Impact of earthquake source complexity and land elevation data resolution on tsunami hazard assessment and fatality estimation

Muhammad, Ario; Goda, Katsuichiro

doi:10.1016/j.cageo.2017.12.009

Cited by 17 publications

(7 citation statements)

References 40 publications

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“…Pakoksung et al (2019) ignored tsunami contributions from co-seismic rupture because of small wave amplitudes and developed a tsunami model solely based on multiple submarine landslide sources (four larger and six smaller sources), involving the total submarine mass movement of 100 × 10 6 m 3 . Their conclusion that a co-seismic tsunami source can be neglected might have been reached because they used a uniform earthquake slip model for representing co-seismic earthquake rupture, which tends to underestimate simulated tsunami waves significantly (Muhammad and Goda, 2018). Another important tsunami simulation set-up adopted by Pakoksung et al (2019) was the consideration of a high tide level of +2.3 m above the MSL at the time of the 2018 Sulawesi earthquake (Lutfi et al, 2018).…”

Section: Tsunami Inundation Simulation Of the 2018 Sulawesi Earthquakmentioning

confidence: 99%

Cascading Geological Hazards and Risks of the 2018 Sulawesi Indonesia Earthquake and Sensitivity Analysis of Tsunami Inundation Simulations

et al. 2019

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Multiple cascading hazards triggered by the 2018 Sulawesi, Indonesia, earthquake caused various compounding consequences. A major strike-slip fault movement with along-dip components resulted in intense ground shaking, liquefaction and lateral spreading, large-scale mudflows, and tsunamis. This paper presents observations of such multi-hazard effects on buildings and infrastructure in areas along Palu Bay, based on field reconnaissance work, and discusses the main causes of the disaster by focusing upon the combined effects of the cascading geological hazards. To evaluate the tsunami risk potential of the strike-slip event, tsunami simulations for the 2018 Sulawesi earthquake are performed by considering different model settings for spatial earthquake slip distribution, rake angle, astronomical tidal effect, and co-seismic ground deformation. The numerical results indicate that the co-seismic rupture of a moment magnitude 7.5 strike-slip earthquake, having notable dip components can generate damaging tsunami waves at coastal locations of Palu Bay. The conclusion has important implications for future tsunami hazard assessments in active seismic regions where major fault systems cut across a bay or into the sea.

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Section: Tsunami Inundation Simulation Of the 2018 Sulawesi Earthquakmentioning

confidence: 99%

Cascading Geological Hazards and Risks of the 2018 Sulawesi Indonesia Earthquake and Sensitivity Analysis of Tsunami Inundation Simulations

et al. 2019

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“…Infrastructure systems Assessment Spatial assessment of risk and resilience of critical infrastructures for flood disaster [119] Critical transportation Prediction Description and prediction of passenger flows, detection of unusual flows and its explanation based on Twitter content during several disasters in Japan [120] Environmental response; Health and safety Monitoring Big Data system for monitoring water pollution after flood disaster [104] Fatality management services Assessment Fatality estimation and tsunami hazard assessment based on big data earthquake source models [121] Fire management and suppression Prediction Real-time prediction of fire department response times in San Francisco [122] Logistics and supply chain management Assessment…”

Section: Nrf Capability Task Examplementioning

confidence: 99%

A Big Data Reference Architecture for Emergency Management

2020

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Nowadays, we are witnessing a shift in the way emergencies are being managed. On the one hand, the availability of big data and the evolution of geographical information systems make it possible to manage and process large quantities of information that can hugely improve the decision-making process. On the other hand, digital humanitarianism has shown to be very beneficial for providing support during emergencies. Despite this, the full potential of combining automatic big data processing and digital humanitarianism approaches has not been fully realized, though there is an initial body of research. This paper aims to provide a reference architecture for emergency management that instantiates the NIST Big Data Reference Architecture to provide a common language and enable the comparison of solutions for solving similar problems.

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“…In particular, tsunami inundation is sensitive to DEM resolution and leads to significantly different results (Satake, 1995;Tang et al, 2009). Similar to flood modeling which is sensitive to spatial resolution (Fewtrell et al, 2008;Sangati and Borga, 2009), resolutions of DEM represent the ability of reflecting the local geographical features and make a significant difference to local tsunami intensity (Griffin et al, 2015;Schäfer and Wenzel, 2017;Muhammad and Goda, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The influenced area by tsunamis is usually confined to coastal areas less than 5 km (mostly less than 3 km) from the sea, and the local tsunami intensity largely depends on the location of buildings as well. The uncertainty in these two aspects has a significant influence on tsunami hazard assessments (Griffin et al, 2015;Muhammad and Goda, 2018). Nevertheless, the impact of the uncertainty to probabilistic tsunami loss estimation has not been investigated nor quantified extensively.…”

Section: Introductionmentioning

confidence: 99%

Influence of Elevation Data Resolution on Tsunami Loss Estimation and Insurance Rate-Making

Jie

Goda

2019

Front. Earth Sci.

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Tsunamis triggered by large offshore earthquakes are devastating, and buildings located near the coast experience damage and loss due to such extreme events. In evaluating regional tsunami impact via numerical tsunami simulations, it is important to pay close attention to local geographical features represented by a digital elevation model (DEM), because tsunami loss estimation is sensitive to its quality and resolution. This study investigates the influence of elevation data resolution on tsunami loss estimation at different scales by comparing tsunami risk results using DEMs of four resolutions (i.e., 10-m, 50-m, 150-m, and 450-m). Using stochastic tsunami modeling, a case study is carried out by focusing on the Tohoku region in Japan to investigate the influence of DEM resolution on tsunami loss estimation considering the effect of location attributes (i.e., coastal topography, distance from the coast, and land elevation) for two building portfolios on plain coast and ria coast. The results indicate the significance of DEM resolution for local tsunami loss estimations at different locations. The local tsunami risk is closely related to the building location, and the increase of distance from the coast and/or land elevation dramatically reduces the local tsunami risk. The investigations extend discussions regarding the calculations of pure insurance premium rate for tsunami loss coverage depending upon structural attributes and location attributes.

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Impact of earthquake source complexity and land elevation data resolution on tsunami hazard assessment and fatality estimation

Cited by 17 publications

References 40 publications

Cascading Geological Hazards and Risks of the 2018 Sulawesi Indonesia Earthquake and Sensitivity Analysis of Tsunami Inundation Simulations

Cascading Geological Hazards and Risks of the 2018 Sulawesi Indonesia Earthquake and Sensitivity Analysis of Tsunami Inundation Simulations

A Big Data Reference Architecture for Emergency Management

Influence of Elevation Data Resolution on Tsunami Loss Estimation and Insurance Rate-Making

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