Articles | Volume 18, issue 8
https://doi.org/10.5194/nhess-18-2183-2018
https://doi.org/10.5194/nhess-18-2183-2018
Research article
 | 
16 Aug 2018
Research article |  | 16 Aug 2018

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Ekrem Canli, Martin Mergili, Benni Thiebes, and Thomas Glade

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Cited articles

Alfieri, L., Salamon, P., Pappenberger, F., Wetterhall, F., and Thielen, J.: Operational early warning systems for water-related hazards in Europe, Environ. Sci. Pol., 21, 35–49, https://doi.org/10.1016/j.envsci.2012.01.008, 2012a. 
Alfieri, L., Thielen, J., and Pappenberger, F.: Ensemble hydro-meteorological simulation for flash flood early detection in southern Switzerland, J. Hydrol., 424–425, https://doi.org/10.1016/j.jhydrol.2011.12.038, 2012b. 
Althuwaynee, O. F., Pradhan, B., Park, H.-J., and Lee, J. H.: A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping, Catena, 114, 21–36, https://doi.org/10.1016/j.catena.2013.10.011, 2014a. 
Althuwaynee, O. F., Pradhan, B., Park, H.-J., and Lee, J. H.: A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping, Landslides, 11, 1063–1078, https://doi.org/10.1007/s10346-014-0466-0, 2014b. 
Alvioli, M. and Baum, R. L.: Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface, Environ. Modell. Softw., 81, 122–135, https://doi.org/10.1016/j.envsoft.2016.04.002, 2016. 
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Short summary
Regional-scale landslide forecasting traditionally strongly relies on empirical approaches and landslide-triggering rainfall thresholds. Today, probabilistic methods utilizing ensemble predictions are frequently used for flood forecasting. In our study, we specify how such an approach could also be applied for landslide forecasts and for operational landslide forecasting and early warning systems. To this end, we implemented a physically based landslide model in a probabilistic framework.
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