Articles | Volume 9, issue 1
https://doi.org/10.5194/essd-9-267-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/essd-9-267-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Brief communication
| 
05 May 2017
Brief communication |
| 05 May 2017
A high-resolution synthetic bed elevation grid of the Antarctic continent
Felicity S. Graham
CORRESPONDING AUTHOR
Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 129, Hobart, Tasmania 7001, Australia
Jason L. Roberts
Australian Antarctic Division, Kingston, Tasmania, Australia
Antarctic Climate & Ecosystems Cooperative Research Centre, Private Bag 80, Hobart, Tasmania 7001, Australia
Ben K. Galton-Fenzi
Australian Antarctic Division, Kingston, Tasmania, Australia
Antarctic Climate & Ecosystems Cooperative Research Centre, Private Bag 80, Hobart, Tasmania 7001, Australia
Duncan Young
Institute for Geophysics, University of Texas at Austin, Austin, Texas 78758, USA
Donald Blankenship
Institute for Geophysics, University of Texas at Austin, Austin, Texas 78758, USA
Martin J. Siegert
Grantham Institute and Department of Earth Sciences and Engineering, Imperial College London, London SW7 2AZ, UK
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Cited
16 citations as recorded by crossref.
- A Multivariate Approach for Mapping Lithospheric Domain Boundaries in East Antarctica T. Stål et al. https://doi.org/10.1029/2019GL083453
- A Method for Automatic Three-Dimensional Reconstruction of Ice Sheets by Using Radar Sounder and Altimeter Data A. Ilisei & L. Bruzzone https://doi.org/10.1109/JSTARS.2017.2787199
- A new bed elevation model for the Weddell Sea sector of the West Antarctic Ice Sheet H. Jeofry et al. https://doi.org/10.5194/essd-10-711-2018
- DeepBedMap: a deep neural network for resolving the bed topography of Antarctica W. Leong & H. Horgan https://doi.org/10.5194/tc-14-3687-2020
- Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning X. Tang et al. https://doi.org/10.3390/rs14184507
- Stochastic modeling of subglacial topography exposes uncertainty in water routing at Jakobshavn Glacier E. MacKie et al. https://doi.org/10.1017/jog.2020.84
- Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage Near Dome Fuji, East Antarctica C. Shackleton et al. https://doi.org/10.1029/2023JF007269
- Incorporating biotic interactions to better model current and future vegetation of the maritime Antarctic B. Rocha et al. https://doi.org/10.1016/j.cub.2024.09.011
- Dating Antarctic ice sheet collapse: Proposing a molecular genetic approach J. Strugnell et al. https://doi.org/10.1016/j.quascirev.2017.11.014
- Antarctic Geothermal Heat Flow Model: Aq1 T. Stål et al. https://doi.org/10.1029/2020GC009428
- Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1) Z. Yin et al. https://doi.org/10.5194/gmd-15-1477-2022
- Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data Y. Cai et al. https://doi.org/10.3390/rs15051359
- The Antarctic Crust and Upper Mantle: A Flexible 3D Model and Software Framework for Interdisciplinary Research T. Stål et al. https://doi.org/10.3389/feart.2020.577502
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al. https://doi.org/10.1016/j.cageo.2025.105857
- Emerging evidence of abrupt changes in the Antarctic environment N. Abram et al. https://doi.org/10.1038/s41586-025-09349-5
- Assimilation of surface observations in a transient marine ice sheet model using an ensemble Kalman filter F. Gillet-Chaulet https://doi.org/10.5194/tc-14-811-2020
16 citations as recorded by crossref.
- A Multivariate Approach for Mapping Lithospheric Domain Boundaries in East Antarctica T. Stål et al. https://doi.org/10.1029/2019GL083453
- A Method for Automatic Three-Dimensional Reconstruction of Ice Sheets by Using Radar Sounder and Altimeter Data A. Ilisei & L. Bruzzone https://doi.org/10.1109/JSTARS.2017.2787199
- A new bed elevation model for the Weddell Sea sector of the West Antarctic Ice Sheet H. Jeofry et al. https://doi.org/10.5194/essd-10-711-2018
- DeepBedMap: a deep neural network for resolving the bed topography of Antarctica W. Leong & H. Horgan https://doi.org/10.5194/tc-14-3687-2020
- Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning X. Tang et al. https://doi.org/10.3390/rs14184507
- Stochastic modeling of subglacial topography exposes uncertainty in water routing at Jakobshavn Glacier E. MacKie et al. https://doi.org/10.1017/jog.2020.84
- Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage Near Dome Fuji, East Antarctica C. Shackleton et al. https://doi.org/10.1029/2023JF007269
- Incorporating biotic interactions to better model current and future vegetation of the maritime Antarctic B. Rocha et al. https://doi.org/10.1016/j.cub.2024.09.011
- Dating Antarctic ice sheet collapse: Proposing a molecular genetic approach J. Strugnell et al. https://doi.org/10.1016/j.quascirev.2017.11.014
- Antarctic Geothermal Heat Flow Model: Aq1 T. Stål et al. https://doi.org/10.1029/2020GC009428
- Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1) Z. Yin et al. https://doi.org/10.5194/gmd-15-1477-2022
- Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data Y. Cai et al. https://doi.org/10.3390/rs15051359
- The Antarctic Crust and Upper Mantle: A Flexible 3D Model and Software Framework for Interdisciplinary Research T. Stål et al. https://doi.org/10.3389/feart.2020.577502
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al. https://doi.org/10.1016/j.cageo.2025.105857
- Emerging evidence of abrupt changes in the Antarctic environment N. Abram et al. https://doi.org/10.1038/s41586-025-09349-5
- Assimilation of surface observations in a transient marine ice sheet model using an ensemble Kalman filter F. Gillet-Chaulet https://doi.org/10.5194/tc-14-811-2020
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
Antarctic bed topography datasets are interpolated onto low-resolution grids because our observed topography data are sparsely sampled. This has implications for ice-sheet model simulations, especially in regions prone to instability, such as grounding lines, where detailed knowledge of the topography is required. Here, we constructed a high-resolution synthetic bed elevation dataset using observed covariance properties to assess the dependence of simulated ice-sheet dynamics on grid resolution.
Antarctic bed topography datasets are interpolated onto low-resolution grids because our...
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