Articles | Volume 23, issue 12
https://doi.org/10.5194/nhess-23-3805-2023
https://doi.org/10.5194/nhess-23-3805-2023
Research article
 | 
14 Dec 2023
Research article |  | 14 Dec 2023

Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling

Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy

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Cited articles

Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the Timing and Location of Shallow Rainfall-Induced Landslides Using a Model for Transient, Unsaturated Infiltration, J. Geophys. Res.-Earth, 115, F03013, https://doi.org/10.1029/2009JF001321, 2010. a
Bordoni, M., Vivaldi, V., Lucchelli, L., Ciabatta, L., Brocca, L., Galve, J. P., and Meisina, C.: Development of a Data-Driven Model for Spatial and Temporal Shallow Landslide Probability of Occurrence at Catchment Scale, Landslides, 18, 1209–1229, https://doi.org/10.1007/s10346-020-01592-3, 2020. a, b, c, d, e
Brocca, L., Ciabatta, L., Moramarco, T., Ponziani, F., Berni, N., and Wagner, W.: Chapter 12 – Use of Satellite Soil Moisture Products for the Operational Mitigation of Landslides Risk in Central Italy, in: Satellite Soil Moisture Retrieval, edited by: Srivastava, P. K., Petropoulos, G. P., and Kerr, Y. H., Elsevier, 231–247, https://doi.org/10.1016/B978-0-12-803388-3.00012-7, 2016. a, b, c
Broeckx, J., Vanmaercke, M., Duchateau, R., and Poesen, J.: A Data-Based Landslide Susceptibility Map of Africa, Earth-Sci. Rev., 185, 102–121, https://doi.org/10.1016/j.earscirev.2018.05.002, 2018. a
Caine, N.: The Rainfall Intensity: Duration Control of Shallow Landslides and Debris Flows, Geogr. Ann. A, 62, 23–27, https://doi.org/10.2307/520449, 1980. a
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The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
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