Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-3889-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-3889-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new global dataset of mountain glacier centerlines and lengths
Dahong Zhang
College of Urban and Environmental Science, Northwest University,
Xi'an 710127, PR China
Shaanxi Key Laboratory of Earth Surface System and Environmental
Carrying Capacity, Northwest University, Xi'an 710127, PR China
Gang Zhou
College of Urban and Environmental Science, Northwest University,
Xi'an 710127, PR China
Shaanxi Key Laboratory of Earth Surface System and Environmental
Carrying Capacity, Northwest University, Xi'an 710127, PR China
Wen Li
College of Urban and Environmental Science, Northwest University,
Xi'an 710127, PR China
Shaanxi Key Laboratory of Earth Surface System and Environmental
Carrying Capacity, Northwest University, Xi'an 710127, PR China
College of Urban and Environmental Science, Northwest University,
Xi'an 710127, PR China
Shaanxi Key Laboratory of Earth Surface System and Environmental
Carrying Capacity, Northwest University, Xi'an 710127, PR China
Xiaojun Yao
College of Geography and Environment Sciences, Northwest Normal
University, Lanzhou 730070, PR China
Shimei Wei
College of Geography and Environment Sciences, Northwest Normal
University, Lanzhou 730070, PR China
Related authors
Dahong Zhang, Xiaojun Yao, Hongyu Duan, Shiyin Liu, Wanqin Guo, Meiping Sun, and Dazhi Li
The Cryosphere, 15, 1955–1973, https://doi.org/10.5194/tc-15-1955-2021, https://doi.org/10.5194/tc-15-1955-2021, 2021
Short summary
Short summary
Glacier centerlines are crucial input for many glaciological applications. We propose a new algorithm to derive glacier centerlines and implement the corresponding program in Python language. Application of this method to 48 571 glaciers in the second Chinese glacier inventory automatically yielded the corresponding glacier centerlines with an average computing time of 20.96 s, a success rate of 100 % and a comprehensive accuracy of 94.34 %.
Guangxi Ding, Jia Qin, Shiqiang Zhang, Bingfeng Yang, Junhao Cui, Feiteng Wang, and Jianfeng Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-5989, https://doi.org/10.5194/egusphere-2025-5989, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
As climate warming accelerates permafrost thaw, significant yet poorly understood shifts are occurring in water cycles across cold regions. This study synthesizes current knowledge through a comprehensive review, establishing an integrated framework that connects surface water, the active layer, and permafrost. We summarize how thawing affects runoff, groundwater, and ecosystems, and highlight key uncertainties. Our findings could enhance understanding of the permafrost hydrology.
Yu Zhu, Shiyin Liu, Junfeng Wei, Kunpeng Wu, Tobias Bolch, Junli Xu, Wanqin Guo, Zongli Jiang, Fuming Xie, Ying Yi, Donghui Shangguan, Xiaojun Yao, and Zhen Zhang
Earth Syst. Sci. Data, 17, 1851–1871, https://doi.org/10.5194/essd-17-1851-2025, https://doi.org/10.5194/essd-17-1851-2025, 2025
Short summary
Short summary
This study compiled a near-complete inventory of glacier mass changes across the eastern Tibetan Plateau using topographical maps. These data enhance our understanding of glacier change variability before 2000. When combined with existing research, our dataset provides a nearly 5-decade record of mass balance, aiding hydrological simulations and assessments of mountain glacier contributions to sea-level rise.
Fuming Xie, Shiyin Liu, Yongpeng Gao, Yu Zhu, Tobias Bolch, Andreas Kääb, Shimei Duan, Wenfei Miao, Jianfang Kang, Yaonan Zhang, Xiran Pan, Caixia Qin, Kunpeng Wu, Miaomiao Qi, Xianhe Zhang, Ying Yi, Fengze Han, Xiaojun Yao, Qiao Liu, Xin Wang, Zongli Jiang, Donghui Shangguan, Yong Zhang, Richard Grünwald, Muhammad Adnan, Jyoti Karki, and Muhammad Saifullah
Earth Syst. Sci. Data, 15, 847–867, https://doi.org/10.5194/essd-15-847-2023, https://doi.org/10.5194/essd-15-847-2023, 2023
Short summary
Short summary
In this study, first we generated inventories which allowed us to systematically detect glacier change patterns in the Karakoram range. We found that, by the 2020s, there were approximately 10 500 glaciers in the Karakoram mountains covering an area of 22 510.73 km2, of which ~ 10.2 % is covered by debris. During the past 30 years (from 1990 to 2020), the total glacier cover area in Karakoram remained relatively stable, with a slight increase in area of 23.5 km2.
