Citation: Lesikikabara, K., Y. Fujii, B. Shibazaki (2023), Enhancing tsunami early warning system in Fijiusing tsunami database, Synopsis of IISEE-GRIPS Master’s Thesis.

ENHANCING TSUNAMI EARLY WARNING SYSTEMIN FIJI USING TSUNAMI DATABASE
Koroi LESIKIKABARA1
MEE22712

Supervisors: Yushiro FUJII2*, Bunichiro SHIBAZAKI2**

ABSTRACTThis study aims to enhance Fiji's tsunami early warning system by employing a tsunami database, which
includes 64 (four magnitudes and depths at four source points) precomputed simulation results stored ina MySQL database. The current system predominantly relies on manual input of forecasting messages,
making it time-consuming and error-prone. Five methods for retrieving tsunami height from the databasewere assessed, revealing significant variations in the estimated tsunami heights. The most simple
approach was defined as Method 2, which selects the maximum tsunami height among the four source
points based on the searched magnitude and depth. This method was used for its simplicity and ease ofimplementation, primarily due to its avoidance of complex interpolation techniques. Compared to theexisting system, which takes 10 to 15 minutes to generate and dispatch tsunami warnings, the enhanced
system can produce forecasting messages in seconds using Method 2 after earthquake parameters are
obtained.
Keywords: Tsunami Early Warning, Tsunami Database, Tsunami Height Estimation, Information
Dissemination, Tide Gauges.
1. INTRODUCTION
Even though Fiji has implemented a tsunami early warning system, the current infrastructure andmethods must be improved indisseminating timely and accurate
information to relevant authorities
and the media. The existing systemrelies heavily on manual processes
for assembling and disseminatingtsunami information, leading todelays and inaccuracies that could
ultimately cost lives. This study
focuses on Fiji's coastal blocks,
initially defined by Rawaikala (2011)
for a tsunami hazard assessment(Figure 1).
Figure 1. Fiji forecasting coastal blocks.
1

Mineral Resources Department, Fiji.
International Institute of Seismology and Earthquake Engineering, Building Research Institute.
*Chief examiner, **Examiner.
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This study aims to create a tsunami database to improve Fiji’s tsunami early warning
system. The study highlighted a remarkable improvement in information dissemination time by using
the tsunami database. The current process takes 10 to 15 minutes to generate a tsunami message, while
utilizing the tsunami database reduced the time to mere seconds.
2. DATAThe original GEBCO bathymetry data (GEBCO Compilation Group, 2023) were resampled to a coarse
resolution of 1 arc minute. In addition to the resampled bathymetry data, the GMT coastline database
was utilized to define the land grid within the computational area where the land grids are set to -999 m
and sea grids are set to positive values.
3. METHODOLOGY
3.1. Setting source points
For this case study, four source points (SP) were
established as shown in Figure 2. Each source point
corresponds to a specific magnitude range: 7, 7.5, 8,and 8.5. The depth range for these source points spansfrom 0 km to 60 km, with increments of 20 km (i.e., 0km, 20 km, 40 km, and 60 km).
3.2. Setting coastal and forecast points
10 coastal points (CPs) and forecast points (FPs) were
established along the coast as shown in Figure 3. CPs
are positioned nearer to the shoreline, specifically inareas where the depth of the sea is less than 10 meters.
On the other hand, FPs are located along the coast
where the water depth is approximately 50 meters.

3.3. Fault parameter setting
Dimensions of fault models, including its length (L),width (W), and slip amount (D), were calculated using
scaling law (Utsu, 1984). The strike angle of each fault Figure 2. Locations of SPs, tige gauge, seismicstations and M8.3 scenario earthquake.
model was set parallel to the Tonga trench.
3.4. Initial condition fornumerical simulation
An initial deformation of the sea surface
is assumed to be identical to thedeformation of the sea floor. It will begiven to the numerical tsunami simulation
as an initial value (Mansinha & Smylie,1971).
Figure 3. Distribution of CPs and FPs.

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3.5. Numerical simulation of tsunami propagation
TUNAMI code by Yanagisawa (2022-2023) was used for tsunami simulation. Non-linear long wave
equations were used as the governing equation using a spherical coordinate system.
3.5.1. Single grid computation
A 1-arc minute grid size was used throughout the computation area for tsunami database computation.
To satisfy the CFL condition, a time step of 3 seconds was used in a tsunami propagation time of 10,800
seconds (3 hours).
3.5.2. Nesting grid computation
Nesting grids shown in Figure 4 were used for computation to test for accuracy of using Green’s law toestimate tsunami height at the coast. The first grid has a grid size of 1 arc minute, the second region has
a grid size of 20 arc seconds and the last region has a grid size of 20/3 arc seconds. To satisfy the CFL
condition a time step of 0.5 seconds was used in a tsunami propagation time of 10,800 seconds (3 hours).

