Spatial pattern of landslides in Swiss Rhone Valley

Marj Tonini (), Andrea Pedrazzini, Ivanna Penna and Michel Jaboyedoff

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2014, vol. 73, issue 1, 97-110

Abstract: The present study analyses the spatial pattern of quaternary gravitational slope deformations (GSD) and historical/present-day instabilities (HPI) inventoried in the Swiss Rhone Valley. The main objective is to test if these events are clustered (spatial attraction) or randomly distributed (spatial independency). Moreover, analogies with the cluster behaviour of earthquakes inventoried in the same area were examined. The Ripley’s K-function was applied to measure and test for randomness. This indicator allows describing the spatial pattern of a point process at increasing distance values. To account for the non-constant intensity of the geological phenomena, a modification of the K-function for inhomogeneous point processes was adopted. The specific goal is to explore the spatial attraction (i.e. cluster behaviour) among landslide events and between gravitational slope deformations and earthquakes. To discover if the two classes of instabilities (GSD and HPI) are spatially independently distributed, the cross K-function was computed. The results show that all the geological events under study are spatially clustered at a well-defined distance range. GSD and HPI show a similar pattern distribution with clusters in the range 0.75–9 km. The cross K-function reveals an attraction between the two classes of instabilities in the range 0–4 km confirming that HPI are more prone to occur within large-scale slope deformations. The K-function computed for GSD and earthquakes indicates that both present a cluster tendency in the range 0–10 km, suggesting that earthquakes could represent a potential predisposing factor which could influence the GSD distribution. Copyright Springer Science+Business Media Dordrecht 2014

Keywords: Ripley’s K-function; Landslides; Cluster; Spatial pattern; Swiss Alps (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11069-012-0522-9

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