Articles | Volume 16, issue 1
https://doi.org/10.5194/tc-16-197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jan 2022
Research article |
| 21 Jan 2022
| 21 Jan 2022
Towards ice-thickness inversion: an evaluation of global digital elevation models (DEMs) in the glacierized Tibetan Plateau
Wenfeng Chen
CORRESPONDING AUTHOR
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Tandong Yao
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
Fei Li
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Guoxiong Zheng
University of Chinese Academy of Sciences, Beijing, China
Xinjiang Institute of Ecology and Geography, Chinese Academy of
Sciences, Urumqi, China
Yushan Zhou
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
Fenglin Xu
State Key Laboratory of Tibetan Plateau Earth System, Resources and
Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
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Cited articles
Allen, S. K., Zhang, G., Wang, W., Yao, T., and Bolch, T.: Potentially
dangerous glacial lakes across the Tibetan Plateau revealed using a large-scale automated assessment approach, Sci. Bull., 64, 435–445,
https://doi.org/10.1016/j.scib.2019.03.011, 2019.
Altunel, A. O.: Evaluation of TanDEM-X 90 m Digital Elevation Model, Int. J.
Remote Sens., 40, 2841–2854, https://doi.org/10.1080/01431161.2019.1585593, 2019.
Azam, M. F., Wagnon, P., Ramanathan, A., Vincent, C., Sharma, P., Arnaud, Y.,
Linda, A., Pottakkal, J. G., Chevallier, P., Singh, V. B., and Berthier, E:
From balance to imbalance: a shift in the dynamic behaviour of Chhota Shigri
glacier, western Himalaya, India, J. Glaciol., 58, 315–324,
https://doi.org/10.3189/2012JoG11J123, 2012.
Bachmann, M., Kraus, T., Bojarski, A., Schandri, M., Boer, J., Busche, T.,
Bueso Bello, J. L., Grigorov, C., Steinbrecher, U., Buckreuss, S., Krieger,
G., and Zink, M.: The TanDEM-X Mission Phases – Ten Years of Bistatic
Acquisition and Formation Planning, IEEE J.-Stars, 14, 3504–3518,
https://doi.org/10.1109/JSTARS.2021.3065446, 2021.
Bhambri, R., Bolch, T., and Chaujar, R. K.: Mapping of debris-covered
glaciers in the Garhwal Himalayas using ASTER DEMs and thermal data, Int. J.
Remote Sens., 32, 8095–8119, https://doi.org/10.1080/01431161.2010.532821, 2011.
Brun, F., Berthier, E., Wagnon, P., Kaab, A., and Treichler, D.: A spatially resolved estimate of High Mountain Asia glacier mass balances, 2000–2016, Nat. Geosci., 10, 668–673, https://doi.org/10.1038/ngeo2999, 2017.
Brunt, K. M., Neumann, T. A., and Smith, B. E.: Assessment of ICESat-2 Ice
Sheet Surface Heights, Based on Comparisons Over the Interior of the Antarctic Ice Sheet, Geophys. Res. Lett., 46, 13072–13078,
https://doi.org/10.1029/2019GL084886, 2019.
Brunt, K. M., Smith, B. E., Sutterley, T. C., Kurtz, N. T., and Neumann, T.
A.: Comparisons of Satellite and Airborne Altimetry with Ground-Based Data
From the Interior of the Antarctic Ice Sheet, Geophys. Res. Lett., 48,
e2020GL090572, https://doi.org/10.1029/2020GL090572, 2021.
Burrough, P. A. and McDonell, R. A.: Principles of Geographical Information
Systems, Oxford University Press, New York, 190 pp., ISBN 9780198742845, 1988.
Chen, W., Yao, T., Zhang, G., Li, S., and Zheng, G.: Accelerated glacier mass loss in the largest river and lake source regions of the Tibetan Plateau and its links with local water balance over 1976–2017, J. Glaciol., 67, 577–591, https://doi.org/10.1017/jog.2021.9, 2021a.
