Access to this full-text is provided by Copernicus Publications on behalf of European Geosciences Union.
Content available from Natural Hazards and Earth System Sciences
This content is subject to copyright.
Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025
https://doi.org/10.5194/nhess-25-1057-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Probabilistic tsunami hazard analysis of Batukaras, a tourism
village in Indonesia
Wiwin Windupranata1, Muhammad Wahyu Al Ghifari2, Candida Aulia De Silva Nusantara3, Marsyanisa Shafa2,
Intan Hayatiningsih2, Iyan Eka Mulia1,4, and Alqinthara Nuraghnia2
1Hydrography Research Group, Faculty of Earth Sciences and Technology,
Institut Teknologi Bandung, Bandung, 40132, Indonesia
2Study Program of Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology,
Institut Teknologi Bandung, Bandung, 40132, Indonesia
3Department of Geomatics Engineering, Faculty of Civil, Planning and Geo Engineering,
Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia
4RIKEN Cluster for Pioneering Research, Prediction Science Laboratory, Wak¯
o, Saitama, Japan
Correspondence: Wiwin Windupranata (w.windupranata@itb.ac.id)
Received: 30 November 2023 – Discussion started: 11 January 2024
Revised: 9 October 2024 – Accepted: 17 December 2024 – Published: 11 March 2025
Abstract. Indonesia’s location in the middle of tectonic
plates makes it vulnerable to earthquakes and tsunamis, es-
pecially in the megathrust zone around the island of Sumatra
and the southern part of the island of Java. Research shows a
seismic gap in southern Java, which poses a potential threat
of megathrust earthquakes and tsunamis, impacting coastal
areas such as Batukaras in West Java, a popular tourist des-
tination. To prepare for disasters, probabilistic tsunami haz-
ard analysis (PTHA), which focuses on seismic factors, was
carried out by modelling tsunamis on 3348 sub-segments of
4 large megathrust segments in the south of Java. Stochas-
tic earthquake modelling was used to simulate the occur-
rence of a tsunami from an earthquake with Mw6.5 to the
highest potential magnitude. This research shows that the
PTHA in Batukaras reveals varying heights of 0.84, 1.63,
2.97, and 5.7 m for each earthquake return period of 250,
500, 1000, and 2500 years, respectively. The dominant threat
arises from the West Java–Central Java megathrust segment,
emphasising the importance of preparedness, although the
annual probability of tsunamis is generally low. Our study
will deepen knowledge of tsunami hazards associated with
megathrust activities near Batukaras for mitigation planning
and decision-making, and it can become a reference for sim-
ilar coastal tourist areas.
1 Background
Indonesia is located in the convergent boundaries between
several tectonic plates, making it prone to earthquakes and
tsunamis. The meeting between these plates causes the emer-
gence of a zone called the megathrust zone. A megathrust is
a type of fault that occurs in the subduction zone of one tec-
tonic plate forced under another plate. High seismic activity
in Indonesia occurs around Sumatra and southern Java due
to the megathrust zone (Koswara et al., 2021; Mulia et al.,
2019; Supendi et al., 2023; Windupranata et al., 2020).
Research conducted by Supendi et al. (2023) shows a seis-
mic gap to the south of the island of Java that has a po-
tential source of future megathrust earthquakes. This poten-
tial earthquake could generate tsunami heights of up to 20 m
on the south coast of West Java, with an average maximum
height of 4.5 m along the south coast of Java. The annual
probability of a tsunami event with a height of more than
3.0 m, which could cause significant loss of life and dam-
age, is 1 %–10% in the south of Java (Horspool et al., 2014).
An earthquake occurred in Pangandaran Regency on 17 July
2006 with a magnitude of Mw7.6 that generated a power-
ful tsunami (Fujii and Satake, 2006; Gunawan et al., 2016;
Hanifa et al., 2014). This event resulted in more than 300
deaths, 301 serious injuries, 551 minor injuries, and 156 peo-
ple missing (Mustafida et al., 2022). Other research shows
Published by Copernicus Publications on behalf of the European Geosciences Union.
1058 W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras
that there is still a high tsunami potential due to the megath-
rust fault in southern Java, which has an earthquake return
period of 500 years (Harris et al., 2019). This condition is re-
inforced by the fact that no major earthquakes have occurred
in the past few years; only earthquakes with a magnitude be-
low Mw<8 have occurred in the past 100 years (Supendi et
al., 2023). This means that megathrust earthquakes still have
the potential to occur.
