the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Oct 2023
Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022
Abstract. Knowledge of the spatial and temporal distribution of surface water is important for water resource management, flood risk assessment, monitoring ecosystem health, constraining estimates of biogeochemical cycles and understanding our climate. While global scale spatial-temporal change detection of surface water has significantly improved in recent years with planetary scale remote sensing and computing, it has remained challenging to distinguish the changing characteristics of rivers and lakes. Here we analyze the spatial extent of permanent and seasonal rivers and lakes globally over the past 38-years based on new data of river system extents and surface water trends. Results show that while the total permanent surface area of both rivers and lakes has remained relatively constant, the area with intermittent seasonal coverage has increased by 12 % and 27 % for rivers and lakes, respectively. The increase is statistically significant in over 84 % of global water catchments based on Spearman rank correlations above 0.05 and p values less than 0.05. The results of our analysis are shared as the Surface Area of Rivers and Lakes (SARL) database, which contributes to improved understanding of the hydrological cycle and management of water resources.
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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
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Supplement
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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.
- Preprint
(1468 KB) - Metadata XML
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Supplement
(1452 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2147', Anonymous Referee #1, 28 Nov 2023
Surface water area plays an important role in ecosystems, the carbon cycle, flood and drought risks, and water resource management. Global surface water datasets built on moderate resolution satellite imagery (e.g., Landsat with 30-m, 16-day resolutions) have been developed, but they do not differentiate types of surface water bodies. The authors created a new dataset of the surface area of rivers and lakes (SARL) with seasonal and permanent surface water data for each year from 1984-2022. They developed this new dataset by combining data from the Global Surface Water dataset (Pekel et al., 2016) and from the global extent of river channel belts dataset (Nyberg et a., 2023). They were also able to assess changes from 1984 to 2022 in permanent and seasonal surface water areas in watersheds across the globe. They found seasonal surface water area had increased for both rivers and lakes, while the global total permanent surface water area was relatively unchanged. This dataset, showing where and when there are changes in lake and river permanent and seasonal surface water areas, can be helpful for water resource management. Such management practices are used to help limit the adverse impacts of extreme events such as floods and droughts, which are becoming more common due to climate change. The SARL dataset can help identify changes in the seasonality or permanence of surface water areas of key stormwater or drinking water reservoirs and help inform decision making about these water bodies in the future. For detailed comments, please see the PDF attached.
- AC1: 'Reply on RC1', Bjorn Nyberg, 27 Jan 2024
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RC2: 'Comment on egusphere-2023-2147', Anonymous Referee #2, 20 Dec 2023
Review Comments:
In this article, the authors analyze the spatial and temporal extent of global surface water changes over the past 38 years (1984 – 2022) using remote sensing methods. The authors create a new dataset or reservoir and lake surface area (SARL) by combining data from the Global Surface Water dataset and from the global extent of river channel belts dataset. From this dataset, the authors are able to determine permanent and seasonal changes in waterbody surface areas. Most notably, they found that seasonal surface water increased in both lakes and reservoirs, while permanent water bodies remained relatively unchanged. The authors also describe the multiple reasons why this dataset would be useful for water management, conservation measurements, and ecosystem health assessments.
Major Comments:
Line 124: Can you clarify why you chose to average 2015 -2017 and use those values where you had missing data? Why did you opt not to interpolate or fill with the long term average or another metric?
Results: It would be nice to include a general comment about regional differences or perhaps even climatic differences. Most importantly, are there regions that stick out or areas that are notably with respect to your results? I know this gets brought up in the discussion, but it might increase the clarity of your results.
Section 4.3: I would suggest changing this section title to something more specific. This section dives into the trends in and potential reasons for why the trends are occurring, but I don’t directly see the link to water management strategies. I also think this would be a better first discussion paragraph as it goes into the reasons behind your results and would prepare the reader for the other two sections on ecosystem health and biogeochemical cycles.
Line 336: You discuss regional differences, but I don’t feel like you dove into the regional differences as much. I would expand this in section 4.3. I would also elaborate on the regional differences you see in the conclusion.
Conclusion: I would include one or two key points from section 4.1 and 4.2. This would make the conclusion a bit stronger and prove why this dataset is so unique and what it can be used for.
Minor comments:
Line 31: The authors cite GRanD (Lehner et al., 2016), but it also might be useful to cite GeoDAR (Wang et al., 2023) as that is a more representative of the total storage that exists globally inside reservoirs.
Figure 2: I would suggest breaking this up into two figures either by rivers and lakes or by plot type since the figure is quite tiny and it’s hard to see the changes in the maps. I would also suggest making the lines on the line plots thicker and changing the colors to be more divergent, especially for the surface area of lakes (Panel B line plot). For the maps, I would include a color bar that shows that white denotes either no data or not enough data (I’m personally not sure which it is). Lastly, it could be beneficial to switch the color bar from rainbow to another one that is more color blind friendly.
Figure 3: I also suggest splitting this figure into two. Perhaps either by the type or by seasonal and permanent as I think it will increase the clarity of your results section. I also don’t know if you need the color legend for each panel, but perhaps that is necessary?
Figure 4: I like this figure; however, I would change the colors to a different set (perhaps one color for each water body type and different shapes for seasonal and permanent). Seasonal Lake gets lost in this figure and my eyes are drawn to permanent lake and seasonal river.