Chen, W., Yao, T., Zhang, G., Li, F., Zheng, G., Zhou, Y., and Xu, F.: Database for Towards ice thickness inversion: an evaluation of global DEMs in the glacierized Tibetan Plateau (Version version 1), Zenodo [data set],
https://zenodo.org/record/5267309, 2021b.
Crippen, R., Buckley, S., Belz, E., Gurrola, E., Hensley, S., Kobrick, M.,
Lavalle, M., Martin, J., Neumann, M., Nguyen, Q., Rosen, P., Shimada, J.,
Simard, M., and Tung, W.: NASADEM global elevation model: methods and
progress, Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci., XLI-B4,
125–128, https://doi.org/10.5194/isprs-archives-XLI-B4-125-2016, 2016.
Cuffey, K. andPaterson, W. S. B.: The Physics of Glaciers, 4th Edn., Academic Press, ISBN 978-0-12-369461-4, 2010.
Dehecq, A., Gourmelen, N., Gardner, A. S., Brun, F., Goldberg, D., Nienow, P. W., Berthier, E., Vincent, C., Wagnon, P., and Trouvé, E.: Twenty-first century glacier slowdown driven by mass loss in High Mountain Asia, Nat. Geosci., 12, 22–27, https://doi.org/10.1038/s41561-018-0271-9, 2019.
DLR – German Aerospace Center: TanDEM-X – Digital Elevation Model (DEM) – Global, 90 m, DLR [data set], https://doi.org/10.15489/ju28hc7pui09, 2018.
Fahnestock, M., Scambos, T., Moon, T., Gardner, A., Haran, T., and Klinger, M.: Rapid large-area mapping of ice flow using Landsat 8, Remote Sens. Environ., 185, 84–94, https://doi.org/10.1016/j.rse.2015.11.023, 2016.
Falorni, G.: Analysis and characterization of the vertical accuracy of digital elevation models from the Shuttle Radar Topography Mission, J. Geophys. Res.-Earth, 110, F02005, https://doi.org/10.1029/2003JF000113, 2005.
Farinotti, D., Huss, M., Bauder, A., Funk, M., and Truffer, M.: A method to
estimate the ice volume and ice-thickness distribution of alpine glaciers, J. Glaciol., 55, 422–430, https://doi.org/10.3189/002214309788816759, 2009.
Farinotti, D., Brinkerhoff, D. J., Clarke, G. K. C., Fürst, J. J., Frey, H., Gantayat, P., Gillet-Chaulet, F., Girard, C., Huss, M., Leclercq, P. W.,
Linsbauer, A., Machguth, H., Martin, C., Maussion, F., Morlighem, M., Mosbeux, C., Pandit, A., Portmann, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Sanchez, O., Stentoft, P. A., Singh Kumari, S., van Pelt, W. J. J., Anderson, B., Benham, T., Binder, D., Dowdeswell, J. A., Fischer, A., Helfricht, K., Kutuzov, S., Lavrentiev, I., McNabb, R., Gudmundsson, G. H.,
Li, H., and Andreassen, L. M.: How accurate are estimates of glacier ice
thickness? Results from ITMIX, the Ice Thickness Models Intercomparison
eXperiment, The Cryosphere, 11, 949–970, https://doi.org/10.5194/tc-11-949-2017, 2017.
Farinotti, D., Huss, M., Furst, J. J., Landmann, J., Machguth, H., Maussion,
F., and Pandit, A.: A consensus estimate for the ice thickness distribution of all glaciers on Earth, Nat. Geosci., 12, 168–173,
https://doi.org/10.1038/s41561-019-0300-3, 2019.
Farinotti, D., Brinkerhoff, D. J., Fürst, J. J., Gantayat, P., Gillet-Chaulet, F., Huss, M., Leclercq, P. W., Maurer, H., Morlighem, M.,
Pandit, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Robo, E., Rouges,
E., Tamre, E., van Pelt, W. J. J., Werder, M. A., Azam, M. F., Li, H., and
Andreassen, L. M.: Results from the Ice Thickness Models Intercomparison
eXperiment Phase 2 (ITMIX2), Front. Earth. Sci., 8, 571923, https://doi.org/10.3389/feart.2020.571923, 2021.
