Abstract
Abstract. We present a database of pre-calculated tsunami waveforms for the entire Mediterranean Sea, obtained by numerical propagation of uniformly spaced Gaussian-shaped elementary sources for the sea level elevation. Based on any initial sea surface displacement, the database allows the fast calculation of full waveforms at the 50 m isobath offshore of coastal sites of interest by linear superposition. A computationally inexpensive procedure is set to estimate the coefficients for the linear superposition based on the potential energy of the initial elevation field. The elementary sources size and spacing is fine enough to satisfactorily reproduce the effects of M> = 6.0 earthquakes. Tsunami propagation is modelled by using the Tsunami-HySEA code, a GPU finite volume solver for the non-linear shallow water equations. Like other existing methods based on the initial sea level elevation, the database is independent on the faulting geometry and mechanism, which makes it applicable in any tectonic environment. We model a large set of synthetic tsunami test scenarios, selected to explore the uncertainty introduced when approximating tsunami waveforms and their maxima by fast and simplified linear combination. This is the first time to our knowledge that the uncertainty associated to such a procedure is systematically analysed and that relatively small earthquakes are considered, which may be relevant in the near-field of the source in a complex tectonic setting. We find that non-linearity of tsunami evolution affects the reconstruction of the waveforms and of their maxima by introducing an almost unbiased (centred at zero) error distribution of relatively modest extent. The uncertainty introduced by our approximation can be in principle propagated to forecast results. The resulting product then is suitable for different applications such as probabilistic tsunami hazard analysis, tsunami source inversions and tsunami warning systems.
Highlights
After the 2004 Indian Ocean tsunami, particular attention has been devoted to the improvement of tsunami warning systems (TWS) and probabilistic tsunami hazard analysis (PTHA), which currently represent two pillars in risk mitigation policies for the authorities of each country exposed to tsunami threat (Satake, 2014)
The chosen σ ensures reaching a compromise between the spatial resolution needed to approximate the relatively small-wavelength deformation field caused by earthquakes down to M = 6.0, while still having a sufficient number of grid points to represent the Gaussian field for unit source propagation
We present here a source mechanism-free tool to rapidly reconstruct the full waveform and the maximum wave heights predicted by any static tsunami initial water displacement, independently from any a priori assumptions on fault geometry
Summary
After the 2004 Indian Ocean tsunami, particular attention has been devoted to the improvement of tsunami warning systems (TWS) and probabilistic tsunami hazard analysis (PTHA), which currently represent two pillars in risk mitigation policies for the authorities of each country exposed to tsunami threat (Satake, 2014). In addition to being independent of the source mechanism, the unit source size and density is suitable to satisfactorily reproduce the tsunamis generated by large earthquakes and those generated by events as small as M6 earthquakes The performance of this tool is analysed by quantifying its limits and errors in recovering an initial water displacement field and by assessing its usability in several different possible applications, such as probabilistic tsunami hazard analysis, tsunami source inversions and tsunami warning systems: for example, by propagating the estimated uncertainty in the probability distribution of the tsunami forecast The performance of this tool is analysed by quantifying its limits and errors in recovering an initial water displacement field and by assessing its usability in several different possible applications, such as probabilistic tsunami hazard analysis, tsunami source inversions and tsunami warning systems: for example, by propagating the estimated uncertainty in the probability distribution of the tsunami forecast (e.g. Annaka et al, 2007; Horspool et al, 2014)
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