Articles | Volume 16, issue 7
https://doi.org/10.5194/tc-16-2671-2022
© 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-16-2671-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jul 2022
Research article |
| 08 Jul 2022
| 08 Jul 2022
Filling and drainage of a subglacial lake beneath the Flade Isblink ice cap, northeast Greenland
School of Geospatial Engineering and Science, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Wanxin Xiao
School of Geospatial Engineering and Science, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Ian Howat
Byrd Polar and Climate Research Center, Columbus, OH, USA
School of Earth Sciences, The Ohio State University, Columbus, OH, USA
Xiao Cheng
School of Geospatial Engineering and Science, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Fengming Hui
School of Geospatial Engineering and Science, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Zhuoqi Chen
School of Geospatial Engineering and Science, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Mi Jiang
School of Geospatial Engineering and Science, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Lei Zheng
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, China
Related authors
Mingyue Nong, Xuying Liu, Teng Li, Baogang Zhang, Qi Liang, Lei Zheng, Tiancheng Zhao, and Xiao Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-1884, https://doi.org/10.5194/egusphere-2025-1884, 2025
Preprint archived
Short summary
Short summary
We extracted nearshore small icebergs in front of Dalk Glacier using UAV high-resolution data and directly obtained geometric parameters of the icebergs and analyzed their distribution patterns. The area/volume relationship of our icebergs aligns with the medium to large icebergs in existing ocean model. The study demonstrates UAVs' effectiveness in polar research and the importance of including all iceberg sizes in ocean modeling for better environmental impact predictions.
Zilong Chen, Xuying Liu, Zhenfu Guan, Teng Li, Xiao Cheng, Tian Li, Yan Liu, Qi Liang, Lei Zheng, and Jiping Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-51, https://doi.org/10.5194/essd-2025-51, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
Our study uses Google Earth Engine to create a dataset of Antarctic icebergs in the Southern Ocean (south of 55°S) from October 2018 to 2023. The dataset includes icebergs larger than 0.04 km², with details on their locations, sizes, and shapes. It shows significant changes in iceberg number and area, mainly driven by major ice shelf calving events – especially in the Weddell Sea. This resource fills key gaps in understanding iceberg impacts on the ocean and climate.
Mingyue Nong, Xuying Liu, Teng Li, Baogang Zhang, Qi Liang, Lei Zheng, Tiancheng Zhao, and Xiao Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-1884, https://doi.org/10.5194/egusphere-2025-1884, 2025
Preprint archived
Short summary
Short summary
We extracted nearshore small icebergs in front of Dalk Glacier using UAV high-resolution data and directly obtained geometric parameters of the icebergs and analyzed their distribution patterns. The area/volume relationship of our icebergs aligns with the medium to large icebergs in existing ocean model. The study demonstrates UAVs' effectiveness in polar research and the importance of including all iceberg sizes in ocean modeling for better environmental impact predictions.
Zilong Chen, Xuying Liu, Zhenfu Guan, Teng Li, Xiao Cheng, Tian Li, Yan Liu, Qi Liang, Lei Zheng, and Jiping Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-51, https://doi.org/10.5194/essd-2025-51, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
Our study uses Google Earth Engine to create a dataset of Antarctic icebergs in the Southern Ocean (south of 55°S) from October 2018 to 2023. The dataset includes icebergs larger than 0.04 km², with details on their locations, sizes, and shapes. It shows significant changes in iceberg number and area, mainly driven by major ice shelf calving events – especially in the Weddell Sea. This resource fills key gaps in understanding iceberg impacts on the ocean and climate.
Runzhuo Fang, Jinfeng Ding, Wenjuan Gao, Xi Liang, Zhuoqi Chen, Chuanfeng Zhao, Haijin Dai, and Lei Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-186, https://doi.org/10.5194/essd-2025-186, 2025
Preprint under review for ESSD
Short summary
Short summary
IMPMCT is a dataset containing a 24-year record (2001–2024) of polar storms in the Nordic Seas. These storms, called Polar Mesoscale Cyclones (PMCs), sometimes cause extreme winds and waves, threatening marine operations. IMPMCT combines remote sensing measurements and reanalysis data to construct a comprehensive PMCs archive. It includes 1,184 PMCs tracks, 16,630 cloud patterns, and 4,373 wind records, providing fundamental data for advancing our understanding of their development mechanisms.