Figure 4. Nesting grid regions.
3.6. Tsunami height estimation at the coast
To optimize simulation computation time, a coarse grid of 1 arc minute is used. Green's law is appliedat the FPs to accurately estimate the tsunami height at the coast, with the water depth of CPs assumed
to be 1 meter based on guidelines from the Japan Meteorological Agency (JMA, 2013).
3.7. Methods of tsunami height retrieval from tsunami database
3.7.1. Method 1a
In this method, all three interpolation techniques (magnitude, depth, and location) were utilized at each
FP. Firstly, two-dimensional linear interpolation was used to interpolate magnitude and depth using twomagnitudes and two depths from the tsunami database. The resulting tsunami heights after magnitude
and depth interpolation was then used in location interpolation. Finally, the maximum tsunami heightfrom all FPs will be used for the tsunami forecasting message.
3.7.2. Method 1b
This method also employs all interpolation techniques. However, instead of interpolation at each FP, themaximum tsunami height from all FPs was used in magnitude and depth two-dimensional linearinterpolation. The resulting interpolated tsunami height was then used in location interpolation. Then
final tsunami height after location interpolation will be used in the tsunami forecasting message.

33.7.3.
Method 2
This involves selecting the closest four SPs. Then the highest closer magnitude and closer shallower
depth is selected from each SP in the database. The highest tsunami height among the four selected SPs
is chosen to be used for tsunami forecasting message.
3.7.4. Method 3aThis method uses the maximum risk method where all SPs in the rectangular fault model are selected.
The rectangular fault model is calculated using Utsu (1984) scaling law equation. The highest closer
magnitude and closer shallower depth will be selected from all selected SPs in the fault model and thehighest estimated tsunami height among them will be used for tsunami forecasting message.
3.7.5. Method 3b
This method also uses the maximum risk method. However, location interpolation is conducted at each
FP. The maximum interpolated tsunami height will be selected from all FP to be used for the tsunami
warning message. In the case where there are less than 4 SPs in the fault model then the four closest SPs
from the epicenter will be selected from the database for location interpolation to be possible.
3.8. Tsunami arrival time estimation
For Tsunami Travel Time (TTT) calculation, TTT Software Package Version 4.0.1 provided byInternational Tsunami Information Center was used together with GMT 4 to make an inverse refraction
diagram. Tsunami travel time from FP to the epicenter is added together with tsunami travel time fromFP to CP. This total tsunami travel time is stored in the tsunami database. To estimate tsunami arrival
time at the coast, the total tsunami travel time stored in the database is added to the earthquake origin
time.
3.9. Tsunami forecasting messages
The determination of the tsunami forecast grade in this study is based on the maximum coastal tsunami
height shown in Table 1. This tsunami height is computed by applying Green's law to the tsunami heightcalculated at the forecast point.
Table 1. Tsunami forecasting messages. H is the estimated tsunami height.
Forecast Grade
Estimated Tsunami Height (m)Warning
Major Tsunami
H >= 3Tsunami
1 >= H < 3Advisory
0.5 >= H <1No tsunami threat
H < 0.5

4. RESULTS AND DISCUSSIONS
4.1. Greens law evaluation
Figure 5 illustrates that the direct measurement of tsunami height at the coast using a coarse grid tends
to underestimate the tsunami height when compared to the more precise fine grid of 20/3 arc seconds.
Furthermore, the graph shows that the tsunami height estimated at the coast using Green’s law inconjunction with the coarse grid aligns well with the tsunami range determined using the fine grid. Thesefindings highlight the significance of utilizing Green's law and coarse grids to accurately estimate

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tsunami height at the coast, thereby
decreasing computation time andmaintaining
tsunamiheightestimation accuracy.

Mean at thecoast (FineGrid)
Median at thecoast (FineGrid)
Min and maxrange (FineGrid)
FP+Greens lawTsunami Height (m)6.00

4.2. Retrieving tsunami heightsfrom the database
The obtained results from the fivemethods, namely Method 1a, Method
1b, Method 2, Method 3a, andMethod 3b, are derived from thescenario earthquake represented by
the red star in Figure 2. This
earthquake scenario has a magnitudeof 8.3 and a depth of 30 km.

