Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5937-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5937-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Nov 2021
Research article |
| 15 Nov 2021
| 15 Nov 2021
Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
Elena Leonarduzzi
CORRESPONDING AUTHOR
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Brian W. McArdell
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Peter Molnar
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
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Cited
17 citations as recorded by crossref.
- Modelling antecedent soil hydrological conditions to improve the prediction of landslide susceptibility in typhoon-prone regions C. Abancó et al. https://doi.org/10.1007/s10346-024-02242-8
- PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale G. Chen et al. https://doi.org/10.1016/j.envsoft.2023.105882
- A new index to quantify the extremeness of precipitation across scales P. Voit & M. Heistermann https://doi.org/10.5194/nhess-22-2791-2022
- Failure Process of High-Loess-Filled-Slopes (HLFSs) during Precipitation under Different Mitigation Measures Y. Zhu et al. https://doi.org/10.3390/app14010419
- Investigations on the effects of parameter selection for unsaturated slopes under Taiwan rainfall conditions J. Mburu et al. https://doi.org/10.1016/j.kscej.2026.100538
- Rainfall-induced landslide risk: the state of the art and future needs T. Wang et al. https://doi.org/10.1007/s12665-025-12541-5
- Recent advancements of landslide hydrology R. Greco et al. https://doi.org/10.1002/wat2.1675
- Optimising rainfall characteristics for determining landslide thresholds H. Abeysiriwardana et al. https://doi.org/10.1007/s11069-025-07835-7
- Optimising landslide initiation modelling with high-resolution saturation prediction based on soil moisture monitoring data T. Halter et al. https://doi.org/10.1007/s10346-024-02304-x
- A Hybrid Physics–Machine Learning Framework for Landslide Susceptibility Assessment with an Improved Non–Landslide Sampling Strategy D. Peng et al. https://doi.org/10.3390/rs18030408
- A systematic review on rainfall thresholds for landslides occurrence F. Gonzalez et al. https://doi.org/10.1016/j.heliyon.2023.e23247
- A prototype adaptive mesh generator for enhancing computational efficiency and accuracy in physically-based modeling of flood-landslide hazards G. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106458
- Rainfall characteristics and magnitude control the volume of shallow and deep-seated landslides: Inferences from analyses using a simple runoff model T. Sato & Y. Shuin https://doi.org/10.1016/j.geomorph.2024.109453
- Temporal prediction of shallow landslides exploiting soil saturation degree derived by ERA5-Land products M. Bordoni et al. https://doi.org/10.1007/s10064-023-03304-2
- Training machine learning with physics-based simulations to predict 2D soil moisture fields in a changing climate E. Leonarduzzi et al. https://doi.org/10.3389/frwa.2022.927113
- Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China J. Zhuang et al. https://doi.org/10.5194/nhess-24-2615-2024
- Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis Z. Yang et al. https://doi.org/10.3390/rs14163879
17 citations as recorded by crossref.
- Modelling antecedent soil hydrological conditions to improve the prediction of landslide susceptibility in typhoon-prone regions C. Abancó et al. https://doi.org/10.1007/s10346-024-02242-8
- PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale G. Chen et al. https://doi.org/10.1016/j.envsoft.2023.105882
- A new index to quantify the extremeness of precipitation across scales P. Voit & M. Heistermann https://doi.org/10.5194/nhess-22-2791-2022
- Failure Process of High-Loess-Filled-Slopes (HLFSs) during Precipitation under Different Mitigation Measures Y. Zhu et al. https://doi.org/10.3390/app14010419
- Investigations on the effects of parameter selection for unsaturated slopes under Taiwan rainfall conditions J. Mburu et al. https://doi.org/10.1016/j.kscej.2026.100538
- Rainfall-induced landslide risk: the state of the art and future needs T. Wang et al. https://doi.org/10.1007/s12665-025-12541-5
- Recent advancements of landslide hydrology R. Greco et al. https://doi.org/10.1002/wat2.1675
- Optimising rainfall characteristics for determining landslide thresholds H. Abeysiriwardana et al. https://doi.org/10.1007/s11069-025-07835-7
- Optimising landslide initiation modelling with high-resolution saturation prediction based on soil moisture monitoring data T. Halter et al. https://doi.org/10.1007/s10346-024-02304-x
- A Hybrid Physics–Machine Learning Framework for Landslide Susceptibility Assessment with an Improved Non–Landslide Sampling Strategy D. Peng et al. https://doi.org/10.3390/rs18030408
- A systematic review on rainfall thresholds for landslides occurrence F. Gonzalez et al. https://doi.org/10.1016/j.heliyon.2023.e23247
- A prototype adaptive mesh generator for enhancing computational efficiency and accuracy in physically-based modeling of flood-landslide hazards G. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106458
- Rainfall characteristics and magnitude control the volume of shallow and deep-seated landslides: Inferences from analyses using a simple runoff model T. Sato & Y. Shuin https://doi.org/10.1016/j.geomorph.2024.109453
- Temporal prediction of shallow landslides exploiting soil saturation degree derived by ERA5-Land products M. Bordoni et al. https://doi.org/10.1007/s10064-023-03304-2
- Training machine learning with physics-based simulations to predict 2D soil moisture fields in a changing climate E. Leonarduzzi et al. https://doi.org/10.3389/frwa.2022.927113
- Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China J. Zhuang et al. https://doi.org/10.5194/nhess-24-2615-2024
- Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis Z. Yang et al. https://doi.org/10.3390/rs14163879
Saved (final revised paper)
Latest update: 05 Jun 2026
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.
Landslides are a dangerous natural hazard affecting alpine regions, calling for effective...
