the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Sep 2022
A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India
Abstract. Delineation of Glaciers is a challenging task in the Himalaya due to its complex topography, cloud cover, seasonal snow cover, hillshade, debris cover. Glacio-hydrological studies including mass balance, run-off, and dynamic modelling rely on the availability of consistent and reliable glacier inventory datasets. This article on data set presents a homogenous, multidecadal inventory of glaciers in the Chandra-Bhaga Basin (CB Basin), western Himalaya, for 1993, 2000, 2010, and 2019. Landsat Thematic Mapper (TM), Enhanced Thematic mapper (ETM+), and Operational Land Imager (OLI) imageries, with minimum snow and cloud cover have been used for enhanced accuracy and consistency. Uncertainty assessment for the generated glacier inventory was performed, following various approaches such as buffer method, standard error estimation, and manual digitisation error and the maximum uncertainty has been quantified. We have identified and manually mapped a total of 251 glaciers with an area > 0.5 km2, and in order to minimise the uncertainty, field surveys were carried out on 6 glaciers in the basin. Out of these 251 glaciers, 217 are clean ice and 35 were debris-covered glaciers. The estimated total glacier area was 996 ± 62 km2 in 1993 that decreased to 973 ± 70 km2 in 2019. Apart from quantifying temporal changes in glacier area, this inventory further allows the estimation of supraglacial debris cover and glacier volume. The supraglacial debris cover area has increased by 14.1 ± 2.54 km2 (15.2 %) during 1993–2019. Accuracy of the debris cover dataset estimated using ground surveys is 82 % with a kappa coefficient of 0.87. Moreover, a glacier ice volume dataset was also generated by incorporating the inventory into Glacier Bed Topography Version 2 (GlabTop2) model and shows a total of 112.5 ± 41 km3 of ice volume stored in the CB Basin glaciers. For accuracy assessment of the DEMs generated using ASTER Stereopairs images, DGPS surveys were carried out on (28 GCPs) and off (6 GCPs) the glaciers. Glacier volume uncertainty with respect to the generated DEMs and model bias is 5.3 km3 and 35.5 km3 respectively. Overestimation of glacier volume due to over deepening is estimated to be 1.2 km3. The impact of climate change on the Himalayan glaciers is a matter of serious concern and such holistic multitemporal inventory datasets can help quantify that impact with improved certainty.
- Preprint
(2259 KB) - Metadata XML
-
Supplement
(120 KB) - BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on essd-2022-311', Anonymous Referee #1, 30 Dec 2022
The manuscript titled “A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India” presents a manual mapping of 251 glaciers with an area > 0.5 km2, over the Chenab Basin, a small basin of the Western Himalaya, associated to an uncertainty analysis. The authors perform a glacier delineation through the manual identification on Landsat and Google Earth images. Then, a debris coverage change evaluation has been done by maximum likelihood classification (MLC) method leveraging on Landsat data. Finally, the glacial ice thickness has been estimated by the Glacier Bed Topography2 (GlabTop2) model.
In general, the text is well-written and structured, though already from the abstract and introduction it becomes clear that there is limited novelty in the type of methodology used in this work, that is based on the use of established methodologies in glacial analysis and glaciers mapping. No less relevant, the area of interest is rather limited, considering the entire Himalayan complex, making this work a simple case study analysis, in contrast to recent studies focused on the creation of consistent wide glacier inventory worldwide (e.g., Mölg et al., 2018; Paul et al., 2020). Overall, these elements, in my opinion, make this work unsuitable for publication in Earth System Science Data, while I would suggest the authors to consider other journals mainly focused on the creation of thematic mapping, where the focus on a small area glacier-mapping and analysis could better fit, as a case study work.
