Articles | Volume 20, issue 11
https://doi.org/10.5194/nhess-20-3057-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-20-3057-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) historical simulations by high-quality observational datasets in southern Italy: insights on drought assessment
David J. Peres
Department of Civil Engineering and Architecture, University of
Catania, Catania, 95123, Italy
Alfonso Senatore
CORRESPONDING AUTHOR
Department of Environmental Engineering, University of Calabria,
Arcavacata di Rende (CS), 87036, Italy
Paola Nanni
Department of Civil Engineering and Architecture, University of
Catania, Catania, 95123, Italy
Antonino Cancelliere
Department of Civil Engineering and Architecture, University of
Catania, Catania, 95123, Italy
Giuseppe Mendicino
Department of Environmental Engineering, University of Calabria,
Arcavacata di Rende (CS), 87036, Italy
Brunella Bonaccorso
Department of Engineering, University of Messina, St Agata, Messina,
98166, Italy
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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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Pierpaolo Distefano, David J. Peres, Pietro Scandura, and Antonino Cancelliere
Nat. Hazards Earth Syst. Sci., 22, 1151–1157, https://doi.org/10.5194/nhess-22-1151-2022, https://doi.org/10.5194/nhess-22-1151-2022, 2022
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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.
Animesh K. Gain, Yves Bühler, Pascal Haegeli, Daniela Molinari, Mario Parise, David J. Peres, Joaquim G. Pinto, Kai Schröter, Ricardo M. Trigo, María Carmen Llasat, and Heidi Kreibich
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Short summary
Short summary
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
Regional climate models (RCMs) are commonly used for high-resolution assessment of climate change impacts. This research assesses the reliability of several RCMs in a Mediterranean area (southern Italy), comparing historic climate and drought characteristics with
high-density and high-quality ground-based observational datasets. We propose a general methodology and identify the more skilful models able to reproduce precipitation and temperature variability as well as drought characteristics.
Regional climate models (RCMs) are commonly used for high-resolution assessment of climate...
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