Fielding, E., Isacks, B., Barazangi, M., and Duncan, C.: How flat is Tibet?,
Geology, 22, 163–167, https://doi.org/10.1130/0091-7613(1994)022<0163:HFIT>2.3.CO;2, 1994.
Frey, H. and Paul, F.: On the suitability of the SRTM DEM and ASTER GDEM for the compilation of topographic parameters in glacier inventories, Int. J. Appl. Earth. Obs., 18, 480–490, https://doi.org/10.1016/j.jag.2011.09.020, 2012.
Frey, H., Paul, F., and Strozzi, T.: Compilation of a glacier inventory for
the western Himalayas from satellite data: methods, challenges, and results,
Remote Sens. Environ., 124, 832–843, https://doi.org/10.1016/j.rse.2012.06.020, 2012.
Frey, H., Machguth, H., Huss, M., Huggel, C., Bajracharya, S., Bolch, T.,
Kulkarni, A., Linsbauer, A., Salzmann, N., and Stoffel, M.: Estimating the
volume of glaciers in the Himalayan–Karakoram region using different
methods, The Cryosphere, 8, 2313–2333, https://doi.org/10.5194/tc-8-2313-2014, 2014.
Fujita, K., Suzuki, R., Nuimura, T., and Sakai, A.: Performance of ASTER and SRTM DEMs, and their potential for assessing glacial lakes in the Lunana
region, Bhutan Himalaya, J. Glaciol., 54, 220–228, https://doi.org/10.3189/002214308784886162, 2017.
Furian, W., Loibl, D., and Schneider, C.: Future glacial lakes in High Mountain Asia: an inventory and assessment of hazard potential from surrounding slopes, J. Glaciol., 67, 653–670, https://doi.org/10.1017/jog.2021.18, 2021.
Gantayat, P., Kulkarni, A. V., and Srinivasan, J.: Estimation of ice thickness using surface velocities and slope: case study at Gangotri Glacier, India, J. Glaciol., 60, 277–282, https://doi.org/10.3189/2014JoG13J078, 2014.
Gardner, A. S., Fahnestock, M. A., and Scambos, T. A.: ITS_LIVE Regional Glacier and Ice Sheet Surface Velocities, National Snow and Ice Data Center [dat aset], https://doi.org/10.5067/6II6VW8LLWJ7, 2019.
Gdulová, K., Marešová, J., and Moudrý, V.: Accuracy assessment of the global TanDEM-X digital elevation model in a mountain
environment, Remote Sens. Environ., 241, 111724, https://doi.org/10.1016/j.rse.2020.111724, 2020.
GitHub: cnugis/ProcessICESat-2, GitHub [code], https://github.com/cnugis/ProcessICESat-2, last access: 20 January 2022.
Glen, J. W.: The Creep of Polycrystalline Ice, P. Roy. Soc. A, 228, 519–538,
https://doi.org/10.1098/rspa.1955.0066, 1955.
González, P. J. and Fernández, J.: Error estimation in multitemporal InSAR deformation time series, with application to Lanzarote, Canary Islands, J. Geophys. Res.-Solid, 116, B10404, https://doi.org/10.1029/2011JB008412, 2011.
González-Moradas, M. D. R. and Viveen, W.: Evaluation of ASTER GDEM2,
SRTMv3.0, ALOS AW3D30 and TanDEM-X DEMs for the Peruvian Andes against highly accurate GNSS ground control points and geomorphological-hydrological metrics, Remote Sens. Environ., 237, 111509, https://doi.org/10.1016/j.rse.2019.111509, 202.
Gorokhovich, Y. and Voustianiouk, A.:. Accuracy assessment of the processed SRTM-based elevation data by CGIAR using field data from USA and Thailand and its relation to the terrain characteristics, Remote Sens. Environ., 104, 409–415, 2006.
Grohmann, C. H.: Evaluation of TanDEM-X DEMs on selected Brazilian sites:
Comparison with SRTM, ASTER GDEM and ALOS AW3D30, Remote Sens. Environ., 212, 121–133, https://doi.org/10.1016/j.rse.2006.05.012, 2018.
