Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5937-2021
https://doi.org/10.5194/hess-25-5937-2021
Research article
 | 
15 Nov 2021
Research article |  | 15 Nov 2021

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

Elena Leonarduzzi, Brian W. McArdell, and Peter Molnar

Related authors

Simulation-based inference for parameter estimation of complex watershed simulators
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024,https://doi.org/10.5194/hess-28-4685-2024, 2024
Short summary
Using simulation-based inference to determine the parameters of an integrated hydrologic model: a case study from the upper Colorado River basin
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-345,https://doi.org/10.5194/hess-2022-345, 2022
Publication in HESS not foreseen
Short summary
Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment
Jacob Hirschberg, Alexandre Badoux, Brian W. McArdell, Elena Leonarduzzi, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 21, 2773–2789, https://doi.org/10.5194/nhess-21-2773-2021,https://doi.org/10.5194/nhess-21-2773-2021, 2021
Short summary
Deriving rainfall thresholds for landsliding at the regional scale: daily and hourly resolutions, normalisation, and antecedent rainfall
Elena Leonarduzzi and Peter Molnar
Nat. Hazards Earth Syst. Sci., 20, 2905–2919, https://doi.org/10.5194/nhess-20-2905-2020,https://doi.org/10.5194/nhess-20-2905-2020, 2020
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Implementation of global soil databases in the Noah-MP model and the effects on simulated mean and extreme soil hydrothermal changes
Kazeem Abiodun Ishola, Gerald Mills, Ankur Prabhat Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci., 29, 2551–2582, https://doi.org/10.5194/hess-29-2551-2025,https://doi.org/10.5194/hess-29-2551-2025, 2025
Short summary
Skilful probabilistic predictions of UK flood risk months ahead using a large-sample machine learning model trained on multimodel ensemble climate forecasts
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025,https://doi.org/10.5194/hess-29-2393-2025, 2025
Short summary
Towards a robust hydrologic data assimilation system for hurricane-induced river flow forecasting
Peyman Abbaszadeh, Fatemeh Gholizadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 29, 2407–2427, https://doi.org/10.5194/hess-29-2407-2025,https://doi.org/10.5194/hess-29-2407-2025, 2025
Short summary
Enhanced evaluation of hourly and daily extreme precipitation in Norway from convection-permitting models at regional and local scales
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025,https://doi.org/10.5194/hess-29-2133-2025, 2025
Short summary
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025,https://doi.org/10.5194/hess-29-2023-2025, 2025
Short summary

Cited articles

Aleotti, P. and Chowdhury, R.: Landslide hazard assessment: summary review and new perspectives, B. Eng. Geol. Environ., 58, 21–44, 1999. a
Anagnostopoulos, G. G., Fatichi, S., and Burlando, P.: An advanced process-based distributed model for the investigation of rainfall-induced landslides: The effect of process representation and boundary conditions, Water Resour. Research, 51, 7501–7523, https://doi.org/10.1002/2015WR016909, 2015. a, b
Anderson, S. A. and Sitar, N.: Analysis of rainfall-induced debris flows, J. Geotechn. Eng., 121, 544–552, 1995. a
Ayalew, L. and Yamagishi, H.: The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan, Geomorphology, 65, 15–31, 2005. a
Baum, R. L., Savage, W. Z., and Godt, J. W.: TRIGRS – a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis, US geological survey open-file report, 424, 38, https://doi.org/10.3133/ofr02424, 2002. a
Download
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.
Share