Articles | Volume 26, issue 9
https://doi.org/10.5194/nhess-26-4457-2026
© Author(s) 2026. 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-26-4457-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Bayesian forecasting of triggered landslides
Flavia Ferriero
CORRESPONDING AUTHOR
Scuola Superiore Meridionale, Via Mezzocannone 4, Napoli 80134, Italy
Fausto Guzzetti
Istituto di Matematica Applicata e Tecnologie Informatiche “Enrico Magenes”, Consiglio Nazionale delle Ricerche, via de Marini 6, Genova 16149, Italy
Institute of Hazard, Risk and Resilience, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
Warner Marzocchi
Scuola Superiore Meridionale, Via Mezzocannone 4, Napoli 80134, Italy
Department of Earth, Environmental and Resources Sciences, Federico II University of Napoli, Complesso Universitario di Monte Sant'Angelo (Edificio L), Via Cinthia 21, Napoli 80126, Italy
Related authors
No articles found.
Fausto Guzzetti, Alessandro Cesare Mondini, Paola Salvati, Antonella Bodini, and Antonio Pievatolo
EGUsphere, https://doi.org/10.5194/egusphere-2026-4342, https://doi.org/10.5194/egusphere-2026-4342, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Using a detailed record of deaths and missing persons caused by floods and landslides in Italy from 1950 to 2024, we examine how human losses changed and what may explain them. Deaths and fatal days fell sharply until about 1970, mainly because of fewer landslide deaths, and then remained much lower. Rainfall drives year-to-year changes, while broad social and economic progress appears to have reduced mortality even though hazardous events continue.
Warner Marzocchi, Alberto Montanari, and the RETURN-uncertainty task force
EGUsphere, https://doi.org/10.5194/egusphere-2026-2254, https://doi.org/10.5194/egusphere-2026-2254, 2026
Short summary
Short summary
Natural systems are complex and uncertain, so we use probabilistic forecasts. Representing and communicating all uncertainties is hard but vital for risk management. A multidisciplinary review found common challenges across hazards and argues for a shared framework: a clear hierarchy of uncertainties, complete forecasts, better model evaluation, and improved communication to support decisions.
Salvatore Ferrara, Jacopo Selva, Jacopo Natale, and Warner Marzocchi
EGUsphere, https://doi.org/10.5194/egusphere-2026-1415, https://doi.org/10.5194/egusphere-2026-1415, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We develop a probabilistic framework for modeling volcanic eruption sizes. Assuming a power-law distribution on erupted volumes, we analyze how measurement error can cause the observed trend to deviate from such a distribution. We apply this framework to Campi Flegrei, Italy, and Taupo, New Zealand, and observe that when the error is properly accounted for, the data distribution is compatible with a power-law, supporting the use of such a distribution in size forecasting.
Vera D'Amico, Francesco Visini, Andrea Rovida, Warner Marzocchi, and Carlo Meletti
Nat. Hazards Earth Syst. Sci., 24, 1401–1413, https://doi.org/10.5194/nhess-24-1401-2024, https://doi.org/10.5194/nhess-24-1401-2024, 2024
Short summary
Short summary
We propose a scoring strategy to rank multiple models/branches of a probabilistic seismic hazard analysis (PSHA) model that could be useful to consider specific requests from stakeholders responsible for seismic risk reduction actions. In fact, applications of PSHA often require sampling a few hazard curves from the model. The procedure is introduced through an application aimed to score and rank the branches of a recent Italian PSHA model according to their fit with macroseismic intensity data.
Silvia Peruccacci, Stefano Luigi Gariano, Massimo Melillo, Monica Solimano, Fausto Guzzetti, and Maria Teresa Brunetti
Earth Syst. Sci. Data, 15, 2863–2877, https://doi.org/10.5194/essd-15-2863-2023, https://doi.org/10.5194/essd-15-2863-2023, 2023
Short summary
Short summary
ITALICA (ITAlian rainfall-induced LandslIdes CAtalogue) is the largest catalogue of rainfall-induced landslides accurately located in space and time available in Italy. ITALICA currently lists 6312 landslides that occurred between January 1996 and December 2021. The information was collected using strict objective and homogeneous criteria. The high spatial and temporal accuracy makes the catalogue suitable for reliably defining the rainfall conditions capable of triggering future landslides.