Haeberli, W. and Hoelzle, M.: Application of inventory data for estimating characteristics of and regional climate-change effects on mountain glaciers: a pilot study with the European Alps. Ann, Glaciol., 21, 206–212, https://doi.org/10.3189/S0260305500015834, 1995.
Han, H., Zeng, Q., and Jiao, J.: Quality Assessment of TanDEM-X DEMs, SRTM
and ASTER GDEM on Selected Chinese Sites, Remote Sens.-Basel, 13, 1304, https://doi.org/10.3390/rs13071304, 2021.
Hawker, L., Neal, J., and Bates, P.: Accuracy assessment of the TanDEM-X 90
Digital Elevation Model for selected floodplain sites, Remote Sens. Environ., 232, 111724–111738, https://doi.org/10.1016/j.rse.2019.111319, 2019.
Höhle, J. and Höhle, M.: Accuracy assessment of digital elevation
models by means of robust statistical methods, ISPRS J. Photogram., 64,
398–406, https://doi.org/10.1016/j.isprsjprs.2009.02.003, 2009.
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L.,
Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.:
Accelerated global glacier mass loss in the early twenty-first century,
Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021.
Huss, M.: Density assumptions for converting geodetic glacier volume change to mass change, The Cryosphere, 7, 877–887, https://doi.org/10.5194/tc-7-877-2013, 2013.
Huss, M. and Farinotti, D.: Distributed ice thickness and volume of all
glaciers around the globe, J. Geophys. Res.-Solid, 117, 1–10,
https://doi.org/10.1029/2012JF002523, 2012.
Huss, M. and Hock, R.: Global-scale hydrological response to future glacier mass loss, Nat. Clim. Change, 8, 135–140, https://doi.org/10.1038/s41558-017-0049-x, 2018.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T.,
Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M.,
Fernandez, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter, T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A. B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.: Importance and vulnerability of the world's water towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4 CGIAR-CSI SRTM 90 m Database, available at: http://srtm.csi.cgiar.org (last access: 21 January 2022), 2008.
Kääb, A.: Combination of SRTM3 and repeat ASTER data for deriving
alpine glacier flow velocities in the Bhutan Himalaya, Remote Sens. Environ., 94, 463–474, https://doi.org/10.1016/j.rse.2004.11.003, 2005.
Kääb, A., Leinss, S., Gilbert, A., Bühler, Y., Gascoin, S.,
Evans, S. G., Bartelt, P., Berthier, E., Brun, F., Chao, W.-A., Farinotti,
D., Gimbert, F., Guo, W., Huggel, C., Kargel, J. S., Leonard, G. J., Tian, L., Treichler, D., and Yao, T.: Massive collapse of two glaciers in western
Tibet in 2016 after surge-like instability, Nat. Geosci., 11, 114–120,
https://doi.org/10.1038/s41561-017-0039-7, 2018.
Kaser, G., Grosshauser, M., and Marzeion, B.: Contribution potential of glaciers to water availability in different climate regimes, P. Natl. Acad. Sci. USA, 107, 20223–20227, https://doi.org/10.1073/pnas.1008162107, 2010.
Ke, L., Ding, X., Zhang, L. E. I., Hu, J. U. N., Shum, C. K., and Lu, Z.:
Compiling a new glacier inventory for southeastern Qinghai–Tibet Plateau
from Landsat and PALSAR data, J. Glaciol., 62, 579–592, https://doi.org/10.1017/jog.2016.58, 2016.
Kienholz, C., Rich, J. L., Arendt, A. A., and Hock, R.: A new method for
deriving glacier centerlines applied to glaciers in Alaska and northwest
Canada, The Cryosphere, 8, 503–519, https://doi.org/10.5194/tc-8-503-2014, 2014.
Koldtoft, I., Grinsted, A., Vinther, B., and Hvidberg, C.: Ice thickness and volume of the Renland Ice Cap, East Greenland, J. Glaciol., 67, 714–726, https://doi.org/10.1017/jog.2021.11, 2021.
Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F., and Immerzeel, W. W.: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's
glaciers, Nature, 549, 257–260, https://doi.org/10.1038/nature23878, 2017.