On the other hand, the southern part of Java has high
tourism potential that can be developed. One of these
tourism opportunities can be seen from the many tourist vil-
lages located on the southern coast of Java, one of which
is Batukaras, located in Pangandaran Regency, West Java
(Koswara et al., 2021; Nijman, 2021; Windupranata et al.,
2020). Batukaras is one of the villages in Indonesia that is
often visited by local and foreign tourists. Every year, the
number of tourists visiting Batukaras can reach 521 000 peo-
ple (before the pandemic) (Batukaras Village Profile, 2022).
Batukaras itself is inhabited by 5172 residents with a den-
sity of 393 people km−2, making this population vulnerable
to tsunami disasters. The coast of Batukaras is dominated by
lodging places, restaurants, and plantation and agricultural
land. Therefore, the preparedness of this tourist village for
facing tsunami disasters needs to be reviewed to minimise
casualties and economic losses.
Tsunami hazard analysis in this tourist village can be done
by modelling tsunamis using probabilistic tsunami hazard
analysis (PTHA). The PTHA method is an approach that
can estimate the tsunami hazard in a certain period of time
in each area that is likely to be exposed to the hazards of
the tsunami disaster (Grezio et al., 2017). This method can
analyse tsunami hazards originating from seismic (plate tec-
tonic activity) or non-seismic (volcanic activity, submarine
landslides, and other events) factors (Grezio et al., 2017;
Salmanidou et al., 2019). However, the scenario of potential
tsunami hazards from these non-seismic factors is unlikely,
as the timing and mechanism of their occurrence are difficult
to estimate (Grezio et al., 2017). In addition, most tsunamis
that occur in Indonesia are caused by seismic factors, i.e.,
vertical displacement of the seabed caused by shallow earth-
quakes in subduction zones (Hamzah et al., 2000).
In this study, tsunami modelling based on seismic factors
is conducted using the parameters of megathrust plate activ-
ity directly adjacent to Batukaras. The results of the PTHA
are expected to characterise the tsunami hazard at the study
site, thereby facilitating mitigation planning and decision-
making. In addition, Batukaras is currently in the process of
being recognised as a tsunami-ready village, which refers to
12 IOC-UNESCO indicators consisting of assessment, pre-
paredness, and response components (Mustafida et al., 2022).
The results of this PTHA can support the assessment compo-
nent to make this village a tsunami-ready village issued by
IOC-UNESCO. Furthermore, the results of the tsunami haz-
ard analysis in this village can be used as a reference for other
tourist villages, especially for areas that have similar charac-
teristics to Batukaras.
2 Data and method
2.1 Data
In this research, accurate bathymetry data will determine the
quality of tsunami modelling results. Bathymetry data were
obtained from National Bathymetry (BATNAS) provided by
the Indonesian Geospatial Information Agency (BIG). These
data were used to create the wave propagation domain of
the tsunami modelling. Furthermore, earthquake generation
parameter data were obtained from the National Centre for
Earthquake Studies (PuSGeN) in 2017 and the United States
Geological Survey (USGS). The earthquake parameter data
are divided into rake, dip, slip, strike, length, and width data
obtained from PuSGeN and the depth of the epicentre data
obtained from USGS.
2.2 Method
PTHA was used for hazard modelling to determine the
tsunami risk in Batukaras. This method is used to determine
the tsunami hazard in an area with a geographically consis-
tent approach to estimating long-term hazards, including un-
certainties in the analysis (Grezio et al., 2017) and modelling
parameters (Thio et al., 2007). In the process of tsunami anal-
ysis using the PTHA method, several stages of data process-
ing must be carried out.
2.2.1 Green’s function for tsunamis
The calculation of Green’s function aims to obtain the height
of a tsunami wave from each observation point. In the calcu-
lation of Green’s function, domain creation, megathrust sub-
segments, and determination of observation points are car-
ried out.
In this tsunami modelling, two types of model domains
are formed, namely domain 1 and domain 2. Domain 1 is a
large area covering the study area, namely Batukaras and the
megathrust segment (Fig. 1). The megathrust segment used
in this tsunami modelling includes the megathrust segments
of the Sunda Strait, West Java–Central Java, East Java, and
Bali (Fig. 2). Domain 2 is a smaller area that only covers
the study area of Batukaras, which is located in Pangandaran
Regency.
