Depending on whether the first part of a tsunami to reach the shore is a crest or a trough, it may appear as a rapidly rising or falling tide, and in some cases the tsunami may appear as a series of breaking waves. People near the beach may also hear a roaring sound, like an approaching train. In this example, the first crest of our tsunami arrives without warning and inundates the beach and low lying land causing extensive damage. After the first wave, the water will draw back and then the second and third waves will repeat the process at 15 to 20 minute intervals. The first wave may not be the biggest. Reefs and offshore islands may help to protect the coast from the devastating effect of a tsunami.

As the tsunami leaves the deep water of the open ocean and approaches the shallower waters near the coast, it slows down and may grow in height depending on the shape of the seafloor. A tsunami that is unnoticeable by ships at sea may grow to be several metres or more in height near the coast. Our example tsunami is now 1.5 metres high with a wavelength of 100 kilometres and is moving at about 400 kilometres an hour.

As the Australian plate slowly pushes under the Eurasian plate, massive stresses build up in the crust. These stresses also cause the Eurasian plate to be slowly forced upwards - part of the process that builds the mountains and volcanoes of Indonesia, as well as creating the many earthquakes felt in that region of the world each year. When the stresses get too great, the plates will suddenly slip causing massive movements in the seafloor. The part of the crust nearest to the fault zone rapidly moves upwards by a metre or so, lifting the entire body of water above it. A hundred kilometres away the opposite may happen: the seafloor drops and the ocean above it also falls. These two movements (the sudden rise and fall of the seafloor hundreds of kilometres apart), combine to cause a series of tsunami waves which move away from the line of the fault in both directions.

Landslides can happen on the seafloor, just like on land. Areas of the seafloor that are steep and loaded with sediment are more prone to undersea landslides, such as the edge of the continental slope. When an undersea landslide occurs (perhaps after a nearby earthquake) a large mass of sand, mud and gravel can move down the slope. This movement will draw the water down and may cause a tsunami that will travel across the ocean.

The development of the Indian Ocean Tsunami Warning and mitigation System (IOTWS) has occurred rapidly over the past few years and there are now a number of centres that perform tsunami modelling within the Indian Ocean, both for risk assessment and for the provision of forecasts and warnings. The aim of this work is to determine to what extent event-specific tsunami forecasts from different numerical forecast systems differ. This will have implications for the inter-operability of the IOTWS. Forecasts from eight separate tsunami forecast systems are considered. Eight hypothetical earthquake scenarios within the Indian Ocean and ten output points at a range of depths were defined. Each forecast centre provided, where possible, time series of sea-level elevation for each of the scenarios at each location. Comparison of the resulting time series shows that the main details of the tsunami forecast, such as arrival times and characteristics of the leading waves are similar. However, there is considerable variability in the value of the maximum amplitude (hmax) for each event and on average, the standard deviation of hmax is approximately 70% of the mean. This variability is likely due to differences in the implementations of the forecast systems, such as different numerical models, specification of initial conditions, bathymetry datasets, etc. The results suggest that it is possible that tsunami forecasts and advisories from different centres for a particular event may conflict with each other. This represents the range of uncertainty that exists in the real-time situation.

We present the first national probabilistic tsunami hazard assessment (PTHA) for Indonesia. This assessment considers tsunami generated from near-field earthquakes sources around Indonesia as well as regional and far-field sources, to define the tsunami hazard at the coastline. The PTHA methodology is based on the established stochastic event-based approach to probabilistic seismic hazard assessment (PSHA) and has been adapted to tsunami. The earthquake source information is primarily based on the recent Indonesian National Seismic Hazard Map and included a consensus-workshop with Indonesia's leading tsunami and earthquake scientists to finalize the seismic source models and logic trees to include epistemic uncertainty. Results are presented in the form of tsunami hazard maps showing the expected tsunami height at the coast for a given return period, and also as tsunami probability maps, showing the probability of exceeding a tsunami height of 0.5m and 3.0m at the coast. These heights define the thresholds for different tsunami warning levels in the Indonesian Tsunami Early Warning System (Ina-TEWS). The results show that for short return periods (100 years) the highest tsunami hazard is the west coast of Sumatra, the islands of Nias and Mentawai. For longer return periods (>500 years), the tsunami hazard in Eastern Indonesia (north Papua, north Sulawesi) is nearly as high as that along the Sunda Arc. A sensitivity analysis of input parameters is conducted by sampling branches of the logic tree using a monte-carlo approach to constrain the relative importance of each input parameter. These results can be used to underpin evidence-based decision making by disaster managers to prioritize tsunami mitigation such as developing detailed inundation simulations for evacuation planning.

