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The hazard from earthquake-generated tsunami waves is not only determined by the earthquake’s magnitude and mechanisms, and distance to the earthquake area, but also by the geomorphology of the nearshore and onshore areas, which can change over time. In coastal hazard assessments, a changing coastal environment is commonly taken into account by increasing the sea-level to projected values (static). However, sea-level changes and other climate-change impacts influence the entire coastal system causing morphological changes near- and onshore (dynamic). We compare the run-up of the same suite of earthquake-generated tsunamis to a barrier island-marsh-lagoon-marsh system for statically adjusted and dynamically adjusted sea level and bathymetry. Sea-level projections from 2000 to 2100 are considered. The dynamical adjustment is based on a morphokinetic model that incorporates sea-level along with other climate-change impacts. We employ Representative Concentration Pathways 2.6 and 8.5 without and with treatment of Antarctic Ice-sheet processes (known as K14 and K17) as different sea-level projections. It is important to note that we do not account for the occurrence probability of the earthquakes. Our results indicate that the tsunami run-up hazard for the dynamic case is approximately three times larger than for the static case. Furthermore, we show that nonlinear and complex responses of the barrier island-marsh-lagoon-marsh system to climate change profoundly impacts the tsunami hazard, and we caution that the tsunami run-up is sensitive to climate-change impacts that are less well-studied than sea-level rise. 

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.

 

Surge hazards induced by tropical cyclones have caused substantial economic losses and casualties for coastal communities worldwide. Under projected sea level rise (SLR), it is not well understood how the probabilistic surge hazard may change. Coastal planning and engineering usually adopt the “bathtub” method to evaluate the surge‐SLR response, where surge with SLR is considered to be the exact summation of the two. This method has been shown by previous studies to be unreliable. Studies on surge‐SLR response either use a low‐fidelity model setup or rely on individual storm's surge to represent the probabilistic surge due to the high computational burden. Herein, we use high‐fidelity numerical models in the Tampa region, West Florida, consider 188 synthetic storms and four SLR scenarios, and investigate the surge‐SLR response and its physical drivers. Compared to the direct summation of present‐day surge and SLR amount, results show that the probabilistic surge with SLR can be 1.0 m larger, while different individual storm's surge with the same magnitude can be 1.5 m larger or 0.1 m smaller, indicating the importance of not relying on a limited number of surge events to assess the probabilistic surge response to SLR. Investigation of the physical drivers shows that distinct topographic features of the study area and storm forward speed notably affect the surge‐SLR response. When considering 1.3 m or larger SLR in the study area, complex topography, and large surge events, the effects of SLR on the probabilistic surge are hard to predict and should be investigated more carefully.