<div>This record links to tarred folders with simulation files used for a study on tsunami hazards in Tongatapu (eCat 146012) - DOI: https://doi.org/10.1093/gji/ggac140. </div><div><br></div><div>Access to this data will only be available by request via datacatalogue@ga.gov.au</div><div><br></div><div>The files were created using code here: </div><div>https://github.com/GeoscienceAustralia/ptha/tree/master/misc/monte_carlo_paper_2021. </div><div><br></div><div>This code should be read to understand the structure and contents of the tar archives. The simulation files are large and for most use cases you won't need them. First check if your needs a met via code and documentation at the link above. If the git repository doesn't include links to what you need, then it may be available in these tar archives. Contents include the datasets used to setup the model and the model outputs for every scenario. While the modelling files and code were developed by GA, at the time of writing, we do not have permission to distribute some of the input datasets outside of GA (including the Tongatapu LIDAR). </div><div><br></div><div>Access to this data will only be available by request via datacatalogue@ga.gov.au</div>

<div>This is for submission to the 2022 ICCE Conference: https://icce2022.com/</div> This Abstract was submitted/presented to the 2022 International Conference on Coastal Engineering (ICCE) 04-09 December (https://icce2022.com/)

PTHA18 estimates the frequency with which tsunamis of any given size occur in deep waters around the Australian coastline. To do this it simulates hundreds of thousands of possible tsunami scenarios from key earthquake sources in the Pacific and Indian Oceans, and models the frequency with which these occur.

This report describes the 2018 Probabilistic Tsunami Hazard Assessment for Australia (henceforth PTHA18). The PTHA18 estimates the frequency with which tsunamis of any given size occur in deep waters around the Australian coastline. To do this it simulates hundreds of thousands of possible tsunami scenarios from key earthquake sources in the Pacific and Indian Oceans, and models the frequency with which these occur. To justify the PTHA18 methodologies a significant fraction of the report is devoted to testing the tsunami scenarios against historical observations, and comparing the modelled earthquake rates against alternative estimates. Although these test provide significant justification for the PTHA18 results, there remain large uncertainties in “how often” tsunamis occur at many sites. This is due to fundamental limitations in present-day scientific knowledge of how often large earthquakes occur.

The 2018 Probabilistic Tsunami Hazard Assessmetn (PTHA18) outputs are can be accessed following the README instructions here: https://github.com/GeoscienceAustralia/ptha/tree/master/ptha_access

Offshore Probabilistic Tsunami Hazard Assessments (offshore PTHAs) provide large-scale analyses of earthquake-tsunami frequencies and uncertainties in the deep ocean, but do not provide high-resolution onshore tsunami hazard information as required for many risk-management applications. To understand the implications of an onshore PTHA for the onshore hazard at any site, in principle the tsunami inundation should be simulated locally for every scenario in the offshore PTHA. In practice this is rarely feasible due to the computational expense of inundation models, and the large number of scenarios in offshore PTHAs. Monte-Carlo methods offer a practical and rigorous alternative for approximating the onshore hazard, using a random subset of scenarios. The resulting Monte-Carlo errors can be quantified and controlled, enabling high-resolution onshore PTHAs to be implemented at a fraction of the computational cost. This study develops novel Monte-Carlo sampling approaches for offshore-to-onshore PTHA. Modelled offshore PTHA wave heights are used to preferentially sample scenarios that have large offshore waves near an onshore site of interest. By appropriately weighting the scenarios, the Monte-Carlo errors are reduced without introducing any bias. The techniques are applied to a high-resolution onshore PTHA for the island of Tongatapu in Tonga. In this region, the new approaches lead to efficiency improvements equivalent to using 4-18 times more random scenarios, as compared with stratified-sampling by magnitude, which is commonly used for onshore PTHA. The greatest efficiency improvements are for rare, large tsunamis, and for calculations that represent epistemic uncertainties in the tsunami hazard. To facilitate the control of Monte-Carlo errors in practical applications, this study also provides analytical techniques for estimating the errors both before and after inundation simulations are conducted. Before inundation simulation, this enables a proposed Monte-Carlo sampling scheme to be checked, and potentially improved, at minimal computational cost. After inundation simulation, it enables the remaining Monte-Carlo errors to be quantified at onshore sites, without additional inundation simulations. In combination these techniques enable offshore PTHAs to be rigorously transformed into onshore PTHAs, with full characterisation of epistemic uncertainties, while controlling Monte-Carlo errors. Appeared online in Geophysical Journal International 11 April 2022.

A mini-poster on GA's capability in tsunami hazard modelling.

The present study reports on recent developments of the Indonesia Tsunami Early Warning System (InaTEWS), especially with respect to the tsunami modeling components used in that system. It is a dual system: firstly, InaTEWS operates a high-resolution scenario database pre-computed with the finite element model TsunAWI; running in parallel, the system also contains a supra real-time modeling component based on the GPU-parallelized linear long-wave model easyWave capable of dealing with events outside the database coverage. The evolution of the tsunami scenario database over time is covered in the first sections. Starting from the mere coverage of the Sunda Arc region, the current state contains scenarios in 15 fault zones. The study is augmented by an investigation of warning products used for early warning like the estimated wave height (EWH) and the estimated time of arrival (ETA). These quantities are determined by easyWave and TsunAWI with model specific approaches. Since the numerical setup of the models is very different, the extent of variations in warning products is investigated for a number of scenarios, where both pure database scenarios and applications to real events are considered.

Numerical codes for probabilistic tsunami hazard assessment, available for download in github: https://github.com/GeoscienceAustralia/ptha

Hazardous tsunamis are rare in Australia but could be generated by several mechanisms, including large plate-boundary earthquakes in locations that efficiently direct wave energy to our coast. With few hours between detection and tsunami arrival, prior planning is important to guide emergency response and risk mitigation. This drives interest in tsunami hazard information; which areas could be inundated, how likely, and how confident can we be? In practice the hazard is uncertain because historical records are short relative to tsunami frequencies, while long-term sedimentary records are sparse. Hazard assessments thus often follow a probabilistic approach where many alternative tsunami scenarios are simulated and assigned uncertain occurrence rates. This relies on models of stochastic earthquakes and their occurrence rates, which are not standardised, but depend on the scenario earthquake magnitude and other information from the source region. In this study we test three different stochastic tsunami models from the 2018 Australian Probabilistic Tsunami Hazard Assessment (PTHA18), an open-source database of earthquake-tsunami scenarios and return periods. The three models are tested against observations from twelve historical tsunamis at multiple tide gauges in Australia. For each historical tsunami, and each of the three models, sixty scenarios with similar earthquake location and magnitude are sampled from the PTHA18 database. A nonlinear shallow water model is used to simulate their effects at tide gauges in NSW, Victoria and Western Australia. The performance and statistical biases of the three models are assessed by comparing observations with the 60 modelled scenarios, over twelve separate tsunamis. Presented at the 30th Conference of the Australian Meteorological and Oceanographic Society (AMOS) 2024.