Citation: https://doi.org/10.5194/essd-2022-311-RC1 -
RC2: 'Comment on essd-2022-311', Anonymous Referee #2, 05 Feb 2023
The manuscript entitled “A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India” introduced the procedures and methodologies on the multi-temporal glacier inventory in the basin of Chandra-Bhaga in very detail, shows the proficiencies of the authors in related fields and their laudable attitudes on doing science. However, the limited number and area of glaciers in the Chandra-Bhaga Basin in contrast to the vast glacier extent in the Great Himalayas not even the Tibetan Plateau makes this study more like a case study than a valuable data paper thus less relevant to the current concerns of ESSD journal. Secondly, in spite of the depth of this work the authors trying to dig, there’s no eye-catching new methodology (except the validation and application of GlabTop2 model), results or opinion all through the manuscript, also makes this study scientifically less referable, although as a data paper the novelties are not always obligatory. So I agree with another referee that this manuscript is not suitable to be publishes on this journal.
Two suggestions I may conclude for the authors: 1) Try to enlarge the spatial coverage of the study coverage to such like entire Western Himalaya, if you insist to resubmit the manuscript to the journal of ESSE; 2) Focus on the glacier thickness estimation and digging into more depth with the valuable in-situ GPR measurements by incorporate more glacier thickness estimation models, and submit to another research journal like Journal of Glaciology or Crysphere, rather than using GlabTop2 only which was known too sensitive to the input data and model parameters so less reliable.
Citation: https://doi.org/10.5194/essd-2022-311-RC2 -
RC3: 'Comment on essd-2022-311', Anonymous Referee #3, 07 Feb 2023
The manuscript titled “A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India” by Vatsal and others presented a multidecadal inventory of glaciers with an area > 0.5 km2 in their study area from 1993 to 2019 with inventories for the years 2000 and 2010 too. The inventory was created using manual delineation. Additionally, they have estimated glacier ice thickness and ice volume using GlabTop model. The methods adopted are standard no novelty/improvement is noticed by me. They also used the available field data on ice thickness, glacier outlines, debris extent to validate their outputs at 6-7 sites.
Though the article contains comprehensive datasets, I feel there are still lot more problems in the presented datasets that needs to be addressed. Moreover, the area is rather small compared to other worldwide/regionwide datasets available ex. RGI, Paul et al 2020. To be more valuable and useful, I suggest the authors to extend their study to Indian Himalayas at least. This will be greatly beneficial to research and policy makers in general and specially for those interested in Himalayas.
Also, I suggest the authors to refer another such inventory prepared by Kashmir University (Romshoo et al 2021) which also cover the present manuscript study area, i.e. Chenab basin
Considering the less reliability of the datasets and substantial overlap with other such studies (ex. Romshoo et al ESDD Discussions) with similar kind of scientific quality issues, I suggest the authors to resubmit after addressing these concerns and suggestions to extend their study to Indian Himalayas/Entire Himalayas
Some of the concerns are
While the intention is to produce accurate inventories, why the authors have restricted themselves to Landsat data only. Why not other datasets such as Sentinel 1 and 2 series and other commercial satellite datasets from Planet labs, which is free for researchers.? By not using the available open data, I feel the authors have not achieved their aim of producing the most/best possible glacier inventory.
I doubt the reliability of the just using a standard Maximum likelihood classifier for the debris cover mapping in spite of not using the thermal band which will help to distinguish the ice mixed with debris, supra glacier debris and peri glacial debris. Needs more clarification on the bands used as inputs for MLC classification.
Authors stated that the estimation of ice volume is important. I agree. However, there are couple of global estimates available (ex. Farinotti et al, 2019, Pandit and Ramsankaran 2020; Millan et al 2021) which covers the present study region too.
In fact, Pandit and Ramsankaran (2020) have used GlabTop2 approach for to estimate glacier volume for Chandra basin. How the present study results compare with them and other cited studies?
Sec 3.4.4 and 3.4.5: how the uncertainty was quantified? No mathematical basis or method details are presented. Only the importance of doing uncertainty estimation are given in the manuscript. Do not confuse with errors and uncertainty. Both are different.
