Articles | Volume 23, issue 1
https://doi.org/10.5194/nhess-23-279-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-279-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jan 2023
Research article |
| 25 Jan 2023
| 25 Jan 2023
Using principal component analysis to incorporate multi-layer soil moisture information in hydrometeorological thresholds for landslide prediction: an investigation based on ERA5-Land reanalysis data
Nunziarita Palazzolo
CORRESPONDING AUTHOR
Department of Civil Engineering and Architecture, University of Pavia, Pavia, 27100, Italy
now at: Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
David J. Peres
Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
Enrico Creaco
Department of Civil Engineering and Architecture, University of Pavia, Pavia, 27100, Italy
Antonino Cancelliere
Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
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Cited
17 citations as recorded by crossref.
- Hydro-meteorological landslide triggering thresholds based on artificial neural networks using observed precipitation and ERA5-Land soil moisture P. Distefano et al. 10.1007/s10346-023-02132-5
- A probabilistic early warning model of rainfall-induced landslides accounting for multiple factors and correlation structures Y. Li et al. 10.1007/s00477-025-02950-0
- Integrating rainfall severity and soil saturation indices to define hydro-meteorological thresholds for landslides S. Zhang et al. 10.1016/j.jhydrol.2025.132873
- Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems B. Mirus et al. 10.5194/nhess-25-169-2025
- Effective landslide risk management in era of climate change, demographic change, and evolving societal priorities V. Capobianco et al. 10.1007/s10346-024-02418-2
- Rainfall- and Irrigation-Induced Landslide Mechanisms in Loess Slopes: An Experimental Investigation in Lanzhou, China W. Liu et al. 10.3390/atmos15020162
- Analysis of Water Migration and Spoil Slope Stability under the Coupled Effects of Rainfall and Root Reinforcement Based on the Unsaturated Soil Theory H. Song et al. 10.3390/f15040640
- Recent advancements of landslide hydrology R. Greco et al. 10.1002/wat2.1675
- An enhanced rainfall-induced landslide catalogue in Italy M. Brunetti et al. 10.1038/s41597-025-04551-6
- From empirically to physically based early warning predictions of rainfall-induced landslides in silty volcanic soils: the Lattari Mountains case study G. Rianna et al. 10.1007/s10064-023-03228-x
- Prognosticators for precipitation variability adopting principal component regression analysis E. Aamir & A. Ghumman 10.1007/s12517-024-12111-2
- Evolution process and failure mechanism of a large expressway roadside landslide J. Zhang et al. 10.1038/s41598-023-32055-z
- Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product K. He et al. 10.1016/j.jhydrol.2024.131536
- Optimising landslide trigger analysis through integration of diverse dendrogeomorphic approaches and multi-source climate and documentary data R. Tichavský 10.1007/s10346-024-02413-7
- Understanding hydrometeorological triggers of natural hazards through dendrogeomorphology: Methods, limitations, and challenges R. Tichavský 10.1016/j.earscirev.2023.104546
- Soil temperature prediction based on explainable artificial intelligence and LSTM Q. Geng et al. 10.3389/fenvs.2024.1426942
- Temporal prediction of shallow landslides exploiting soil saturation degree derived by ERA5-Land products M. Bordoni et al. 10.1007/s10064-023-03304-2
16 citations as recorded by crossref.
- Hydro-meteorological landslide triggering thresholds based on artificial neural networks using observed precipitation and ERA5-Land soil moisture P. Distefano et al. 10.1007/s10346-023-02132-5
- A probabilistic early warning model of rainfall-induced landslides accounting for multiple factors and correlation structures Y. Li et al. 10.1007/s00477-025-02950-0
- Integrating rainfall severity and soil saturation indices to define hydro-meteorological thresholds for landslides S. Zhang et al. 10.1016/j.jhydrol.2025.132873
- Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems B. Mirus et al. 10.5194/nhess-25-169-2025
- Effective landslide risk management in era of climate change, demographic change, and evolving societal priorities V. Capobianco et al. 10.1007/s10346-024-02418-2
- Rainfall- and Irrigation-Induced Landslide Mechanisms in Loess Slopes: An Experimental Investigation in Lanzhou, China W. Liu et al. 10.3390/atmos15020162
- Analysis of Water Migration and Spoil Slope Stability under the Coupled Effects of Rainfall and Root Reinforcement Based on the Unsaturated Soil Theory H. Song et al. 10.3390/f15040640
- Recent advancements of landslide hydrology R. Greco et al. 10.1002/wat2.1675
- An enhanced rainfall-induced landslide catalogue in Italy M. Brunetti et al. 10.1038/s41597-025-04551-6
- From empirically to physically based early warning predictions of rainfall-induced landslides in silty volcanic soils: the Lattari Mountains case study G. Rianna et al. 10.1007/s10064-023-03228-x
- Prognosticators for precipitation variability adopting principal component regression analysis E. Aamir & A. Ghumman 10.1007/s12517-024-12111-2
- Evolution process and failure mechanism of a large expressway roadside landslide J. Zhang et al. 10.1038/s41598-023-32055-z
- Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product K. He et al. 10.1016/j.jhydrol.2024.131536
- Optimising landslide trigger analysis through integration of diverse dendrogeomorphic approaches and multi-source climate and documentary data R. Tichavský 10.1007/s10346-024-02413-7
- Understanding hydrometeorological triggers of natural hazards through dendrogeomorphology: Methods, limitations, and challenges R. Tichavský 10.1016/j.earscirev.2023.104546
- Soil temperature prediction based on explainable artificial intelligence and LSTM Q. Geng et al. 10.3389/fenvs.2024.1426942
Latest update: 23 Apr 2025
Short summary
We propose an approach exploiting PCA to derive hydrometeorological landslide-triggering thresholds using multi-layered soil moisture data from ERA5-Land reanalysis. Comparison of thresholds based on single- and multi-layered soil moisture information provides a means to identify the significance of multi-layered data for landslide triggering in a region. In Sicily, the proposed approach yields thresholds with a higher performance than traditional precipitation-based ones (TSS = 0.71 vs. 0.50).
We propose an approach exploiting PCA to derive hydrometeorological landslide-triggering...
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