The hypermobility of huge landslides and avalanches

Catastrophic failure of large land masses, which generate landslides, rockfalls and debris avalanches, can have hazardous consequences extending far beyond the source. Observations show that the mobility of such events depends strongly on the volume for volumes larger than 10(6) m(3), with many different processes invoked to explain higher mobilities (hypermobility) for both terrestrial and extraterrestrial events. Although the mobility of large events has been extensively studied, there is no generally accepted mechanism for predicting extreme travel distances because the underlying physical processes are poorly understood. Here we show using physical and rheological arguments that the wide scatter observed for very large mass wasting events in all environments collapses to a single relationship between event volume or inundation area and mobility. Hypermobility is defined to be the reciprocal of the effective friction coefficient p, where the scale-dependent mu(e) is derived analytically as a function of the mechanical, volumetric and topographical parameters of the flow. The dominant term in the coefficient is the degree of fluidization involved in the flow; our results show that fluidization is limited in extraterrestrial events, that significant fluidization occurs in non-volcanic and volcanic events, and fluidization dominates submarine events. This analysis demonstrates that fluidization is associated with long run-out distances, and that the degree of fluidization can be predicted by the volume, and physical and topographic parameters. The methodology is simple, physically-based and validated with datasets of very large terrestrial and extraterrestrial avalanche events. We demonstrate that the effective Coulomb friction rheology and the hypermobility function are applicable to avalanche events of any size, providing an opportunity to simulate past and/or potential huge landslide and debris avalanche events, run-out distances, destructive impact and assessment of risk. The model can be used to estimate the overrun area and volume in terms of known mobility data. (C) 2013 Elsevier B.V. All rights reserved.