Preprints
https://doi.org/10.5194/nhess-2021-360
https://doi.org/10.5194/nhess-2021-360
 
06 Dec 2021
06 Dec 2021
Status: a revised version of this preprint is currently under review for the journal NHESS.

Estimating global landslide susceptibility and its uncertainty through ensemble modelling

Anne Felsberg1, Jean Poesen1,2, Michel Bechtold1, Matthias Vanmaercke1, and Gabriëlle J. M. De Lannoy1 Anne Felsberg et al.
  • 1KU Leuven, Department of Earth and Environmental Sciences, Belgium
  • 2Maria-Curie Sklodowska University, Faculty of Earth Sciences and Spatial Management, Lublin, Poland

Abstract. This study assesses global landslide susceptibility (LSS) at the coarse 36-km spatial resolution of global satellite soil moisture observations, to prepare for a subsequent combination of a global LSS map with dynamic soil moisture estimates for landslide modelling. Global LSS estimation intrinsically contains uncertainty, arising from errors in the underlying data, the spatial mismatch between landslide events and predictor information, and large-scale model generalizations. For a reliable uncertainty assessment, this study combines methods from the landslide community with common practices in meteorological modelling to create an ensemble of global LSS maps. The predictive LSS models are obtained from a mixed effects logistic regression, associating hydrologically triggered landslide data from the Global Landslide Catalog (GLC) with predictor variables from the Catchment land surface modeling system (incl. input parameters of soil (hydrological) properties and resulting climatological statistics of water budget estimates), geomorphological and lithological data. Road network density is introduced as a random effect to mitigate potential landslide inventory bias. We use a blocked random cross validation to assess the model uncertainty that propagates into the LSS maps. To account for other uncertainty sources, such as input uncertainty, we also perturb the predictor variables and obtain an ensemble of LSS maps. The perturbations are optimized so that the total predicted uncertainty fits the observed discrepancy between the ensemble average LSS and the landslide presence or absence from the GLC. We find that the most reliable total uncertainty estimates are obtained through the inclusion of a topography-dependent perturbation between 15 % and 20 % to the predictor variables. The areas with the largest LSS uncertainty coincide with moderate ensemble average LSS. The spatial patterns of the average LSS agree well with previous global studies and yield areas under the Receiver Operation Characteristic between 0.63 and 0.9 for independent regional to continental landslide inventories.

Anne Felsberg et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-360', Anonymous Referee #1, 18 Jan 2022
    • AC1: 'Reply on RC1', Anne Felsberg, 24 Feb 2022
  • RC2: 'Comment on nhess-2021-360', Anonymous Referee #2, 21 Jan 2022
    • AC2: 'Reply on RC2', Anne Felsberg, 24 Feb 2022

Anne Felsberg et al.

Anne Felsberg et al.

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Short summary
In this study we assessed global landslide susceptibility at the coarse 36-km spatial resolution of global satellite soil moisture observations, to prepare for a subsequent combination of the two. Specifically, we focus therefore on the susceptibility of hydrologically triggered landslides. We introduce ensemble techniques, common in – for example – meteorology but not yet in the landslide community, to retrieve reliable estimates of the total prediction uncertainty.
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