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

Palazzolo, Nunziarita; Peres, David J.; Creaco, Enrico; Cancelliere, Antonino

doi:https://doi.org/10.5194/nhess-23-279-2023

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.

Special issue:

Hydro-meteorological extremes and hazards: vulnerability,...

https://doi.org/10.5194/nhess-23-279-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 1

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

Data sets

FraneItalia: a catalog of recent Italian landslides (version 2.0) M. Calvello and G. Pecoraro https://doi.org/10.17632/zygb8jygrw.2

ERA5-Land hourly data from 1950 to present J Muñoz Sabater https://doi.org/10.24381/cds.e2161bac

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).