Source modeling and inversion with near real-time GPS: a GITEWS perspective for Indonesia

176

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.

“…3). The vertical deformation is calculated using the layered earth model of Wang et al (2003) and the crustal parameters from the Crust 2.0 model (Bassin et al, 2000).…”

Section: Unit Sourcesmentioning

confidence: 99%

A probabilistic tsunami hazard assessment for Indonesia

Horspool

Pranantyo

Griffin

et al. 2014

176

“…The first digits give the location along the Sunda Trench (starting with 1 at the south-eastern end), the following digits give the location with respect to the distance to the coast (increasing number = shorter distance). For further information please see corresponding articles by Behrens et al (2010) and Babeyko et al (2010), also published in this volume. Out of all scenarios computed, the worst cases (9.0 magnitude scenarios) shown here were those with epicentres ∼120 km south of Padang, ∼220 km southeast of Cilacap, and 180 km south-east of Kuta.…”

Section: Worst Case Scenariosmentioning

confidence: 99%

Tsunami inundation modelling based on detailed roughness maps of densely populated areas

Gayer

Leschka²,

Nohren

et al. 2010

Abstract. An important part within the German-Indonesian Tsunami Early Warning System (GITEWS) project was the detailed numerical investigation of the impact of tsunamis in densely populated coastal areas of Indonesia. This work, carried out by the German Research Centre Geesthacht (GKSS), in co-operation with DHI-WASY, also provides the basis for the preparation of high resolution hazard and risk maps by the German Aerospace Center (DLR).In this paper a method is described of how to prepare very detailed roughness maps for scenario computations performed with the MIKE 21 Flow Model FM in three highly resolved (∼10 m) priority regions, namely Kuta (Bali), Padang (West-Sumatra), and Cilacap (southern coast of Java). Roughness values are assigned to 43 land use classes, e.g. different types of buildings, rural and urban subareas, by using equivalent coefficients found in literature or by performing numerical experiments.Comparisons of simulations using differentiated roughness maps with simulations using constant values (a widely used approach) are presented and it is demonstrated that roughness takes considerable influence on run-up and inundation.Out of all simulations, the results of the worst case scenarios for each of the three priority areas are discussed. Earthquakes with magnitudes of M W =8.5 or higher lead to considerable inundation in all study sites. A spatially distinguished consideration of roughness has been found to be necessary for detailed modelling onshore.

“…For the studied area of Kuta in Bali, high resolution tsunami modeling with a spatial resolution of up to 10 m was provided by GKSS/DHI-WASY (Gayer et al, 2008). (Babeyko et al, 2010). The likelihood of earthquake occurrence at a specific source location and of a certain magnitude varies along the Sunda Trench (McCloskey et al, 2008;Clieh et al, 2008;Latief et al, 2000).…”

Section: Hazard Assessmentmentioning

confidence: 99%

Generating tsunami risk knowledge at community level as a base for planning and implementation of risk reduction strategies

Wegscheider

Post

Zoßeder

et al. 2011

Abstract. More than 4 million Indonesians live in tsunamiprone areas along the southern and western coasts of Sumatra, Java and Bali. Although a Tsunami Early Warning Center in Jakarta now exists, installed after the devastating 2004 tsunami, it is essential to develop tsunami risk knowledge within the exposed communities as a basis for tsunami disaster management. These communities need to implement risk reduction strategies to mitigate potential consequences.The major aims of this paper are to present a risk assessment methodology which (1) identifies areas of high tsunami risk in terms of potential loss of life, (2) bridges the gaps between research and practical application, and (3) can be implemented at community level. High risk areas have a great need for action to improve people's response capabilities towards a disaster, thus reducing the risk. The methodology developed here is based on a GIS approach and combines hazard probability, hazard intensity, population density and people's response capability to assess the risk.Within the framework of the GITEWS (GermanIndonesian Tsunami Early Warning System) project, the methodology was applied to three pilot areas, one of which is southern Bali. Bali's tourism is concentrated for a great part in the communities of Kuta, Legian and Seminyak. Here alone, about 20 000 people live in high and very high tsunami risk areas. The development of risk reduction strategies is therefore of significant interest. A risk map produced for the study area in Bali can be used for local planning activities and the development of risk reduction strategies.