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

Related subject area

Landslides and Debris Flows Hazards
Large-scale assessment of rainfall-induced landslide hazard based on hydrometeorological information: application to Partenio Massif (Italy)
Daniel Camilo Roman Quintero, Pasquale Marino, Abdullah Abdullah, Giovanni Francesco Santonastaso, and Roberto Greco
Nat. Hazards Earth Syst. Sci., 25, 2679–2698, https://doi.org/10.5194/nhess-25-2679-2025,https://doi.org/10.5194/nhess-25-2679-2025, 2025
Short summary
Transformations in exposure to debris flows in post-earthquake Sichuan, China
Isabelle Utley, Tristram Hales, Ekbal Hussain, and Xuanmei Fan
Nat. Hazards Earth Syst. Sci., 25, 2699–2716, https://doi.org/10.5194/nhess-25-2699-2025,https://doi.org/10.5194/nhess-25-2699-2025, 2025
Short summary
Is higher resolution always better? A comparison of open-access DEMs for optimized slope unit delineation and regional landslide prediction
Mahnoor Ahmed, Giacomo Titti, Sebastiano Trevisani, Lisa Borgatti, and Mirko Francioni
Nat. Hazards Earth Syst. Sci., 25, 2519–2539, https://doi.org/10.5194/nhess-25-2519-2025,https://doi.org/10.5194/nhess-25-2519-2025, 2025
Short summary
Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan
Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, and Filippo Catani
Nat. Hazards Earth Syst. Sci., 25, 2371–2377, https://doi.org/10.5194/nhess-25-2371-2025,https://doi.org/10.5194/nhess-25-2371-2025, 2025
Short summary
Landslide activation during deglaciation in a fjord-dominated landscape: observations from southern Alaska (1984–2022)
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
Nat. Hazards Earth Syst. Sci., 25, 2045–2073, https://doi.org/10.5194/nhess-25-2045-2025,https://doi.org/10.5194/nhess-25-2045-2025, 2025
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