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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 6
Nonlin. Processes Geophys., 20, 1071–1078, 2013
https://doi.org/10.5194/npg-20-1071-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 20, 1071–1078, 2013
https://doi.org/10.5194/npg-20-1071-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 05 Dec 2013

Research article | 05 Dec 2013

Recurrence time distribution and temporal clustering properties of a cellular automaton modelling landslide events

E. Piegari1, R. Di Maio1, and A. Avella2 E. Piegari et al.
  • 1Dipartimento di Scienze della Terra, dell'Ambiente e delle Risorse, Università degli Studi di Napoli "Federico II", Naples, Italy
  • 2Dipartimento di Fisica "E. R. Caianiello", Università degli Studi di Salerno, Fisciano (SA), Italy

Abstract. Reasonable prediction of landslide occurrences in a given area requires the choice of an appropriate probability distribution of recurrence time intervals. Although landslides are widespread and frequent in many parts of the world, complete databases of landslide occurrences over large periods are missing and often such natural disasters are treated as processes uncorrelated in time and, therefore, Poisson distributed. In this paper, we examine the recurrence time statistics of landslide events simulated by a cellular automaton model that reproduces well the actual frequency-size statistics of landslide catalogues. The complex time series are analysed by varying both the threshold above which the time between events is recorded and the values of the key model parameters. The synthetic recurrence time probability distribution is shown to be strongly dependent on the rate at which instability is approached, providing a smooth crossover from a power-law regime to a Weibull regime. Moreover, a Fano factor analysis shows a clear indication of different degrees of correlation in landslide time series. Such a finding supports, at least in part, a recent analysis performed for the first time of an historical landslide time series over a time window of fifty years.

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