Tsunami modelling over global oceans

Tsunamis are massive waves generated by sudden water displacement on the ocean surface, causing devastation as they sweep across the coastlines, posing a global threat. The aftermath of the 2004 Indian Ocean tsunami led to the establishment of the Indian Tsunami Early Warning System (ITEWS). Predict...

Full description

Saved in:
Bibliographic Details
Main Authors: Siva Srinivas Kolukula, P. L. N. Murty, T. Srinivasa Kumar, E. Pattabhi Ramarao, Ramana Murthy M. V
Format: Article
Language:English
Published: The Royal Society 2025-01-01
Series:Royal Society Open Science
Subjects:
Online Access:https://royalsocietypublishing.org/doi/10.1098/rsos.241128
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Tsunamis are massive waves generated by sudden water displacement on the ocean surface, causing devastation as they sweep across the coastlines, posing a global threat. The aftermath of the 2004 Indian Ocean tsunami led to the establishment of the Indian Tsunami Early Warning System (ITEWS). Predicting real-time tsunami heights and the resulting coastal inundation is crucial in ITEWS to safeguard the coastal communities. Global tsunamis other than those in the Indian Ocean might weaken at Indian coasts due to distance yet still cause significant damage due to local coastal morphological amplification. The current study focuses on tsunami simulations over global oceans. A finite element (FE)-based ADvanced CIRCulation (ADCIRC) model is configured to the global domain to model global tsunamis accurately and efficiently. The model mesh has a spatial resolution of 2 km in the shallow waters and relaxed to 20 km in the deeper waters. Model simulations are performed for significant historical events, assessing their effect on near and far field regions. Computed results are compared with the observations, and it is found that the model’s predictions align well with the observations. The simulation results demonstrate that ADCIRC can be applied to real-time tsunami predictions due to its computational efficiency and accuracy.
ISSN:2054-5703