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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Landslides are a dangerous natural hazard affecting alpine regions, calling for effective warning systems. Here we consider different approaches for the prediction of rainfall-induced shallow landslides at the regional scale, based on open access datasets and operational hydrological forecasting systems. We find antecedent wetness useful to improve upon the classical rainfall thresholds and the resolution of the hydrological model used for its estimate to be a critical aspect.
Preprints
https://doi.org/10.5194/hess-2020-624
https://doi.org/10.5194/hess-2020-624

  08 Dec 2020

08 Dec 2020

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

Rainfall-induced shallow landslides and soil wetness: comparison of physically-based and probabilistic predictions

Elena Leonarduzzi1,2, Brian W. McArdell2, and Peter Molnar1 Elena Leonarduzzi et al.
  • 1Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
  • 2Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

Abstract. Landslides are an impacting natural hazard in alpine regions, calling for effective forecasting and warning systems. Here we compare two methods (physically-based and probabilistic) for the prediction of shallow rainfall-induced landslides in an application to Switzerland, with a specific focus on the value of antecedent soil wetness. First, we show that landslide susceptibility predicted by the factor of safety in the infinite slope model is strongly dependent on soil data inputs, limiting the hydrologically active range where landslides can occur to only ~20 % of the area with typical soil parameters and soil depth models. Second, the physically-based approach with a coarse resolution model setup (TerrSysMP) 12.5 km × 12.5 km downscaled to 25 m × 25 m with the TopographicWetness Index to provide water table simulations for the infinite slope stability model did not succeed in predicting local scale landsliding satisfactorily, despite spatial downscaling. We argue that this is due to inadequacies of the infinite slope model, soil parameter uncertainty, and the coarse resolution of the hydrological model. Third, soil saturation estimates provided by a higher resolution 500 m × 500 m conceptual hydrological model (PREVAH) provided added value to rainfall threshold curves for landslide prediction in the probabilistic approach, with potential to reduce false alarms and misses. We conclude that although combined physically-based hydrological-geotechnical modelling is the desired goal, we still need to overcome problems of model resolution, parameter constraints, and landslide validation for successful prediction at regional scales.

Elena Leonarduzzi et al.

 
Status: open (until 11 Feb 2021)
Status: open (until 11 Feb 2021)
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Elena Leonarduzzi et al.

Elena Leonarduzzi et al.

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Short summary
Landslides are a dangerous natural hazard affecting alpine regions, calling for effective warning systems. Here we consider different approaches for the prediction of rainfall-induced shallow landslides at the regional scale, based on open access datasets and operational hydrological forecasting systems. We find antecedent wetness useful to improve upon the classical rainfall thresholds and the resolution of the hydrological model used for its estimate to be a critical aspect.
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