Fukai Peng, Xiaoli Deng, Yunzhong Shen, and Xiao Cheng
Earth Syst. Sci. Data, 17, 1441–1460, https://doi.org/10.5194/essd-17-1441-2025, https://doi.org/10.5194/essd-17-1441-2025, 2025
Short summary
Short summary
A new reprocessed altimeter coastal sea level dataset, International Altimetry Service 2024 (IAS2024), for monitoring sea level changes along the world’s coastlines is presented. The evaluation and validation results confirm the reliability of this dataset. The altimeter-based virtual stations along the world’s coastlines can be built using this dataset to monitor the coastal sea level changes where tide gauges are unavailable. Therefore, it is beneficial for both oceanographic communities and policymakers.
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2760, https://doi.org/10.5194/egusphere-2024-2760, 2025
Short summary
Short summary
This manuscript proposes to combine semantic segmentation of ice region using a U-Net model and multi-stage detection of ice pixels using the Multi-textRG algorithm to achieve fine ice-water classification. Novel proccessings for the HV/HH polarization ratio and the GLCM textures, as well as the usage of regional growing, largely improve the method accuracy and robustness. The proposed algorithm framework achieved automated sea-ice labelling.
Allison M. Chartrand, Ian M. Howat, Ian R. Joughin, and Benjamin E. Smith
The Cryosphere, 18, 4971–4992, https://doi.org/10.5194/tc-18-4971-2024, https://doi.org/10.5194/tc-18-4971-2024, 2024
Short summary
Short summary
This study uses high-resolution remote-sensing data to show that shrinking of the West Antarctic Thwaites Glacier’s ice shelf (floating extension) is exacerbated by several sub-ice-shelf meltwater channels that form as the glacier transitions from full contact with the seafloor to fully floating. In mapping these channels, the position of the transition zone, and thinning rates of the Thwaites Glacier, this work elucidates important processes driving its rapid contribution to sea level rise.
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-1177, https://doi.org/10.5194/egusphere-2024-1177, 2024
Preprint archived
Short summary
Short summary
Arctic sea ice has rapidly declined due to global warming, leading to extreme weather events. Accurate ice monitoring is vital for understanding and forecasting these impacts. Combining SAR and AMSR2 data with machine learning is efficient but requires sufficient labels. We propose a framework integrating the U-Net model with the Multi-textRG algorithm to achieve ice-water classification at SAR-level resolution and to generate accurate labels for improved U-Net model training.
Haihan Hu, Jiechen Zhao, Petra Heil, Zhiliang Qin, Jingkai Ma, Fengming Hui, and Xiao Cheng
The Cryosphere, 17, 2231–2244, https://doi.org/10.5194/tc-17-2231-2023, https://doi.org/10.5194/tc-17-2231-2023, 2023
Short summary
Short summary
The oceanic characteristics beneath sea ice significantly affect ice growth and melting. The high-frequency and long-term observations of oceanic variables allow us to deeply investigate their diurnal and seasonal variation and evaluate their influences on sea ice evolution. The large-scale sea ice distribution and ocean circulation contributed to the seasonal variation of ocean variables, revealing the important relationship between large-scale and local phenomena.
Yufang Ye, Yanbing Luo, Yan Sun, Mohammed Shokr, Signe Aaboe, Fanny Girard-Ardhuin, Fengming Hui, Xiao Cheng, and Zhuoqi Chen
The Cryosphere, 17, 279–308, https://doi.org/10.5194/tc-17-279-2023, https://doi.org/10.5194/tc-17-279-2023, 2023
Short summary
Short summary
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study gives a systematic inter-comparison and evaluation of eight SITY products. Main results include differences in SITY products being significant, with average Arctic multiyear ice extent up to 1.8×106 km2; Ku-band scatterometer SITY products generally performing better; and factors such as satellite inputs, classification methods, training datasets and post-processing highly impacting their performance.
