Estimating global landslide susceptibility and  its uncertainty through ensemble modeling

Felsberg, Anne; Poesen, Jean; Bechtold, Michel; Vanmaercke, Matthias; De Lannoy, Gabriëlle J. M.

doi:https://doi.org/10.5194/nhess-22-3063-2022

Articles | Volume 22, issue 9

https://doi.org/10.5194/nhess-22-3063-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-22-3063-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 9

Research article

|

19 Sep 2022

Research article |

| 19 Sep 2022

Estimating global landslide susceptibility and its uncertainty through ensemble modeling

Anne Felsberg, Jean Poesen, Michel Bechtold, Matthias Vanmaercke, and Gabriëlle J. M. De Lannoy

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Final revised paper (published on 19 Sep 2022)
Preprint (discussion started on 06 Dec 2021)

Interactive discussion

Status: closed

RC1:
'Comment on nhess-2021-360', Anonymous Referee #1, 18 Jan 2022

The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2021-360/nhess-2021-360-RC1-supplement.pdf

Citation: https://doi.org/10.5194/nhess-2021-360-RC1
- AC1: 'Reply on RC1', Anne Felsberg, 24 Feb 2022
  
  We thank the reviewer for the feedback on the methods. We improved the readability of the paper for people in various research fields, because we hope to bridge multiple disciplines in this paper and appreciate feedback from outside the landslide community.
  
  Please find enclosed the pdf document with the complete and detailed responses.
  
  Citation: https://doi.org/10.5194/nhess-2021-360-AC1
RC2:
'Comment on nhess-2021-360', Anonymous Referee #2, 21 Jan 2022

I have revised the manuscript "Estimating global landslide susceptibility and its uncertainty through ensemble modelling” by Anne Felsberg and co-authors. The procedure is very interesting and it would be interesting to apply it to a smaller area where you can control better the training and the validation landslide and thematic data. The manuscript is well written and well organized but there are few major thinks nor really convincing.

Citation: https://doi.org/10.5194/nhess-2021-360-RC2
- AC2: 'Reply on RC2', Anne Felsberg, 24 Feb 2022
  
  We thank the reviewer for the detailed feedback.
  
  We improved the rationale for large-scale landslide susceptibility estimates and spatial representativeness error, and addressed all comments in detail.
  
  Please find enclosed the pdf document with the complete and detailed responses.
  
  Citation: https://doi.org/10.5194/nhess-2021-360-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (27 Feb 2022) by Paolo Tarolli

AR by Anne Felsberg on behalf of the Authors (21 Mar 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (02 Apr 2022) by Paolo Tarolli

RR by Anonymous Referee #1 (01 May 2022)

RR by Anonymous Referee #2 (11 May 2022)

ED: Publish subject to minor revisions (review by editor) (03 Jun 2022) by Paolo Tarolli

AR by Anne Felsberg on behalf of the Authors (13 Jun 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (18 Jul 2022) by Paolo Tarolli

AR by Anne Felsberg on behalf of the Authors (24 Jul 2022)

Post-review adjustments

AA: Author's adjustment | EA: Editor approval

AA by Anne Felsberg on behalf of the Authors (22 Aug 2022) Author's adjustment Manuscript

EA: Adjustments approved (07 Sep 2022) by Paolo Tarolli

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.