the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Feb 2023
Glacier-level and gridded mass change in the source rivers in the eastern Tibetan Plateau (ETPR) from 1970s to 2000
Abstract. The highly glacierised eastern part of the Tibetan Plateau is the key source region of the five major rivers Yangtze, Yellow, Lancang-Mekong, Nu-Salween and Brahmaputra rivers. These exotic rivers are vital freshwater resources for more than one billion people downstream for their daily life, irrigation, industrial use, and hydropower. However, the glaciers have been receding during the last decades and are projected to further decline which will profoundly impact the water availability of these larger river systems. Although few studies have investigated glacier mass changes in these river basins since the 1970s, they are site and temporal specific and limited by data availability. Hence, knowledge of glacier mass changes is especially lacking for years prior to 2000. We therefore applied digital elevation models (DEMs) derived from large scale topographic maps based on aerial photogrammetry from the 1970s and 1980s and compared them to the SRTM DEM to provide a complete picture of mass change of glaciers in the region. The mass changes are presented on individual glacier bases with a resolution of 30 m and the grided (0.1° and 0.5°). Our database consists of 17444 glaciers with a total area of 17426 ± 523 km2. The annual mean mass loss of glaciers is -0.28 ± 0.15 m w.e. in the whole region. This is larger than the previous site-specific findings, the surface thinning increases on average from west to east along the Himalayas-Hengduan mountains with the largest thinning in the Salween basin. Comparisons between the topographic map-based DEMs and DEMs generated based on Hexagon KH-9 metric camera data for parts in the Himalayas demonstrate that our dataset provides a robust estimation of glacier mass changes. However, the uncertainty is high in high altitudes due to the saturation of aerial photos over low contrast areas like snow surface on a steep terrain. The dataset is well suited for supporting more detailed climatical and hydrological analyses and are available at https://doi.org/10.11888/Cryos.tpdc.272884 (Liu et al., 2022).
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Interactive discussion
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RC1: 'Comment on essd-2022-473', Romain Hugonnet, 16 Mar 2023
Review of Zhu et al., “Glacier-level and gridded mass change in the source rivers in the eastern Tibetan Plateau (ETPR) from 1970s to 2000” in discussion in ESSD, March 2023
by Romain Hugonnet, University of Washington
General
The study by Zhu et al. presents an elevation change dataset for glaciers in the Tibetan Plateau. This dataset is based on historical topographic maps compared to the SRTM.
While digging into historical archives is of great interest to the field of glaciology (and others), many processing steps in the study are flawed. My three most concerning issues are the radar penetration assumptions, the bias correction of elevation differences, and the spatial correlations used during error analysis.
Individually, each of these steps can impact the current estimates to a magnitude close to that of the signal (or error) estimated.In addition, many statements are unclear, and many references in the text are out-of-date or irrelevant.
Main comments
1/ General error reporting
The authors do not specify which confidence level is reported, so I assume it is 1-sigma (68% confidence) everywhere. To make robust statements, one should rather use 2-sigma (95% confidence), as +/-1-sigma is too narrow a confidence interval.
With 2-sigma, the current regional result of the authors is not statistically different from zero at -0.28 +/- 0.30 m yr-1. From basic error propagation theory, any result aggregated at a larger-scale has lower uncertainty than its smaller-scale components. This implies that all other results of the study (from pixel scale to subregions elevation change) must also not be statistically significant.
In short, if the errors provided by the authors are realistic, then the results are largely useless for the community. This is worrying, but might only be a question of whether the error estimation is reliable or not (some sources of error might be overestimated). However, looking into the details, several sources are actually highly underestimated in the current approach, as I further describe in my main comments 2/, 3/ and 4/.
In short, the authors should strive to improve their entire error analysis (systematic AND random) and paint a clear picture of the confidence in their dataset and result. However, even if they do, they might find that the data is not of good enough quality, and that the results are not statistically significant (which would not surprise me, given the structures of noise known to exist in some of the DEMs used, see comment 3/).2/ Penetration of X-band is a systematic error and not negligible
The penetration of X-band in glaciers of the Tibetan plateau is not negligible, especially given the period of year where SRTM was acquired. Many studies provide such estimates of X-band penetration, see for instance Li et al. (2021) or Zhou et al. (2022), to only point out recent work in HMA.
