A granular scaling approach to landslide runout

TL;DR

This paper introduces a simplified granular scaling method to predict landslide runout distances by linking laboratory experiments with field data, emphasizing the roles of fall height and grain size distribution.

Contribution

It presents a novel scaling approach that unifies laboratory and field data, improving prediction accuracy of landslide runout by considering fall height and grain size skewness.

Findings

Mobility increases with the square root of fall height.

Mobility correlates with grain size distribution skewness.

Normalized runout correlates better when accounting for these parameters.

Abstract

A main objective in landslide research is to predict how far they will travel. Landslides are complex, and a complete understanding in principle requires accounting for numerous parameters. Here we engender a simplification by investigating the maximum landslide runout using granular laboratory experiments and a scaling analysis. We find that correctly accounting for the fall height and grain size distribution not only yields an improved correlation of normalized runout, but also quantitatively unites laboratory and field data. In particular, we find that the mobility of landslides increases with the square root of the fall height and with the skewness of the grain size distribution.