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Real‐time tsunami prediction is necessary for tsunami forecasting. Although tsunami forecasting based on a precomputed tsunami simulation database is fast, it is difficult to respond to earthquakes that are not in the database. As the computation speed increases, various alternatives based on physics‐based models have been proposed. However, physics‐based models still require several minutes to simulate tsunamis and can have numerical stability issues that potentially make them unreliable for use in forecasting—particularly in the case of near‐field tsunamis. This paper presents a data‐driven model called the tsunami runup response function for finite faults (TRRF‐FF) model that can predict alongshore near‐field tsunami runup distribution from heterogeneous earthquake slip distribution in less than a second. Once the TRRF‐FF model is trained and calibrated based on a discrete set of tsunami simulations, the TRRF‐FF model can predict alongshore tsunami runup distribution from any combination of finite fault parameters. The TRRF‐FF model treats the leading‐order contribution and the residual part of the alongshore tsunami runup distribution separately. The interaction between finite faults is modeled based on the leading‐order alongshore tsunami runup distribution. We validated the TRRF‐FF modeling approach with more than 200 synthetic tsunami scenarios in eastern Japan. We further explored the performance of the TRRF‐FF model by applying it to the 2011 Tohoku (Japan) tsunami event. The results show that the TRRF‐FF model is more flexible, occupies much less storage space than a precomputed tsunami simulation database, and is more rapid and reliable than real‐time physics‐based numerical simulation.

 

Understanding a tsunami source and its impact is vital to assess a tsunami hazard. Thanks to the efforts of the tsunami survey teams, high‐quality tsunami run‐up data exist for contemporary events. Still, it has not been widely used to infer a tsunami source and its impact mainly due to the computational burden of the tsunami forward model. In this study, we propose a TRRF‐INV (Tsunami Run‐up Response Function‐based INVersion) model that can provide probabilistic estimates of a near‐field tsunami source and tsunami run‐up distribution from a small number of run‐up records. We tested the TRRF‐INV model with synthetic tsunami scenarios in northern Chile and applied it to the 2014 Iquique, Chile, tsunami event as a case study. The results demonstrated that the TRRF‐INV model can provide a reasonable tsunami source estimate to first order and estimate tsunami run‐up distribution well. Moreover, the case‐study results agree well with the United States Geological Survey report and the global Centroid Moment Tensor solution. We also analyzed the performance of the TRRF‐INV model depending on the number and the uncertainty of run‐up records. We believe that the TRRF‐INV model has the potential for supporting accurate hazard assessment by (1) providing new insights from tsunami run‐up records into the tsunami source and its impact, (2) using the TRRF‐INV model as a tool to support existing tsunami inversion models, and (3) estimating a tsunami source and its impact for ancient events where no data other than estimated run‐up from sediment deposit data exist.

 

Barrier island models that include marsh and lagoon processes are highly parameterized. To constrain model uncertainty, those desiring to use these models should seek a robust understanding of the parameter sensitivities. In this study, global sensitivity analysis was performed on a long-term barrier island model to yield insights into the modeled barrier-backbarrier system. Given that a variety of global sensitivity analysis methods exist, each one appearing to differ in its implementation, computational burden, and output, three methods (i.e., the Two-Level Full Factorial Method, Morris Method, and Sobol Method) were applied to the model for the purposes of comparison. Key influential parameters (e.g., sea level rise rate, equilibrium/critical barrier width, and reference wind speed) were consistently identified by all three sensitivity analysis methods. Despite the relatively low number of simulations required by the Morris Method, the Two-Level Method computationally outperformed the others, warranting further exploration of the Morris Method’s parallelization structure. These results may be used to help identify parameter constraints and characterize model uncertainty toward more confident predictions and management decisions for coastal barrier systems. 

This paper will discuss the beginnings of a sensitivity analysis of barrier island breaching. The study area of Mantoloking, New Jersey, USA is used as the barrier island breached significantly during Hurricane Sandy in 2012. The numerical model XBeach is used to conduct this study. The study investigates the affects that back-bay currents, water-level timing, and barrier-island configuration have on barrier island breaching.