Following Li et al. (2021), about 2 meters of average X-band penetration would still be present. Such a systematic error for a period of ~25 years will affect thinning estimates by 0.08 m yr-1, which is 30% of the signal measured by the authors of -0.28 m yr-1.
In short, X-band penetration needs to be corrected, not neglected.
3/ Long-range spatial correlations of errors are not accounted for
All of the DEMs used by the authors contain long-range correlated noise (that can be seen on Figure 4, top panel): KH-9, topographic maps, SRTM-C and -X. All of these have especially strong structures of noises. For more visual examples, see Hugonnet et al. (2022), Fig. 1 and Supplementary Figs.
Not accounting for these patterns of errors can underestimate final mean elevation change uncertainties by a factor of 50 on high-res modern DEMs, and much more on historic ones. Right now the authors use a correlation length of 600 m, which is ridiculously small when it is known that KH-9 has errors correlated up to dozens of kilometers (Dehecq et al. (2020)), and similar for other DEMs as can be seen on the visual examples listed in the previous paragraph.
Hugonnet et al. (2022) developed a framework to account for these errors reliably for any DEM, with open-source tools to perform error propagation, used by other studies while still in development (Dehecq et al. (2020) for KH-9, Zhou et al. (2022) for SRTM).
It is also a bad habit to show elevation difference maps without stable terrain (Figure 10), as it helps visually assess the data quality. Authors should add this back.
4/ Elevation-dependent corrections introduce important systematic errors
Elevation-dependent biases generally arise from differences in high curvature terrain (peaks and cavities) rather than GCP alignment, as justified by the authors that apply such a correction. See Gardelle et al. (2013).
Problematically, curvature biases are specific to stable terrain (where the peaks are) but do not have an impact on glaciers. Applying a correction to all terrain will therefore introduce systematic errors for the entire elevation change maps, including glaciers. Those can have a magnitude in the order of meters.
In short, the authors should either better justify the relevance of such a correction, or not apply any.
Line-by-line comments:
There were too many unclear or exaggerated statements, so I stopped taking notes entirely.
I advise the authors to:
1/ Revise their statements carefully for scientific clarity and accuracy,
2/ Revise their references, as many are out-of-date or biased towards a group (too many Bolch papers) or simply irrelevant,
3/ Ask for English proofreading.References
Dehecq, A., Gardner, A. S., Alexandrov, O., McMichael, S., Hugonnet, R., Shean, D., & Marty, M. (2020). Automated Processing of Declassified KH-9 Hexagon Satellite Images for Global Elevation Change Analysis Since the 1970s. Frontiers of Earth Science, 8, 516.
Gardelle, J., Berthier, E., & Arnaud, Y. (2012). Impact of resolution and radar penetration on glacier elevation changes computed from DEM differencing. Journal of Glaciology, 58(208), 419–422.
Hugonnet, R., Brun, F., Berthier, E., Dehecq, A., Mannerfelt, E. S., Eckert, N., & Farinotti, D. (2022). Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 6456–6472.
Li, J., Li, Z.-W., Hu, J., Wu, L.-X., Li, X., Guo, L., Liu, Z., Miao, Z.-L., Wang, W., & Chen, J.-L. (2021). Investigating the bias of TanDEM-X digital elevation models of glaciers on the Tibetan Plateau: impacting factors and potential effects on geodetic mass-balance measurements. Journal of Glaciology, 67(264), 613–626.
Zhou, Y., Li, X., Zheng, D., & Li, Z. (2022). Evolution of geodetic mass balance over the largest lake-terminating glacier in the Tibetan Plateau with a revised radar penetration depth based on multi-source high-resolution satellite data. Remote Sensing of Environment, 275, 113029.
