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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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (28 May 2018) by Samuele Segoni
AR by Ekrem Canli on behalf of the Authors (04 Jul 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (09 Jul 2018) by Samuele Segoni
RR by Anonymous Referee #3 (14 Jul 2018)
RR by Anonymous Referee #1 (17 Jul 2018)
ED: Publish subject to minor revisions (review by editor) (22 Jul 2018) by Samuele Segoni
AR by Ekrem Canli on behalf of the Authors (26 Jul 2018)  Author's response    Manuscript
ED: Publish as is (27 Jul 2018) by Samuele Segoni
AR by Ekrem Canli on behalf of the Authors (27 Jul 2018)  Author's response    Manuscript
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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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