Mapping Warning Level Specific Tsunami Inundation

Hazard assessment in inhabited areas shall lay open the spatial distribution of hazard inundation probabilities. The assessment shall be able to identify areas that show higher probabilities of tsunami inundation than others. With such information optional measures can be generated enabling to decide which hazard inundation probability should be given priority for investment in warning dissemination and evacuation structures. The assessment shall be conducted for a predefined set of warning levels as a specific warning level, e.g. 4 metres (major warning) may lead to different inundations at different locations. The warning levels are specified by InaTEWS (Table 10)

Table 10: Defined Warning levels in Ina-TEWS (BMKG 2008)

Tsunami Category Warning Level Wave Height (WH) range [m]

<none> <none> 0,0 = WH < 0,1

Minor Tsunami Advisory 0,1 = WH < 0,5

Tsunami Warning 0,5 = WH <3,0

Major Tsunami Major Warning WH ≥ 3,0

To acknowledge this, the assessment and anticipation of differential tsunami inundation scenarios at the local level are important.

VII.3.1 Methods

The calculation of tsunami hazard inundation scenarios is derived from combining probabilistic with multi-scenario tsunami modelling¹⁷ linked to a set of pre-defined warning levels. The goal is to assess the likelihood of tsunamis of various sizes that can then be simplified into tsunami

17 The tsunami modelling was performed by AWI (Alfred Wegener Institute) at epicentre locations (source grid) for tsunami scenarios provided by GFZ (German Research Centre for Geosciences, 2008). For the inundation modelling, the MIKE21 FM model from Wasy GmbH was used, and the run-up modelling was performed by GKSS and

DHI-hazard zones (tsunami probability and intensity distribution at the coast and the spatial distribution of the maximum inundation) linked with predefined warning levels by the Tsunami Warning Centre. The “Advisory Level” causes only a very small inundation area or no inundation at the coast. Hence, in the hazard mapping approach the “Advisory Level” and the “Warning Level” are used in combination. The assessment is based on an “event tree technique” with different steps to be accomplished to arrive at a warning level specific tsunami probability map.

The method has been invented by the GITEWS consortium partner DLR; consequently, the following summary is based on the work of the DLR scientists (LIPI et al. 2011b; Post et al.

2009):

 Tsunami modelling: Along the Sunda Trench several thousands of realistic tsunami scenarios with different tsunami source locations and earthquake magnitudes (7.5 – 9.0) has been calculated. All scenarios together cover the south coast of Sumatera, Java and Bali. These scenarios are used as input data for the hazard maps. Additional parameters include detailed local topography and bathymetry (elevation data on land and underwater, respectively). Modelling results shall include areas flooded as well as estimated water depths, current strengths, wave heights, and wave arrival times; with a spatial resolution between several hundreds of meters to ten meters, allowing for representation at a map scale of 1:25,000.

 Determine tsunami scenarios affecting the area of interest: All the scenarios which affect the area of interest are chosen from the GITEWS “Tsunami Scenario Database”.

This is realized by a spatial data query and selects all scenarios which at least inundate one point on land of the area of interest (e.g. a map sheet). The selected scenarios represent the basis for the further assessment.

 Classification of the scenarios depending on the warning levels: All chosen scenarios were grouped in the two warning level classes. By defining the outline of the consolidated inundation of these two classes a first map showing the maximum inundation areas for the different warning levels was developed.

 Estimation of the spatial distributed probability for earthquake occurrence: First, the Sunda Trench region is zoned into three smaller regions each representing different seismic activities (Latief Hamza et al. 2000). For each seismic zone the probability for an annually recurring earthquake magnitude is estimated using the historical earthquake

occurrence probabilities between 1 (for determined hot spots with a high probability for an occurrence of a strong earthquake) and 0.1 (for determined more or less “inactive”

spots) (Cf. Figure 12). Thus, every tsunami-genic source applied has an own occurrence probability.

Figure 16: Assessment of the spatially differentiated likelihoods for the occurrence of an earthquake with a specific magnitude along the Sunda Trench; left: Mw 8.0, right: Mw 9.0 (Source: adapted from Babeyko et al. 2010)

 Determination of a spatially distributed inundation probability: In the next step a spatial differentiation for the possibility that a coastal area will be inundated was specified. The results of the modelled tsunami scenarios include impact on land, and the area on land which will be inundated by a tsunami with a specific magnitude. As the single impact areas from the different scenarios can overlap each other (because every point on land can be inundated several times by different scenarios), the spatially distributed inundation probability represents the probability that this point will be hit by a tsunami within a year. The yielded values are combined and quantified by a logical tree technique.

 Combination of the continuous probability with the “warning level” zone: In a final step the continuous tsunami impact probability is overlaid by the derived “warning level” zones. The threshold for the minimum Estimated Time of Arrival (ETA) is defined by the 1st percentile from the ETAs of all modelled tsunami scenarios at the displaced region. The median ETA describes the 50th percentile. For more information on this assessment step see (LIPI et al. 2011b; Post et al. 2009).

the coast. The ETA can vary to a great extent for the various scenarios depending generally on the distance from the coast to the tsunami-genic source and the earthquake magnitude. The Median (50%-value) of the minimum ETAs of all relevant scenarios have been used for the mapping and the calculation of the evacuation capacity (Cf. VII.7).

VII.3.2 Results

Map 2 is produced at a scale of 1:25 000. The High Tsunami Hazard Probability Zone (dark red) shows the areas with a high probability of being affected by every tsunami with a wave height at the coast greater than 3 m (warning level “major warning”). For this warning level continuous hazard probability visualization from moderate tsunami probability (light red) to low tsunami probability (yellow) is displayed. Only for the hazard zone linked to the level “major warning”

hazard probabilities are shown (moderate to low probability). The area which will be inundated by the warning level “warning” is displayed as red zone summarizing quantified tsunami probabilities to high tsunami probability. The results show that the probability that a tsunami occurs decreases with an increasing distance from the coast and associated water bodies, such as rivers connected with the ocean. Thus, the probability of being inundated by a tsunami within a year ranges from about 0.03 ‰ (light yellow areas) to about 7% (red areas). Areas not affected by tsunamis are visualized in grey.

The map shows that the inundation probabilities at the coast are much higher in the eastern part of Cilacap bay than close to the city centre in the western part. This is due to the fact that the western part is protected by an island (Nusa Kambangan, not visible on the map).

Nevertheless, high hazard probabilities occur along the inland reaching water bodies, due to the channelling effect of a tsunami wave. It has to be pointed out that the hazard information provided is based on modelling results which naturally hold some uncertainties. An additional important hazard assessment parameter is the Estimated Time of Arrival. For the case of Cilacap the calculated minimum ETA is less than 50 minutes, whereas the median ETA is less than 90 minutes.

The hazard maps and the respective detailed and differentiated hazard parameters have only to be seen as best available reference information for the development of local specific disaster preparedness strategies, such as defining an evacuation zone, increasing evacuation preparedness and managing warning dissemination. The utility of the results for other R&V-A information packages as well as for elaborating, assessing, selecting, and implementing R&V-R-task specific measures are discussed in the respective empirical sub-chapters as well as chapter VIII.