Articles | Volume 23, issue 1
https://doi.org/10.5194/nhess-23-279-2023
© Author(s) 2023. This work is distributed under
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the Creative Commons Attribution 4.0 License.
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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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We investigate whether ERA5-Land reanalysis soil moisture data, despite their 5-days publication delay, can be useful for improving the performance of relationships providing triggering conditions for landslides. Using artificial neural networks, we find that soil moisture delayed even up to 15 days allows an improvement of performance respect to precipitation-based models, therefore corroborating the potential use of ERA5-Land soil moisture for improving landslide early warning.
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We investigate whether ERA5-Land reanalysis soil moisture data, despite their 5-days publication delay, can be useful for improving the performance of relationships providing triggering conditions for landslides. Using artificial neural networks, we find that soil moisture delayed even up to 15 days allows an improvement of performance respect to precipitation-based models, therefore corroborating the potential use of ERA5-Land soil moisture for improving landslide early warning.
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In the communication, we introduce the use of artificial neural networks (ANNs) for improving the performance of rainfall thresholds for landslide early warning. Results show how ANNs using rainfall event duration and mean intensity perform significantly better than a classical power law based on the same variables. Adding peak rainfall intensity as input to the ANN improves performance even more. This further demonstrates the potentialities of the proposed machine learning approach.
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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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