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Preprints
https://doi.org/10.5194/nhess-2020-125
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-125
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 27 Apr 2020

Submitted as: research article | 27 Apr 2020

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This preprint is currently under review for the journal NHESS.

Data limitations and potential of hourly and daily rainfall thresholds for shallow landslides

Elena Leonarduzzi1,2 and Peter Molnar1 Elena Leonarduzzi and Peter Molnar
  • 1Institute of Environmental Engineering, ETH Zurich, Switzerland
  • 2Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

Abstract. Rainfall thresholds are a simple and widely used method to predict landslide occurrence. In this paper we provide a comprehensive data-driven assessment of the effects of rainfall temporal resolution (hourly versus daily) on landslide prediction performance in Switzerland, with sensitivity to two other important aspects which appear in many landslide studies – the normalisation of rainfall, which accounts for local climatology, and the inclusion of antecedent rainfall as a proxy of soil water state prior to landsliding. We use an extensive landslide inventory with over 3800 events and several daily and hourly, station and gridded rainfall datasets, to explore different scenarios of rainfall threshold estimation. Our results show that although hourly rainfall did show best predictive performance for landslides, daily data were not far behind, and the benefits of hourly resolutions can be masked by the higher uncertainties in threshold estimation connected to using short records. We tested the impact of several typical actions of users, like assigning the nearest raingauge to a landslide location and filling in unknown timing, and report their effects on predictive performance. We find that localisation of rainfall thresholds through normalisation compensates for the spatial heterogeneity in rainfall regimes and landslide erosion process rates and is a good alternative to regionalisation. On top of normalisation by mean annual precipitation or a high rainfall quantile, we recommend that non-triggering rainfall be included in rainfall threshold estimation if possible. Finally, we demonstrate that there is predictive skill in antecedent rain as a proxy of soil wetness state, despite the large heterogeneity of the study domain, although it may not be straightforward to build this into rainfall threshold curves.

Elena Leonarduzzi and Peter Molnar

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Elena Leonarduzzi and Peter Molnar

Elena Leonarduzzi and Peter Molnar

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Latest update: 31 May 2020
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Short summary
Landslides are a natural hazard that affects alpine regions. Here we focus on rainfall induced shallow landslides and one of the most widely approaches used for their predictions, rainfall thresholds. We design several comparisons utilizing a landslide database and rainfall records in Switzerland. We find that using daily rather than hourly rainfall might be a better option in some circumstances, and mean annual precipitation and antecedent wetness can improve predictions at the regional scale.
Landslides are a natural hazard that affects alpine regions. Here we focus on rainfall induced...
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