Citation: https://doi.org/10.5194/essd-2022-473-RC1 -
RC2: 'Comment on essd-2022-473', Anonymous Referee #2, 17 Mar 2023
Review of “Glacier-level and gridded mass change in the source rivers in the eastern Tibetan Plateau (ETPR) from 1970s to 2000” from Yu Zhu and others
In this data paper the authors present a dataset that consists in glacier elevation and mass changes at various resolution (glacier level to 0.5° gridded values) for the eastern Tibetan Plateau from 1970 to 2000. Glacier elevation changes are estimated by differencing digital elevation models (DEMs) derived from topographic maps with the SRTM DEM. While I commend the authors for writing a data paper on this topic, and while I acknowledge the need and interest of such data, I think that the overall quality of the manuscript prevents the readers from having a rigorous and usable description of the data for future use. I list my main comments below, but I did not make specific comments as too much work is required before addressing specific comments.
Quality of the English
I am sorry for pointing this out, and being my self a non-native English speaker I understand the challenge to write in English, but the quality of the language prevents from understanding in many places. This is just an example, but there are several sentences like L60-61: “In the area with annual precipitation more than 2000 mm (e.g. Kangri Karpo Mountains), a high accumulation may be presented during monsoon at meanwhile (Wu et al., 2018).” Or L194-195: “In order to reduce the error propagation, we filled the data holes where area more than 0.5 km2 by using the original values.” I recommend to carefully check the manuscript.
Structure
This manuscript would be much more useful for the users if its focus would closely follow the data production, description and handling. The section 2 should be separated into three sections (“Raw data”, “Methods” and “Uncertainties and quality assessment”). The product description (fig. A2 and A3) could be moved to the main text.
Use of the SRTM DEM
There are two major issues in the use of SRTM by the authors. First, they use the void filled version of SRTM (L126-127). This is not valid for the purpose of tracking elevation changes of glaciers, as the acquisition date of the data used to fill the SRTM voids is not known. I suggest using a non-void filled version of SRTM and interpolate the dh maps instead of the SRTM DEM (McNabb et al., 2019). Second, the authors assume a C-band penetration depth based on the difference between SRTM-X and SRTM-C DEMs. This strategy has been wildly applied in the field of glaciology, but it is important to note that it relies on the assumption of no penetration of the X-band. Unfortunately, it is proven that this penetration is clearly non zero, and that is can reach several meters in the case of cold snow and ice conditions, as expected during the SRTM campaign of February 2000 (Dehecq et al., 2016; Lambrecht et al., 2018; Li et al., 2021; Zhou et al., 2022). As the mean elevation change is less than 10 m over the whole study period (L338), this penetration bias is proportionally very large, and should at least be included in the uncertainty calculation. This issue would ultimately question the usefulness of this dataset for the community.
L208: the authors wrote that “The penetration depth was estimated from the off-glacier region (Fig. 3) and presented a mean of 3.8-6.2 m along the altitude.” I hope that this is a typo and that the penetration depth was actually estimated on glacier.
More specific details needed in the processing
The level of details is not well chosen in the method section (L155-335). First, I do not think it is useful to recall the co-registration procedure with such a large amount of details, including equations from previous well established methods and L156-168 should be deleted (Nuth and Kääb, 2011). Second the elevation dependent bias mentioned L175 could originate from curvature differences (Gardelle et al., 2012) mentioned by the authors. However, it is not clear whether they actually implemented this correction or another one. Regarding the histograms on figure 2, it seems that the applied corrections tend to shift the distribution away from 0 in some cases (e.g. the third panel). When the authors apply the sigmoid function for filtering on glacier values (L184-195), they should tell which percentage of the data are filtered out.
Uncertainty estimate completely unclear
I feel that there a confusion between systematic and random errors in the approach here. The mean elevation difference (MED) on stable ground does not allow to evaluate systematic biases in DEM differences. If I take the example of a tilted DEM, then there is a systematic bias in the elevation difference, but still the MED can be zero (especially after a co-registration procedure). Additionally, the authors do not account for the spatial correlation of the errors (Rolstad et al., 2009; Hugonnet et al., 2022) and for missing data/gaps in the elevation change maps. In short, the uncertainty assessment should be done again from the start.
L319: glaciers that fulfill the coverage criteria have still about 45% of coverage. It is not clear how the voids in the elevation change maps are filled in order to calculate the mean glacier elevation change.