Line 460: It is stated the present study inventory is compared with RGI and GAMDAM and results are shown in Fig. 5.. Which year’s inventory of yours have been used for comparison? In RGI and GAMDAM, I guess they mentioned the year of update, kindly use that information and appropriate inventory from your study and report the comparison results to have a better understanding.
In Sec 4.2.1, you have presented only the accuracy assessment results. Though the uncertainty in debris cover is stated but no details are given how it was computed. Kindly update it.Though comparisons were made with other studies relate to glacier inventory and debris cover extent, no such discussions are made for volume estimations. Why it so?
Considering the focus of the work as glacier inventory in terms of its area and debris cover change, I feel the authors can remove the ice thickness estimates. Moreover, I feel the Ice thickness/volume estimates given here are not robust as those given in Millan et al 2021. It is because no detailed information on how the uncertainty was quantified in the manuscript.
General comment and suggestion;
How the authors wold like to ensure the continuity of updating the inventories in the upcoming years? Inventories like RGI, GAMDAM are community efforts and there is a scope for continuous updation though it may have errors. As a standalone inventory for available few decades without any guarantee for further updates, the researchers may not use such inventories defeating the main purpose of the bringing out such inventories. Hence, I suggest the authors to make necessary efforts to incorporate such inventories with global database lie RGI, GAMDAM etc for the larger benefit of all the stake holders, especially researchers. However, before doing this, kindly address the concerns.
Reference
Romshoo, S. A., Abdullah, T., and Bhat, M. H.: Evaluation of the global glacier inventories and assessment of glacier elevation changes over north-western Himalaya, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-28, 2021.
Farinotti, D., Huss, M., Fürst, J.J. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019). https://doi.org/10.1038/s41561-019-0300-3
Ankur Pandit and RAAJ Ramsankaran .(2020). Modeling ice thickness distribution and storage volume of glaciers in Chandra Basin, western Himalayas. Journal of Mountain Science , vol. 17, pp: 2011–2022.
Paul, F., Rastner, P., Azzoni, R. S., Diolaiuti, G., Fugazza, D., Le Bris, R., Nemec, J., Rabatel, A., Ramusovic, M., Schwaizer, G., and Smiraglia, C.: Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2, Earth Syst. Sci. Data, 12, 1805–1821, https://doi.org/10.5194/essd-12-1805-2020, 2020.
Millan, R, Mouginot, J, Rabatel, A and Morlighem, M. (2022) Ice velocity and thickness of the world's glaciers. Nature Geoscience 15(2), 124–129. doi: 10.1038/s41561-021-00885-zCrossRefGoogle Scholar
Citation: https://doi.org/10.5194/essd-2022-311-RC3
Status: closed
-
RC1: 'Comment on essd-2022-311', Anonymous Referee #1, 30 Dec 2022
The manuscript titled “A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India” presents a manual mapping of 251 glaciers with an area > 0.5 km2, over the Chenab Basin, a small basin of the Western Himalaya, associated to an uncertainty analysis. The authors perform a glacier delineation through the manual identification on Landsat and Google Earth images. Then, a debris coverage change evaluation has been done by maximum likelihood classification (MLC) method leveraging on Landsat data. Finally, the glacial ice thickness has been estimated by the Glacier Bed Topography2 (GlabTop2) model.
In general, the text is well-written and structured, though already from the abstract and introduction it becomes clear that there is limited novelty in the type of methodology used in this work, that is based on the use of established methodologies in glacial analysis and glaciers mapping. No less relevant, the area of interest is rather limited, considering the entire Himalayan complex, making this work a simple case study analysis, in contrast to recent studies focused on the creation of consistent wide glacier inventory worldwide (e.g., Mölg et al., 2018; Paul et al., 2020). Overall, these elements, in my opinion, make this work unsuitable for publication in Earth System Science Data, while I would suggest the authors to consider other journals mainly focused on the creation of thematic mapping, where the focus on a small area glacier-mapping and analysis could better fit, as a case study work.
