TSUNAMI DAMAGE ESTIMATION ALONG THE
COAST OF LAOAG CITY USING TSUNAMIFRAGILITY FUNCTIONS
Bhenz Rodriguez
MEE17721

Supervisor:

Dr. Shunichi KOSHIMURAABSTRACTThe objective of this study is to assess the tsunami hazard and to estimate the tsunami damage in Laoag
City. The scenarios used for the assessment of tsunami were obtained from different studies of theManila Trench. For damage estimation, tsunami fragility functions developed for Banda Aceh were used.
The worst scenario S2 has a magnitude of Mw 8.4, with a fault length of 277 km, a fault width of 91 km
and a top depth of 5 km. The slip amount was set to 3.72 m with strike, dip and rake angles of 20°, 41°,and 79°, respectively. The maximum uplift and subsidence obtained for this scenario were 2.08 m and0.25 m. The maximum tsunami height resulted on 4.47 m and the arrival time to 20 min after theearthquake. Also, the inundation area and maximum inundation depth values were 6.79 km2 and 6.8 m.
The estimated damage on exposed buildings and casualties were 93%, and 70%, respectively. Thesefindings can be used as a reference by local governments, however careful assessment of uncertainties
not considered here are necessary for future disaster planning and mitigation.
Keywords: Tsunami simulations, Fragility Functions, Damage Estimation, Laoag City.

1. INTRODUCTION
The archipelago of the Philippines is located an active seismic region where tsunamis may also occur.
Events like the 1976 Moro Gulf M 8.1 and the 1994 Mindoro Ms 7.1 earthquakes generated tsunamis
and damage on structures and loss of human lives. Studying tsunami hazard as well as estimating thedamage to exposed infrastructure and people would be the key to prepare for a future possible disaster.
The study area chosen for this research is Laoag City which is one of the chartered cities in thePhilippines. Various studies using Manila Trench as a tsunami generator were taken into consideration
to develop earthquake source models. The subduction beneath this trench has been in the process ofstrain accumulation over a period of 440 years in which could be a source of Mw 9.0 earthquake(Megawati et al., 2009). The scenario which generates the highest output data for seafloor deformation,
tsunami heights, inundation depth and area will be used as the worst case scenario. The maximum
inundation depth value for each “barangay” is used for damage estimation. Barangay is the smallest
administrative division in the Philippines. The damage probability and death ratio were collected per
barangay using the maximum inundation depth and fragility functions. The results to be presented arelimited by the resolution and quality of the data used such as the bathymetry and topography, together
with the exposed data. Thus, these results can only be used as a guide and not as exact values for disasterplanning.

Philippine Institute of Volcanology and Seismology (PHIVOLCS), Philippines.
Laboratory of Remote Sensing and Geoinformatics for Disaster Management, International Research Instituteof Disaster Science, Tohoku University, Japan.
12. THEORETICAL CONCEPTS
2.1. Tsunami ModelingIn this study, the governing equations were derived based on the nonlinear long wave theory(Yanagisawa, 2018). The differential equations for continuity and momentum conservation in thespherical coordinate system discretized using the staggered leapfrog method. Moreover, to attainstability in the simulation, the program code follows Courant-Freidrichs-Lewy (CFL) condition.
2.2. Fragility Functions
Tsunami Fragility functions are used to estimate the damage probability on structures and human loss
using the hydrodynamic features of tsunami such as inundation depths, current velocity andhydrodynamic force (Koshimura et al., 2009a).
This study only uses the inundation depth values to obtain the damage probabilities. The
equation (1) is the formulation for finding the probabilities while Table 1 presents the constantparameters used for this study which were obtained from Banda Aceh damage data after the 2004 Indian
Ocean tsunami.

 xu P  x     (1)where parameter 𝑥 is for the inundation depth while 𝜇 and 𝜎 are for the mean and standard
deviation, respectively.
Table 1. Parameters for fragility functions (Koshimura et al., 2009b).
x for fragility functions P(x)𝜇𝜎
Building Damage
2.99 1.12
Casualty
3.75 1.35

3. DATA AND METHODOLOGY
3.1. Tsunami Hazard Modeling
The bathymetry and topography data used in thisstudy were the GEBCO 30 arc-sec and SRTM 3 arcsecond. The computational domains are presentedin Figure 1. The grid sizes for Regions 1 to 4 wereapproximately 810 m, 270 m, 90 m and 30 m
respectively. Region 4 contains the study area.
To perform a tsunami simulation,earthquake source is needed. The source models
adopted for this study are listed in Table 2.
Different output points of tsunami
waveforms are assigned for each region in which
one point is placed in Region 4.
The simulation code introduced byYanagisawa (2018) during the IISEE training class
was used for this study. Most of the outputs can beplotted in maps through QGIS. The scenario which
has the highest results will be identified as the worst