Comparison with KH-9 and ICESat-2 very difficult to follow
I was wondering why comparing the SRTM-KH-9 difference with the SRTM-Topo DEM difference, and why not comparing directly the Topo DEM and the KH-9 DEM? I think the latter would be easier to interpret (even though the KH-9 DEM has its one biases and problems). Maybe I missed something, but in the figures, I have the feeling the only the off-glacier terrain is compared. What is the interest of data acquired at the same time then?
The comparison with ICESat-2 is just impossible to understand, especially when the conclusion is “We thus find that the accuracy of corrected Topo difference is not less accurate than the ATL06 difference below 5700 m.”
Inventories in the 1970
What are the dates of the inventories used in the study? Wouldn’t be useful to update the CGI with the glacier outlines from 1970’s? No area change is mentioned in this study. Area changes are not expected to be very large, but they should still be non zero.
References
Dehecq, A., Millan, R., Berthier, E., Gourmelen, N., Trouvé, E., and Vionnet, V.: Elevation Changes Inferred From TanDEM-X Data Over the Mont-Blanc Area: Impact of the X-Band Interferometric Bias, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 3870–3882, https://doi.org/10.1109/JSTARS.2016.2581482, 2016.
Gardelle, J., Berthier, E., and Arnaud, Y.: Impact of resolution and radar penetration on glacier elevation changes computed from DEM differencing, J. Glaciol., 58, 419–422, https://doi.org/doi:10.3189/2012JoG11J175, 2012.
Hugonnet, R., Brun, F., Berthier, E., Dehecq, A., Mannerfelt, E. S., Eckert, N., and Farinotti, D.: Uncertainty analysis of digital elevation models by spatial inference from stable terrain, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 1–17, https://doi.org/10.1109/JSTARS.2022.3188922, 2022.
Lambrecht, A., Mayer, C., Wendt, A., Floricioiu, D., and Völksen, C.: Elevation change of Fedchenko Glacier, Pamir Mountains, from GNSS field measurements and TanDEM-X elevation models, with a focus on the upper glacier, J. Glaciol., 64, 637–648, https://doi.org/10.1017/jog.2018.52, 2018.
Li, J., Li, Z.-W., Hu, J., Wu, L.-X., Li, X., Guo, L., Liu, Z., Miao, Z.-L., Wang, W., and Chen, J.-L.: Investigating the bias of TanDEM-X digital elevation models of glaciers on the Tibetan Plateau: impacting factors and potential effects on geodetic mass-balance measurements, J. Glaciol., 67, 613–626, https://doi.org/10.1017/jog.2021.15, 2021.
McNabb, R., Nuth, C., Kääb, A., and Girod, L.: Sensitivity of glacier volume change estimation to DEM void interpolation, The Cryosphere, 13, 895–910, https://doi.org/10.5194/tc-13-895-2019, 2019.
Nuth, C. and Kääb, A.: Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change, The Cryosphere, 5, 271–290, https://doi.org/10.5194/tc-5-271-2011, 2011.
Rolstad, C., Haug, T., and Denby, B.: Spatially integrated geodetic glacier mass balance and its uncertainty based on geostatistical analysis: application to the western Svartisen ice cap, Norway, J. Glaciol., 55, 666–680, https://doi.org/10.3189/002214309789470950, 2009.
Zhou, Y., Li, X., Zheng, D., and Li, Z.: Evolution of geodetic mass balance over the largest lake-terminating glacier in the Tibetan Plateau with a revised radar penetration depth based on multi-source high-resolution satellite data, Remote Sens. Environ., 275, 113029, https://doi.org/10.1016/j.rse.2022.113029, 2022.
Citation: https://doi.org/10.5194/essd-2022-473-RC2
Interactive discussion
Status: closed
-
RC1: 'Comment on essd-2022-473', Romain Hugonnet, 16 Mar 2023
Review of Zhu et al., “Glacier-level and gridded mass change in the source rivers in the eastern Tibetan Plateau (ETPR) from 1970s to 2000” in discussion in ESSD, March 2023
by Romain Hugonnet, University of Washington
General
The study by Zhu et al. presents an elevation change dataset for glaciers in the Tibetan Plateau. This dataset is based on historical topographic maps compared to the SRTM.
