Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-763-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-11-763-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
06 Oct 2025
Original research article |
| 06 Oct 2025
| 06 Oct 2025
Terrain is a stronger predictor of peat depth than airborne radiometrics in Norwegian landscapes
Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Naomi Gatis
Department of Geography, University of Exeter, Exeter, United Kingdom
Mette Kusk Gillespie
Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
VIA University College, Nørre Nissum, Denmark
Karl-Kristian Muggerud
Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Sigurd Daniel Nerhus
Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Knut Rydgren
Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Mikko Sparf
Department of Civil Engineering and Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
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Henning Åkesson, Kamilla Hauknes Sjursen, Thomas Vikhamar Schuler, Thorben Dunse, Liss Marie Andreassen, Mette Kusk Gillespie, Benjamin Aubrey Robson, Thomas Schellenberger, and Jacob Clement Yde
EGUsphere, https://doi.org/10.5194/egusphere-2025-467, https://doi.org/10.5194/egusphere-2025-467, 2025
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We model the historical and future evolution of the Jostedalsbreen ice cap in Norway, projecting substantial and largely irreversible mass loss for the 21st century, and that the ice cap will split into three parts. Further mass loss is in the pipeline, with a disappearance during the 22nd century under high emissions. Our study demonstrates an approach to model complex ice masses, highlights uncertainties due to precipitation, and calls for further research on long-term future glacier change.
Mette K. Gillespie, Liss M. Andreassen, Matthias Huss, Simon de Villiers, Kamilla H. Sjursen, Jostein Aasen, Jostein Bakke, Jan M. Cederstrøm, Hallgeir Elvehøy, Bjarne Kjøllmoen, Even Loe, Marte Meland, Kjetil Melvold, Sigurd D. Nerhus, Torgeir O. Røthe, Eivind W. N. Støren, Kåre Øst, and Jacob C. Yde
Earth Syst. Sci. Data, 16, 5799–5825, https://doi.org/10.5194/essd-16-5799-2024, https://doi.org/10.5194/essd-16-5799-2024, 2024
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We present an extensive ice thickness dataset from Jostedalsbreen ice cap that will serve as a baseline for future studies of regional climate-induced change. Results show that Jostedalsbreen currently (~2020) has a maximum ice thickness of ~630 m, a mean ice thickness of 154 ± 22 m and an ice volume of 70.6 ±10.2 km3. Ice of less than 50 m thickness covers two narrow regions of Jostedalsbreen, and the ice cap is likely to separate into three parts in a warming climate.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
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This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
Trevor R. Hillebrand, John O. Stone, Michelle Koutnik, Courtney King, Howard Conway, Brenda Hall, Keir Nichols, Brent Goehring, and Mette K. Gillespie
The Cryosphere, 15, 3329–3354, https://doi.org/10.5194/tc-15-3329-2021, https://doi.org/10.5194/tc-15-3329-2021, 2021
Short summary
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We present chronologies from Darwin and Hatherton glaciers to better constrain ice sheet retreat during the last deglaciation in the Ross Sector of Antarctica. We use a glacier flowband model and an ensemble of 3D ice sheet model simulations to show that (i) the whole glacier system likely thinned steadily from about 9–3 ka, and (ii) the grounding line likely reached the Darwin–Hatherton Glacier System at about 3 ka, which is ≥3.8 kyr later than was suggested by previous reconstructions.
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Short summary
Peat depth is crucial to peatland management but often unknown. We used machine learning to map peat depth in two Norwegian landscapes, based on terrain and remotely sensed radiation. We found that terrain, especially elevation and valley bottom flatness, predicted peat depth better than radiation. Our approach improved existing maps but struggled to identify very deep peat, demonstrating that it can support regional planning but not replace field measurements for local carbon stock assessments.
Peat depth is crucial to peatland management but often unknown. We used machine learning to map...