2

Figure 1. Overview of computational domains.

case scenario and be used for the damage estimation part.
3.2. Tsunami Damage Estimation
The methodology for the damage estimation was adopted from Vera (2015). The maximum inundation
depth at each barangay is needed to be obtained from the worst-case scenario. Using the equation (1),the probability of building damage and casualty can be identified per barangay. Multiplying these values
to the number of exposed data will lead to an estimated number of building damage and casualties. Thismethodology will be used for both Methodologies A and B but using a different set of exposed data.
The data set provided by Philippine Statistics Authority (PSA) which is the Census for 2015
will be used for Methodology A. For Methodology B, building points were identified inside theinundated grid data. In the case of the population, data from PSA (2015) was uniformly distributed inside
the barangay and only the population inside the inundation area.

CodeM

S1
S2
S3
S4
W1*
N1*
N2*
N3*
N4*

8.48.48.4
8.4
8.1
8.3
8.3
8.3
8.3

Table 2. Earthquake source parameters.
Location
Fault
Slip
Depth
AmountLong.
Lat.
(km) Length Width
(m)
(oE)
(oN)
(km)
(km)
119.2
17.75
0
277
91.00
3.72
119.2
17.75
5277
91.00
3.72
119.2
17.75
10
277
91.00
3.72
119.1
16.06
5254
91.16
3.69
119.36 17.65
40
180
75.08
2.46
119.36 17.65
5250
90.35
3.63
119.36 17.65
10
250
90.35
3.63
119.36 17.65
20
250
90.35
3.63
119.36 17.65
30
250
90.35
3.63

Angle (o)StrikeDip

Rake

20202012222222222414141362020202020797979959090909090

4. RESULTS AND DISCUSSION
4.1. Seafloor Deformation
The seafloor deformations were computed using Manshinha andSmylie (1971) equations which is part of the TUNAMI code by
Yanagisawa (2018). The ranges of the results were from 0.77 m to
2.08 m and -0.52 m to -0.19 m for uplift and subsidence,respectively.
Scenario S2 reported the highest uplift value of 2.08 m andsubsidence of 0.26 m (Figure 2). The results were affected by large
fault geometry and also the focal mechanism of this scenario. Whilescenario W1 resulted in the lowest uplift of 0.77 m among all thescenarios.
Figure 2. Seafloor deformation map of scenario S2
4.2. Tsunami Heights
calculated using the formula of Okada (1985).
The maximum tsunami height gives us the idea of the possible tsunami inundation that can happen to acertain area. The tsunami caused by scenario S2 can be the result of the larger geometry of the earthquake
source. Scenario S4 was also comparable. Since this scenario or the fault area does not directly face the3study area, the tsunami height was
not as high as scenario S2. Lastly,
the tsunami of scenario W1 wasrelatively low due to the deep depth(40 km) of the source. Themaximum tsunami height in Laoag
City in each scenario was obtained(Figure 3). The ranges were from
2.31 m (scenario W1) to 4.67 m(scenario S2).

Figure 3.
Maximum tsunami
height map of scenarios N2 (topleft), S2(top right), S4 (bottomleft) and W1(bottom right)
processed in QGIS.
4.3. Tsunami Propagation and Waveforms
Figure 4 shows the tsunami waveform in the output point of Laoag City which is placed in the smallest
regions which have a grid size of 1 arc second from scenario S2. After 20 mins, the Laoag City will
experience the arrival of the tsunami (positive amplitude). The arrival times are the key factor for theevacuation planning, giving the community the idea of how much time they need to evacuate and also
for the establishment of evacuation shelters.
The shape of the coastline affected thepropagations of the tsunami as well as thetsunami heights and inundation in the coastalareas.
The initial negative waves are theeffect of the subsidence pattern of the seafloor
deformation after the earthquake. It can beidentified that there are sharp peaks that can be
related to the grid size. Moreover, its location
allowed the propagation of tsunami to the area
which leads to almost two peaks of 4 m.
Figure 4. Calculated waveform in Laoag City.
4.4. Tsunami Inundation
The inundation simulation results were only taken in the smallest grid. Figure 5 presents the inundation
map of scenarios N2, S2, S4 and W1. The maximum inundation depth was about 6.8 m at scenario S2
while the lowest one was 3.5 m at scenario W1. The scenarios S1-S3 and N1-N4 had the same
parameters except for the depth that was varied in each scenario. It could be the reason that the results
were almost the same with small differences from each other.
The inundation area should also be taken into consideration in finding the maximum
credible scenario from the Manila Trench. Scenario S2 delivered the largest inundation area while W1delivered the lowest. The inundation area and maximum inundation depth were almost at the coastlineof Laoag City. One reason could be the topography of the study area. The land area at the coastline
elevated made the inundation area smaller.

