Many earthquakes in Indonesia have caused a large number of fatalities. Disaster risk-reduction of fatalities requires a representative fatality model derived from fatality data caused by historical earthquakes in Indonesia. We develop an empirical fatality model for Indonesia by relating macroseismic intensity to fatality rate using compiled subdistrict level fatality rate data and numerically simulated ground shaking intensity for four recent damaging events. The fatality rate data are compiled by collecting population and fatality statistics of the regions impacted by the selected events. The ground shaking intensity is numerically estimated by incorporating a finite fault model of each event and local site conditions approximated by topographically-based site amplifications. The macroseismic intensity distribution of each event is generated by using ShakeMap software with a selected pair of ground motion predictive equation (GMPE) and ground motion to intensity conversion equation (GMICE). The developed fatality model is a Bayesian generalized linear model where the fatality rate is assumed to follow a mixture of a Bernoulli and a gamma distribution. The probability of zero fatality rate and the mean non-zero fatality rate is linked to a linear function of shaking intensity by the logit and the log link functions, respectively. We estimate posterior distribution of the parameters of the model based on the Hamilton Monte Carlo algorithm. For validation of the developed model we calculate fatalities of the past events from the EXPO-CAT catalog and compare the estimates with the EXPO-CAT fatality records. While the developed fatality model can provide an estimate of the range of fatalities for future events it needs on-going refinement by incorporation of additional fatality rate data from past and future events.

Indonesia is one of the most disaster prone countries in the world due to its hazard profile and high population exposure. Despite its risk profile, disaster management has not traditionally been informed by best available information. Since 2008, the Australian and Indonesian governments have partnered to increase the use of science and technology in Indonesia to support decision making in disaster management. Our partnership has concentrated on strengthening the evidence base for informed disaster management by improving: - hazard information for earthquake, tsunami, volcano and flood - spatial data for exposure (population, building, roads and infrastructure) - decision support tools such as InaSAFE that assist disaster managers to combine hazard and exposure data to inform disaster response and management. We have worked alongside technical and disaster management agencies, universities, non-government organisations and the private sector to develop Indonesian capacity to manage disasters and institutionalise best-practices. Partnerships have facilitated science-to-policy and science-to-programming outcomes in disaster management that help people prepare for, respond to and recover from crises. Ten years is a good time for a partnership to form, blossom and deliver effective and sustainable changes. The achievements over this time are too many to list in entirety. Science and Technology for Disaster Management merely scratches the surface to highlight the most significant achievements. There is a tendency in doing so to focus on achievements over the last three years. In most cases the achievements of this later period of the program have only been possible because they have built on and extended the achievements of the former seven years. Science and Technology for Disaster Management demonstrates how our collaboration has increased the use of science and technology in Indonesian disaster management by developing new knowledge and enhancing capacity, both within the scientists and within policy and decision makers. In doing so, tangible policies have been developed and implemented and practices have changed. The program has helped to strengthen relationships between agencies within Indonesia and also between Indonesia and Australia in the areas of disaster management and science more broadly. New relationships manifest as distinct government-to-government collaborations and strong peer-to-peer links.

Indonesia is located in one of the most seismically active regions in the world and often experiences damaging earthquakes. In the past the housing sector has sustained more damage and losses than other sectors due to earthquakes. This is often attributed to the fact that the most common houses in Indonesia are non-engineered, built with poor quality workmanship, poor quality materials and without resilient seismic design features. However little effort has been made to quantify how fragile these houses are, or how the fragility of these houses may vary according to location or wealth. It is not possible to derive empirical fragility functions for Indonesia due to insufficient damage data. The aim of this study is to determine whether existing earthquake fragility functions can be used for common houses in Indonesia. Scenario damage analyses were undertaken several times using different sets of fragility functions for the 2006 Yogyakarta and 2009 Padang events. The simulated damage results were then compared to the damage observed post event to determine whether an accurate damage prediction could be achieved. It was found that the common houses in Yogyakarta and Central Java vary according to age, location and wealth and can be reasonably well represented by existing fragility functions. However, the houses in Padang and surrounding West Sumatra did not vary in a predictable manner and are more fragile than anticipated. Therefore, the fragility of the most common houses in Indonesia is not uniform across the country. This has important implications for seismic damage and risk assessment undertaken in Indonesia. <b>Citation:</b> Weber, R., Cummins, P. & Edwards, M. Fragility of Indonesian houses: scenario damage analysis of the 2006 Yogyakarta and 2009 Padang earthquakes. <i>Bull Earthquake Eng</i> (2024). https://doi.org/10.1007/s10518-024-01930-z

