Articles | Volume 26, issue 10
https://doi.org/10.5194/nhess-26-4675-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-4675-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Oct 2026
Research article |
| 01 Oct 2026
| 01 Oct 2026
Spatial machine learning modelling reveals that soil indicators and tree type best explain shallow landslide release
Denise Christina Rüther
CORRESPONDING AUTHOR
Department of Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Kristine Flacké Haualand
Department of Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Iris Louisa Johanna Peeters
Department of Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Department of Technology and Safety, The Arctic University of Norway, Tromsø, Norway
Mark Andrew Kusk Gillespie
Department of Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, Norway
Department of Ecoscience, Aarhus University, Aarhus, Denmark
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Melting glaciers worldwide cause changes in land surface type and elevation that may impact regional climate. In a weather and climate model, we find that these changes result in warming and less precipitation, particularly less snow, over Jostedalsbreen ice cap in western Norway. Most of these impacts are related to thinning of the ice cap and the associated lowering of the surface and reduction in orographic lifting of moist air masses. The findings suggest accelerated melting of the ice cap.
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Short summary
We use several machine learning models to explore which factors best explain landslide release during an extreme rainfall event in eastern Norway. As landslides often occur in clusters, methods must be chosen carefully to account for any spatial effects. When considering this, we find that south-facing slopes, thicker soils and more water made landslides most likely. On forested slopes, landslides are most likely in deciduous rather than spruce or pine stands.
We use several machine learning models to explore which factors best explain landslide release...
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