Felsberg, AnneHeyvaert, ZdenkoPoesen, JeanStanley, ThomasDe Lannoy, Gabriëlle J. M.2023-06-082023-06-082023-12-14Felsberg, Anne, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy. “Probabilistic Hydrological Estimation of LandSlides (PHELS): Global Ensemble Landslide Hazard Modelling.” Natural Hazards and Earth System Sciences 23, no. 12 (December 14, 2023): 3805–21. https://doi.org/10.5194/nhess-23-3805-2023.https://doi.org/10.5194/nhess-23-3805-2023http://hdl.handle.net/11603/28130In this study we present a model for the global Probabilistic Hydrological Estimation of LandSlides (PHELS). PHELS estimates the daily hazard of hydrologically triggered landslides at a coarse spatial resolution of 36 km, by combining landslide susceptibility (LSS) and (percentiles of) hydrological variable(s). The latter include daily rainfall, a 7 d antecedent rainfall index (ARI7) or root-zone soil moisture content (rzmc) as hydrological predictor variables, or the combination of rainfall and rzmc. The hazard estimates with any of these predictor variables have areas under the receiver operating characteristic curve (AUC) above 0.68. The best performance was found with combined rainfall and rzmc predictors (AUC = 0.79), which resulted in the lowest number of missed alarms (especially during spring) and false alarms. Furthermore, PHELS provides hazard uncertainty estimates by generating ensemble simulations based on repeated sampling of LSS and the hydrological predictor variables. The estimated hazard uncertainty follows the behaviour of the input variable uncertainties, is about 13.6 % of the estimated hazard value on average across the globe and in time and is smallest for very low and very high hazard values.17 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/Text