5.00
4.00
3.00
2.00

1.00
0.00

1234 5 6 7 8Location (G1-G10)

9 10Median at thecoast (CoarseGrid)
Min and max(Coarse grid)

Figure 5. Green’s law evaluation.

4.3. Comparing the five methods
Method 2 and Method 3a result produce
the highest estimated tsunami heightbecause both methods choose themaximum tsunami height from thetsunami database among the four closest
SPs (Figure 6). The simplicity of Method
2 offers practical advantages in terms ofimplementation. It avoids the need forcomplex interpolation techniques.

Tsunami Height (m)

5

4.35

4.35
3.57

43

2.37

2.61

2

10Method Method Method Method Method
1a1b2
3a3b

Figure 6. Estimated tsunami heights using the five methods.
Figure 7 presents the total tsunami
travel time for each location in thestudy area. The quickest tsunami
travel time among all FPs isrecorded in the tsunami databaseand utilized in the computation of
the tsunami's arrival time. By
selecting the quickest time among
these nearest SPs, we can offer
more crucial minutes for people toevacuate and seek safety.

Tsunami Travel Time (H:mm)

4.4. Tsunami travel time
2:24
2:16
2:09

SP1

2:02

SP2SP3

1:55

SP4
1:48

M8.3

1:40
G1 G2 G3 G4 G5 G6 G7 G8 G9 G104.5. Tool to generate tsunami
forecasting message

Location
Figure 7. Total tsunami travel time to each coastal area.

Tsunami simulation results are
first stored in Excel files, then imported from MySQL to create a tsunami database. We used Python
programming language to create a Graphical User Interface (GUI) to input earthquake parameters.
In the automated generated message shown in Figure 8, the content of the tsunami
forecasting message will be determined based on the tsunami height at the coast, as indicated in Table
2. The specific type of forecasting message, whether it be a major tsunami warning, tsunami warning,5

advisory or no tsunami threat will be
determined by the calculated tsunami
height value. Additionally, the evaluation
message and recommendations message
will be tailored according to the type of
forecasting message selected.

5. CONCLUSIONSIn conclusion, this study focused onenhancing the tsunami early warning
system in Fiji through the utilization of atsunami database. the study highlighted aremarkable improvement in information
dissemination time by using the tsunamidatabase. The current process takes 10 to
15 minutes to generate a tsunami message,
while utilizing the tsunami database
reduced the time to mere seconds. Thisswift access to pre-computed tsunami
simulation results and messages stored
Figure 8. Sample of "Major Tsunami Warning” message. within the database enables the rapid
The preprepared message is also stored in the tsunami generation of accurate tsunami messages
for forecasting, facilitating timely
database.
response and decision-making during
tsunami events. Furthermore, considering the results obtained from the Nuku’alofa tide gauge, the study
demonstrates the usefulness of tide gauges around Fiji in monitoring tsunami sources near SPs in thetsunami database. The evaluation of tsunami travel time differences between Nuku’alofa and Suva canaid MRD in promptly responding to potential tsunami threats and issuing timely warnings to coastal
communities, further enhancing Fiji’s disaster preparedness and safety.
ACKNOWLEDGEMENTS
I would like to express my deepest appreciation and gratitude to my supervisors, Dr. Yushiro Fujii andDr. Bunichiro Shibazaki, for their exceptional guidance, unwavering support, and extensive expertisethroughout the entire process of completing this report. Their valuable insights and advice have playeda crucial role in shaping the outcome of this study, and I am sincerely indebted to their mentorship andencouragement.
REFERENCES
GEBCO Compilation Group (2023). GEBCO 2023 Grid (doi:10.5285/f98b053b-0cbc-6c23-e0536c86abc0af7b).
JMA (2013). Tsunami Forecasting Technology in Japan Meteorological Agency.
Mansinha, L., & Smylie, D. (1971). The displacement fields of inclined faults. Bulletin of theSeismological Society of America, 61(5), 1433-1440.
Rawaikala, U. R. (2011). TSUNAMI SIMULATION AND HAZARD ASSESSMENT ALONG THE
COASTS OF FIJI. Bulletin of the International Institute of Seismology and Earthquake
Engineering, 45, 103-108.
Utsu, T. (1984). Seismology [2nd edition]. Kyoritsu Shuppan.
Yanagisawa, H. (2022-2023). Lecture notes on Numerical Simulation of Tsunami Inundation and its
Application.

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