Articles | Volume 24, issue 11
https://doi.org/10.5194/nhess-24-3815-2024
© Author(s) 2024. 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-24-3815-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Nov 2024
Research article |
| 08 Nov 2024
| 08 Nov 2024
Size scaling of large landslides from incomplete inventories
Oliver Korup
CORRESPONDING AUTHOR
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Institute of Geosciences, University of Potsdam, Potsdam, Germany
Lisa V. Luna
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Potsdam Institute for Climate Impact Research PIK, Potsdam, Germany
Joaquin V. Ferrer
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Potsdam Institute for Climate Impact Research PIK, Potsdam, Germany
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Short summary
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Melanie Fischer, Oliver Korup, Georg Veh, and Ariane Walz
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Short summary
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Guilherme S. Mohor, Annegret H. Thieken, and Oliver Korup
Nat. Hazards Earth Syst. Sci., 21, 1599–1614, https://doi.org/10.5194/nhess-21-1599-2021, https://doi.org/10.5194/nhess-21-1599-2021, 2021
Short summary
Short summary
We explored differences in the damaging process across different flood types, regions within Germany, and six flood events through a numerical model in which the groups can learn from each other. Differences were found mostly across flood types, indicating the importance of identifying them, but there is great overlap across regions and flood events, indicating either that socioeconomic or temporal information was not well represented or that they are in fact less different within our cases.
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Short summary
Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Catalogues of mapped landslides are useful for learning and forecasting how frequently they...
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