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Abstract 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. 

Abstract 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. 

Abstract 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. 

Earthquakes pose a major threat to the people of Haiti, as tragically shown by the catastrophic 2010 Mw 7.0 earthquake and more recently by the 2021 Mw 7.2 earthquake. Both events occurred within the transpressional Enriquillo–Plantain Garden fault zone (EPGFZ), which runs through the southern peninsula of Haiti and is a major source of seismic hazard for the region. Satellite-based Interferometric Synthetic Aperture Radar (InSAR) data are used to illuminate the ground deformation patterns associated with the 2021 event. The analysis of Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 InSAR data shows (1) the broad coseismic deformation field; (2) detailed secondary fault structures as far as 12 km from the main Enriquillo–Plantain Garden fault (EPGF), which are active during and after the earthquake; and (3) postseismic shallow slip, which migrates along an ∼40 km unruptured section of the EPGF for approximately two weeks following the earthquake. The involvement of secondary faults in this rupture requires adjustments to the representation of hazard that assumes a simple segmented strike-slip EPGF. This work presents the first successful use of phase gradient techniques to map postseismic deformation in a vegetated region, which opens the door to future studies of a larger number of events in a wider variety of climates. 

In recent years, several locations in the United States have been experiencing a significant increase in seismicity that has been attributed to oil and gas production. As oil and natural gas production in the United States continues to increase, it is expected that the seismic hazard in these locations will continue to experience a corresponding upsurge. However, many urban structures in these locations are not designed to withstand these increasing levels of seismicity. Accordingly, it is crucial to develop methodologies that can help us quantify the seismic performance of these structures, establish their risk levels, and identify optimal retrofit strategies that will enhance the seismic resilience of these structures. In this context, structural health monitoring (SHM) plays an important role in understanding the seismic performance of structures. SHM can be used, in conjunction with finite element modelling, to provide a realistic representation of the structural performance during a seismic event. In this paper, a framework for seismic risk assessment of reinforced concrete buildings based on SHM is presented. The framework combines nonlinear finite element modeling and SHM data to establish the seismic fragility profile of the structure. The approach is illustrated on a multi- story reinforced concrete structure located on the Oklahoma State University Campus.