While digging into historical archives is of great interest to the field of glaciology (and others), many processing steps in the study are flawed. My three most concerning issues are the radar penetration assumptions, the bias correction of elevation differences, and the spatial correlations used during error analysis.
Individually, each of these steps can impact the current estimates to a magnitude close to that of the signal (or error) estimated.In addition, many statements are unclear, and many references in the text are out-of-date or irrelevant.
Main comments
1/ General error reporting
The authors do not specify which confidence level is reported, so I assume it is 1-sigma (68% confidence) everywhere. To make robust statements, one should rather use 2-sigma (95% confidence), as +/-1-sigma is too narrow a confidence interval.
With 2-sigma, the current regional result of the authors is not statistically different from zero at -0.28 +/- 0.30 m yr-1. From basic error propagation theory, any result aggregated at a larger-scale has lower uncertainty than its smaller-scale components. This implies that all other results of the study (from pixel scale to subregions elevation change) must also not be statistically significant.
In short, if the errors provided by the authors are realistic, then the results are largely useless for the community. This is worrying, but might only be a question of whether the error estimation is reliable or not (some sources of error might be overestimated). However, looking into the details, several sources are actually highly underestimated in the current approach, as I further describe in my main comments 2/, 3/ and 4/.
In short, the authors should strive to improve their entire error analysis (systematic AND random) and paint a clear picture of the confidence in their dataset and result. However, even if they do, they might find that the data is not of good enough quality, and that the results are not statistically significant (which would not surprise me, given the structures of noise known to exist in some of the DEMs used, see comment 3/).2/ Penetration of X-band is a systematic error and not negligible
The penetration of X-band in glaciers of the Tibetan plateau is not negligible, especially given the period of year where SRTM was acquired. Many studies provide such estimates of X-band penetration, see for instance Li et al. (2021) or Zhou et al. (2022), to only point out recent work in HMA.
Following Li et al. (2021), about 2 meters of average X-band penetration would still be present. Such a systematic error for a period of ~25 years will affect thinning estimates by 0.08 m yr-1, which is 30% of the signal measured by the authors of -0.28 m yr-1.
In short, X-band penetration needs to be corrected, not neglected.
3/ Long-range spatial correlations of errors are not accounted for
All of the DEMs used by the authors contain long-range correlated noise (that can be seen on Figure 4, top panel): KH-9, topographic maps, SRTM-C and -X. All of these have especially strong structures of noises. For more visual examples, see Hugonnet et al. (2022), Fig. 1 and Supplementary Figs.
Not accounting for these patterns of errors can underestimate final mean elevation change uncertainties by a factor of 50 on high-res modern DEMs, and much more on historic ones. Right now the authors use a correlation length of 600 m, which is ridiculously small when it is known that KH-9 has errors correlated up to dozens of kilometers (Dehecq et al. (2020)), and similar for other DEMs as can be seen on the visual examples listed in the previous paragraph.
Hugonnet et al. (2022) developed a framework to account for these errors reliably for any DEM, with open-source tools to perform error propagation, used by other studies while still in development (Dehecq et al. (2020) for KH-9, Zhou et al. (2022) for SRTM).
It is also a bad habit to show elevation difference maps without stable terrain (Figure 10), as it helps visually assess the data quality. Authors should add this back.
4/ Elevation-dependent corrections introduce important systematic errors
Elevation-dependent biases generally arise from differences in high curvature terrain (peaks and cavities) rather than GCP alignment, as justified by the authors that apply such a correction. See Gardelle et al. (2013).
Problematically, curvature biases are specific to stable terrain (where the peaks are) but do not have an impact on glaciers. Applying a correction to all terrain will therefore introduce systematic errors for the entire elevation change maps, including glaciers. Those can have a magnitude in the order of meters.
In short, the authors should either better justify the relevance of such a correction, or not apply any.
Line-by-line comments:
There were too many unclear or exaggerated statements, so I stopped taking notes entirely.
I advise the authors to:
1/ Revise their statements carefully for scientific clarity and accuracy,
2/ Revise their references, as many are out-of-date or biased towards a group (too many Bolch papers) or simply irrelevant,
3/ Ask for English proofreading.References
Dehecq, A., Gardner, A. S., Alexandrov, O., McMichael, S., Hugonnet, R., Shean, D., & Marty, M. (2020). Automated Processing of Declassified KH-9 Hexagon Satellite Images for Global Elevation Change Analysis Since the 1970s. Frontiers of Earth Science, 8, 516.
