Articles | Volume 11, issue 3
https://doi.org/10.5194/tc-11-1403-2017
© 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/tc-11-1403-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
19 Jun 2017
Research article |
| 19 Jun 2017
Determining the terrain characteristics related to the surface expression of subsurface water pressurization in permafrost landscapes using susceptibility modelling
Jean E. Holloway
CORRESPONDING AUTHOR
Department of Geography and Planning, Queen's University, Kingston, ON K7L 3N6, Canada
Ashley C. A. Rudy
Department of Geography and Planning, Queen's University, Kingston, ON K7L 3N6, Canada
Scott F. Lamoureux
Department of Geography and Planning, Queen's University, Kingston, ON K7L 3N6, Canada
Paul M. Treitz
Department of Geography and Planning, Queen's University, Kingston, ON K7L 3N6, Canada
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Cited
17 citations as recorded by crossref.
- Multi‐criteria decision analysis integrated with GIS to determine land suitability for cultivation and best harvest time of vine H. Amin et al. https://doi.org/10.1002/jsfa.12380
- Remote sensing of biogeophysical variables at the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut, Canada P. Treitz et al. https://doi.org/10.1139/as-2023-0043
- 多年冻土过渡带研究进展与展望 D. Luo et al. https://doi.org/10.3799/dqkx.2024.075
- Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India S. Saha et al. https://doi.org/10.1007/s12303-024-0044-y
- Evaluating of Quantitative Geomorphometric Parameters Efficiency in Increasing the Accuracy of Landslide Sensitivity Maps (Case Study: Fereydoun Shahr Basin, Isfahan Province) a. arabameri et al. https://doi.org/10.29252/jwmr.9.18.220
- Multi-scale site evaluation of a relict active layer detachment in a High Arctic landscape M. Paquette et al. https://doi.org/10.1016/j.geomorph.2020.107159
- Determining prone areas to gully erosion and the impact of land use change on it by using multiple-criteria decision-making algorithm in arid and semi-arid regions M. Mokarram & A. Zarei https://doi.org/10.1016/j.geoderma.2021.115379
- Machine learning-based mapping of fog water harvesting potential in Pithoragarh, Uttarakhand: Evaluating climate scenarios and geospatial influences C. Singha et al. https://doi.org/10.1016/j.pce.2025.104138
- Robustness of Optimized Decision Tree-Based Machine Learning Models to Map Gully Erosion Vulnerability H. Eloudi et al. https://doi.org/10.3390/soilsystems7020050
- Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere J. Luo et al. https://doi.org/10.1016/j.accre.2024.03.001
- Investigation of water quality and its spatial distribution in the Kor River basin, Fars province, Iran M. Mokarram et al. https://doi.org/10.1016/j.envres.2021.112294
- Effects of permafrost stability changes on vegetation dynamics in the middle part of the Greater Khingan Mountains J. Sun et al. https://doi.org/10.1088/2515-7620/ada673
- GIS-based Gully Erosion Susceptibility Evaluation Using Frequency Ratio, Cosine Amplitude and Logistic Regression Ensembled with fuzzy logic in Hinglo River Basin, India J. Roy & D. Saha https://doi.org/10.1016/j.rsase.2019.100247
- Machine learning-based thermokarst landslide susceptibility modeling across the permafrost region on the Qinghai-Tibet Plateau G. Yin et al. https://doi.org/10.1007/s10346-021-01669-7
- Retrogressive failure mechanisms of ice-rich permafrost slopes under thermal disturbance on the Qinghai–Tibet plateau T. Wei et al. https://doi.org/10.1016/j.enggeo.2026.108823
- Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm A. Arabameri et al. https://doi.org/10.1002/ldr.3151
- Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India S. Saha et al. https://doi.org/10.3390/s20051313
17 citations as recorded by crossref.
- Multi‐criteria decision analysis integrated with GIS to determine land suitability for cultivation and best harvest time of vine H. Amin et al. https://doi.org/10.1002/jsfa.12380
- Remote sensing of biogeophysical variables at the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut, Canada P. Treitz et al. https://doi.org/10.1139/as-2023-0043
- 多年冻土过渡带研究进展与展望 D. Luo et al. https://doi.org/10.3799/dqkx.2024.075
- Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India S. Saha et al. https://doi.org/10.1007/s12303-024-0044-y
- Evaluating of Quantitative Geomorphometric Parameters Efficiency in Increasing the Accuracy of Landslide Sensitivity Maps (Case Study: Fereydoun Shahr Basin, Isfahan Province) a. arabameri et al. https://doi.org/10.29252/jwmr.9.18.220
- Multi-scale site evaluation of a relict active layer detachment in a High Arctic landscape M. Paquette et al. https://doi.org/10.1016/j.geomorph.2020.107159
- Determining prone areas to gully erosion and the impact of land use change on it by using multiple-criteria decision-making algorithm in arid and semi-arid regions M. Mokarram & A. Zarei https://doi.org/10.1016/j.geoderma.2021.115379
- Machine learning-based mapping of fog water harvesting potential in Pithoragarh, Uttarakhand: Evaluating climate scenarios and geospatial influences C. Singha et al. https://doi.org/10.1016/j.pce.2025.104138
- Robustness of Optimized Decision Tree-Based Machine Learning Models to Map Gully Erosion Vulnerability H. Eloudi et al. https://doi.org/10.3390/soilsystems7020050
- Machine learning-based predictions of current and future susceptibility to retrogressive thaw slumps across the Northern Hemisphere J. Luo et al. https://doi.org/10.1016/j.accre.2024.03.001
- Investigation of water quality and its spatial distribution in the Kor River basin, Fars province, Iran M. Mokarram et al. https://doi.org/10.1016/j.envres.2021.112294
- Effects of permafrost stability changes on vegetation dynamics in the middle part of the Greater Khingan Mountains J. Sun et al. https://doi.org/10.1088/2515-7620/ada673
- GIS-based Gully Erosion Susceptibility Evaluation Using Frequency Ratio, Cosine Amplitude and Logistic Regression Ensembled with fuzzy logic in Hinglo River Basin, India J. Roy & D. Saha https://doi.org/10.1016/j.rsase.2019.100247
- Machine learning-based thermokarst landslide susceptibility modeling across the permafrost region on the Qinghai-Tibet Plateau G. Yin et al. https://doi.org/10.1007/s10346-021-01669-7
- Retrogressive failure mechanisms of ice-rich permafrost slopes under thermal disturbance on the Qinghai–Tibet plateau T. Wei et al. https://doi.org/10.1016/j.enggeo.2026.108823
- Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm A. Arabameri et al. https://doi.org/10.1002/ldr.3151
- Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India S. Saha et al. https://doi.org/10.3390/s20051313
Saved (final revised paper)
Latest update: 28 May 2026
Short summary
Below ground pressurization occurs when there is more moisture in the soil pores than normal, and it can potentially result in landscape degradation. We mapped features that are caused by this overpressurization and generated susceptibility maps to find other areas on the landscape that could be susceptible in the future. The susceptibility maps identified areas that may be sensitive to pressurization and help improve our understanding of potentially hazardous permafrost degradation.
Below ground pressurization occurs when there is more moisture in the soil pores than normal,...
