Rapid prediction of alongshore run-up distribution from near-field tsunamis
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Rapid prediction of alongshore run‑up distribution from near‑field tsunamis Jun‑Whan Lee1 · Jennifer L. Irish1,3 · Robert Weiss2,3 Received: 12 February 2020 / Accepted: 25 July 2020 © Springer Nature B.V. 2020
Abstract Rapid prediction of the spatial distribution of the run-up from near-field tsunamis is critically important for tsunami hazard characterization. Even though significant advances have been made over the last decade, physics-based numerical models are still computationally intensive. Here, we present a response surface methodology (RSM)-based model called the tsunami run-up response function (TRRF). Derived from a discrete set of tsunami simulations, TRRF can produce a rapid prediction of a near-field tsunami run-up distribution that takes into account the influence of variable local topographic and bathymetric characteristics in a given region. This new method reduces the number of simulations required to build an RSM model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Using the northern region of Puerto Rico as a case study, we investigated the performance (accuracy, computational time) of the TRRF. The results reveal that the TRRF achieves reliable prediction while reducing the prediction time by six orders of magnitude (computational time: < 1 s per earthquake). Keywords Tsunami · Run-up · Response surface methodology · Puerto Rico
1 Introduction Tsunamis are some of the most destructive and costly natural hazards for coastal areas around the world. The 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami are prime examples of how tsunamis can cause extensive damage to coastal communities, especially in near-field areas (Titov et al. 2005; Wei et al. 2013). A near-field tsunami, which is a tsunami generated close to the coastline, involves a high risk for coastal communities because the first waves can arrive on shore in minutes (National Research Council 2011). To mitigate damage and build resilient coastal communities, it is critically important to develop rapid prediction capacities for a near-field tsunami run-up distribution along the coastlines. * Jun‑Whan Lee [email protected] 1
Department of Civil and Environmental Engineering, Virginia Tech, 750 Drillfield Dr, Blacksburg, VA 24061, USA
2
Department of Geosciences, Virginia Tech, 926 W Campus Dr, Blacksburg, VA 24061, USA
3
Center for Coastal Studies, Virginia Tech, 926 W Campus Dr, Blacksburg, VA 24061, USA
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Natural Hazards
Physics-based numerical simulation is currently the most accurate method for predicting a tsunami run-up distribution. Though significant advances have been made over the last decade (LeVeque et al. 2011; Lin et al. 2015; Popinet 2015; Shi et al. 2012), these physicsbased numerical models still remain time consuming. For example, robust probabilistic tsunami hazard assessment (PTHA) requires tsunami run-up estimates for a large number of scenarios to allow for accurate quantification of the hazard and related unce
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