Articles | Volume 25, issue 12
https://doi.org/10.5194/nhess-25-4907-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-4907-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Use of delayed ERA5-Land soil moisture products for improving landslide early warning
Nunziarita Palazzolo
CORRESPONDING AUTHOR
Department of Civil Engineering and Architecture, University of Catania, Catania, 95125, Italy
Antonino Cancelliere
Department of Civil Engineering and Architecture, University of Catania, Catania, 95125, Italy
Robert D. Zofei
Department of Civil Engineering and Architecture, University of Catania, Catania, 95125, Italy
David J. Peres
Department of Civil Engineering and Architecture, University of Catania, Catania, 95125, Italy
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Nunziarita Palazzolo, David J. Peres, Enrico Creaco, and Antonino Cancelliere
Nat. Hazards Earth Syst. Sci., 23, 279–291, https://doi.org/10.5194/nhess-23-279-2023, https://doi.org/10.5194/nhess-23-279-2023, 2023
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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).
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Nat. Hazards Earth Syst. Sci., 23, 279–291, https://doi.org/10.5194/nhess-23-279-2023, https://doi.org/10.5194/nhess-23-279-2023, 2023
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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).
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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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To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years.
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Short summary
We investigate whether ERA5-Land reanalysis soil moisture data, despite their 5 d 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 d 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.
We investigate whether ERA5-Land reanalysis soil moisture data, despite their 5 d publication...
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