The Attorney General's Department (AGD) has supported Geoscience Australia (GA) to develop inundation models for selected Northern Territory communities with the view of building the tsunami planning and preparation capacity of the Northern Territory Government. The communities chosen were Darwin, Palmerston, Wagait Beach and Dundee Beach. These locations were selected in collaboration with the Northern Territory Emergency Service (NTES) and Department of Natural Resources, Environment, The Arts and Sport (NRETAS) and the Australian Government based on a combination of the offshore Probabilistic Tsunami Hazard Assessment of Australia (PTHA)[1], the availability of suitable elevation data and the location of low lying communities. Three tsunamigenic events were selected for modelling from the scenario database that was calculated as part of the national offshore probabilistic tsunami hazard assessment (PTHA) [1]. The events selected are hypothetical and are based on the current understanding of the tsunami hazard. Only earthquake sources are considered as these account for the majority of tsunami. The suite of events includes three 'worst-case' or 1 in 10 000 year hazard events as well as more frequent events. Source zones considered are the Timor Trough, Flores-Wetar Thrust Fault and the Java Trench as these regions make the highest contribution to the offshore tsunami hazard for Darwin.

The major tsunamis of the last few years in the southern hemisphere have raised awareness of the possibility of potentially damaging tsunami to Australia and countries in the Southwest Pacific region. Here we present a probabilistic hazard assessment for Australia and for the SOPAC countries in the Southwest Pacific for tsunami generated by subduction zone earthquakes. To conduct a probabilistic tsunami hazard assessment, we first need to estimate the likelihood of a tsunamigeneic earthquake occurring. Here we will discuss and present our method of estimate the likely return period a major megathrust earthquake on each of the subduction zones surrounding the Pacific. Our method is based on the global rate of occurrence of such events and the rate of convergence and geometry of each particular subduction zone. This allows us to create a synthetic catalogue of possible megathrust earthquakes in the region with associated probabilities for each event. To calculate the resulting tsunami for each event we create a library of "unit source" tsunami for a set of 100km x 50km unit sources along each subduction zone. For each unit source, we calculate the sea floor deformation by modelling the slip along the unit source as a dislocation in a stratified, linear elastic half-space. This sea floor deformation is then fed into a tsunami propagation model to calculate the wave height off the coast for each unit source. Our propagation model uses a staggered grid, finite different scheme to solve the linear, shallow water wave equations for tsunami propagation. The tsunami from any earthquake in the synthetic catalogue can then be quickly calculated by summing the unit source tsunami from all the unit sources that fall within the rupture zone of the earthquake. The results of these calculations can then be combined with our estimate of the probability of the earthquake to produce hazard maps showing (for example) the probability of a tsunami exceeding a given height offshore from a given stretch of coastline. These hazard maps can then be used to guide emergency managers to focus their planning efforts on regions and countries which have the greatest likelihood of producing a catastrophic tsunami.

The Natural Hazard Impacts Project (NHIP) at Geoscience Australia has developed modelling techniques that enable coastal inundation to be predicted during a tsunami. A Collaborative Research Agreement between Geoscience Australia and the Fire and Emergency Services Authority (FESA) was formed in 2005 to understand tsunami risk and inform emergency management in WA. Through this partnership a significant tsunami risk was identified in NW Western Australia, leading to the development of inundation models for several coastal communities in this region, including Onslow and Exmouth. Recognising the importance of this research to Geoscience Australia, FESA and the communities of Onslow and Exmouth, this year's graduate project was designed to assist the NHIP and to further strengthen ties with FESA and community organisations. The project had several distinct outcomes which can be divided into data acquisition and community interaction. High quality elevation data was gathered by GPS surveying in order to ascertain the quality of the Digital Elevation Model (DEM) that is currently used in inundation models. Improved accuracy in the elevation data allows the capture of subtle changes in topography that may not be present in the existing DEM and so may improve model accuracy. Secondly, ground-truthing of predicted inundation areas supplements the survey data, provides critical assistance in the production of accurate inundation models and potentially aids in the production of emergency plans. Prior to fieldwork a community-specific tsunami awareness brochure was designed and produced for Onslow. This brochure was presented to Onslow local emergency managers and FESA personnel, and subsequently to Emergency Management Australia and the Bureau of Meteorology. It has received widespread positive feedback, and consequently may provide a template for other community brochures in similarly vulnerable regions of Australia. Finally, graduates represented Geoscience Australia at several community meetings in Onslow where NHIP research was presented. These meetings provided insight into specific community concerns in the event of a tsunami and provided an opportunity for the attendees to ask questions about tsunamis and their impacts. Fortuitously this community interaction also led to the discovery of anecdotal evidence of past tsunami events in Onslow, including the tsunami triggered by the 1883 Krakatau eruption, a 1937 tsunami that may be attributed to an earthquake near Java, and the 1994 and 2004 tsunamis.

The maps contained on this DVD are designed to provide emergency managers and others with an estimate of the probability of large tsunami generated by a large subduction zone earthquake reaching the 100m contour offshore Australia. These maps were created by generating a synthetic catalogue of possible earthquakes with associated probabilities and estimating the maximum wave height of the resulting tsunami off the coast using numerical modelling.