Articles | Volume 22, issue 8
https://doi.org/10.5194/nhess-22-2611-2022
https://doi.org/10.5194/nhess-22-2611-2022
Research article
 | 
15 Aug 2022
Research article |  | 15 Aug 2022

Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations

Feiko Bernard van Zadelhoff, Adel Albaba, Denis Cohen, Chris Phillips, Bettina Schaefli, Luuk Dorren, and Massimiliano Schwarz

Related authors

Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart N. Lane, and Francesco Comiti
Hydrol. Earth Syst. Sci., 29, 1725–1748, https://doi.org/10.5194/hess-29-1725-2025,https://doi.org/10.5194/hess-29-1725-2025, 2025
Short summary
Implementing the Equations of Motion in the Energy Line Principle to Simulate the Runout Zones of Gravitational Natural Hazards
Elisa Marras, Dominik May, Luuk Dorren, and Filippo Giadrossich
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-226,https://doi.org/10.5194/nhess-2024-226, 2025
Preprint under review for NHESS
Short summary
Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, and Joshua R. Larsen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1214,https://doi.org/10.5194/egusphere-2025-1214, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Predicting the thickness of shallow landslides in Switzerland using machine learning
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025,https://doi.org/10.5194/nhess-25-467-2025, 2025
Short summary
Separating snow and ice melt using water stable isotopes and glacio-hydrological modelling: towards improving the application of isotope analyses in highly glacierized catchments
Tom Müller, Mauro Fischer, Stuart N. Lane, and Bettina Schaefli
The Cryosphere, 19, 423–458, https://doi.org/10.5194/tc-19-423-2025,https://doi.org/10.5194/tc-19-423-2025, 2025
Short summary

Related subject area

Landslides and Debris Flows Hazards
Characterizing the scale of regional landslide triggering from storm hydrometeorology
Jonathan Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
Nat. Hazards Earth Syst. Sci., 25, 1037–1056, https://doi.org/10.5194/nhess-25-1037-2025,https://doi.org/10.5194/nhess-25-1037-2025, 2025
Short summary
A participatory approach to determine the use of road cut slope design guidelines in Nepal to lessen landslides
Ellen B. Robson, Bhim Kumar Dahal, and David G. Toll
Nat. Hazards Earth Syst. Sci., 25, 949–973, https://doi.org/10.5194/nhess-25-949-2025,https://doi.org/10.5194/nhess-25-949-2025, 2025
Short summary
An integrated method for assessing vulnerability of buildings caused by debris flows in mountainous areas
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci., 25, 709–726, https://doi.org/10.5194/nhess-25-709-2025,https://doi.org/10.5194/nhess-25-709-2025, 2025
Short summary
Identifying unrecognised risks to life from debris flows
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
Nat. Hazards Earth Syst. Sci., 25, 647–656, https://doi.org/10.5194/nhess-25-647-2025,https://doi.org/10.5194/nhess-25-647-2025, 2025
Short summary
Predicting the thickness of shallow landslides in Switzerland using machine learning
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025,https://doi.org/10.5194/nhess-25-467-2025, 2025
Short summary

Cited articles

Amishev, D., Basher, L., Phillips, C. J., Hill, S., Marden, M., Bloomberg, M., and Moore, J. R.: New forest management approaches to steep hills, Ministry for Primary Industries, ISBN 9780478437867, 2014. a
Askarinejad, A., Casini, F., Bischof, P., Beck, A., and Springman, S. M.: Rainfall induced instabilities: a field experiment on a silty sand slope in northern Switzerland, rivista italiana di geotecnica, 12, 50–71, http://www.associazionegeotecnica.it/rig/archivio (last access: 20 April 2021), 2012. a, b
Askarinejad, A., Akca, D., and Springman, S. M.: Precursors of instability in a natural slope due to rainfall: a full-scale experiment, Landslides, 15, 1745–1759, https://doi.org/10.1007/s10346-018-0994-0, 2018. a
Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747–2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016. a, b
Baeza, C. and Corominas, J.: Assessment of shallow landslide susceptibility by means of multivariate statistical techniques, Earth Surf. Processes, 26, 1251–1263, https://doi.org/10.1002/esp.263, 2001. a
Download
Short summary
Shallow landslides pose a risk to people, property and infrastructure. Assessment of this hazard and the impact of protective measures can reduce losses. We developed a model (SlideforMAP) that can assess the shallow-landslide risk on a regional scale for specific rainfall events. Trees are an effective and cheap protective measure on a regional scale. Our model can assess their hazard reduction down to the individual tree level.
Share
Altmetrics
Final-revised paper
Preprint