The Tsunami Scenario Database of the Indonesia Tsunami Early Warning System (InaTEWS): Evolution of the Coverage and the Involved Modeling Approaches

Harig, Sven; Immerz, Antonia; Weniza,; Griffin, Jonathan; Weber, Bernd; Babeyko, Andrey; Rakowsky, Natalja; Hartanto, Dwi; Nurokhim, Arif; Handayani, Tri; Weber, Rikki

doi:10.1007/s00024-019-02305-1

Cited by 38 publications

(31 citation statements)

References 29 publications

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“…According to the mean resolution of the triangular mesh, a raster with grid cell dimensions of 10x10m was chosen. Details of this method are reported in Harig et al, (2020). Tsunami inundation height for the six scenarios over the two most tsunami-prone areas in the Lima city -La Punta and Chorrillos district-(see the white square in tsunami maps of https://doi.org/10.5194/nhess-2021-70 Preprint.…”

Section: Construction Of Earthquake and Tsunami Scenarios For Limamentioning

confidence: 99%

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

Zapata¹,

Brinckmann²,

Harig³

et al. 2021

Preprint

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Abstract. We propose the use of variable resolution boundaries based on Central Voronoi Tessellations (CVT) to spatially aggregate building exposure models for risk assessment to various natural hazards. Such a framework is especially beneficial when the spatial distribution of the considered hazards present intensity measures with contrasting footprints and spatial correlations such as in coastal environments. This proposal avoids the incorrect assumption that a single intensity value from hazards with low spatial correlation (e.g. tsunami) are considered as representative within large sized geocells for physical vulnerability assessment, without, at the same time, increasing the complexity of the overall model. We present decoupled earthquake and tsunami scenario-based risk estimates for the residential building stock of Lima (Peru). We observe that earthquake loss models for far-field subduction sources are practically insensitive to the exposure resolution. Conversely, tsunami loss models and associated uncertainties depend on the spatial correlations of the hazard intensities as well as on the resolution of the exposure models. We observe that for the portfolio located in the coastal area exposed to both perils in Lima, the ground-shaking dominates the losses for lower magnitudes whilst the tsunami does for the larger ones. For the latter, two sets of existing empirical flow-depth fragility models are used, finding large differences in the losses. This study arises awareness about the uncertainties in the selection of fragility models and aggregations entities for exposure modelling and loss mapping.

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Section: Construction Of Earthquake and Tsunami Scenarios For Limamentioning

confidence: 99%

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

Zapata¹,

Brinckmann²,

Harig³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Construction Of Earthquake and Tsunami Scenarios For Limamentioning

confidence: 99%

Comment on nhess-2021-70

2021

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“…Since 2005, through the contribution of the German-Indonesia Tsunami Early Warning System (GITEWS), the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) operates the Indonesia Tsunami Early Warning System (InaTEWS) [7]. InaTEWS is the National Tsunami Warning Center (NTWC) for the Indonesian region and is also a Tsunami Service Provider (TSP) for the Indian Ocean region [8].…”

Section: Introductionmentioning

confidence: 99%

“…Tsunami warning information can be estimated through numerical tsunami modeling [9][10][11]. Furthermore, studies from Rakowsky, et al [11], Immerz, et al [12] and Harig, et al [8] have produced the tsunami scenario database of TsunAWI, including inundation data and covering all Indonesian fault zones. As a result, InaTEWS is able to issue early warning information on tsunamis caused by earthquakes that occur in area A of NTWC (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Tsunami Impact Prediction System Based on TsunAWI Inundation Data

Dermadi

Bandung

2021

J. ICT Res. Appl.

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It is very important for tsunami early warning systems to provide inundation predictions within a short period of time. Inundation is one of the factors that directly cause destruction and damage from tsunamis. This research proposes a tsunami impact prediction system based on inundation data analysis. The inundation data used in this analysis were obtained from the tsunami modeling called TsunAWI. The inundation data analysis refers to the coastal forecast zones for each city/regency that are currently used in the Indonesia Tsunami Early Warning System (InaTEWS). The data analysis process comprises data collection, data transformation, data analysis (through GIS analysis, predictive analysis, and simple statistical analysis), and data integration, ultimately producing a pre-calculated inundation database for inundation prediction and tsunami impact prediction. As the outcome, the tsunami impact prediction system provides estimations of the flow depth and inundation distance for each city/regency incorporated into generated tsunami warning bulletins and impact predictions based on the Integrated Tsunami Intensity Scale (ITIS-2012). In addition, the system provides automatic sea level anomaly detection from tide gauge sensors by applying a tsunami detection algorithm. Finally, the contribution of this research is expected to bring enhancements to the tsunami warning products of InaTEWS.

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The Tsunami Scenario Database of the Indonesia Tsunami Early Warning System (InaTEWS): Evolution of the Coverage and the Involved Modeling Approaches

Cited by 38 publications

References 29 publications

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru

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Tsunami Impact Prediction System Based on TsunAWI Inundation Data

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