Citation: Sinki, K., Y. Fujii, B. Shibazaki (2021), Numerical simulations of the 1992 Flores tsunami
using earthquake fault and landslide models, Synopsis of IISEE-GRIPS Masterʼs Thesis.

NUMERICAL SIMULATIONS OF THE 1992 FLORESTSUNAMI USING EARTHQUAKE FAULT AND
LANDSLIDE MODELS
Kian Purna Sinki1,2Supervisor: Yushiro FUJII3, Bunichiro SHIBAZAKI3
ABSTRACTThe Flores back-arc thrust is an active earthquake source in northern Flores Island, and this trenchextends from Bali to East Flores. On December 12, 1992, there was an earthquake that caused a massive
tsunami. The tsunami that hit along the northern coast of Flores caused many economic andinfrastructure damages and loss of life. The field survey measurements indicate that the highest tsunami
was 26 m in northern coast of the Flores, revealing significantly high tsunami run-ups which might becaused by landslides because there were some evidences of landslide slumps in several areas.
This study reproduces the tsunami propagation and inundation for the 1992 Flores tsunami andcompares the numerical results with the observed data obtained by the field survey. We applied thenumerical simulation using a finite difference TUNAMI code that solves the non-linear shallow waterequations. We used BATNAS bathymetric data and DEMNAS topographic data, which were provided
by Indonesia Geospatial Agency for two-layer computations to simulate tsunamis form the fault models
and three-layer computations for fault and landslide models. We also propose a scenario earthquake forcreating a tsunami hazard map with a reference fault model from the previous study.
Using the data we collected, we have successfully performed the tsunami modeling for wave
propagation to the northern coast of Flores. The simulated tsunami by using the three-layer computations
with the fault and landslide models generated higher tsunamis than the two-layer computations onlywith the fault models. It is emphasized that the landslide will impact the tsunami generation higher than
the fault only. Besides, we also obtained the maximum tsunami run-up heights and inundation distances.
This study will bring many advantages to enhance the source input for tsunami simulations since wefound that the results are applicable to create the tsunami hazard map in the Flores area.
Keywords: 1992 Flores Earthquake and Tsunami, Earthquake Fault and Landslide Model, Tsunami
Hazard Map.
1. INTRODUCTION
Indonesia is located between the Pacific Ocean and the Indian Ocean and lies on the boundaries of major
tectonic plates: the Indo-Australian Plate that subducts beneath the Eurasian Plate (Sunda Plate), thePacific Plate in the eastern part, Philippine Sea Plate in the northeastern part (Hamilton, 1979). Flores
Island located in the Eastern Nusa Tenggara, a seismically active area. In 1992, the earthquake of Mw

1Agency for Meteorology, Climatology and Geophysics (BMKG), Jakarta, Indonesia.
IISEE-GRIPS Master’s course student.
3International Institute of Seismology and Earthquake Engineering (IISEE), Building Research Institute (BRI).
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7.2 caused the devastating tsunami (Figure 1). The fault
extended from the north of the Flores Island to thenorth-sea of Bali with a length around 1,130 km andlocated between the subduction boundary of the IndoAustralian and Eurasian plates (Daryono, 2011). Tsujiet al. (1995) reported that the people felt the great
shaking at Maumere in Wuring city with the maximum
intensity recorded up to VII MMI. This tsunami caused
catastrophic damage along the coast of northeast Flores,
based on the field survey along coastal parts of theFlores Island, they found the highest tsunami run-up
Figure 1. Epicenter (blue star) of the 1992
with the average of 18 m in the most northeast shore.
Flores earthquake and the aftershocks (redYeh et al. (1993) found some evidences of landslides circles) distribution from USGS.
along the southern coast of Hading Bay, the eastern partof Flores, which may have caused the extremely high tsunami run-ups. In order to contribute to a better
quantitative understanding of the tsunami propagation and inundation around Flores, as well as thosefor the 1992 Flores tsunami we adopt some earthquake fault and landslide models. We will evaluate thesimulated tsunami by comparing with the field survey data by Tsuji et al. (1995) and also create atsunami hazard map of the western part of Flores Island assuming an earthquake scenario model.
2. DATA
2.1 Tectonic source – Earthquake’s fault models
Imamura et al. (1995) and Pranantyo and Cummins(2019) constructed the different fault models for thisearthquake as shown in Figure 2. Imamura et al. (1995)
proposed a two-fault model and we modified theirparameters as listed in Table 1. Pranantyo andCummins (2019) constructed the subfault model by thefinite-fault source inversion using the seismic data,
which can be used for tsunami simulations. The
inverted slip model have 360 subfaults to obtain theheterogeneous slips on the fault. The maximum slip on
the fault is about 20 m, and large slips focused on thewestern part near the hypocenter.
Table 1. Parameters of fault model modified from Imamura et al.