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
Short summary
Short summary
Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Yijing Lin, Yan Liu, Zhitong Yu, Xiao Cheng, Qiang Shen, and Liyun Zhao
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-325, https://doi.org/10.5194/tc-2021-325, 2021
Preprint withdrawn
Short summary
Short summary
We introduce an uncertainty analysis framework for comprehensively and systematically quantifying the uncertainties of the Antarctic mass balance using the Input and Output Method. It is difficult to use the previous strategies employed in various methods and the available data to achieve the goal of estimation accuracy. The dominant cause of the future uncertainty is the ice thickness data gap. The interannual variability of ice discharge caused by velocity and thickness is also nonnegligible.
Mengzhen Qi, Yan Liu, Jiping Liu, Xiao Cheng, Yijing Lin, Qiyang Feng, Qiang Shen, and Zhitong Yu
Earth Syst. Sci. Data, 13, 4583–4601, https://doi.org/10.5194/essd-13-4583-2021, https://doi.org/10.5194/essd-13-4583-2021, 2021
Short summary
Short summary
A total of 1975 annual calving events larger than 1 km2 were detected on the Antarctic ice shelves from August 2005 to August 2020. The average annual calved area was measured as 3549.1 km2, and the average calving rate was measured as 770.3 Gt yr-1. Iceberg calving is most prevalent in West Antarctica, followed by the Antarctic Peninsula and Wilkes Land in East Antarctica. This annual iceberg calving dataset provides consistent and precise calving observations with the longest time coverage.
Xuguo Shi, Shaocheng Zhang, Mi Jiang, Yuanyuan Pei, Tengteng Qu, Jinhu Xu, and Chen Yang
Nat. Hazards Earth Syst. Sci., 21, 2285–2297, https://doi.org/10.5194/nhess-21-2285-2021, https://doi.org/10.5194/nhess-21-2285-2021, 2021
Short summary
Short summary
We mapped the subsidence of Wuhan using Sentinel-1 synthetic aperture radar (SAR) images acquired during 2015–2019. Overall subsidence coincides with the distribution of engineered geological regions with soft soils, while the subsidence centers shifted with urban construction activities. Correlation between karst subsidence and concentrated rainfall was identified in Qingling–Jiangdi. Results indicate that interferometric SAR can be employed to routinely monitor and identify geohazards.
Linlu Mei, Vladimir Rozanov, Evelyn Jäkel, Xiao Cheng, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021, https://doi.org/10.5194/tc-15-2781-2021, 2021
Short summary
Short summary
This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 2 of two companion papers and shows the results and validation. The paper performs the new retrieval algorithm on the Sea and Land
Surface Temperature Radiometer (SLSTR) instrument and compares the retrieved snow properties with ground-based measurements, aircraft measurements and other satellite products.
Yu Zhou, Jianlong Chen, and Xiao Cheng
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2021-21, https://doi.org/10.5194/esurf-2021-21, 2021
Preprint withdrawn
Cited articles
Aðalgeirsdóttir, G., Gudmundsson, G. H., and Björnsson, H.: The
response of a glacier to a surface disturbance: a case study on
Vatnajökull ice cap, Iceland, Ann. Glaciol., 31, 104–110,
https://doi.org/10.3189/172756400781819914, 2000.
Björnsson, H.: Subglacial lakes and jökulhlaups in Iceland, Global
Planet. Change, 35, 255–271, https://doi.org/10.1016/S0921-8181(02)00130-3,
2003.
Bowling, J. S., Livingstone, S. J., Sole, A. J., and Chu, W.: Distribution
and dynamics of Greenland subglacial lakes, Nat. Commun., 10, 2810,
https://doi.org/10.1038/s41467-019-10821-w, 2019.
Davison, B. J., Sole, A. J., Livingstone, S. J., Cowton, T. R., and Nienow,
P. W.: The Influence of Hydrology on the Dynamics of Land-Terminating
Sectors of the Greenland Ice Sheet, Front. Earth Sci., 7, 10,
https://doi.org/10.3389/feart.2019.00010, 2019.
Davison, B. J., Sole, A. J., Cowton, T. R., Lea, J. M., Slater, D. A.,
Fahrner, D., and Nienow, P. W.: Subglacial Drainage Evolution Modulates
Seasonal Ice Flow Variability of Three Tidewater Glaciers in Southwest
Greenland, J. Geophys. Res.-Earth Surf., 125, e2019JF005492,
https://doi.org/10.1029/2019JF005492, 2020.