Hongyu Duan, Xiaojun Yao, Yuan Zhang, Huian Jin, Qi Wang, Zhishui Du, Jiayu Hu, Bin Wang, and Qianxun Wang
The Cryosphere, 17, 591–616, https://doi.org/10.5194/tc-17-591-2023, https://doi.org/10.5194/tc-17-591-2023, 2023
Short summary
Short summary
We conducted a comprehensive investigation of Bienong Co, a moraine-dammed glacial lake on the southeastern Tibetan Plateau (SETP), to assess its potential hazards. The maximum lake depth is ~181 m, and the lake volume is ~102.3 × 106 m3. Bienong Co is the deepest known glacial lake with the same surface area on the Tibetan Plateau. Ice avalanches may produce glacial lake outburst floods that threaten the downstream area. This study could provide new insight into glacial lakes on the SETP.
Yu Zhu, Shiyin Liu, Junfeng Wei, Kunpeng Wu, Tobias Bolch, Junli Xu, Wanqin Guo, Zongli Jiang, Fuming Xie, Ying Yi, Donghui Shangguan, Xiaojun Yao, and Zhen Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-473, https://doi.org/10.5194/essd-2022-473, 2023
Preprint withdrawn
Short summary
Short summary
In this study, we presented a nearly complete inventory of glacier mass change dataset across the eastern Tibetan Plateau by using topographical maps, which will enhance the knowledge on the heterogeneity of glacier change before 2000. Our dataset, in combination with the published results, provide a nearly five decades mass balance to support hydrological simulation, and to evaluate the contribution of mountain glacier loss to sea level.
Xinde Chu, Xiaojun Yao, Hongyu Duan, Cong Chen, Jing Li, and Wenlong Pang
The Cryosphere, 16, 4273–4289, https://doi.org/10.5194/tc-16-4273-2022, https://doi.org/10.5194/tc-16-4273-2022, 2022
Short summary
Short summary
The available remote-sensing data are increasingly abundant, and the efficient and rapid acquisition of glacier boundaries based on these data is currently a frontier issue in glacier research. In this study, we designed a complete solution to automatically extract glacier outlines from the high-resolution images. Compared with other methods, our method achieves the best performance for glacier boundary extraction in parts of the Tanggula Mountains, Kunlun Mountains and Qilian Mountains.
Meiping Sun, Sugang Zhou, Xiaojun Yao, Hongyu Duan, and Yuan Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2022-765, https://doi.org/10.5194/egusphere-2022-765, 2022
Preprint withdrawn
Short summary
Short summary
For understanding the occurrence mechanism of surging glaciers in High Mountain Asia, it is essential to ascertain their amounts, distribution and periodicity. Based on images from Landsat satellite from 1986–2021, we identified 244 surging glaciers with high confidence and 2802 events of glacier surge. We also analyzed the periodicity of 36 glaciers which experienced two or more surges. The findings will benefit to enrich dataset and provide basic information of surging glaciers in HMA.
Dahong Zhang, Xiaojun Yao, Hongyu Duan, Shiyin Liu, Wanqin Guo, Meiping Sun, and Dazhi Li
The Cryosphere, 15, 1955–1973, https://doi.org/10.5194/tc-15-1955-2021, https://doi.org/10.5194/tc-15-1955-2021, 2021
Short summary
Short summary
Glacier centerlines are crucial input for many glaciological applications. We propose a new algorithm to derive glacier centerlines and implement the corresponding program in Python language. Application of this method to 48 571 glaciers in the second Chinese glacier inventory automatically yielded the corresponding glacier centerlines with an average computing time of 20.96 s, a success rate of 100 % and a comprehensive accuracy of 94.34 %.
Cited articles
Abrams, M., Crippen, R., and Fujisada, H.: ASTER Global Digital Elevation
Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD), Remote Sensing,
12, 1156, https://doi.org/10.3390/rs12071156, 2020.