John Douglas, Helen Crowley, Vitor Silva, Warner Marzocchi, Laurentiu Danciu, and Rui Pinho
EGUsphere, https://doi.org/10.5194/egusphere-2023-991, https://doi.org/10.5194/egusphere-2023-991, 2023
Preprint withdrawn
Short summary
Short summary
Estimates of the earthquake ground motions expected during the lifetime of a building or the length of an insurance policy are frequently calculated for locations around the world. Estimates for the same location from different studies can show large differences. These differences affect engineering, financial and risk management decisions. We apply various approaches to understand when such differences have an impact on such decisions and when they are expected because data are limited.
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023, https://doi.org/10.5194/npg-30-167-2023, 2023
Short summary
Short summary
Despite being among the most severe climate extremes, it is still challenging to assess droughts’ features for specific regions. In this paper we study meteorological droughts in Europe using concepts derived from climate network theory. By exploring the synchronization in droughts occurrences across the continent we unveil regional clusters which are individually examined to identify droughts’ geographical propagation and source–sink systems, which could potentially support droughts’ forecast.
Warner Marzocchi, Jacopo Selva, and Thomas H. Jordan
Nat. Hazards Earth Syst. Sci., 21, 3509–3517, https://doi.org/10.5194/nhess-21-3509-2021, https://doi.org/10.5194/nhess-21-3509-2021, 2021
Short summary
Short summary
Eruption forecasting and volcanic hazard analysis are pervaded by uncertainty of different kinds, such as the natural randomness, our lack of knowledge, and the so-called unknown unknowns. After discussing the limits of how classical probabilistic frameworks handle these uncertainties, we put forward a unified probabilistic framework which unambiguously defines uncertainty of different kinds, and it allows scientific validation of the hazard model against independent observations.
Cited articles
Bean, M. A.: Probability: The Science of Uncertainty with Applications to Investments, Insurance, and Engineering, American Mathematical Society, https://api.semanticscholar.org/CorpusID:106862438 (last access: 14 September 2025), 2000.
Berti, M., Martina, M. L. V., Franceschini, S., Pignone, S., Simoni, A., and Pizziolo, M.: Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach, J. Geophys. Res., 117, https://doi.org/10.1029/2012jf002367, 2012.
Brunetti, M. T., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., and Guzzetti, F.: Rainfall thresholds for the possible occurrence of landslides in Italy, Nat. Hazards Earth Syst. Sci., 10, 447–458, https://doi.org/10.5194/nhess-10-447-2010, 2010.
Calcaterra, D., Parise, M., Palma, B., and Pelella, L.: The May 5th, 1998, landsliding event in Campania (Southern Italy): inventory of slope movements in the Quindici area, in: Proceedings of the Symposium on Slope Stability Engineering, edited by: Yagi, Y. and Jiang, J., Balkema, Rotterdam, 1361–1366, ISBN 90 5809 149 X, 1999.
Calvello, M., Pecoraro, G., and Piciullo, L.: The regional early warning system for rainfall-induced landslides operating in Campania (Italy): performance evaluation of two warning strategies, 1st IMEKO TC-4 International Workshop on Metrology for Geotechnics, Benevento, Italy, 17–18 March, ISBN 978-92-990075-0-1, 2016.
Cascini, L., Cuomo, S., and Guida, D.: Typical source areas of May 1998 flow-like mass movements in the Campania region, Southern Italy, Eng. Geol., 96, 107–125, https://doi.org/10.1016/j.enggeo.2007.10.003, 2008.
Chen, C., Shen, Z., Fang, L., Lin, J., Li, S., and Wang, K.: Incorporating modelling uncertainty and prior knowledge into landslide susceptibility mapping using Bayesian neural networks, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 19, 513–532, https://doi.org/10.1080/17499518.2024.2422498, 2025.
Crosta, G. and Frattini, P.: Rainfall thresholds for triggering soil slips and debris flow, in: Proceedings of the EGS 2nd Plinius Conference on Mediterranean Storms, Siena, 2000, Bios, 2000.
de Riso, R., Budetta, P., Calcaterra, D., De Luca, C., Del Prete, S., Di Crescenzo, G., Guarino, P. M., Mele, R., Palma, B., Santo, A., and Sgambati, D.: Fenomeni di instabilità dei Monti Lattari e dell'area flegrea (Campania): scenari di suscettibilità da frana in aree-campione, Quaderni di Geologia Applicata, 11, 1–26, 2004.
De Vita, P., Di Clemente, E., Rolandi, M., and Celico, P.: Engineering geological models of the initial landslides occurred on the April 30th, 2006, at the Mount Di Vezzi (Ischia Island, Italy), Ital. J. Eng. Geol. Environ., https://doi.org/10.4408/IJEGE.2007-02.O-08, 2007.