ESA: Copernicus, https://scihub.copernicus.eu/dhus/#/home, last access: 1 October 2021.
Forster, R. R., Box, J. E., van den Broeke, M. R., Miège, C., Burgess,
E. W., van Angelen, J. H., Lenaerts, J. T. M., Koenig, L. S., Paden, J.,
Lewis, C., Gogineni, S. P., Leuschen, C., and McConnell, J. R.: Extensive
liquid meltwater storage in firn within the Greenland ice sheet, Nat.
Geosci., 7, 95–98, https://doi.org/10.1038/ngeo2043, 2014.
Fricker, H. A., Scambos, T., Bindschadler, R., and Padman, L.: An Active
Subglacial Water System in West Antarctica Mapped from Space, Science, 315,
1544–1548, https://doi.org/10.1126/science.1136897, 2007.
Gray, L., Joughin, I., Tulaczyk, S., Spikes, V. B., Bindschadler, R., and
Jezek, K.: Evidence for subglacial water transport in the West Antarctic Ice
Sheet through three-dimensional satellite radar interferometry, Geophys.
Res. Lett., 32, L03501, https://doi.org/10.1029/2004GL021387, 2005.
Haran, T., Bohlander, J., Scambos, T., Painter, T., and Fahnestock, M.:
MEaSUREs MODIS Mosaic of Greenland (MOG) 2005, 2010, and 2015 Image Maps,
Version 2, National Snow and Ice Data Center [data set],
https://doi.org/10.5067/9ZO79PHOTYE5, 2018.
Harper, J., Humphrey, N., Pfeffer, W. T., Brown, J., and Fettweis, X.:
Greenland ice-sheet contribution to sea-level rise buffered by meltwater
storage in firn, Nature, 491, 240–243, https://doi.org/10.1038/nature11566, 2012.
Hoffman, A. O., Christianson, K., Shapero, D., Smith, B. E., and Joughin, I.: Brief communication: Heterogenous thinning and subglacial lake activity on Thwaites Glacier, West Antarctica, The Cryosphere, 14, 4603–4609, https://doi.org/10.5194/tc-14-4603-2020, 2020.
Howat, I. M., Porter, C., Noh, M. J., Smith, B. E., and Jeong, S.: Brief Communication: Sudden drainage of a subglacial lake beneath the Greenland Ice Sheet, The Cryosphere, 9, 103–108, https://doi.org/10.5194/tc-9-103-2015, 2015.
Joughin, I.: MEaSUREs Greenland Monthly Ice Sheet Velocity Mosaics from SAR and Landsat, Version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA [data set], https://doi.org/10.5067/YDLH5QG02XKC, 2021.
Joughin, I., Shean, D. E., Smith, B. E., and Dutrieux, P.: Grounding Line
Variability and Subglacial Lake Drainage on Pine Island Glacier, Antarctica,
Geophys. Res. Lett., 43, 9093–9102, https://doi.org/10.1002/2016gl070259, 2016.
Joughin, I., Smith, B. E., and Howat, I.: Greenland Ice Mapping Project: ice flow velocity variation at sub-monthly to decadal timescales, The Cryosphere, 12, 2211–2227, https://doi.org/10.5194/tc-12-2211-2018, 2018.
Koziol, C., Arnold, N., Pope, A., and Colgan, W.: Quantifying supraglacial
meltwater pathways in the Paakitsoq region, West Greenland, J. Glaciol., 63,
464–476, https://doi.org/10.1017/jog.2017.5, 2017.
Li, T., Dawson, G. J., Chuter, S. J., and Bamber, J. L.: Mapping the grounding zone of Larsen C Ice Shelf, Antarctica, from ICESat-2 laser altimetry, The Cryosphere, 14, 3629–3643, https://doi.org/10.5194/tc-14-3629-2020, 2020.
Livingstone, S. J., Sole, A. J., Storrar, R. D., Harrison, D., Ross, N., and Bowling, J.: Brief communication: Subglacial lake drainage beneath Isunguata Sermia, West Greenland: geomorphic and ice dynamic effects, The Cryosphere, 13, 2789–2796, https://doi.org/10.5194/tc-13-2789-2019, 2019.
