 227: please replace the word 'proven' with 'demonstrated', here and elsewhere in the document. Likewise, replace 'proves' with 'demonstrates'. 'Proven' and 'proves' should be reserved for contexts where there is little room for interpretation.
7. Line 316: replace ‘product’ with 'LAWSD30 data set'.
8. Line 339: replace 'researches' with ‘studies’ (here and elsewhere in the manuscript).
9. Line 348: replace 'Similarly, some studies found the same SD ....' with 'Other studies found the same SD ...'.
10. Line 462: replace 'hydrometeor' with 'weather'. (hydrometeor has a different meaning to that which I assume is intended)
11. Lines 523 - 24: replace 'to reflect the comprehensive conditions in water bodies' with 'of water quality'
12. Conclusion: include commentary that alerts the reader to the notion that an informed interpretation of the lake clarity patterns presented in this manuscript will require an understanding of the relative importance of local and regional driving forces; and that such an informed interpretation will be required in order to prioritise management actions.
For example, the work of Yu and Zhai (https://doi.org/10.1038/s41598-020-71312-3) demonstrate that compound drought and heat events have impacted China more severly since ca. the year 2000. Have such events led to changes in SD patterns in some lakes, for example by making wind-driven resuspension more or less likely?
- AC1: 'Reply on RC1', Liangyun Liu, 19 Apr 2022
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RC2: 'Comment on hess-2021-630', Anonymous Referee #2, 18 Mar 2022
This paper developed a 30 m long-term Lake Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) using the robust water-color-parameter-based SD model. The LAWSD30 dataset can be used to study the temporal and spatial changes of Lake SD. Therefore, it is of great significance to the study of long-term trend of Lake SD and lake ecological environment management.
Specific comments
- The RMSE was used for evaluating the accuracy of the model. However, in my opinion, this index is not very appropriate. For example, when the real value is equal to 2 or 20, although the RMSE value is 0.2, the accuracy of the model is very different. Thus, this index does not show how close the real value is to the estimated value I suggest using MAPE or similar indicators to evaluate the accuracy of the model.
- Line 125-126. “The summer months were chosen because the water clarity is relatively stable in this season and suitable for monitoring with remote sensing imagery”. I understand that calculating SD in the same season can enhance the comparability of data, but I don’t think the clarity is relatively stable in the summer, because heavy rainfall and algal bloom often occur in summer, resulting in the change of suspended solids and therefore affecting the SD.
- Line 163-164. The collected SD measurements were within seven days of satellite overpasses. I suggest the meteorological conditions should be considered since both heavy rain and strong wind could affect the SD.
- Figure 4. When SD is less than 2, the covariance relationship between in-situ SD and LAWSD30 is very weak because many data are vertical lines. The accuracy should be analyzed in this situation.
- Line 332. Why Selinco Lake and Hongze Lake were chosen to illustrate the LAWSD30?
- Figure 10. The figures present the different SD trends of lakes with area<=1 km2 and >1km2, in different region of China. The five regions have different socio-economic, geological and climatic conditions, should the driver factors of SD changes be further explained?
- AC2: 'Reply on RC2', Liangyun Liu, 19 Apr 2022
Peer review completion
Interactive discussion
Status: closed
-
RC1: 'Comment on hess-2021-630', Anonymous Referee #1, 17 Mar 2022
This manuscript represents a significant contribution in the form of an extensive application ready lake clarity data product covering small and large lakes of China over a period of 35 years. This data product, LAWSD30 (and associated derivations), will be a valuable asset to future endeavours that seek to understand the drivers of lake clarity change in China.
Specific comments:
1. Include the normalised RMSE in addition to the RMSE; the normalised RMSE summarises the explanatory power of a model in a manner that is independent of scale, and so enables a ready comparison across modelling efforts.
2. Line 118 : replace 'product' with 'application ready data product'.
3. Lines 147 - 48: it is not clear how 'user' and 'producer' map to the terminology used in Peckel et al 2016. Please clarify or re-word.
4. Line 211: replace 'Following him' with 'Following Chen et al. (2021)', assuming that is what is meant?
5. Line 215: for the convenience of the reader, please briefly describe the constituent BAP criteria and how the BAP was calculated, rather than pointing to Chen et al. 2021 (which then on-refers the reader to White et al. 2014 for further details).
6. Line 227: please replace the word 'proven' with 'demonstrated', here and elsewhere in the document. Likewise, replace 'proves' with 'demonstrates'. 'Proven' and 'proves' should be reserved for contexts where there is little room for interpretation.
7. Line 316: replace ‘product’ with 'LAWSD30 data set'.
8. Line 339: replace 'researches' with ‘studies’ (here and elsewhere in the manuscript).
9. Line 348: replace 'Similarly, some studies found the same SD ....' with 'Other studies found the same SD ...'.
10. Line 462: replace 'hydrometeor' with 'weather'. (hydrometeor has a different meaning to that which I assume is intended)
11. Lines 523 - 24: replace 'to reflect the comprehensive conditions in water bodies' with 'of water quality'
12. Conclusion: include commentary that alerts the reader to the notion that an informed interpretation of the lake clarity patterns presented in this manuscript will require an understanding of the relative importance of local and regional driving forces; and that such an informed interpretation will be required in order to prioritise management actions.
For example, the work of Yu and Zhai (https://doi.org/10.1038/s41598-020-71312-3) demonstrate that compound drought and heat events have impacted China more severly since ca. the year 2000. Have such events led to changes in SD patterns in some lakes, for example by making wind-driven resuspension more or less likely?
- AC1: 'Reply on RC1', Liangyun Liu, 19 Apr 2022
-
RC2: 'Comment on hess-2021-630', Anonymous Referee #2, 18 Mar 2022
This paper developed a 30 m long-term Lake Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) using the robust water-color-parameter-based SD model. The LAWSD30 dataset can be used to study the temporal and spatial changes of Lake SD. Therefore, it is of great significance to the study of long-term trend of Lake SD and lake ecological environment management.
Specific comments
- The RMSE was used for evaluating the accuracy of the model. However, in my opinion, this index is not very appropriate. For example, when the real value is equal to 2 or 20, although the RMSE value is 0.2, the accuracy of the model is very different. Thus, this index does not show how close the real value is to the estimated value I suggest using MAPE or similar indicators to evaluate the accuracy of the model.
- Line 125-126. “The summer months were chosen because the water clarity is relatively stable in this season and suitable for monitoring with remote sensing imagery”. I understand that calculating SD in the same season can enhance the comparability of data, but I don’t think the clarity is relatively stable in the summer, because heavy rainfall and algal bloom often occur in summer, resulting in the change of suspended solids and therefore affecting the SD.
- Line 163-164. The collected SD measurements were within seven days of satellite overpasses. I suggest the meteorological conditions should be considered since both heavy rain and strong wind could affect the SD.
- Figure 4. When SD is less than 2, the covariance relationship between in-situ SD and LAWSD30 is very weak because many data are vertical lines. The accuracy should be analyzed in this situation.
- Line 332. Why Selinco Lake and Hongze Lake were chosen to illustrate the LAWSD30?
- Figure 10. The figures present the different SD trends of lakes with area<=1 km2 and >1km2, in different region of China. The five regions have different socio-economic, geological and climatic conditions, should the driver factors of SD changes be further explained?
- AC2: 'Reply on RC2', Liangyun Liu, 19 Apr 2022
Peer review completion
Journal article(s) based on this preprint
Xidong Chen et al.
Data sets
The 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) Xidong Chen; Liangyun Liu https://doi.org/10.5281/zenodo.5734071
Xidong Chen et al.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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