Preprints
https://doi.org/10.5194/nhess-2021-30
https://doi.org/10.5194/nhess-2021-30

  15 Feb 2021

15 Feb 2021

Review status: this preprint is currently under review for the journal NHESS.

Tsunami propagation kernel and its applications

Takenori Shimozono Takenori Shimozono
  • The University of Tokyo, Tokyo, Japan

Abstract. Tsunamis rarely occur in a specific area, and their occurrence is highly uncertain. Generated from their sources in deep water, they occasionally undergo tremendous amplification over decreasing water depth to devastate low-lying coastal areas. Despite the advancement of computational power and simulation algorithms, there is a need for novel and rigorous approaches to efficiently predict coastal amplification of tsunamis during different disaster management phases, such as tsunami risk assessment and real-time forecast. This study presents convolution kernels that can instantly predict onshore waveforms of water surface elevation and flow velocity from observed/simulated wavedata apart from the shore. Kernel convolution involves isolating an incident-wave component from the offshore wavedata and transforming it into the onshore waveform. Moreover, unlike previous derived ones, the present kernels are based on shallow-water equations with a damping term and can account for tsunami attenuation on its path to the shore with a damping parameter. Kernel convolution can be implemented at a low computational cost compared to conventional numerical models that discretise the spatial domain. The prediction capability of the kernel method was demonstrated through application to real-world tsunami cases.

Takenori Shimozono

Status: open (until 29 Mar 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-30', Efim Pelinovsky, 16 Feb 2021 reply
  • RC2: 'Comment on nhess-2021-30', Anonymous Referee #2, 08 Mar 2021 reply

Takenori Shimozono

Takenori Shimozono

Viewed

Total article views: 202 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
153 46 3 202 0 0
  • HTML: 153
  • PDF: 46
  • XML: 3
  • Total: 202
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 15 Feb 2021)
Cumulative views and downloads (calculated since 15 Feb 2021)

Viewed (geographical distribution)

Total article views: 199 (including HTML, PDF, and XML) Thereof 199 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Mar 2021
Download
Short summary
Tsunamis are a major threat to low-lying coastal communities. Generated from their sources in deep water, tsunamis occasionally undergo tremendous amplification in shallow water. There is a need for efficient ways of predicting coastal tsunami transformation during different disaster management phases. The study proposed a novel and rigorous method based on kernel convolution for fast prediction of onshore tsunami waveforms from the observed/simulated wave data apart from the coast.
Altmetrics