Li, G. and Lin, H.: Recent decadal glacier mass balances over the Western
Nyainqentanglha Mountains and the increase in their melting contribution to
Nam Co Lake measured by differential bistatic SAR interferometry, Global
Planet. Change, 149, 177–190, https://doi.org/10.1016/j.gloplacha.2016.12.018, 2017.
Li, R., Li, H., Hao, T., Qiao, G., Cui, H., He, Y., Hai, G., Xie, H., Cheng,
Y., and Li, B.: Assessment of ICESat-2 ice surface elevations over the Chinese Antarctic Research Expedition (CHINARE) route, East Antarctica,
based on coordinated multi-sensor observations, The Cryosphere, 15, 3083–3099, https://doi.org/10.5194/tc-15-3083-2021, 2021.
Li, S., Yao, T., Yang, W., Yu, W., and Zhu, M.: Glacier Energy and Mass Balance in the Inland Tibetan Plateau: Seasonal and Interannual Variability
in Relation to Atmospheric Changes, J. Geophys. Res.-Atmos., 123, 6390–6409,
https://doi.org/10.1029/2017JD028120, 2018.
Linsbauer, A., Paul, F. and Haeberli, W.: Modeling glacier thickness distribution and bed topography over entire mountain ranges with GlabTop:
Application of a fast and robust approach, J. Geophys. Res.-Solid, 117,
1–17, https://doi.org/10.1029/2011JF002313, 2012.
Linsbauer, A., Frey, H., Haeberli, W., Machguth, H., Azam, M. F., and Allen,
S.: Modelling glacier-bed overdeepenings and possible future lakes for the
glaciers in the Himalaya–Karakoram region, Ann. Glaciol., 57, 119–130,
https://doi.org/10.3189/2016AoG71A627, 2016.
Liu, K., Song, C., Ke, L., Jiang, L., Pan, Y., and Ma, R.: Global open-access DEM performances in Earth's most rugged region High Mountain Asia: A multi-level assessment, Geomorphology, 338, 16–26, https://doi.org/10.1016/j.geomorph.2019.04.012, 2019.
Magruder, L. A., Brunt, K. M., and Alonzo, M.: Early ICESat-2 on-orbit
geolocation validation using ground-based corner cube retroreflectors, Remote Sens., 12, 3653, https://doi.org/10.3390/rs12213653, 2020.
Maurer, J. M., Schaefer, J. M., Rupper, S., and Corley, A.: Acceleration of
ice loss across the Himalayas over the past 40 years, Sci. Adv., 5, eaav7266, https://doi.org/10.1126/sciadv.aav7266, 2019.
Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau,
K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B.,
Rothenpieler, T., Vlug, A., Wild, C. T., and Marzeion, B.: The Open Global
Glacier Model (OGGM) v1.1, Geosci. Model. Dev., 12, 909–931,
https://doi.org/10.5194/gmd-12-909-2019, 2019.
Mölg, N., Bolch, T., Rastner, P., Strozzi, T., and Paul, F.: A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges, Earth Syst. Sci. Data, 10, 1807–1827, https://doi.org/10.5194/essd-10-1807-2018, 2018.
Mukherjee, S., Joshi, P. K., Mukherjee, S., Ghosh, A., Garg, R. D., and
Mukhopadhyay, A.: Evaluation of vertical accuracy of open source Digital
Elevation Model (DEM), Int. J. Appl. Earth. Obs., 21, 205–217,
https://doi.org/10.1016/j.jag.2012.09.004, 2013.
NASA JPL: NASA Shuttle Radar Topography Mission Global 1 arc second, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003, 2013.
NASA JPL: NASA SRTM-only Height and Height Precision Global 1 arc second V001, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MEaSUREs/NASADEM/NASADEM, 2020.
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.
Paul, F. and Linsbauer, A.: Modeling of glacier bed topography from glacier outlines, central branch lines, and a DEM, Int. J. Geogr. Inf. Sci., 26, 1173–1190, https://doi.org/10.1080/13658816.2011.627859, 2012.
Pelto, B. M., Maussion, F., Menounos, B., Radić, V., and Zeuner, M.:
Bias-corrected estimates of glacier thickness in the Columbia River Basin,
Canada, J. Glaciol., 66, 1051–1063, https://doi.org/10.1017/jog.2020.75, 2020.