The Government of Indonesia has committed to deploying a network of 500 strong-motion sensors throughout the nation. The data from these sensors have the potential to provide critical near-real-time information on the level of ground shaking and potential impact from Indonesian earthquakes near communities. We describe the implementation of real-time ‘ShakeMaps’ within Indonesia's Agency of Meteorology, Climatology and Geophysics (BMKG). These ShakeMaps are intended to underpin real-time earthquake situational awareness tools. The use of the new strong-motion network is demonstrated for two recent earthquakes in northern Sumatra: the 2 July 2013 Mw 6.1 Bener Meriah, Sumatra and the 10 October 2013 Mw 5.4 Aceh Besar earthquakes. The former earthquake resulted in 35 fatalities, with a further 2400 reported injuries. The recently integrated ShakeMap system automatically generated shaking estimates calibrated by BMKG's strong-motion network within 7 min of the Bener Meriah earthquake's origin, which assisted the emergency response efforts. Recorded ground motions are generally consistent with theoretical models. However, more analysis is required to fully characterize the attenuation of strong ground motion in Indonesia.

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.

Tsunami hazard maps are generated for the Mentawai Islands, West Sumatra, Indonesia, to support evacuation and disaster response planning. A random heterogeneous slip generator is used to forward model a suite of earthquake rupture scenarios on the Mentawai Segment of the Sunda Subduction Zone. A total of 1000 rupture models that fit constraints provided by coral and geodetic records of coseismic vertical deformation from great earthquakes in 1797, 1833 and 2007 are used to model inundation and define a maximum inundation zone that envelopes all of these scenarios. Results are compared with single scenario hazard assessments developed by experts and agreed through scientific consensus building processes to assess the additional value of modelling a suite of scenarios to obtain a more robust estimate of potential inundated areas by incorporating uncertainty in the earthquake source. The model presented here, like all tsunami hazard assessments, is based on assumptions about the characteristics of future events based on past events, however by sampling a range of plausible outcomes we gain a more robust estimate of which areas may be inundated during a tsunami within the bounds of our assumptions.

Natural Hazards and Earth Systems Science

Heterogeneous distribution of slip during megathrust earthquakes has been shown to significantly affect the spatial distribution of tsunami height in both numerical studies and field observations. This means that tsunami hazard maps generated using uniform slip distributions in their tsunami source models may underestimate tsunami inundation in some locations compared with real events of the same magnitude in the same location. In order to more completely define areas that may be inundated during a tsunami it is important to consider how different possible distributions of slip will impact different parts of the coastline. We generate tsunami inundation maps for the Mentawai Islands, West Sumatra, Indonesia, from a composite suite of possible source models that are consistent with current knowledge of the source region. First, a suite of earthquake source models with randomly distributed slip along the Mentawai Segment of the Sunda Subduction Zone is generated using a k-2 rupture model. From this suite we select source models that generate vertical deformation consistent with that observed in coral palaeogeodetic records of previous ruptures of the Mentawai Segment in 1797 and 1833, minus deformation observed in the 2007 Bengkulu earthquake sequence. Tsunami inundation is then modelled using high resolution elevation data for selected source models and the results compiled to generate a maximum tsunami inundation zone. This method allows us to constrain the slip distribution beneath the Mentawai Islands, where coral palaeogeodetic data is available, while allowing for greater variation in the slip distribution away from the islands, in particular near the trench where large slip events can generate very large tsunami. This method also allows us to consider high slip events on deeper portions of the megathrust between the Mentawai Islands and the Sumatran Mainland, which give greater tsunami inundation on the eastern part of the Mentawai Islands and the west coast of Sumatra compared with near-trench event. By accounting for uncertainty in slip distribution, the resulting hazard maps give a more complete picture of the areas that may be inundated compared with hazard maps derived from a single 'worst case' source model. These maps allow for more robust tsunami evacuation plans to be developed to support immediate community evacuation in response to strong or long-lasting earthquake ground shaking. From the American Geophysical Union Fall Meeting Abstracts

