Preprints
https://doi.org/10.5194/nhess-2022-154
https://doi.org/10.5194/nhess-2022-154
 
03 Jun 2022
03 Jun 2022
Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria)

Raphael Knevels1, Helene Petschko1, Herwig Proske2, Philip Leopold3, Aditya N. Mishra4, Douglas Maraun4, and Alexander Brenning1 Raphael Knevels et al.
  • 1Department of Geography, Friedrich Schiller University Jena, Jena, 07743, Germany
  • 2Remote Sensing and Geoinformation Department, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, 8010, Austria
  • 3Centre for Low-Emission Transport, AIT Austrian Institute of Technology GmbH, Vienna, 1210, Austria
  • 4Wegener Centre for Climate and Global Change, Regional Climate Research Group, Karl-Franzens-University Graz, Graz, 8010, Austria

Abstract. The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for pre-industrial climate, while for future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated by much drier soils (-45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties of the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 K and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties of landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.

Raphael Knevels et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Reviewer Comment on nhess-2022-154', Anonymous Referee #1, 30 Jun 2022
  • RC2: 'Comment on nhess-2022-154', Anonymous Referee #2, 23 Jul 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Reviewer Comment on nhess-2022-154', Anonymous Referee #1, 30 Jun 2022
  • RC2: 'Comment on nhess-2022-154', Anonymous Referee #2, 23 Jul 2022

Raphael Knevels et al.

Data sets

Event-based landslide susceptibility models (Styrian Basin, Austria) Knevels, Raphael; Petschko, Helene; Proske, Herwig; Leopold, Philip; Maraun, Douglas; Brenning, Alexander https://doi.org/10.5281/zenodo.6365228

Raphael Knevels et al.

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Short summary
In the summer 2009 & 2014, rainfall events occurred in the Styrian basin (Austria), triggering thousands of landslides. Landslide storylines help to show potential future changes under changing environmental conditions. The often neglected uncertainty quantification was the aim of this study. We found uncertainty arising from the landslide model to be of the same order as climate scenario uncertainty. Understanding the dimensions of uncertainty is crucial for allowing informed decision making.
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