Rankl, M. and Braun, M.: Glacier elevation and mass changes over the central Karakoram region estimated from TanDEM-X and SRTM/X-SAR digital elevation models, Ann. Glaciol., 57, 273–281, https://doi.org/10.3189/2016AoG71A024, 2016.
Reuter, H. I., Nelson, A., and Jarvis, A.: An evaluation of void-filling
interpolation methods for SRTM data, Int. J. Geogr. Inf. Sci., 21, 983–1008,
https://doi.org/10.1080/13658810601169899, 2007.
RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 6.0 Technical Report, Global Land Ice Measurements from Space, Colorado, USA, Digital Media [data set], https://doi.org/10.7265/N5-RGI-60, 2017.
Rignot, E., Echelmeyer, K., and Krabill, W.: Penetration depth of interferometric synthetic-aperture radar signals in snow and ice, Geophys.
Res. Lett., 28, 3501–3504, https://doi.org/10.1029/2000gl012484, 2001.
Scambos, T., Fahnestock, M., Moon, T., Gardner, A., and Klinger, M.: Global
Land Ice Velocity Extraction from Landsat 8 (GoLIVE), Version 1, NSIDC – National Snow and Ice Data Center, Boulder, Colorado, USA [data set], https://doi.org/10.7265/N5ZP442B, 2016.
Shean, D. and Bhushan, S.: dshean/hma_mb_paper: Release for accepted article (v1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.3600624, 2020.
Shean, D. E.: High Mountain Asia 8-meter DEM Mosaics Derived from Optical
Imagery, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/KXOVQ9L172S2, 2017.
Shean, D. E., Bhushan, S., Montesano, P., Rounce, D. R., Arendt, A., and
Osmanoglu, B.: A Systematic, Regional Assessment of High Mountain Asia Glacier Mass Balance, Front. Earth. Sci., 7, 363, https://doi.org/10.3389/feart.2019.00363, 2020.
Shortridge, A. and Messina, J.: Spatial structure and landscape associations of SRTM error, Remote Sens. Environ., 115, 1576–1587,
https://doi.org/10.1016/j.rse.2011.02.017, 2011.
Smith, B., Fricker, H. A., Holschuh, N., Gardner, A. S., Adusumilli, S., Brunt, K. M., Csatho, B., Harbeck, K., Huth, A., Neumann, T., Nilsson, J.,
and Siegfried, M. R.: Land ice height-retrieval algorithm for NASA's ICESat-2 photon-counting laser altimeter, Remote Sens. Environ., 233, 111352, https://doi.org/10.1016/j.rse.2019.111352, 2019.
Smith, B., Adusumilli, S., Csathó, B. M., Felikson, D., Fricker, H. A., Gardner, A., Holschuh, N., Lee, J., Nilsson, J., Paolo, F. S., Siegfried, M. R., Sutterley, T., and the ICESat-2 Science Team: ATLAS/ICESat-2 L3A Land Ice Height, Version 5, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA [dataset], https://doi.org/10.5067/ATLAS/ATL06.005, 2021.
Takaku, J., Tadono, T., Doutsu, M., Ohgushi, F., and Kai, H.: Updates of
`Aw3d30' Alos Global Digital Surface Model with Other Open Access Datasets,
Int. Arch. Photogram., XLIII-B4-2020, 183–189,
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-183-2020, 2020.
Thompson, L. G., Yao, T., Davis, M. E., Mosley-Thompson, E., Wu, G., Porter,
S. E., Xu, B., Lin, P.-N., Wang, N., Beaudon, E., Duan, K., Sierra-Hernández, M. R., and Kenny, D. V.: Ice core records of climate
variability on the Third Pole with emphasis on the Guliya ice cap, western
Kunlun Mountains, Quaternary. Sci. Rev., 188, 1–14, 2018
Uuemaa, E., Ahi, S., Montibeller, B., Muru, M., and Kmoch, A.: Vertical
Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30,
MERIT, TanDEM-X, SRTM, and NASADEM), Remote Sens.-Basel, 12, 3482, https://doi.org/10.3390/rs12213482, 2020.