Figure 2. Fault models form (top) Imamura et al.
(1995) and (bottom) Pranantyo and Cummins(2019), and the calculated seafloor deformations.

2.2. Field survey data

As the observed data in this study, we referred to Hidayat et al. (1995). They conducted tsunami
modeling in 37 locations by compiling the tsunami run-up heights (Figure 3) from the field survey resultsfrom Tsuji et al. (1995). The tsunami field survey was conducted along the northeast shore of Flores
Island. The yellow number indicates the tsunami run-up heights and the green numbers shows thelocation of the field survey measurement. For this study we will discuss the results for Wuring (11),Maumere (12), Babi Island (25) and (27) Uepadung (33), Waibalen (34), Pantai Lela (35), Riangkroko(36) and Bunga (37).

22.3. Nontectonic source – Landslide models
Based on the information reported by Tsuji et al (1995) and Yeh et al. (1993), we adopted the slumptype submarine landslide model by Watts et al. (2005). Figure 4 shows the calculated sea surface
elevations from the constructed three landslide models. The first landslide model near Waibalen has themaximum uplift of 18.7 m and subsidence of -21.0 m. For the second model near Riangkroko, themaximum uplift is 18.3 m and the subsidence is -20.6 m, and for the third model near Bunga the valueof the maximum uplift is 20.1 m and the subsidence value is -21 m. The uplift and subsidence
information are considered as the bathymetry and topography deformation for the initial tsunami wave.

Figure 3. Observed tsunami run-ups plottedwith scale bars in a 3D view. The data
source is from Hidayat et al. (1995).

Figure 4. Three landslide models. The red andblue contours with the interval of 1 m indicate theuplift and subsidence predicted from the slumpmodels.

3. METHODOLOGY
3.1. Tsunami simulation and computational setting
For the tsunami simulations in this study, we used TUNAMI code originally developed by Tohoku
University and provided by Yanagisawa (2021). The governing equations are based on the non-linier
long wave theory with the spherical coordinate system. For the study area, we confirmed that the recent
data for the Flores, BATNAS is more adequate than GEBCO 2020 (GEBCO Compilation Group, 2020).
The resolution of GEBCO 2020 is 15 arc-second (https://www.gebco.net/). Meanwhile, they are 6 arcsecond and 0.27 arc-second for BATNAS and DEMNAS, respectively. This study enhances the tsunami
modeling by applying tectonic (earthquake fault) and non-tectonic (landslide) sources. For the tectonic
source, we applied two-layer computations by using the two reference fault models. For the non-tectonicsources, we applied three-layer simulations using the fault and three landslide models.
3.2. Validation of tsunami simulations results
Validation of the calculations are required in tsunami modeling studies as the confirmation whether
the simulation results reproduce the observations. For the validations we refer to the K and 𝜅 numbers(Aida, 1978). K is a ratio between the measurements and the simulations, 𝜅 is a standard deviation
indicating the acceptability of the modeling, proposed values of 0.8 < 𝐾 < 1.2 (20% error) and κ <1.45.

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4. RESULTS and DISCUSSION
4.1. Two-layer computation with fault models(m)

We applied the two-layer nested grids for the numerical tsunami simulations by using two fault models
from Imamura et al. (1995) and Pranantyo and Cummins (2019) as shown in Figure 5. Our tsunami
modeling results from Imamura et al. (1995) showed thatfor a tsunami in the densely populated area, Wuring (11)and Maumere (12), the estimated tsunami heights
obtained were 1.46 m and 1.44 m, respectively. In Babi
Island, as reported by Tsuji et al. (1995), the tsunami
wiped away the whole island and the heights we obtained
were 2.44 m at Babi North (25) and 1.97 m at Babi East(27). The tsunami in the northern shore of Flores, whichapproached to Bunga was 12 m, and our obtained resultof the model was 2.8 m which was not fitted well withthe measurement.
Meanhwile our modelling from Pranantyo andCummins (2019) for locations of (11) and (12) theestimated tsunami heights obtained were 3.2 m and 3 m,respectively. For locations of 25 and 27, we obtained 4m and 5.6 m respectively and at Bunga (37) obtainedheight was 1.03 m. The previous study by Hidayat et al.
(1995) also performed tsunami simulations with the fault
source inverted from seismic data. For the comparison,
we show the bar graph of the simulation results against
Figure 5. Distribution of calsulated maximum
the observations at 37 outpoints (Figure 6). The twotsunami heights from (top) Imamura et al.
layer simulation results from the fault models showed1995) and (bottom) Pranantyo and Cumminsthat the obtained tsunami run-up heights underestimated(2019) fault models.
the observed values.

Figure 6. Comparison of tsunami heights for two-layer computations.