Aciego, S. M., Stevenson, E. I., and Arendt, C. A.: Climate versus
geological controls on glacial meltwater micronutrient production in
southern Greenland, Earth Planet. Sc. Lett., 424, 51–58,
https://doi.org/10.1016/j.epsl.2015.05.017, 2015.
Carabajal, C. C. and Boy, J. P.: Evaluation of Aster Gdem V3 Using Icesat
Laser Altimetry, Int. Arch. Photogramm., XLI-B4, 117–124,
https://doi.org/10.5194/isprsarchives-XLI-B4-117-2016, 2016.
Carrera-Hernández, J. J.: Not all DEMs are equal: An evaluation of six
globally available 30 m resolution DEMs with geodetic benchmarks and LiDAR
in Mexico, Remote Sens. Environ., 261, 112474,
https://doi.org/10.1016/j.rse.2021.112474, 2021.
Fan, Y., Ke, C.-Q., and Shen, X.: A new Greenland digital elevation model derived from ICESat-2 during 2018–2019, Earth Syst. Sci. Data, 14, 781–794, https://doi.org/10.5194/essd-14-781-2022, 2022.
Farinotti, D., Huss, M., Fürst, 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.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, 1–33,
https://doi.org/10.1029/2005rg000183, 2007.
Franke, S., Jansen, D., Binder, T., Paden, J. D., Dörr, N., Gerber, T. A., Miller, H., Dahl-Jensen, D., Helm, V., Steinhage, D., Weikusat, I., Wilhelms, F., and Eisen, O.: Airborne ultra-wideband radar sounding over the shear margins and along flow lines at the onset region of the Northeast Greenland Ice Stream, Earth Syst. Sci. Data, 14, 763–779, https://doi.org/10.5194/essd-14-763-2022, 2022.
Gao, Y. P., Yao, X. J., Liu, S. Y., Qi, M. M., Gong, P., An, L. N., Li, X.
F., and Duan, H. Y.: Methods and future trend of ice volume calculation of
glacier, Arid Land Geography, 41, 1204–1213, 2018.
Hansen, K., Hasenstab, K., and Schwartzman, A.: Estimating Mountain Glacier
Flowlines by Local Linear Regression Gradient Descent, IEEE T.
Geosci. Remote, 59, 10022–10034,
https://doi.org/10.1109/tgrs.2020.3035513, 2020.
Heid, T. and Kääb, A.: Repeat optical satellite images reveal widespread and long term decrease in land-terminating glacier speeds, The Cryosphere, 6, 467–478, https://doi.org/10.5194/tc-6-467-2012, 2012.
Herla, F., Roe, G. H., and Marzeion, B.: Ensemble statistics of a geometric
glacier length model, Ann. Glaciol., 58, 130–135,
https://doi.org/10.1017/aog.2017.15, 2017.
Herreid, S. and Pellicciotti, F.: The state of rock debris covering Earth's
glaciers, Nat. Geosci., 13, 621–627,
https://doi.org/10.1038/s41561-020-0615-0, 2020.
Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P.: The Reference Elevation Model of Antarctica, The Cryosphere, 13, 665–674, https://doi.org/10.5194/tc-13-665-2019, 2019.
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L.,
Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kaab, 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.
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.,
Fernández, 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, 2019.
Ji, Q., Yang, T.-b., He, Y., Qin, Y., Dong, J., and Hu, F.-s.: A simple
method to extract glacier length based on Digital Elevation Model and
glacier boundaries for simple basin type glacier,
J. Mt. Sci., 14, 1776–1790,
https://doi.org/10.1007/s11629-016-4243-5, 2017.
Kääb, A., Jacquemart, M., Gilbert, A., Leinss, S., Girod, L., Huggel, C., Falaschi, D., Ugalde, F., Petrakov, D., Chernomorets, S., Dokukin, M., Paul, F., Gascoin, S., Berthier, E., and Kargel, J. S.: Sudden large-volume detachments of low-angle mountain glaciers – more frequent than thought?, The Cryosphere, 15, 1751–1785, https://doi.org/10.5194/tc-15-1751-2021, 2021.
