An Evaluation of Tsunami Forecasts from the T2 Scenario Database

Greenslade, Diana; Allen, Stewart C. R.; Simanjuntak, M. Arthur

doi:10.1007/s00024-010-0229-3

Cited by 30 publications

(23 citation statements)

References 7 publications

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“…The use of unit sources have become increasingly popular for PTHA (Geist and Parsons, 2006;Burbidge et al, 2009;Power et al, 2012) and tsunami early warning systems (Titov et al, 2005;Greenslade et al, 2011) due to their computational efficiency at calculating the tsunami hazard for a large number of sites over a large area. The fundamental assumption in using a unit source approach is that tsunami waves behave linearly in deep to moderate water depths .…”

Section: Unit Source Methodsmentioning

confidence: 99%

A probabilistic tsunami hazard assessment for Indonesia

Horspool

Pranantyo

Griffin

et al. 2014

Nat. Hazards Earth Syst. Sci.

176

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Abstract. Probabilistic hazard assessments are a fundamental tool for assessing the threats posed by hazards to communities and are important for underpinning evidence-based decision-making regarding risk mitigation activities. Indonesia has been the focus of intense tsunami risk mitigation efforts following the 2004 Indian Ocean tsunami, but this has been largely concentrated on the Sunda Arc with little attention to other tsunami prone areas of the country such as eastern Indonesia. We present the first nationally consistent probabilistic tsunami hazard assessment (PTHA) for Indonesia. This assessment produces time-independent forecasts of tsunami hazards at the coast using data from tsunami generated by local, regional and distant earthquake sources. The methodology is based on the established monte carlo approach to probabilistic seismic hazard assessment (PSHA) and has been adapted to tsunami. We account for sources of epistemic and aleatory uncertainty in the analysis through the use of logic trees and sampling probability density functions. For short return periods (100 years) the highest tsunami hazard is the west coast of Sumatra, south coast of Java and the north coast of Papua. For longer return periods (500-2500 years), the tsunami hazard is highest along the Sunda Arc, reflecting the larger maximum magnitudes. The annual probability of experiencing a tsunami with a height of > 0.5 m at the coast is greater than 10 % for Sumatra, Java, the Sunda islands (Bali, Lombok, Flores, Sumba) and north Papua. The annual probability of experiencing a tsunami with a height of > 3.0 m, which would cause significant inundation and fatalities, is 1-10 % in Sumatra, Java, Bali, Lombok and north Papua, and 0.1-1 % for north Sulawesi, Seram and Flores. The results of this national-scale hazard assessment provide evidence for disaster managers to prioritise regions for risk mitigation activities and/or more detailed hazard or risk assessment.

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Section: Unit Source Methodsmentioning

confidence: 99%

A probabilistic tsunami hazard assessment for Indonesia

Horspool

Pranantyo

Griffin

et al. 2014

Nat. Hazards Earth Syst. Sci.

176

View full text Add to dashboard Cite

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“…Note that this length will not be exact due to the curvature of the subduction zone. Further details on the rupture construction are given by Greenslade et al [2010].…”

Section: Application To T2 Scenariosmentioning

confidence: 99%

“…All other rupture and model details are kept fixed. The appropriate scaling [ Greenslade et al , 2010] was then applied to the original T2 scenario (in this case, a factor of 0.89) to match this magnitude. The scaled maximum amplitudes ( η scaled ) are then subtracted from the alternate maximum amplitudes ( η a ) at each grid point in the domain to obtain the bias: …”

Section: Application To T2 Scenariosmentioning

confidence: 99%

Magnitude‐based scaling of tsunami propagation

Simanjuntak

Greenslade

2011

J. Geophys. Res.

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[1] Most current operational tsunami prediction systems are based upon databases of precomputed tsunami scenarios, where some form of linear scaling is applied to the precomputed model runs in order to represent specific earthquake magnitudes. This can introduce errors due to assumptions made about the rupture width and possible effects on dispersion. In this paper, we perform a series of numerical experiments on uniform depth domains, using the Method of Splitting Tsunamis (MOST) model, and develop estimates of the maximum error that an assumed discrepancy in the width of a rupture will produce in the resulting field of maximum tsunami amplitude. This estimate was produced from fitting the decay of maximum amplitude with normalized distance for various resolutions of the source widths to the grid size, resulting in a simple power law whose coefficients effectively vary with wavelength resolution. This provides a quantification of the effect that linear scaling of precomputed scenarios will have on forecasts of tsunami amplitude. This estimate of scaling bias is investigated in relation to the scenario database that is currently in use within the Joint Australian Tsunami Warning Centre.Citation: Simanjuntak, M. A., and D.

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“…This event has been considered in detail by several studies, such as Tang et al (2008), Greenslade and Titov (2009) and, in the specific context of T2, Greenslade et al (2010). This event produced no impacts on the Australian coastline or external territories and the desired warning scheme consists of no threats in any coastal zone (not shown).…”

Section: Tonga 2006mentioning

confidence: 99%

Model-based tsunami warnings derived from observed impacts

Allen

Greenslade

2010

Nat. Hazards Earth Syst. Sci.

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Abstract. The Joint Australian Tsunami Warning Centre uses the T2 tsunami scenario database to provide forecast guidance for potential tsunami threats to the coastlines of mainland Australia and its external territories. This study describes a method for generating coastal tsunami warnings from model data obtained from the T2 scenario database. Consideration of observed coastal impacts for nine past events leads to retrospective or "ideal" warning schemes being designed. The 95th percentile values of maximum amplitude within designated coastal zones are examined and thresholds that produce the best match for the ideal schemes are selected. This empirical method is impact-based and allows the T2 scenarios to be used as a proxy for potential impacts on the coast in order to generate warnings for the Australian region.

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An Evaluation of Tsunami Forecasts from the T2 Scenario Database

Cited by 30 publications

References 7 publications

A probabilistic tsunami hazard assessment for Indonesia

A probabilistic tsunami hazard assessment for Indonesia

Magnitude‐based scaling of tsunami propagation

Model-based tsunami warnings derived from observed impacts

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