Citation: https://doi.org/10.5194/essd-2022-311-RC1 -
RC2: 'Comment on essd-2022-311', Anonymous Referee #2, 05 Feb 2023
The manuscript entitled “A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India” introduced the procedures and methodologies on the multi-temporal glacier inventory in the basin of Chandra-Bhaga in very detail, shows the proficiencies of the authors in related fields and their laudable attitudes on doing science. However, the limited number and area of glaciers in the Chandra-Bhaga Basin in contrast to the vast glacier extent in the Great Himalayas not even the Tibetan Plateau makes this study more like a case study than a valuable data paper thus less relevant to the current concerns of ESSD journal. Secondly, in spite of the depth of this work the authors trying to dig, there’s no eye-catching new methodology (except the validation and application of GlabTop2 model), results or opinion all through the manuscript, also makes this study scientifically less referable, although as a data paper the novelties are not always obligatory. So I agree with another referee that this manuscript is not suitable to be publishes on this journal.
Two suggestions I may conclude for the authors: 1) Try to enlarge the spatial coverage of the study coverage to such like entire Western Himalaya, if you insist to resubmit the manuscript to the journal of ESSE; 2) Focus on the glacier thickness estimation and digging into more depth with the valuable in-situ GPR measurements by incorporate more glacier thickness estimation models, and submit to another research journal like Journal of Glaciology or Crysphere, rather than using GlabTop2 only which was known too sensitive to the input data and model parameters so less reliable.
Citation: https://doi.org/10.5194/essd-2022-311-RC2 -
RC3: 'Comment on essd-2022-311', Anonymous Referee #3, 07 Feb 2023
The manuscript titled “A comprehensive multidecadal glacier inventory dataset for the Chandra-Bhaga Basin, Western Himalaya, India” by Vatsal and others presented a multidecadal inventory of glaciers with an area > 0.5 km2 in their study area from 1993 to 2019 with inventories for the years 2000 and 2010 too. The inventory was created using manual delineation. Additionally, they have estimated glacier ice thickness and ice volume using GlabTop model. The methods adopted are standard no novelty/improvement is noticed by me. They also used the available field data on ice thickness, glacier outlines, debris extent to validate their outputs at 6-7 sites.
Though the article contains comprehensive datasets, I feel there are still lot more problems in the presented datasets that needs to be addressed. Moreover, the area is rather small compared to other worldwide/regionwide datasets available ex. RGI, Paul et al 2020. To be more valuable and useful, I suggest the authors to extend their study to Indian Himalayas at least. This will be greatly beneficial to research and policy makers in general and specially for those interested in Himalayas.
Also, I suggest the authors to refer another such inventory prepared by Kashmir University (Romshoo et al 2021) which also cover the present manuscript study area, i.e. Chenab basin
Considering the less reliability of the datasets and substantial overlap with other such studies (ex. Romshoo et al ESDD Discussions) with similar kind of scientific quality issues, I suggest the authors to resubmit after addressing these concerns and suggestions to extend their study to Indian Himalayas/Entire Himalayas
Some of the concerns are
While the intention is to produce accurate inventories, why the authors have restricted themselves to Landsat data only. Why not other datasets such as Sentinel 1 and 2 series and other commercial satellite datasets from Planet labs, which is free for researchers.? By not using the available open data, I feel the authors have not achieved their aim of producing the most/best possible glacier inventory.
I doubt the reliability of the just using a standard Maximum likelihood classifier for the debris cover mapping in spite of not using the thermal band which will help to distinguish the ice mixed with debris, supra glacier debris and peri glacial debris. Needs more clarification on the bands used as inputs for MLC classification.