De Vita, P., Napolitano, E., Godt, J. W., and Baum, R. L.: Deterministic estimation of hydrological thresholds for shallow landslide initiation and slope stability models: case study from the Somma-Vesuvius area of southern Italy, Landslides, 10, 713–728, https://doi.org/10.1007/s10346-012-0348-2, 2013.
D. P. G. R. n. 299 30/06/2005: Decreto del Presidente della Giunta Regionale della Campania: Il Sistema di Allertamento Regionale per il rischio Idrogeologico e Idraulico ai fini di protezione civile, Bollettino Ufficiale della Regione Campania, n. speciale 01/08/2005, https://sito.regione.campania.it/burc/pdf05/burcsp01_08_05/decpregiure299_05allsub_A.pdf (last access: 4 August 2025), 2005.
Ducci, D. and Tranfaglia, G.: Effects of climate change on groundwater resources in Campania (southern Italy), Geol. Soc. London Spec. Publ., 288, 25–38, https://doi.org/10.1144/sp288.3, 2008.
Felsberg, A., Heyvaert, Z., Poesen, J., Stanley, T., and De Lannoy, G. J. M.: Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling, Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, 2023.
Fischhoff, B., Lichtenstein, S., Slovic, P., Derby, S. L., and Keeney, R. L.: Acceptable Risk, Cambridge University Press, New York, ISBN 0521241642, 9780521241649, 1981.
Forte, G., Pirone, M., Santo, A., Nicotera, M. V., and Urciuoli, G.: Triggering and predisposing factors for flow-like landslides in pyroclastic soils: the case study of the Lattari Mts. (southern Italy), Eng. Geol., 257, 105137, https://doi.org/10.1016/j.enggeo.2019.05.014, 2019.
Fusco, F., Tufano, R., De Vita, P., Di Martire, D., Di Napoli, M., Guerriero, L., Mileti, F. A., Terribile, F., and Calcaterra, D.: A revised landslide inventory of the Campania region (Italy), Sci. Data, 10, 355, https://doi.org/10.1038/s41597-023-02155-6, 2023.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B.: Bayesian Data Analysis, Chapman & Hall/CRC, Boca Raton, FL, ISBN 1439840954, 9781439840955, 2014.
Guadagno, F. M.: Debris flows in the Campanian volcaniclastic soils, in: Slope Stability Engineering: Developments and Applications, Emerald Publishing Limited, 125–130, https://doi.org/10.1680/ssedaa.16606.0021, 1991.
Guzzetti, F.: Landslide Cartography, Hazard Assessment and Risk Evaluation: Overview, Limits and Prospective, in: Proceedings of 3rd MITCH Workshop Floods, droughts and landslides who plans, who pays, Potsdam, Germany, November 24–26, https://geomorphology.irpi.cnr.it/publications/repository/public/ proceedings/2002/landslide-hazard-assessment-and-risk-evaluation-overview-limits-and-prospective.pdf (last access: 14 September 2025), 2002.
Guzzetti, F., Cardinali, M., Reichenbach, P., and Carrara, A.: Comparing landslide maps: a case study in the Upper Tiber River Basin, Central Italy, Environ. Manage., 25, 247–263, https://doi.org/10.1007/s002679910020, 2000.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: Rainfall thresholds for the initiation of landslides in central and southern Europe, Meteorol. Atmos. Phys., 98, 239–267, https://doi.org/10.1007/s00703-007-0262-7, 2007.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall intensity–duration control of shallow landslides and debris flows: an update, Landslides, 5, 3–17, https://doi.org/10.1007/s10346-007-0112-1, 2008.
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I., Rossi, M., and Melillo, M.: Geographical landslide early warning systems, Earth-Sci. Rev., 200, 102973, https://doi.org/10.1016/j.earscirev.2019.102973, 2020.
Guzzetti, F., Berti, M., Reichenbach, P., and Tofani, V.: Landslide risk management in Italy: practices, advances, and future directions, Rend. Fis. Acc. Lincei, 36, 1165–1173, https://doi.org/10.1007/s12210-025-01382-w, 2025.
Hutchinson, J. N.: Keynote paper: landslide hazard assessment, in: Proceedings of the 6th International Symposium on Landslides, edited by: Bell, D. H., Balkema, Rotterdam, 1805–1841, 1995.
Jackob, M. and Hungr, O.: Debris-flow Hazards and Related Phenomena, Springer-Verlag, Berlin, Heidelberg, ISBN 3-540-20726-0, 2005.
Jenkins, S., Magill, C., McAneney, J., and Blong, R.: Regional ash fall hazard I: a probabilistic assessment methodology, B. Volcanol., 74, 1699–1712, https://doi.org/10.1007/s00445-012-0627-8, 2012.
