Preprints
https://doi.org/10.5194/egusphere-2026-4604
https://doi.org/10.5194/egusphere-2026-4604
19 Aug 2026
 | 19 Aug 2026
Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

National-scale debris-flow hazard indication modelling for Swiss railway infrastructure

Feiko Bernard van Zadelhoff, Yves Bühler, and Michael Bründl

Abstract. Debris flows are among the most destructive alpine hazards, threatening settlements and critical infrastructure with average annual damages in Switzerland of CHF 321 million. Hazard indication modelling of debris flows, among other hazards, offers the possibility to identify hazard hot spots at a regional and national scale. Additionally it lays the foundation for future scenario modelling. We present the Large-Scale Hazard Indication Modelling (LSHIM) framework for debris flows as commissioned by the Swiss federal railway (SBB) with guiding value return periods of 30, 100 and 300 years. For disposition, we combine high-resolution digital terrain models, climatic and geologic layers, and statistical modelling with a Random Forest approach. Disposition is cross-validated against the Swiss event database (StorMe) with an average accuracy of 81.4 % and application results in 16,756 possible release areas. Dynamic runout is calculated with a tailored version of RAMMS::Debrisflow, where we define hydrograph volume from MeteoSchweiz-modelled extreme precipitation. Landscape erosivity is assessed by geology and geomorphology. Results indicate 74, 99 and 110 km of railway affected by debris flow hazard for a 30, 100 and 300 year return period, respectively. The highest intensity class shows the highest relative increase under greater return periods. This approach enables a large scale hazard indication assessment based on high-quality input data. The results are a valuable base for further planning and investigations that incorporate climate change. Limitations in the methodology lie in the static definition of release area and deterministic parameterization.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Feiko Bernard van Zadelhoff, Yves Bühler, and Michael Bründl

Status: open (until 03 Oct 2026)

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Feiko Bernard van Zadelhoff, Yves Bühler, and Michael Bründl
Feiko Bernard van Zadelhoff, Yves Bühler, and Michael Bründl

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Short summary
Debris flows are a major hazard in mountainous areas, causing millions of francs in damage in Switzerland every year. Therefore, the Swiss national railway company aims to identify dangerous areas along their infrastructure. We perform this analysis by identifying critical areas using machine learning and detailed simulation modelling of debris flow events caused by extreme rainfall. The results are a valuable baseline to plan mitigation measures and be better prepared for the future.
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