Citations:
Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations, Earth Syst. Sci. Data, 14, 1869–1899, https://doi.org/10.5194/essd-14-1869-2022, 2022.
Citation: https://doi.org/10.5194/egusphere-2023-2147-RC2 - AC2: 'Reply on RC2', Bjorn Nyberg, 27 Jan 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2147', Anonymous Referee #1, 28 Nov 2023
Surface water area plays an important role in ecosystems, the carbon cycle, flood and drought risks, and water resource management. Global surface water datasets built on moderate resolution satellite imagery (e.g., Landsat with 30-m, 16-day resolutions) have been developed, but they do not differentiate types of surface water bodies. The authors created a new dataset of the surface area of rivers and lakes (SARL) with seasonal and permanent surface water data for each year from 1984-2022. They developed this new dataset by combining data from the Global Surface Water dataset (Pekel et al., 2016) and from the global extent of river channel belts dataset (Nyberg et a., 2023). They were also able to assess changes from 1984 to 2022 in permanent and seasonal surface water areas in watersheds across the globe. They found seasonal surface water area had increased for both rivers and lakes, while the global total permanent surface water area was relatively unchanged. This dataset, showing where and when there are changes in lake and river permanent and seasonal surface water areas, can be helpful for water resource management. Such management practices are used to help limit the adverse impacts of extreme events such as floods and droughts, which are becoming more common due to climate change. The SARL dataset can help identify changes in the seasonality or permanence of surface water areas of key stormwater or drinking water reservoirs and help inform decision making about these water bodies in the future. For detailed comments, please see the PDF attached.
- AC1: 'Reply on RC1', Bjorn Nyberg, 27 Jan 2024
-
RC2: 'Comment on egusphere-2023-2147', Anonymous Referee #2, 20 Dec 2023
Review Comments:
In this article, the authors analyze the spatial and temporal extent of global surface water changes over the past 38 years (1984 – 2022) using remote sensing methods. The authors create a new dataset or reservoir and lake surface area (SARL) by combining data from the Global Surface Water dataset and from the global extent of river channel belts dataset. From this dataset, the authors are able to determine permanent and seasonal changes in waterbody surface areas. Most notably, they found that seasonal surface water increased in both lakes and reservoirs, while permanent water bodies remained relatively unchanged. The authors also describe the multiple reasons why this dataset would be useful for water management, conservation measurements, and ecosystem health assessments.
Major Comments:
Line 124: Can you clarify why you chose to average 2015 -2017 and use those values where you had missing data? Why did you opt not to interpolate or fill with the long term average or another metric?
Results: It would be nice to include a general comment about regional differences or perhaps even climatic differences. Most importantly, are there regions that stick out or areas that are notably with respect to your results? I know this gets brought up in the discussion, but it might increase the clarity of your results.
Section 4.3: I would suggest changing this section title to something more specific. This section dives into the trends in and potential reasons for why the trends are occurring, but I don’t directly see the link to water management strategies. I also think this would be a better first discussion paragraph as it goes into the reasons behind your results and would prepare the reader for the other two sections on ecosystem health and biogeochemical cycles.
Line 336: You discuss regional differences, but I don’t feel like you dove into the regional differences as much. I would expand this in section 4.3. I would also elaborate on the regional differences you see in the conclusion.
Conclusion: I would include one or two key points from section 4.1 and 4.2. This would make the conclusion a bit stronger and prove why this dataset is so unique and what it can be used for.
Minor comments:
Line 31: The authors cite GRanD (Lehner et al., 2016), but it also might be useful to cite GeoDAR (Wang et al., 2023) as that is a more representative of the total storage that exists globally inside reservoirs.
Figure 2: I would suggest breaking this up into two figures either by rivers and lakes or by plot type since the figure is quite tiny and it’s hard to see the changes in the maps. I would also suggest making the lines on the line plots thicker and changing the colors to be more divergent, especially for the surface area of lakes (Panel B line plot). For the maps, I would include a color bar that shows that white denotes either no data or not enough data (I’m personally not sure which it is). Lastly, it could be beneficial to switch the color bar from rainbow to another one that is more color blind friendly.
Figure 3: I also suggest splitting this figure into two. Perhaps either by the type or by seasonal and permanent as I think it will increase the clarity of your results section. I also don’t know if you need the color legend for each panel, but perhaps that is necessary?
Figure 4: I like this figure; however, I would change the colors to a different set (perhaps one color for each water body type and different shapes for seasonal and permanent). Seasonal Lake gets lost in this figure and my eyes are drawn to permanent lake and seasonal river.
Citations:
Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations, Earth Syst. Sci. Data, 14, 1869–1899, https://doi.org/10.5194/essd-14-1869-2022, 2022.
Citation: https://doi.org/10.5194/egusphere-2023-2147-RC2 - AC2: 'Reply on RC2', Bjorn Nyberg, 27 Jan 2024
Peer review completion
Journal article(s) based on this preprint
Data sets
Surface Area of Rivers and Lakes (SARL) database Björn Nyberg https://zenodo.org/record/6895820
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Roger Sayre
Elco Luijendijk
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1468 KB) - Metadata XML
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Supplement
(1452 KB) - BibTeX
- EndNote
- Final revised paper