4Figure 5. Inundation maps for scenarios N2, S2, S4 and W1.
4.5. Tsunami Damage Estimation
To estimate the potential damage from scenario S2, inundation depth values at each barangay are needed.
Exposed data were different for Methodologies A and B. For exposed building, PSA data and identified
building points in satellite imageries were used for Methodology A and B, respectively. The damage
map for each methodology is presented in Figure 6.
The results using Methodology A produced the higher number. Since this methodology usedaggregated data considering the inundated area, the approach may have led to overestimation. Most of
the barangays’ inundated area lies only on the coast, but the estimation was done for the whole area. InMethodology B, the estimation was done only to the inundated area which led to smaller values. In thisapproach, the buildings inside the inundation area were taken into consideration for the estimation andcan lead to a more accurate estimation.
The maximum inundated depth was obtained at Barangay 37 with 6.7 m while at lowest in
Barangay 32-C with 2.5 m due to its location. The estimated number of building damage and casualty
results of Methodology A were 3,972 (77%) and 12,800 (59%), respectively. In the case of methodology
B, the estimated damage on buildings and casualties were 538 (93%) and 3,144 (70%) respectively.

Figure 6. Tsunami Damage Map of Methodology A and B.
The estimation for the number of casualties in this study did not include the perception of thepeople about an evacuation. If the evacuation is to be considered, the estimation results for casualty will
be smaller. Also, knowledge about the signs of a tsunami as well as alerting stations will also be one of
the factors to lessen the estimates.

55. CONCLUSIONSThe tsunami hazard was assessed along the coast of Laoag City using single-fault models of the Manila
Trench. The results such as the seafloor deformations, tsunami heights, inundation area and depth foreach case were considered to produce a maximum credible scenario. In the worst case scenario, thedamage was estimated using tsunami fragility functions developed for Banda Aceh in the 2004 Indian
Ocean tsunami.
The fault dimension of scenario S2 was 277 km by 91 km, and its focal mechanism leads tothe highest maximum uplift of 2.08 m among all the scenarios. Also, this scenario resulted in a maximum
tsunami height of 4.67 m and led to a maximum inundation depth of 6.7 m. Since scenario S2 produced
the highest values, its inundation map was used for the damage estimation.
The estimation of tsunami damage was done using the fragility functions in two differentapproaches. The information on satellite imageries and street maps were used as data in a newmethodology. The identified number of the exposed building were 581 in which 93% (540) wereexpected to be damaged by the tsunami. For the exposed population, the data from PSA were distributed
to each grid inside a barangay. The total number of the exposed population was 4,474 in which 70%(3,144) was expected to be affected by the tsunami. These findings can only be used as a guide and notas exact values for the disaster planning.

6. RECOMMENDATIONThis research has certain limitations in terms of the data used. It is recommended to have accurate
information about tsunami propagation and inundation. Finer and high-resolution data for bathymetry
and topography should be used for the smallest region. For the exposure data such as building andpopulation, a field survey should be done to have an exact number of these data.

ACKNOWLEDGEMENTS
I would like to express my deepest gratitude to my supervisors Dr. Shunichi Koshimura, Dr. Erick Mas,and Dr. Yushiro Fujii for their guidance that was valuable in making this research a good one. Also, toall of my professors who made lectures that were the building blocks for this research.

REFERENCES
Koshimura, S., et al., 2009a, Jour. Dis. Res., 4, 6, 479 – 488.
Koshimura, S., et al., 2009b, Coast. Eng. Jour., 51, 3, 243 – 273.
Manshinha, L. and Smylie, D. E., 1971, Bull. Seism. Soc. Am., 61, 1433-1440.
Megawati, K., et al., 2009, Jour. Earth Sci., 36, 13 – 20.
Okada, Y., 1985, Bull. Seism. Soc. Am., 75, 4, 1135 - 1154.
Philippine Statistics Authority (PSA), 2015, Housing Census for 2015.
Vera, MT., 2015, Individual studies by participant at the IISEE. 1-38.
Yanagisawa, H., 2018, IISEE Lecture Note 2017-2018, IISEE, BRI.

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