Gardelle, J., Berthier, E., & Arnaud, Y. (2012). Impact of resolution and radar penetration on glacier elevation changes computed from DEM differencing. Journal of Glaciology, 58(208), 419–422.
Hugonnet, R., Brun, F., Berthier, E., Dehecq, A., Mannerfelt, E. S., Eckert, N., & Farinotti, D. (2022). Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 6456–6472.
Li, J., Li, Z.-W., Hu, J., Wu, L.-X., Li, X., Guo, L., Liu, Z., Miao, Z.-L., Wang, W., & Chen, J.-L. (2021). Investigating the bias of TanDEM-X digital elevation models of glaciers on the Tibetan Plateau: impacting factors and potential effects on geodetic mass-balance measurements. Journal of Glaciology, 67(264), 613–626.
Zhou, Y., Li, X., Zheng, D., & Li, Z. (2022). Evolution of geodetic mass balance over the largest lake-terminating glacier in the Tibetan Plateau with a revised radar penetration depth based on multi-source high-resolution satellite data. Remote Sensing of Environment, 275, 113029.
Citation: https://doi.org/10.5194/essd-2022-473-RC1 -
RC2: 'Comment on essd-2022-473', Anonymous Referee #2, 17 Mar 2023
Review of “Glacier-level and gridded mass change in the source rivers in the eastern Tibetan Plateau (ETPR) from 1970s to 2000” from Yu Zhu and others
In this data paper the authors present a dataset that consists in glacier elevation and mass changes at various resolution (glacier level to 0.5° gridded values) for the eastern Tibetan Plateau from 1970 to 2000. Glacier elevation changes are estimated by differencing digital elevation models (DEMs) derived from topographic maps with the SRTM DEM. While I commend the authors for writing a data paper on this topic, and while I acknowledge the need and interest of such data, I think that the overall quality of the manuscript prevents the readers from having a rigorous and usable description of the data for future use. I list my main comments below, but I did not make specific comments as too much work is required before addressing specific comments.
Quality of the English
I am sorry for pointing this out, and being my self a non-native English speaker I understand the challenge to write in English, but the quality of the language prevents from understanding in many places. This is just an example, but there are several sentences like L60-61: “In the area with annual precipitation more than 2000 mm (e.g. Kangri Karpo Mountains), a high accumulation may be presented during monsoon at meanwhile (Wu et al., 2018).” Or L194-195: “In order to reduce the error propagation, we filled the data holes where area more than 0.5 km2 by using the original values.” I recommend to carefully check the manuscript.
Structure
This manuscript would be much more useful for the users if its focus would closely follow the data production, description and handling. The section 2 should be separated into three sections (“Raw data”, “Methods” and “Uncertainties and quality assessment”). The product description (fig. A2 and A3) could be moved to the main text.
Use of the SRTM DEM
There are two major issues in the use of SRTM by the authors. First, they use the void filled version of SRTM (L126-127). This is not valid for the purpose of tracking elevation changes of glaciers, as the acquisition date of the data used to fill the SRTM voids is not known. I suggest using a non-void filled version of SRTM and interpolate the dh maps instead of the SRTM DEM (McNabb et al., 2019). Second, the authors assume a C-band penetration depth based on the difference between SRTM-X and SRTM-C DEMs. This strategy has been wildly applied in the field of glaciology, but it is important to note that it relies on the assumption of no penetration of the X-band. Unfortunately, it is proven that this penetration is clearly non zero, and that is can reach several meters in the case of cold snow and ice conditions, as expected during the SRTM campaign of February 2000 (Dehecq et al., 2016; Lambrecht et al., 2018; Li et al., 2021; Zhou et al., 2022). As the mean elevation change is less than 10 m over the whole study period (L338), this penetration bias is proportionally very large, and should at least be included in the uncertainty calculation. This issue would ultimately question the usefulness of this dataset for the community.
