Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1425-2025
https://doi.org/10.5194/nhess-25-1425-2025
Brief communication
 | 
14 Apr 2025
Brief communication |  | 14 Apr 2025

Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping

Matthias Schlögl, Anita Graser, Raphael Spiekermann, Jasmin Lampert, and Stefan Steger

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Cited articles

Achu, A., Aju, C., Di Napoli, M., Prakash, P., Gopinath, G., Shaji, E., and Chandra, V.: Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis, Geosci. Front., 14, 101657, https://doi.org/10.1016/j.gsf.2023.101657, 2023. a
Brenning, A., Schwinn, M., Ruiz-Páez, A. P., and Muenchow, J.: Brenning, A., Schwinn, M., Ruiz-Páez, A. P., and Muenchow, J.: Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province, Nat. Hazards Earth Syst. Sci., 15, 45–57, https://doi.org/10.5194/nhess-15-45-2015, 2015. a
Chung, C.-J. F. and Fabbri, A. G.: Validation of Spatial Prediction Models for Landslide Hazard Mapping, Nat. Hazards, 30, 451–472, https://doi.org/10.1023/b:nhaz.0000007172.62651.2b, 2003. a
Conoscenti, C., Rotigliano, E., Cama, M., Caraballo-Arias, N. A., Lombardo, L., and Agnesi, V.: Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy, Geomorphology, 261, 222–235, https://doi.org/10.1016/j.geomorph.2016.03.006, 2016. a
Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D., and Chacón, J.: Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D., and Chacón, J.: Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain), Nat. Hazards Earth Syst. Sci., 12, 327–340, https://doi.org/10.5194/nhess-12-327-2012, 2012. a
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Short summary
Communicating uncertainties is a crucial yet challenging aspect of spatial modelling – especially in applied research, where results inform decisions. In disaster risk reduction, susceptibility maps for natural hazards guide planning and risk assessment, yet their uncertainties are often overlooked. We present a new type of landslide susceptibility map that visualizes both susceptibility and associated uncertainty alongside guidelines for creating such maps using free and open-source software.
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