Heatmap analysis of modeled coastal tsunamis using different bathymetry data resolutions

• Main Authors: Felix, Raquel, Hubbard, Judith, Wilson, Kaya, Switzer, Adam D.
• Other Authors: Asian School of the Environment
• Format: Article
• Language: English
• Published: 2025
• Subjects: Earth and Environmental Sciences; Bathymetry; Coastal tsunami
• Online Access: https://hdl.handle.net/10356/182065
• Institution: Nanyang Technological University

We examined how variations in the horizontal resolution of bathymetry influence the behavior of modeled tsunamis at shallow depths nearshore. This was done using the Cornell Multi-grid Coupled Tsunami Model (COMCOT) to simulate tsunamis with resampled bathymetric data at resolutions of 5, 10, 20, 30, 40, 50, 100, 200, and 300 meters, derived from 1-m resolution NOAA coastal LiDAR data sets (at water depths of less than or equal to 30 m) and soundings. In total, we utilized 1,080 data sets, comprising 9 resolutions across 30 sets at 4 different sites. In addition, we included the 15-arc second grid (∼455 m) 2021 GEBCO data for comparison. We initiated a 5-m high tsunami wave offshore and propagated it towards the coast, then used the resulting maximum wave heights for each resolution to quantify the differences across varying resolutions. Using the 5 m bathymetry as the reference model, we observed that data sets with 10–50 m resolutions can reproduce tsunamis reasonably well. The maximum heights are overestimated by less than or equal to 5% or underestimated by less than or equal to 10%, and the first wave arrival time is ∼10% earlier than expected. Coarser bathymetries show an increasing trend of height underestimation, with the GEBCO model underestimating it by as much as 70%. Coarser bathymetry models have more variable first wave arrival time, with waves arriving up to 20% later or up to 10% earlier than expected. Overall, a reasonably accurate result can be achieved using a bathymetric resolution in the 10 m–50 m range, and is achievable with reasonable computational efficiency (at least 80% faster than simulations using the 5 m model on high-performance computing). This study highlights the importance of shallow bathymetry data quality in the numerical modeling of tsunami propagation.