Articles | Volume 23, issue 1
https://doi.org/10.5194/nhess-23-279-2023
https://doi.org/10.5194/nhess-23-279-2023
Research article
 | 
25 Jan 2023
Research article |  | 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, David J. Peres, Enrico Creaco, and Antonino Cancelliere

Viewed

Total article views: 2,983 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,054 849 80 2,983 76 101
  • HTML: 2,054
  • PDF: 849
  • XML: 80
  • Total: 2,983
  • BibTeX: 76
  • EndNote: 101
Views and downloads (calculated since 04 Jul 2022)
Cumulative views and downloads (calculated since 04 Jul 2022)

Viewed (geographical distribution)

Total article views: 2,983 (including HTML, PDF, and XML) Thereof 2,879 with geography defined and 104 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 09 Oct 2025
Download
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).
Share
Altmetrics
Final-revised paper
Preprint