Kienholz, C., Hock, R., and Arendt, A. A.: A new semi-automatic approach for
dividing glacier complexes into individual glaciers, J. Glaciol.,
59, 925–937, https://doi.org/10.3189/2013JoG12J138, 2013.
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.
Le Bris, R. and Paul, F.: An automatic method to create flow lines for
determination of glacier length: A pilot study with Alaskan glaciers,
Comput. Geosci., 52, 234–245,
https://doi.org/10.1016/j.cageo.2012.10.014, 2013.
Le Moine, N. and Gsell, P.-S.: A graph-based approach to glacier flowline
extraction: An application to glaciers in Switzerland, Comput.
Geosci., 85, 91–101,
https://doi.org/10.1016/j.cageo.2015.09.010, 2015.
Leclercq, P. W. and Oerlemans, J.: Global and hemispheric temperature
reconstruction from glacier length fluctuations, Clim. Dynam., 38,
1065–1079, https://doi.org/10.1007/s00382-011-1145-7, 2011.
Leclercq, P. W., Oerlemans, J., Basagic, H. J., Bushueva, I., Cook, A. J., and Le Bris, R.: A data set of worldwide glacier length fluctuations, The Cryosphere, 8, 659–672, https://doi.org/10.5194/tc-8-659-2014, 2014.
Li, H., Ng, F., Li, Z., Qin, D., and Cheng, G.: An extended
“perfect-plasticity” method for estimating ice thickness along the flow
line of mountain glaciers, J. Geophys. Res.-Earth,
117, F01020, https://doi.org/10.1029/2011jf002104, 2012.
Li, X., Ding, Y., Hood, E., Raiswell, R., Han, T., He, X., Kang, S., Wu, Q.,
Yu, Z., Mika, S., Liu, S., and Li, Q.: Dissolved Iron Supply from Asian
Glaciers: Local Controls and a Regional Perspective, Global Biogeochem.
Cy., 33, 1223–1237, https://doi.org/10.1029/2018gb006113,
2019.
Li, Y., Li, F., Shangguan, D., and Ding, Y.: A new global gridded glacier
dataset based on the Randolph Glacier Inventory version 6.0, J.
Glaciol., 67, 773–776, https://doi.org/10.1017/jog.2021.28,
2021.
Lüthi, M. P., Bauder, A., and Funk, M.: Volume change reconstruction of
Swiss glaciers from length change data, J. Geophys. Res.,
115, F04022, https://doi.org/10.1029/2010jf001695, 2010.
Machguth, H. and Huss, M.: The length of the world's glaciers – a new approach for the global calculation of center lines, The Cryosphere, 8, 1741–1755, https://doi.org/10.5194/tc-8-1741-2014, 2014.
Mankoff, K. D., Fettweis, X., Langen, P. L., Stendel, M., Kjeldsen, K. K., Karlsson, N. B., Noël, B., van den Broeke, M. R., Solgaard, A., Colgan, W., Box, J. E., Simonsen, S. B., King, M. D., Ahlstrøm, A. P., Andersen, S. B., and Fausto, R. S.: Greenland ice sheet mass balance from 1840 through next week, Earth Syst. Sci. Data, 13, 5001–5025, https://doi.org/10.5194/essd-13-5001-2021, 2021.
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.
Melkonian, A. K., Willis, M. J., and Pritchard, M. E.: Satellite-derived
volume loss rates and glacier speeds for the Juneau Icefield, Alaska,
J. Glaciol., 60, 743–760,
https://doi.org/10.3189/2014JoG13J181, 2017.
Noel, B., Jakobs, C. L., van Pelt, W. J. J., Lhermitte, S., Wouters, B.,
Kohler, J., Hagen, J. O., Luks, B., Reijmer, C. H., van de Berg, W. J., and
van den Broeke, M. R.: Low elevation of Svalbard glaciers drives high mass
loss variability, Nat. Commun., 11, 4597,
https://doi.org/10.1038/s41467-020-18356-1, 2020.
Oerlemans, J.: A flowline model for Nigardsbreen, Norway: projection of
future glacier length based on dynamic calibration with the historic record,
Ann. Glaciol., 24, 382–389,
https://doi.org/10.1017/S0260305500012489, 1997.