Reports of bitumen stranding on the ocean beaches of southern Australia date back to the early days of European settlement. Previous investigations have shown that this ‘coastal bitumen’ comprises three categories of stranded petroleum: waxy bitumen, asphaltite and oil slicks. All three varieties are physically and chemically distinct from each other, and bear no geochemical resemblance to any indigenous Australian crude oil. This study focuses on the most common variety, waxy bitumen, which accounted for 90% of the strandings on six South Australian beaches repeatedly surveyed during 1991–1992. Geochemical analysis of 96 individual specimens collected from these survey sites and other beaches in South Australia and western Victoria has shown them to be variously weathered high-wax crude oils of paraffinic to aromatic-intermediate bulk composition. Elemental, isotopic and biomarker differences allow their assignment to at least five oil families with inferred source facies that range from deep freshwater lacustrine through paludal and deltaic to euxinic marine, possibly deposited within different sedimentary basins. Family 1, 2 and 3 waxy bitumens all contain biomarkers derived from the freshwater alga Botryococcus sp. and tropical angiosperms (notably dipterocarps). Similar biomarker assemblages are unknown in Australian sedimentary basins but are common in Cenozoic crude oils and source rocks throughout western Indonesia. Family 4 waxy bitumens lack these biomarkers, but do contain dinosterane and 24-n-propylcholestane, indicative of a marine source affinity, while the carbon isotopic signatures and high pristane/phytane (Pr/Ph) ratios of Family 5 waxy bitumens are consistent with their origin from coal-rich source rocks deposited in fluvial to deltaic sedimentary successions. The majority of these waxy bitumens represent an oceanic influx of non-indigenous, Southeast Asian crude oils carried into the waters of southern Australia by the Leeuwin Current. Although they are likely to originate from natural seepage within the Indonesian Archipelago, it is unknown whether the parent oils emanate from submarine seeps or from inland seepages which are then carried to the sea by rivers. The common practice of tanker cleaning operations in the Java and Banda seas may augment the supply of natural bitumen to the beaches of Australia.

Historical reports of earthquake effects from the period 1681 to 1877 in Java, Bali and Nusa Tenggara are used to independently test ground motion predictions in Indonesia’s 2010 national probabilistic seismic hazard assessment (PSHA). Assuming that strong ground motion occurrence follows a Poisson distribution, we cannot reject Indonesia’s current PSHA for key cities in Java at 95% confidence. However, the results do suggest that seismic hazard may be underestimated for the megacity Jakarta. Ground motion simulations for individual large damaging events are used to identify plausible source mechanisms, providing insights into the major sources of earthquake hazard in the region and possible maximum magnitudes for these sources. The results demonstrate that large intraslab earthquakes have been responsible for major earthquake disasters in Java, including a ~Mw 7.5 intraslab earthquake near Jakarta in 1699 and a ~Mw 7.8 event in 1867 in Central Java. The results also highlight the potential for large earthquakes to occur on the Flores Thrust. We require an earthquake with Mw 8.4 on the Flores Thrust to reproduce tsunami observation from Sulawesi and Sumbawa in 1820. Furthermore, large shallow earthquakes (Mw > 6) have occurred in regions where active faults have not been mapped identifying the need for further research to identify and characterize these faults for future seismic hazard assessments. <b>Citation:</b> Jonathan Griffin, Ngoc Nguyen, Phil Cummins, Athanasius Cipta; Historical Earthquakes of the Eastern Sunda Arc: Source Mechanisms and Intensity‐Based Testing of Indonesia’s National Seismic Hazard Assessment. <i>Bulletin of the Seismological Society of America </i>2018; 109 (1): 43–65. doi: https://doi.org/10.1785/0120180085