Authors stated that the estimation of ice volume is important. I agree. However, there are couple of global estimates available (ex. Farinotti et al, 2019, Pandit and Ramsankaran 2020; Millan et al 2021) which covers the present study region too.
In fact, Pandit and Ramsankaran (2020) have used GlabTop2 approach for to estimate glacier volume for Chandra basin. How the present study results compare with them and other cited studies?
Sec 3.4.4 and 3.4.5: how the uncertainty was quantified? No mathematical basis or method details are presented. Only the importance of doing uncertainty estimation are given in the manuscript. Do not confuse with errors and uncertainty. Both are different.
Line 460: It is stated the present study inventory is compared with RGI and GAMDAM and results are shown in Fig. 5.. Which year’s inventory of yours have been used for comparison? In RGI and GAMDAM, I guess they mentioned the year of update, kindly use that information and appropriate inventory from your study and report the comparison results to have a better understanding.
In Sec 4.2.1, you have presented only the accuracy assessment results. Though the uncertainty in debris cover is stated but no details are given how it was computed. Kindly update it.Though comparisons were made with other studies relate to glacier inventory and debris cover extent, no such discussions are made for volume estimations. Why it so?
Considering the focus of the work as glacier inventory in terms of its area and debris cover change, I feel the authors can remove the ice thickness estimates. Moreover, I feel the Ice thickness/volume estimates given here are not robust as those given in Millan et al 2021. It is because no detailed information on how the uncertainty was quantified in the manuscript.
General comment and suggestion;
How the authors wold like to ensure the continuity of updating the inventories in the upcoming years? Inventories like RGI, GAMDAM are community efforts and there is a scope for continuous updation though it may have errors. As a standalone inventory for available few decades without any guarantee for further updates, the researchers may not use such inventories defeating the main purpose of the bringing out such inventories. Hence, I suggest the authors to make necessary efforts to incorporate such inventories with global database lie RGI, GAMDAM etc for the larger benefit of all the stake holders, especially researchers. However, before doing this, kindly address the concerns.
Reference
Romshoo, S. A., Abdullah, T., and Bhat, M. H.: Evaluation of the global glacier inventories and assessment of glacier elevation changes over north-western Himalaya, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-28, 2021.
Farinotti, D., Huss, M., Fürst, J.J. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019). https://doi.org/10.1038/s41561-019-0300-3
Ankur Pandit and RAAJ Ramsankaran .(2020). Modeling ice thickness distribution and storage volume of glaciers in Chandra Basin, western Himalayas. Journal of Mountain Science , vol. 17, pp: 2011–2022.
Paul, F., Rastner, P., Azzoni, R. S., Diolaiuti, G., Fugazza, D., Le Bris, R., Nemec, J., Rabatel, A., Ramusovic, M., Schwaizer, G., and Smiraglia, C.: Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2, Earth Syst. Sci. Data, 12, 1805–1821, https://doi.org/10.5194/essd-12-1805-2020, 2020.
Millan, R, Mouginot, J, Rabatel, A and Morlighem, M. (2022) Ice velocity and thickness of the world's glaciers. Nature Geoscience 15(2), 124–129. doi: 10.1038/s41561-021-00885-zCrossRefGoogle Scholar
Citation: https://doi.org/10.5194/essd-2022-311-RC3
Data sets
Glacier_inventory_debris_cover_ice_thickness_dataset_Chandra_Bhaga_basin_Himalaya Sarvagya Vatsal; Anshuman Bhardwaj; Mohd Farooq Azam; Arindan Mandal; Alagappan Ramanathan; I. M. Bahuguna; N. Janardhana Raju; Sangita Singh Tomar https://doi.org/10.5281/zenodo.6595546
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 962 | 456 | 55 | 1,473 | 96 | 53 | 56 |
- HTML: 962
- PDF: 456
- XML: 55
- Total: 1,473
- Supplement: 96
- BibTeX: 53
- EndNote: 56
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