Livingstone, S. J., Li, Y., Rutishauser, A., Sanderson, R. J., Winter, K.,
Mikucki, J. A., Björnsson, H., Bowling, J. S., Chu, W., Dow, C. F.,
Fricker, H. A., McMillan, M., Ng, F. S. L., Ross, N., Siegert, M. J.,
Siegfried, M., and Sole, A. J.: Subglacial lakes and their changing role in
a warming climate, Nature Reviews Earth & Environment, 3, 106–124,
https://doi.org/10.1038/s43017-021-00246-9, 2022.
MacFerrin, M., Machguth, H., As, D. v., Charalampidis, C., Stevens, C. M.,
Heilig, A., Vandecrux, B., Langen, P. L., Mottram, R., Fettweis, X., Broeke,
M. R. v. d., Pfeffer, W. T., Moussavi, M. S., and Abdalati, W.: Rapid
expansion of Greenland's low-permeability ice slabs, Nature, 573, 403–407,
https://doi.org/10.1038/s41586-019-1550-3, 2019.
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B.,
Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R.,
Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R.,
Neuenschwander, A., Palm, S., Popescu, S., Shum, C. K., Schutz, B. E.,
Smith, B., Yang, Y., and Zwally, J.: The Ice, Cloud, and land Elevation
Satellite-2 (ICESat-2): Science requirements, concept, and implementation,
Remote Sens. Environ., 190, 260–273, https://doi.org/10.1016/j.rse.2016.12.029, 2017.
Meierbachtol, T., Harper, J., and Humphrey, N.: Basal Drainage System
Response to Increasing Surface Melt on the Greenland Ice Sheet, Science,
341, 777–779, https://doi.org/10.1126/science.1235905, 2013.
Miller, J. Z., Culberg, R., Long, D. G., Shuman, C. A., Schroeder, D. M., and Brodzik, M. J.: An empirical algorithm to map perennial firn aquifers and ice slabs within the Greenland Ice Sheet using satellite L-band microwave radiometry, The Cryosphere, 16, 103–125, https://doi.org/10.5194/tc-16-103-2022, 2022.
Mottram, R., Boberg, F., Langen, P., Yang, S., Rodehacke, C., Christensen,
J. H., and Madsen, M. S.: Surface mass balance of the Greenland ice sheet in
the regional climate model HIRHAM5: Present state and future prospects, Low
Temperature Science, 75, 105–115, https://doi.org/10.14943/lowtemsci.75.105, 2017.
Munneke, P. K., M. Ligtenberg, S. R., van den Broeke, M. R., van Angelen, J.
H., and Forster, R. R.: Explaining the presence of perennial liquid water
bodies in the firn of the Greenland Ice Sheet, Geophys. Res. Lett., 41,
476–483, https://doi.org/10.1002/2013GL058389, 2014.
Nienow, P. W., Sole, A. J., Slater, D. A., and Cowton, T. R.: Recent
Advances in Our Understanding of the Role of Meltwater in the Greenland Ice
Sheet System, Current Climate Change Reports, 3, 330–344,
https://doi.org/10.1007/s40641-017-0083-9, 2017.
Neckel, N., Franke, S., Helm, V., Drews, R., and Jansen, D.: Evidence of
cascading subglacial water flow at Jutulstraumen Glacier (Antarctica)
derived from Sentinel-1 and ICESat-2 measurements, Geophys. Res. Lett., 48, e2021GL094472,
https://doi.org/10.1029/2021GL094472, 2021.
Noël, B., van de Berg, W. J., van Wessem, J. M., van Meijgaard, E., van As, D., Lenaerts, J. T. M., Lhermitte, S., Kuipers Munneke, P., Smeets, C. J. P. P., van Ulft, L. H., van de Wal, R. S. W., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 1: Greenland (1958–2016), The Cryosphere, 12, 811–831, https://doi.org/10.5194/tc-12-811-2018, 2018.
Noël, B., Berg, W. J. v. d., Lhermitte, S., and Broeke, M. R. v. d.:
Rapid ablation zone expansion amplifies north Greenland mass loss, Science
Advances, 5, eaaw0123, https://doi.org/10.1126/sciadv.aaw0123, 2019.
Palmer, S., McMillan, M., and Morlighem, M.: Subglacial lake drainage
detected beneath the Greenland ice sheet, Nat. Commun., 6, 8408,
https://doi.org/10.1038/ncomms9408, 2015.