Van Niel, T. G., McVicar, T. R., Li, L., Gallant, J. C., and Yang, Q.: The
impact of misregistration on SRTM and DEM image differences, Remote Sens.
Environ., 112, 2430–2442, https://doi.org/10.1016/j.rse.2007.11.003, 2008.
Welty, E., Zemp, M., Navarro, F., Huss, M., Fürst, J. J., Gärtner-Roer, I., Landmann, J., Machguth, H., Naegeli, K., Andreassen, L. M., Farinotti, D., and Li, H.: Worldwide version-controlled database of glacier thickness observations, Earth Syst. Sci. Data, 12, 3039–3055,
https://doi.org/10.5194/essd-12-3039-2020, 2020.
Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., and Roth, A.: Accuracy assessment of the global TanDEM-X Digital Elevation Model with
GPS data, ISPRS J. Photogram., 139, 171–182,
https://doi.org/10.1016/j.isprsjprs.2018.02.017, 2018.
Wu, K., Liu, S., Zhu, Y., Liu, Q., and Jiang, Z.: Dynamics of glacier surface velocity and ice thickness for maritime glaciers in the southeastern Tibetan Plateau, J. Hydrol., 590, 125527, https://doi.org/10.1016/j.jhydrol.2020.125527, 2020.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853,
https://doi.org/10.1002/2017GL072874, 2017.
Yao, T., Thompson, L. G., Mosbrugger, V., Zhang, F., Ma, Y., Luo, T., Xu, B.,
Yang, X., Joswiak, D. R., Wang, W., Joswiak, M. E., Devkota, L. P., Tayal, S., Jilani, R., and Fayziev, R.: Third Pole Environment (TPE), Environ. Dev.,
3, 52–64, 2012.
Zhang, G., Yao, T., Xie, H., Kang, S., and Lei, Y.: Increased mass over the
Tibetan Plateau: From lakes or glaciers?, Geophys. Res. Lett., 40, 2125–2130, https://doi.org/10.1002/grl.50462, 2013.
Zhang, G., Bolch, T., Allen, S., Linsbauer, A., Chen, W., and Wang, W.: Glacial lake evolution and glacier–lake interactions in the Poiqu River
basin, central Himalaya, 1964–2017, J. Glaciol., 65, 347–365,
https://doi.org/10.1016/j.envdev.2012.04.002, 2019.
Zhang, Y., Pang, Y., Cui, D., Ma, Y., and Chen, L.: Accuracy Assessment of
the ICESat-2/ATL06 Product in the Qilian Mountains Based on CORS and UAV
Data, IEEE J.-Stars, 14, 1558–1571, https://doi.org/10.1109/JSTARS.2020.3044463, 2020.
Zheng, G., Allen, S. K., Bao, A. M., Juan, A. B., Matthias, H., Zhang, G. Q., Li, J. L., Ye, Y., Jiang, L. L., Yu, T., Chen, W. F., Stoffel, M.: Increasing
risk of glacial lake outburst floods from future Third Pole deglaciation, Nat. Clim. Change., 11, 411–417, https://doi.org/10.1038/s41558-021-01028-3, 2021.
Zhou, Y., Li, Z., Li, J., Zhao, R., and Ding, X.: Glacier mass balance in
the Qinghai–Tibet Plateau and its surroundings from the mid-1970s to 2000
based on Hexagon KH-9 and SRTM DEMs, Remote Sens. Environ., 210, 96–112,
https://doi.org/10.1016/j.rse.2018.03.020, 2018.
Short summary
A digital elevation model (DEM) is a prerequisite for estimating regional glacier thickness. Our study first compared six widely used global DEMs over the glacierized Tibetan Plateau by using ICESat-2 (Ice, Cloud and land Elevation Satellite) laser altimetry data. Our results show that NASADEM had the best accuracy. We conclude that NASADEM would be the best choice for ice-thickness estimation over the Tibetan Plateau through an intercomparison of four ice-thickness inversion models.
A digital elevation model (DEM) is a prerequisite for estimating regional glacier thickness. Our...
