Preprints
https://doi.org/10.5194/nhess-2021-135
https://doi.org/10.5194/nhess-2021-135

  06 May 2021

06 May 2021

Review status: this preprint is currently under review for the journal NHESS.

Limitations of rainfall thresholds for debris-flow prediction in an Alpine catchment

Jacob Hirschberg1,2, Alexandre Badoux1, Brian W. McArdell1, Elena Leonarduzzi2,1, and Peter Molnar2 Jacob Hirschberg et al.
  • 1Mountain Hydrology and Mass Movements, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
  • 2Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland

Abstract. The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity-duration (ID) thresholds. Unfortunately, no standardized procedures exist for the determination of such ID thresholds, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record length. Furthermore, we compared two methods for rainfall definition which consider both triggering and non-triggering events, based on linear regression and/or True Skill Statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered here for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set was used instead of a local one, with important implications for ID-threshold determination. Furthermore, we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the 30-min maximum accumulated rainfall was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

Jacob Hirschberg 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-2021-135', Ben Mirus, 11 Jun 2021
  • RC2: 'Comment on nhess-2021-135', Clàudia Abancó, 17 Jun 2021

Jacob Hirschberg et al.

Jacob Hirschberg et al.

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Short summary
Debris-flow prediction is often based on rainfall thresholds but uncertainty assessments are rare. We established rainfall thresholds using two approaches and find that 25 debris flows are needed for uncertainties to converge in an Alpine basin and that the suitable method differs for regional compared to local thresholds. Finally, we demonstrate the potential of a statistical learning algorithm to improve threshold performance. These findings are helpful for early warning system development.
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