From granular collapses to shallow water waves: A predictive model for tsunami generation

TL;DR

This paper introduces a predictive model for tsunami wave amplitude caused by granular collapses, integrating grain dynamics and shallow water hydrodynamics, validated with experimental data and key dimensionless parameters.

Contribution

The model combines granular spreading and wave hydrodynamics, providing a new comprehensive approach to predict tsunami generation from landslides and collapses.

Findings

Model accurately predicts wave amplitudes across various conditions.

The Froude number and volume ratio significantly influence wave generation.

Laboratory data confirms the model's effectiveness in real-world scenarios.

Abstract

In this article, we present a predictive model for the amplitude of impulse waves generated by the collapse of a granular column into a water layer. The model, which combines the spreading dynamics of the grains and the wave hydrodynamics in shallow water, is successfully compared to a large dataset of laboratory experiments, and captures the influence of the initial parameters while giving an accurate prediction. Furthermore, the role played on the wave generation by two key dimensionless numbers, i.e., the global Froude number and the relative volume of the immersed deposit, is rationalized. These results provide a simplified, yet comprehensive, physical description of the generation of tsunami waves engendered by large-scale subaerial landslides, rockfalls, or cliff collapses in a shallow water.