L208: the authors wrote that “The penetration depth was estimated from the off-glacier region (Fig. 3) and presented a mean of 3.8-6.2 m along the altitude.” I hope that this is a typo and that the penetration depth was actually estimated on glacier.
More specific details needed in the processing
The level of details is not well chosen in the method section (L155-335). First, I do not think it is useful to recall the co-registration procedure with such a large amount of details, including equations from previous well established methods and L156-168 should be deleted (Nuth and Kääb, 2011). Second the elevation dependent bias mentioned L175 could originate from curvature differences (Gardelle et al., 2012) mentioned by the authors. However, it is not clear whether they actually implemented this correction or another one. Regarding the histograms on figure 2, it seems that the applied corrections tend to shift the distribution away from 0 in some cases (e.g. the third panel). When the authors apply the sigmoid function for filtering on glacier values (L184-195), they should tell which percentage of the data are filtered out.
Uncertainty estimate completely unclear
I feel that there a confusion between systematic and random errors in the approach here. The mean elevation difference (MED) on stable ground does not allow to evaluate systematic biases in DEM differences. If I take the example of a tilted DEM, then there is a systematic bias in the elevation difference, but still the MED can be zero (especially after a co-registration procedure). Additionally, the authors do not account for the spatial correlation of the errors (Rolstad et al., 2009; Hugonnet et al., 2022) and for missing data/gaps in the elevation change maps. In short, the uncertainty assessment should be done again from the start.
L319: glaciers that fulfill the coverage criteria have still about 45% of coverage. It is not clear how the voids in the elevation change maps are filled in order to calculate the mean glacier elevation change.
Comparison with KH-9 and ICESat-2 very difficult to follow
I was wondering why comparing the SRTM-KH-9 difference with the SRTM-Topo DEM difference, and why not comparing directly the Topo DEM and the KH-9 DEM? I think the latter would be easier to interpret (even though the KH-9 DEM has its one biases and problems). Maybe I missed something, but in the figures, I have the feeling the only the off-glacier terrain is compared. What is the interest of data acquired at the same time then?
The comparison with ICESat-2 is just impossible to understand, especially when the conclusion is “We thus find that the accuracy of corrected Topo difference is not less accurate than the ATL06 difference below 5700 m.”
Inventories in the 1970
What are the dates of the inventories used in the study? Wouldn’t be useful to update the CGI with the glacier outlines from 1970’s? No area change is mentioned in this study. Area changes are not expected to be very large, but they should still be non zero.
References
Dehecq, A., Millan, R., Berthier, E., Gourmelen, N., Trouvé, E., and Vionnet, V.: Elevation Changes Inferred From TanDEM-X Data Over the Mont-Blanc Area: Impact of the X-Band Interferometric Bias, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 3870–3882, https://doi.org/10.1109/JSTARS.2016.2581482, 2016.
Gardelle, J., Berthier, E., and Arnaud, Y.: Impact of resolution and radar penetration on glacier elevation changes computed from DEM differencing, J. Glaciol., 58, 419–422, https://doi.org/doi:10.3189/2012JoG11J175, 2012.
Hugonnet, R., Brun, F., Berthier, E., Dehecq, A., Mannerfelt, E. S., Eckert, N., and Farinotti, D.: Uncertainty analysis of digital elevation models by spatial inference from stable terrain, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 1–17, https://doi.org/10.1109/JSTARS.2022.3188922, 2022.
Lambrecht, A., Mayer, C., Wendt, A., Floricioiu, D., and Völksen, C.: Elevation change of Fedchenko Glacier, Pamir Mountains, from GNSS field measurements and TanDEM-X elevation models, with a focus on the upper glacier, J. Glaciol., 64, 637–648, https://doi.org/10.1017/jog.2018.52, 2018.
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Citation: https://doi.org/10.5194/essd-2022-473-RC2
Data sets
Glacier-level and gridded mass change in the source rivers in the eastern Tibetan Plateau (1970s-2000) S. Liu, Y. Zhu, J. Wei, K. Wu, J. Xu, W. Guo, Z. Jiang, F. Xie, Y. Yi, D. Shangguan, X. Yao, and Z. Zhang https://doi.org/10.11888/Cryos.tpdc.272884
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