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We face a new era in the assessment of multiple natural hazards whose statistics are becoming alarmingly non‐stationary due to ubiquitous long‐term changes in climate. One particular case is tsunami hazard affected by climate‐change‐driven sea level rise (SLR). A traditional tsunami hazard assessment approach where SLR is omitted or included as a constant sea‐level offset in a probabilistic calculation may misrepresent the impacts of climate‐change. In this paper, a general method called non‐stationary probabilistic tsunami hazard assessment (nPTHA), is developed to include the long‐term time‐varying changes in mean sea level. The nPTHA is based on a non‐stationary Poisson process model, which takes advantage of the independence of arrivals within non‐overlapping time‐intervals to specify a temporally varying hazard mean recurrence rate, affected by SLR. The nPTHA is applied to the South China Sea (SCS) for tsunamis generated by earthquakes in the Manila Subduction Zone. The method provides unique and comprehensive results for inundation hazard, combining tsunami and SLR at a specific location over a given exposure time. The results show that in the SCS, SLR has a significant impact when its amplitude is comparable to that of tsunamis with moderate probability of exceedance. The SLR and its associated uncertainty produce an impact on nPTHA results comparable to that caused by the uncertainty in the earthquake recurrence model. These findings are site‐specific and must be analyzed for different regions. The proposed methodology, however, is sufficiently general to include other non‐stationary phenomena and can be exploited for other hazards affected by SLR.

Models of bathymetry derived from satellite radar altimetry are essential for modeling many marine processes. They are affected by uncertainties which require quantification. We propose an uncertainty model that assumes errors are caused by the lack of high‐wavenumber content within the altimetry data. The model is then applied to a tsunami hazard assessment. We build a bathymetry uncertainty model for northern Chile. Statistical properties of the altimetry‐predicted bathymetry error are obtained using multibeam data. We find that a Von Karman correlation function and a Laplacian marginal distribution can be used to define an uncertainty model based on a random field. We also propose a method for generating synthetic bathymetry samples conditional to shipboard measurements. The method is further extended to account for interpolation uncertainties, when bathymetry data resolution is finer than∼10 km. We illustrate the usefulness of the method by quantifying the bathymetry‐induced uncertainty of a tsunami hazard estimate. We demonstrate that tsunami leading wave predictions at middle/near field tide gauges and buoys are insensitive to bathymetry uncertainties in Chile. This result implies that tsunami early warning approaches can take full advantage of altimetry‐predicted bathymetry in numerical simulations. Finally, we evaluate the feasibility of modeling uncertainties in regions without multibeam data by assessing the bathymetry error statistics of 15 globally distributed regions. We find that a general Von Karman correlation and a Laplacian marginal distribution can serve as a first‐order approximation. The standard deviation of the uncertainty random field model varies regionally and is estimated from a proposed scaling law.

In this paper, we have conducted a probabilistic tsunami hazard assessment (PTHA) for Hong Kong (China) and Kao Hsiung (Taiwan), considering earthquakes generated in the Manila subduction zone. The new PTHA methodology with the consideration of uncertainties of slip distribution and location of future earthquakes extends the stochastic approach of Sepúlveda et al. (2017). Using sensitivity analyses, we further investigate the uncertainties of probability properties defining the slip distribution, the location, and the occurrence of earthquakes. We demonstrate that Kao Hsiung and Hong Kong would be significantly impacted by tsunamis generated byM_W > 8.5 earthquakes in the Manila subduction zone. For instance, a specificM_W9.0 earthquake scenario is capable of producing tsunami amplitudes exceeding 4.0 and 3.5 m in Kao Hsiung and Hong Kong, respectively, with a probability of 50%. Despite the significant tsunami impact, great earthquakes have long mean return periods. As a result, the PTHA shows that Kao Hsiung and Hong Kong are exposed to a relatively small tsunami hazard. For instance, maximum tsunami amplitudes in the assessed locations of Kao Hsiung and Hong Kong exceed 0.32 and 0.18 m, respectively, with a mean return period of 100 years. The inundation hazard in populated areas is small as well, with mean return periods exceeding 1,000 years. Sensitivity analyses demonstrate that the PTHA can be affected by the uncertainties of the probability properties defining the slip distribution, the location, and the occurrence of earthquakes. However, PTHA results are most sensitive to the choice of the earthquake occurrence model.

The 2018 Palu tsunami contributed significantly to the devastation caused by the associated7.5 earthquake. This began a debate about how the moderate size earthquake triggered such a large tsunami within Palu Bay, with runups of more than 10 m. The possibility of a large component of vertical coseismic deformation and submarine landslides have been considered as potential explanations. However, scarce instrumental data have made it difficult to resolve the potential contributions from either type of source. We use tsunami waveforms derived from social media videos in Palu Bay to model the possible sources of the tsunami. We invert InSAR data with different fault geometries and use the resulting seafloor displacements to simulate tsunamis. The coseismic sources alone cannot match both the video‐derived time histories and surveyed runups. Then we conduct a tsunami source inversion using the video‐derived time histories and a tide gauge record as inputs. We specify hypothetical landslide locations and solve for initial tsunami elevation. Our results, validated with surveyed runups, show that a limited number of landslides in southern Palu Bay are sufficient to explain the tsunami data. The Palu tsunami highlights the difficulty in accurately capturing with tide gauges the amplitude and timing of short period waves that can have large impacts at the coast. The proximity of landslides to locations of high fault slip also suggests that tsunami hazard assessment in strike‐slip environments should include triggered landslides, especially for locations where the coastline morphology is strongly linked to fault geometry.