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner,
A. S., Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S.,
Moholdt, G., Mölg, N., Paul, F., Radiæ, V., Rastner, P., Raup, B.
H., Rich, J., and Sharp, M. J.: The Randolph Glacier Inventory: a globally
complete inventory of glaciers, J. Glaciol., 60, 537–552,
https://doi.org/10.3189/2014JoG13J176, 2014.
Pritchard, H. D.: Asia's shrinking glaciers protect large populations from
drought stress, Nature, 569, 649–654,
https://doi.org/10.1038/s41586-019-1240-1, 2019.
Radiæ, V. and Hock, R.: Regional and global volumes of glaciers derived
from statistical upscaling of glacier inventory data, J. Geophys. Res., 115, F01010, https://doi.org/10.1029/2009jf001373, 2010.
RGI Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier
Outlines: Version 6.0: Technical Report, Global Land Ice Measurements from
Space, Colorado, USA, https://doi.org/10.7265/N5-RGI-60, 2017.
Scherler, D., Wulf, H., and Gorelick, N.: Global Assessment of Supraglacial
Debris-Cover Extents, Geophys. Res. Lett., 45, 11798–11805,
https://doi.org/10.1029/2018gl080158, 2018.
Schiefer, E., Menounos, B., and Wheate, R.: An inventory and morphometric
analysis of British Columbia glaciers, Canada, J. Glaciol.,
54, 551–560, 2008.
Shukla, A., Garg, S., Mehta, M., Kumar, V., and Shukla, U. K.: Temporal inventory of glaciers in the Suru sub-basin, western Himalaya: impacts of regional climate variability, Earth Syst. Sci. Data, 12, 1245–1265, https://doi.org/10.5194/essd-12-1245-2020, 2020.
Shukla, T. and Sen, I. S.: Preparing for floods on the Third Pole, Science,
372, 232–234, https://doi.org/10.1126/science.abh3558, 2021.
Solgaard, A., Kusk, A., Merryman Boncori, J. P., Dall, J., Mankoff, K. D., Ahlstrøm, A. P., Andersen, S. B., Citterio, M., Karlsson, N. B., Kjeldsen, K. K., Korsgaard, N. J., Larsen, S. H., and Fausto, R. S.: Greenland ice velocity maps from the PROMICE project, Earth Syst. Sci. Data, 13, 3491–3512, https://doi.org/10.5194/essd-13-3491-2021, 2021.
Sommer, C., Malz, P., Seehaus, T. C., Lippl, S., Zemp, M., and Braun, M. H.:
Rapid glacier retreat and downwasting throughout the European Alps in the
early 21(st) century, Nat. Commun., 11, 3209,
https://doi.org/10.1038/s41467-020-16818-0, 2020.
Stuart-Smith, R. F., Roe, G. H., Li, S., and Allen, M. R.: Increased
outburst flood hazard from Lake Palcacocha due to human-induced glacier
retreat, Nat. Geosci., 14, 85–90,
https://doi.org/10.1038/s41561-021-00686-4, 2021.
Sugiyama, S., Bauder, A., Zahno, C., and Funk, M.: Evolution of
Rhonegletscher, Switzerland, over the past 125 years and in the future :
application of an improved flowline model, Ann. Glaciol., 46,
268–274, 2007.
Thogersen, K., Gilbert, A., Schuler, T. V., and Malthe-Sorenssen, A.:
Rate-and-state friction explains glacier surge propagation, Nat. Commun., 10,
2823, https://doi.org/10.1038/s41467-019-10506-4, 2019.
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 Sensing, 12, 3482,
https://doi.org/10.3390/rs12213482, 2020.
Vargo, L. J., Anderson, B. M., Dadiæ, R., Horgan, H. J., Mackintosh, A.
N., King, A. D., and Lorrey, A. M.: Anthropogenic warming forces extreme
annual glacier mass loss, Nat. Clim. Change, 10, 856–861,
https://doi.org/10.1038/s41558-020-0849-2, 2020.
WCRP Global Sea Level Budget Group: Global sea-level budget 1993–present, Earth Syst. Sci. Data, 10, 1551–1590, https://doi.org/10.5194/essd-10-1551-2018, 2018.
