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A sensitivity study is undertaken to assess the utility of different onshore digital elevation models (DEMs) for simulating the extent of tsunami inundation using case studies from two locations in Indonesia. We compare airborne IFSAR, ASTER, and SRTM against high resolution LiDAR and stereo-camera data in locations with different coastal morphologies. Tsunami inundation extents modeled with airborne IFSAR DEMs are comparable with those modeled with the higher resolution datasets and are also consistent with historical run-up data, where available. Large vertical errors and poor resolution of the coastline in the ASTER and SRTM elevation datasets cause the modeled inundation extent to be much less compared with the other datasets and observations. Therefore, ASTER and SRTM should not be used to underpin tsunami inundation models. A model mesh resolution of 25 m was sufficient for estimating the inundated area when using elevation data with high vertical accuracy in the case studies presented here. Differences in modeled inundation between digital terrain models (DTM) and digital surface models (DSM) for LiDAR and IFSAR are greater than differences between the two data types. Models using DTM may overestimate inundation while those using DSM may underestimate inundation when a constant Manning's roughness value is used. We recommend using DTM for modeling tsunami inundation extent with further work needed to resolve the scale at which surface roughness should be parameterized.

This paper considers the importance of model parameterization, including dispersion, source kinematics, and source discretization, in tsunami source inversion. We implement single and multiple time window methods for dispersive and nondispersive wave propagation to estimate source models for the tsunami generated by the 2011 Tohoku‐Oki earthquake. Our source model is described by sea surface displacement instead of fault slip, since sea surface displacement accounts for various tsunami generation mechanisms in addition to fault slip. The results show that tsunami source models can strongly depend on such model choices, particularly when high‐quality, open‐ocean tsunami waveform data are available. We carry out several synthetic inversion tests to validate the method and assess the impact of parameterization including dispersion and variable rupture velocity in data predictions on the inversion results. Although each of these effects has been considered separately in previous studies, we show that it is important to consider them together in order to obtain more meaningful inversion results. Our results suggest that the discretization of the source, the use of dispersive waves, and accounting for source kinematics are all important factors in tsunami source inversion of large events such as the Tohoku‐Oki earthquake, particularly when an extensive set of high‐quality tsunami waveform recordings are available. For the Tohoku event, a dispersive model with variable rupture velocity results in a profound improvement in waveform fits that justify the higher source complexity and provide a more realistic source model.

We have previously developed a tsunami source inversion method based on “Time Reverse Imaging” and demonstrated that it is computationally very efficient and has the ability to reproduce the tsunami source model with good accuracy using tsunami data of the 2011 Tohoku earthquake tsunami. In this paper, we implemented this approach in the 2009 Samoa earthquake tsunami triggered by a doublet earthquake consisting of both normal and thrust faulting. Our result showed that the method is quite capable of recovering the source model associated with normal and thrust faulting. We found that the inversion result is highly sensitive to some stations that must be removed from the inversion. We applied an adjoint sensitivity method to find the optimal set of stations in order to estimate a realistic source model. We found that the inversion result is improved significantly once the optimal set of stations is used. In addition, from the reconstructed source model we estimated the slip distribution of the fault from which we successfully determined the dipping orientation of the fault plane for the normal fault earthquake. Our result suggests that the fault plane dip toward the northeast.