Jiang, W., Chen, G., Meng, X., Jin, J., Zhao, Y., Lin, L., Li, Y., and Zhang, Y.: Probabilistic rainfall threshold of landslides in data-scarce mountainous areas: a case study of the Bailong River Basin, China, Catena, 213, 106190, https://doi.org/10.1016/j.catena.2022.106190, 2022.
Kass, R. E. and Raftery, A. E.: Bayes factors, J. Am. Stat. Assoc., 90, 773–795, 1995.
Lombardo, L., Opitz, T., Ardizzone, F., Guzzetti, F., and Huser, R.: Space-time landslide predictive modelling, Earth-Sci. Rev., 209, 103318, https://doi.org/10.1016/j.earscirev.2020.103318, 2020.
Longobardi, A. and Boulariah, O.: Long-term regional changes in inter-annual precipitation variability in the Campania Region, Southern Italy, Theor. Appl. Climatol., 148, 869–879, https://doi.org/10.1007/s00704-022-03972-2, 2022.
Lyell, C.: The Principles of Geology, Being an Attempt to Explain the Former Changes of the Earth's Surface by Reference to Causes Now in Operation, John Murray, London, 1830.
Marzocchi, W.: Predictive seismology, Seismol. Res. Lett., 89, 1998–2000, https://doi.org/10.1785/0220180238, 2018.
Marzocchi, W. and Jordan, T. H.: Testing for ontological errors in probabilistic forecasting models of natural systems, P. Natl. Acad. Sci. USA, 111, 11973–11978, https://doi.org/10.1073/pnas.1410183111, 2014.
Marzocchi, W., Sandri, L., and Selva, J.: BET_EF: a probabilistic tool for long- and short-term eruption forecasting, B. Volcanol., 70, 623–632, https://doi.org/10.1007/s00445-007-0157-y, 2008.
Mondini, A. C., Guzzetti, F., and Melillo, M.: Deep learning forecast of rainfall-induced shallow landslides, Nat. Commun., 14, 2466, https://doi.org/10.1038/s41467-023-38135-y, 2023.
Napolitano, E., Fusco, F., Baum, R. L., Godt, J. W., and De Vita, P.: Effect of antecedent-hydrological conditions on rainfall triggering of debris flows in ashfall pyroclastic mantled slopes of Campania (southern Italy), Landslides, 13, 967–983, https://doi.org/10.1007/s10346-015-0647-5, 2016.
Nocentini, M., Tofani, V., Gigli, G., Fidolini, F., and Casagli, N.: Modeling debris flows in volcanic terrains for hazard mapping: the case study of Ischia Island (Italy), Landslides, 12, 831–846, https://doi.org/10.1007/s10346-014-0524-7, 2015.
Pecoraro, G. and Calvello, M.: Definition and first application of a probabilistic warning model for rainfall-induced landslides, in: Understanding and Reducing Landslide Disaster Risk, ICL Contribution to Landslide Disaster Risk Reduction, edited by: Casagli, N., Tofani, V., Sassa, K., Bobrowsky, P. T., and Takara, K., Springer, Cham, https://doi.org/10.1007/978-3-030-60311-3_20, 2021.
Peres, D. J. and Cancelliere, A.: Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach, Hydrol. Earth Syst. Sci., 18, 4913–4931, https://doi.org/10.5194/hess-18-4913-2014, 2014.
Peres, D. J., Cancelliere, A., Greco, R., and Bogaard, T. A.: Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds, Nat. Hazards Earth Syst. Sci., 18, 633–646, https://doi.org/10.5194/nhess-18-633-2018, 2018.
Peruccacci, S., Brunetti, M. T., Gariano, S. L., Melillo, M., Rossi, M., and Guzzetti, F.: Rainfall thresholds for possible landslide occurrence in Italy, Geomorphology, 290, 39–57, https://doi.org/10.1016/j.geomorph.2017.03.031, 2017.
Peruccacci, S., Gariano, S. L., Melillo, M., Solimano, M., Guzzetti, F., and Brunetti, M. T.: The ITAlian rainfall-induced LandslIdes CAtalogue, an extensive and accurate spatio-temporal catalogue of rainfall-induced landslides in Italy, Earth Syst. Sci. Data, 15, 2863–2877, https://doi.org/10.5194/essd-15-2863-2023, 2023.
Piciullo, L., Gariano, S. L., Melillo, M., Brunetti, M. T., Peruccacci, S., Guzzetti, F., and Calvello, M.: Definition and performance of a threshold-based regional early warning model for rainfall-induced landslides, Landslides, 14, 995–1008, https://doi.org/10.1007/s10346-016-0750-2, 2017.