Porter, C., Morin, P., Howat, I., Noh, M.-J., Bates, B., Peterman, K.,
Keesey, S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C.,
Cloutier, M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington,
M., Jr., Williamson, C., Bauer, G., Enos, J., Arnold, G., Kramer, W.,
Becker, P., Doshi, A., D'Souza, C., Cummens, P., Laurier, F., and Bojesen,
M.: ArcticDEM (V1), Harvard Dataverse [data set],
https://doi.org/10.7910/DVN/OHHUKH, 2018.
Schoof, C.: Ice-sheet acceleration driven by melt supply variability,
Nature, 468, 803–806, https://doi.org/10.1038/nature09618, 2010.
Sellevold, R. and Vizcaino, M.: First Application of Artificial Neural
Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt, Geophys.
Res. Lett., 48, e2021GL092449, https://doi.org/10.1029/2021GL092449, 2021.
Siegfried, M. R. and Fricker, H. A.: Thirteen years of subglacial lake
activity in Antarctica from multi-mission satellite altimetry, Ann.
Glaciol., 59, 42–55, https://doi.org/10.1017/aog.2017.36, 2018.
Siegfried, M. R. and Fricker, H. A.: Illuminating Active Subglacial Lake
Processes With ICESat-2 Laser Altimetry, Geophys. Res. Lett., 48,
e2020GL091089, https://doi.org/10.1029/2020GL091089, 2021.
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 [data set], https://doi.org/10.5067/ATLAS/ATL06.005, 2021.
Smith, L. C., Yang, K., Pitcher, L. H., Overstreet, B. T., Chu, V. W.,
Rennermalm, A. K., Ryan, J. C., Cooper, M. G., Gleason, C. J., Tedesco, M.,
Jeyaratnam, J., van As, D., van den Broeke, M. R., van de Berg, W. J., Noel,
B., Langen, P. L., Cullather, R. I., Zhao, B., Willis, M. J., Hubbard, A.,
Box, J. E., Jenner, B. A., and Behar, A. E.: Direct measurements of
meltwater runoff on the Greenland ice sheet surface, Proc. Natl. Acad. Sci.
USA, 114, E10622–E10631, https://doi.org/10.1073/pnas.1707743114, 2017.
Tadono, T., Ishida, H., Oda, F., Naito, S., Minakawa, K., and Iwamoto, H.: Precise Global DEM Generation by ALOS PRISM, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4, 71–76, https://doi.org/10.5194/isprsannals-II-4-71-2014, 2014 (data available at: https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm, last access: 1 October 2021).
Takaku, J., Tadono, T., and Tsutsui, K.: Generation of High Resolution Global DSM from ALOS PRISM, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4, 243–248, https://doi.org/10.5194/isprsarchives-XL-4-243-2014, 2014 (data available at: https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm, last access: 1 October 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. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 183–189, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-183-2020, 2020 (data available at: https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm, last access: 1 October 2021).
USGS: EarthExplorer, https://earthexplorer.usgs.gov/, last access: 1 October 2021.
Willis, M. J., Herried, B. G., Bevis, M. G., and Bell, R. E.: Recharge of a
subglacial lake by surface meltwater in northeast Greenland, Nature, 518,
223–227, https://doi.org/10.1038/nature14116, 2015.
Yang, K., Smith, L. C., Fettweis, X., Gleason, C. J., Lu, Y., and Li, M.:
Surface meltwater runoff on the Greenland ice sheet estimated from remotely
sensed supraglacial lake infilling rate, Remote Sens. Environ., 234, 111459,
https://doi.org/10.1016/j.rse.2019.111459, 2019.
Short summary
Using multi-temporal ArcticDEM and ICESat-2 altimetry data, we document changes in surface elevation of a subglacial lake basin from 2012 to 2021. The long-term measurements show that the subglacial lake was recharged by surface meltwater and that a rapid drainage event in late August 2019 induced an abrupt ice velocity change. Multiple factors regulate the episodic filling and drainage of the lake. Our study also reveals ~ 64 % of the surface meltwater successfully descended to the bed.
Using multi-temporal ArcticDEM and ICESat-2 altimetry data, we document changes in surface...
