Preprints
https://doi.org/10.5194/nhess-2022-175
https://doi.org/10.5194/nhess-2022-175
 
04 Jul 2022
04 Jul 2022
Status: a revised version of this preprint is currently under review for the journal NHESS.

Potential improvements of landslide prediction by hydro-meteorological thresholds: an investigation based on reanalysis soil moisture data and principal component analysis

Nunziarita Palazzolo1,a, David Johnny Peres2, Enrico Creaco3, and Antonino Cancelliere2 Nunziarita Palazzolo et al.
  • 1Department of Civil Engineering and Architecture, University of Pavia, Italy
  • 2Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
  • 3Department of Civil Engineering and Architecture, University of Pavia, Pavia, 27100, Italy
  • anow at: Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy

Abstract. In recent times, several efforts have been addressed to understand the extent to which soil moisture estimations may improve the performance of landslide early warning systems (LEWSs). These systems have been traditionally based on rainfall intensity-duration thresholds. Still a limited number of studies explore the possible enhancement of the performance of LEWSs through the identification of hydro-meteorological thresholds. In this study, we propose a methodology for developing regional hydro-meteorological landslide triggering thresholds coupling mean rainfall intensity and soil moisture information. To test the potential improvements in prediction we use ERA5-Land reanalysis soil moisture data, available at four depth levels and hourly resolution. Two different instances are investigated, namely the identification of triggering thresholds using rainfall intensity and the soil moisture at each of four depth levels, and the identification of triggering thresholds using rainfall intensity and a combination of soil moisture at the four depths as obtained by principal component analysis (PCA). We propose thresholds in the form of a piece-wise linear equation. The equation’s parameters are optimized in order to maximize the ROC True Skill Statistic (TSS) prediction performance metric. The proposed hydro-meteorological thresholds are tested on the case of Sicily Island (south Italy) and the performance is compared with those obtained through the traditional rainfall intensity-duration (ID) power-law thresholds. Overall, the results show that the soil moisture information adds a considerable value to the improved thresholds’ performance since the ROC True Skill Statistic increases from 0.50 to 0.71. A similar performance is obtained when the first principal component derived from the PCA is used, proving PCA to be a valuable support tool for the identification of the proposed hydro-meteorological thresholds, as it allows to take into account the multi-layer information while keeping the thresholds two-dimensional.

Nunziarita Palazzolo et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-175', Anonymous Referee #1, 27 Jul 2022
    • AC1: 'Reply on RC1', Nunziarita Palazzolo, 05 Oct 2022
  • RC2: 'Comment on nhess-2022-175', Anonymous Referee #2, 29 Jul 2022
    • AC2: 'Reply on RC2', Nunziarita Palazzolo, 05 Oct 2022

Nunziarita Palazzolo et al.

Nunziarita Palazzolo et al.

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Short summary
We explore the development of regional hydro-meteorological landslide triggering thresholds combining rainfall and soil moisture information at different depths. We apply PCA to condense multi-dimensional information in a simple 2D threshold. Based on ERA5-Land reanalysis soil moisture and observed precipitation we find that hydro-meteorological thresholds may significantly enhance landslide prediction (TSS = 0.71) compared to power-law rainfall intensity-duration thresholds (TSS = 0.50).
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