Piciullo, L., Calvello, M., and Cepeda, J. M.: Territorial early warning systems for rainfall-induced landslides, Earth-Sci. Rev., 179, 228–247, https://doi.org/10.1016/j.earscirev.2018.02.013, 2018.
Prete, M., Guadagno, M., and Hawkins, A.: Preliminary report on the landslides of 5 May 1998, Campania, southern Italy, B. Eng. Geol. Environ., 57, 113–129, 1998.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria [code], https://www.R-project.org/ (last access: 27 July 2025), 2025.
Revellino, P., Hungr, O., Guadagno, F. M., and Evans, S. G.: Velocity and runout simulation of destructive debris flows and debris avalanches in pyroclastic deposits, Campania region, Italy, Environ. Geol., 45, 295–311, https://doi.org/10.1007/s00254-003-0885-z, 2004.
Rianna, G., Pagano, L., and Urciuoli, G.: Rainfall patterns triggering shallow flowslides in pyroclastic soils, Eng. Geol., 174, 22–35, https://doi.org/10.1016/j.enggeo.2014.03.004, 2014.
Rossi, M., Witt, A., Guzzetti, F., Malamud, B. D., and Peruccacci, S.: Analysis of historical landslide time series in the Emilia-Romagna region, northern Italy, Earth Surf. Proc. Land., 35, 1123–1137, https://doi.org/10.1002/esp.1858, 2010.
Rossi, M., Luciani, S., Valigi, D., Kirschbaum, D., Brunetti, M. T., Peruccacci, S., and Guzzetti, F.: Statistical approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data, Geomorphology, 285, 16–27, 2017.
Segoni, S., Piciullo, L., and Gariano, S. L.: A review of the recent literature on rainfall thresholds for landslide occurrence, Landslides, 15, 1483–1501, https://doi.org/10.1007/s10346-018-0966-4, 2018.
Staley, D., Kean, J., Cannon, S., Schmidt, K. M., and Laber, J. L.: Objective definition of rainfall intensity–duration thresholds for the initiation of post-fire debris flows in southern California, Landslides, 10, 547–562, https://doi.org/10.1007/s10346-012-0341-9, 2013.
Strandberg, G. and Lind, P.: The importance of horizontal model resolution on simulated precipitation in Europe – from global to regional models, Weather Clim. Dynam., 2, 181–204, https://doi.org/10.5194/wcd-2-181-2021, 2021.
The MathWorks Inc.: Optimization Toolbox version 24.2 (R2024b), The MathWorks Inc., Natick, Massachusetts [code], https://www.mathworks.com (last access: May 2025), 2024.
Thiessen, A. J. and Alter, J. C.: Precipitation for large areas, Mon. Weather Rev., 39, 1082–1084, 1911.
Varnes, D. J. and Commission on Landslides and Other Mass-Movements: Landslide Hazard Zonation: A Review of Principles and Practice, UNESCO Press, Paris, ISBN 92-3-101895-7, 1984.
Zanchetta, G., Sulpizio, R., Pareschi, M. T., Leoni, F. M., and Santacroce, R.: Characteristics of May 5–6, 1998 volcaniclastic debris flows in the Sarno area (Campania, southern Italy): relationships to structural damage and hazard zonation, J. Volcanol. Geoth. Res., 133, 377–393, https://doi.org/10.1016/s0377-0273(03)00409-8, 2004.
Zhang, S., Pecoraro, G., Jiang, Q., and Calvello, M.: A probabilistic procedure to define multidimensional rainfall thresholds for territorial landslide warning models, Landslides, 22, 1773–1787, https://doi.org/10.1007/s10346-025-02461-7, 2025.
Zhao, B., Zhang, L., Gu, X., Luo, W., Yu, Z., and Yuan, L.: How is the occurrence of rainfall-triggered landslides related to extreme rainfall?, Geomorphology, 475, https://doi.org/10.1016/j.geomorph.2025.109666, 2025.
Short summary
Landslides cause thousands of deaths and billions in damages yearly, yet predicting them remains a major challenge. We developed a Bayesian method that estimates landslide probability as a function of rainfall, explicitly accounting for uncertainty. Applied in southern Italy, landslide probability increases gradually with rainfall, with no sharp thresholds in the triggering conditions. This approach supports a more uncertainty-aware landslide risk management.
Landslides cause thousands of deaths and billions in damages yearly, yet predicting them remains...
Altmetrics
Final-revised paper
Preprint