4.2. Three-layer computation with fault and landslide models
We set the three landslide models located near the coast and the slide directions perpendicular to thecoast lines. As the output points for the calculated tsunami waveforms, we selected five locations,Waibalen, Riangkroko, Bunga, Uepadung and Pantai Lela in Layer 3. Uepadung and Pantai Lela arelocated behind and in front of the landslide model near Waibalen, respectively. For the two-subfault
model by Imamura et al. (1995) and three landslide models, we obtained the maximum tsunami heights

4(m)of 8.12 m at Waibalen (34) and 11.2 m at Bunga (37). The results slightly underestimated the measured
tsunami heights. At Riangkroko (36), the calculated maximum tsunami height was 12 m. We can see
the estimated maximum tsunami at all the locations in Figure 7. The other results at location (33) and(35) show the maximum tsunami heightsof 5.2 m and 4.5 m.
For the 360-subfault model byPranantyo and Cummins (2019) and thethree landslide models, we obtained theresults for locations (34), (36) and (37) of
7.9 m, 8.5 m and 10.4 m, respectively.
Figure 8 shows the comparison of thecalculated maximum tsunami heights andthe observed ones by the field survey andthe ones simulated by the previous study(Hidayat et al., 1995). In the Eastern partof Flores, the landslide models had a
Figure 7. Distribution of calculated maximum tsunami
good certainty to reproduce the tsunami
heights from (left) Imamura et al. (1995) and (right) Pranantyo
heights. To validate the simulation
and Cummins (2019) fault models and three landslide models.
results, we calculated the K and κ for the37 locations. For the first model referredto Imamura et al. (1995) with the landslide
models, we obtained K = 1.49 and κ =1.39, and for the second model ofPranantyo and Cummins (2019) with thelandslide models, we obtained K = 1.47
and κ = 1. The validation results showedthat the second fault model explained theobservation results slightly better than thefirst simulation.
Figure 8. Comparison of tsunami heights for three-layercomputations.

4.3. Tsunami inundation simulation for a scenario earthquake
We assumed the fault model refer to Lovholt et al. (2012) and Pusgen (2017) along the Flores back-arc
thrust and calculated the seafloor deformation (Figure 9). The maximum uplift was 2.1 m and themaximum subsidence was -0.47 m. This scenario generated huge tsunami and the inundation tsunami
in Pota Pota, western Flores (Figure 10). The maximum tsunami run-up of 16 m with the inundation
distance of 1,700 m. For this simulation, we did not adopt landslide models, since we need morereference data for potential landslide events. The results can support the creation of a tsunami hazard
map. However, to make a tsunami evacuation map for this scenario, further studies are needed

Figure 10. Result of the tsunami inundation
simulation from the scenario earthquake for
Pota Pota,western Flores.

Figure 9. Seafloor deformation fromthe scenario fault model.

55. CONCLUSIONSIn this study, we modeled the 1992 Flores tsunami using TUNAMI code by adopting twofault models and three landslide models. The two-fault model (Mw 7.8) referred to Imamura et al. (1995)
and the 360-subfault model (Mw 7.8) from Pranantyo and Cummins (2019) reproduced higher tsunamis
in the eastern part and western part, respectively. This is because of the different position of the large
slips on the fault models.
We performed two tsunami inundation simulations with two-layer and three-layer nested
grids and evaluated the results with the observed tsunami heights at 37 output points from the fieldsurvey measurements Table 3. As the results from the two-layer simulations, the tsunami waveforms
and the maximum heights were provided along the coast of Flores. As the results from the three-layer
calculation, we involved five locations as the output points in the finest grid (Layer 3). Since we only
assumed three locations by using three landslide models, the tsunami heights at the five output points
were impacted higher than the two-layer computation.
As the validation of the tsunami simulation results comparing with the tsunami field survey
data, we calculated the values of K and κ. We found that both of the fault models almost explain thetsunami heights by being unified with the three landslide models, especially in the eastern area, although
the fitting values of K and κ still show the slight underestimation.
We designed the size of an earthquake (Mw 8.3) from the reference studies as our scenario
to simulate the tsunami propagation. We also performed a tsunami inundation simulation assuming thescenario earthquake to prepare a tsunami hazard map at Pota-Pota in western Flores. If we could find an
evidence of landslide potential in future, we are ready for the tsunami modeling using the unified
scenario earthquake and landslide models.
ACKNOWLEDGEMENTS
This research was conducted as the individual study of the training course “Seismology, Earthquake
Engineering and Tsunami Disaster Mitigation” by IISEE/BRI, JICA, and GRIPS. I would like to express
my sincere gratitude to my supervisor Dr. Yushiro Fujii for his valuable support and experience which
helped me to complete my research successfully.

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