Articles | Volume 24, issue 8
https://doi.org/10.5194/nhess-24-2689-2024
https://doi.org/10.5194/nhess-24-2689-2024
Research article
 | 
09 Aug 2024
Research article |  | 09 Aug 2024

Temporal clustering of precipitation for detection of potential landslides

Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele

Related authors

A local model of snow–firn dynamics and application to the Colle Gnifetti site
Fabiola Banfi and Carlo De Michele
The Cryosphere, 16, 1031–1056, https://doi.org/10.5194/tc-16-1031-2022,https://doi.org/10.5194/tc-16-1031-2022, 2022
Short summary
A local model of snow-firn dynamics and application to Colle Gnifetti site
Fabiola Banfi and Carlo De Michele
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-357,https://doi.org/10.5194/tc-2020-357, 2021
Manuscript not accepted for further review
Short summary

Cited articles

Abbate, A., Papini, M., and Longoni, L.: Analysis of meteorological parameters triggering rainfall-induced landslide: a review of 70 years in Valtellina, Nat. Hazards Earth Syst. Sci., 21, 2041–2058, https://doi.org/10.5194/nhess-21-2041-2021, 2021. a
Banfi, F. and De Michele, C.: Compound flood hazard at Lake Como, Italy, is driven by temporal clustering of rainfall events, Commun. Earth Environ., 3, 234, https://doi.org/10.1038/s43247-022-00557-9, 2022. a, b
Barton, Y., Giannakaki, P., von Waldow, H., Chevalier, C., Pfahl, S., and Martius, O.: Clustering of regional-scale extreme precipitation events in Southern Switzerland, Mon. Weather Rev., 144, 347–369, https://doi.org/10.1175/MWR-D-15-0205.1, 2016. a
Belo-Pereira, M., Dutra, E., and Viterbo, P.: Evaluation of global precipitation data sets over the Iberian Peninsula, J. Geophys. Res.-Atmos., 116, D20101, https://doi.org/10.1029/2010JD015481, 2011. a
Benjamini, Y. and Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. Roy. Stat. Soc. Ser. B, 57, 289–300, https://doi.org/10.1111/j.2517-6161.1995.tb02031.x, 1995. a
Download
Short summary
Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Share
Altmetrics
Final-revised paper
Preprint