Numerical modeling of tsunami: advances and future challenges after the 2011 Tohoku earthquake and tsunami

This paper reviews achievements and findings from studies associated with numerical modeling of tsunami since the 2011 Tohoku earthquake, and addresses challenges for future advances. The topics cover improvements in tsunami numerical modeling including multi-physics simulations, applications to source modeling, hazard assessment and real-time forecasting and warning. During the last 10 years, tsunami numerical modeling has been improved, validated and applied to better predict tsunami's behavior and impacts, with aids from the groundbreaking data of the 2011 Tohoku earthquake tsunami. Marked progresses have been made for propagation and inundation modeling, multi-physics simulations such as sediment transport modeling and real-time forecasting and warning. Sediment transport modeling since the Tohoku earthquake tsunami has shown that it may profit from bridging different disciplines to gain further advance in disaster science. Despite the progresses, further exploration for better accuracy and capability, and improved understanding of model limitations and uncertainties is needed. Source modeling of the Tohoku earthquake tsunami, with aids from tsunami records and other geophysical datasets, have provided detailed depiction of the spatial distribution of initial water level, fault slip and mechanism and process of tsunami amplification. To improve assessment of tsunami hazards and related damages, the source features of the Tohoku earthquake tsunami were characterized and applied to hazard assessments of future tsunamis in other areas. The characterization will benefit from not overlooking ‘unexpected’ hazards and damages, although some other processes, such as rupture propagation and submarine mass failure, cannot be excluded from uncertainty quantification in tsunami source modeling. As in past years, tsunami numerical modeling will play a crucial role in policy making and consensus building for tsunami disaster mitigation actions, with efforts for better transmission of the model's intent, limitation and uncertainty.