GeoClaw-STRICHE: A coupled model for Sediment TRansport In Coastal Hazard Events

TL;DR

GeoClaw-STRICHE is a coupled model that simulates tsunami-induced erosion, sediment transport, and deposition, incorporating multiple grain sizes and layers, validated against experimental and real-world data, and showing improved predictive capabilities.

Contribution

It introduces a novel coupled model with multiple sediment layers and grain sizes, using the Van Leer method for sediment flux, enhancing tsunami sediment transport simulation accuracy.

Findings

Accurately predicts sediment thickness and grain-size distribution in experiments.

Shows good agreement with real tsunami data, outperforming some existing models.

Demonstrates the model's capability to simulate complex sediment transport processes.

Abstract

GeoClaw-STRICHE is designed for simulating the physical impacts of tsunami as it relates to erosion, transport and deposition. GeoClaw-STRICHE comprises of three components: (1) nonlinear shallow water equations; (2) advection-diffusion equation; (3) an equation for morphology updating. Multiple grain sizes and sediment layers are added into GeoClaw-STRICHE to simulate grain-size distribution and add the capability to develop grain-size trends from bottom to the top of a simulated deposit as well as along the inundation. Unlike previous models based on empirical equations or sediment concentration gradient, the standard Van Leer method is applied to calculate sediment flux. We tested and verified GeoClaw-STRICHE with flume experiment by \citet{johnson2016experimental} and data from the 2004 Indian Ocean tsunami in Kuala Meurisi as published in \citet{JGRF:JGRF786}. The comparison with experimental data shows GeoClaw-STRICHE's capability to simulate sediment thickness and grain-size distribution in experimental conditions. The agreement between experimental and model results is generally quite good, especially for sediment thickness, which builds confidence that sediment transport is correctly predicted by this model. The comparison with the data from the 2004 Indian Ocean tsunami reveals that the pattern of sediment thickness is well predicted and is of similar quality, if not better than the established computational models such as Delft3D.