Winsvold, S. H., Andreassen, L. M., and Kienholz, C.: Glacier area and length changes in Norway from repeat inventories, The Cryosphere, 8, 1885–1903, https://doi.org/10.5194/tc-8-1885-2014, 2014.
Wu, K., Liu, S., Jiang, Z., Liu, Q., Zhu, Y., Yi, Y., Xie, F., Ahmad Tahir,
A., and Saifullah, M.: Quantification of glacier mass budgets in the
Karakoram region of Upper Indus Basin during the early twenty-first century,
J. Hydrol., 603, 127095,
https://doi.org/10.1016/j.jhydrol.2021.127095, 2021.
Xia, W.: An Automatic Extraction Method of Glacier Length Based on Voronoi Algorithm – A Pilot Study in the Sanjiangyuan Region, M.S. thesis, Northwest University, China, https://doi.org/10.27405/d.cnki.gxbdu.2020.000585, 2020.
Yang, B. Y., Zhang, L. X., Gao, Y., Xiang, Y., Mou, N. X., and Suo, L. D.
B.: An integrated method of glacier length extraction based on Gaofen
satellite data, Journal of Glaciology and Geocryology, 38, 1615–1623,
https://doi.org/10.7522/j.issn.1000-0240.2016.0189, 2016.
Yao, X. J., Liu, S. Y., Zhu, Y., Gong, P., An, L. N., and Li, X. F.: Design
and implementation of an automatic method for deriving glacier centerlines
based on GIS, Journal of Glaciology and Geocryology, 37, 1563–1570,
2015.
Zemp, M., Huss, M., Thibert, E., Eckert, N., McNabb, R., Huber, J.,
Barandun, M., Machguth, H., Nussbaumer, S. U., Gartner-Roer, I., Thomson,
L., Paul, F., Maussion, F., Kutuzov, S., and Cogley, J. G.: Global glacier
mass changes and their contributions to sea-level rise from 1961 to 2016,
Nature, 568, 382–386,
https://doi.org/10.1038/s41586-019-1071-0, 2019.
Zhang, B., Wang, Z., An, J., Liu, T., and Geng, H.: A 30-year monthly 5 km gridded surface elevation time series for the Greenland Ice Sheet from multiple satellite radar altimeters, Earth Syst. Sci. Data, 14, 973–989, https://doi.org/10.5194/essd-14-973-2022, 2022.
Zhang, D. and Zhang, S.: A new global dataset of mountain glacier centerline
and length, Science Data Bank [data set],
https://doi.org/10.11922/sciencedb.01643, 2022.
Zhang, D., Yao, X., Duan, H., Liu, S., Guo, W., Sun, M., and Li, D.: A new automatic approach for extracting glacier centerlines based on Euclidean allocation, The Cryosphere, 15, 1955–1973, https://doi.org/10.5194/tc-15-1955-2021, 2021.
Zheng, G., Allen, S. K., Bao, A., Ballesteros-Cánovas, J. A., Huss, M.,
Zhang, G., Li, J., Yuan, Y., Jiang, L., Yu, T., Chen, W., and 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, S., Yao, X., Zhang, D., Zhang, Y., Liu, S., and Min, Y.: Remote
Sensing Monitoring of Advancing and Surging Glaciers in the Tien Shan,
1990–2019, Remote Sensing, 13, 1973,
https://doi.org/10.3390/rs13101973, 2021a.
Zhou, Y., Li, X., Zheng, D., Li, Z., An, B., Wang, Y., Jiang, D., Su, J.,
and Cao, B.: The joint driving effects of climate and weather changes caused
the Chamoli glacier-rock avalanche in the high altitudes of the India
Himalaya, Science China Earth Sciences, 64, 1909–1921,
https://doi.org/10.1007/s11430-021-9844-0, 2021b.
Short summary
The length of a glacier is a key determinant of its geometry; glacier centerlines are crucial inputs for many glaciological applications. Based on the European allocation theory, we present a new global dataset that includes the centerlines and lengths of 198 137 mountain glaciers. The accuracy of the glacier centerlines was 89.68 %. The constructed dataset comprises 17 sub-datasets which contain the centerlines and lengths of glacier tributaries.
The length of a glacier is a key determinant of its geometry; glacier centerlines are crucial...
Special issue
Altmetrics
Final-revised paper
Preprint