Here, data on the four segments, such as location and seg-
ment size, were sourced from PuSGeN in 2017. Further-
more, the four segments were modelled as megathrust sub-
segments with a grid size of 10 km ×10km, resulting in 3348
sub-segments divided into
–the Sunda Strait segment (612 sub-segments)
–the West Java–Central Java segment (800 sub-segments)
Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025 https://doi.org/10.5194/nhess-25-1057-2025
W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras 1059
Figure 1. Modelling domain illustration. (a) Domain 1. (b) Domain 2. Imagery © BATNAS 2018.
Figure 2. Megathrust sub-segment used in PTHA modelling.
–the East Java segment (736 sub-segments)
–the Bali segment (1200 sub-segments).
In this tsunami modelling, the location of observa-
tion points was determined to evaluate the height of the
tsunami generated at each observation point. The locations
of these observation points are spread along the coastline in
Batukaras (Fig. 3). In this modelling, 20 observation points
were used based on bathymetry data, where the location of
each point has an isobath (depth) of 1 m.
We used the Cornell Multi-grid Coupled Tsunami (COM-
COT) software version 1.7 for tsunami modelling. In COM-
COT, modelling is carried out by generating waves from
earthquakes originating from megathrust segments based
on earthquake parameter data and propagating the tsunami
waves using the shallow-water equation to obtain tsunami
heights at each observation point. This tool only simulates
tsunami waves with no influence of wind and tides.
The COMCOT software uses the linear shallow-water
equation (LSWE) and the non-linear shallow-water equation
(NLSWE). The LSWE is used when tsunami waves are still
in the open sea with smaller wave amplitude compared to
depth. The following is the LSWE used (Wang, 2009).
δη
δt +1
RcosϕδP
δψ +δ
δψ (cos ϕQ)=−δh
δt (1)
δP
δt +gh
Rcosϕ
δη
δψ −f Q =0 (2)
δQ
δt +gh
R
δη
δψ +f P =0 (3)
δη
δt +1
RcosϕδP
δψ +δ
δϕ (cos ϕ Q)=−δh
δt (4)
δP
δt +1
Rcosϕ
δ
δϕ P2
H+1
R
δ
δϕ P Q
H
+gh
Rcosϕ
δη
δϕ −f Q +Fx=0 (5)
δP
δt +1
Rcosϕ
δ
δϕ P2
H+1
R
δ
δϕ P Q
H
+gh
Rcosϕ
δη
δϕ −f Q +Fx=0 (6)
δQ
δt +1
Rcosϕ
δ
δψ P2
H+1
R
δ
δϕ P Q
H
+gh
R
δη
δϕ +f P +Fy=0 (7)
Then, when the tsunami wave travels in shallow waters, the
NLSWE is used. This is because the wavelength becomes
shorter and the wave amplitude becomes larger when pass-
ing through shallow water. This means that the shape of the
https://doi.org/10.5194/nhess-25-1057-2025 Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025
1060 W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras
Figure 3. Observation point along Batukaras coast. Imagery © Google Earth 2022.
seabed influences the wave amplitude. The following is the
NLSWE used (Wang, 2009).
H=η+h(8)
f=sinϕ(9)
Fx=gn2
H7/3PpP2+Q2(10)
Fy=gn2
H7/3QpP2+Q2(11)
P=
η
Z
−h
udz=u(h+η)=uH (12)
Q=
η
Z
−h
vdz=v(h+η)=uH (13)
Here, gis the acceleration of gravity (m s−2), Pis the vol-
ume flux in −x(west–east) (m s−2), Qis the volume flux
in −y(south–north) (m s−2), fis the Coriolis force coeffi-
cient, (ϕ, ψ) is latitude and longitude (°), Ris the radius of
the Earth (m), his the water depth (m), ηis the water surface
height (m), His the total water depth (m), is the Earth ro-
tation rate (7.2921 ×10−5rad s−1), (F x, F y) is bottom fric-
tion at −xand −y;nis the Manning roughness coefficient
(s m−1/3); uis the current velocity in −x(m s−1), and vis
the current velocity in −y(m s−1).
2.2.2 Stochastic earthquake modelling
Stochastic earthquake modelling aims to simulate the slip
amount on the fault plane, determining the initial seafloor
displacement and the corresponding tsunamis. Mai and
Beroza (2002) developed a method to characterise the com-
plexity of earthquake slip represented by spatially random
fields of anisotropic wave number spectra according to the
von Karman autocorrelation function. The stochastic nature
of the method is associated with uniformly distributed ran-
dom phase angles embedded in the domain. In this study,
random slips are initially calculated at a 1 km grid spacing
and then interpolated into the sub-segment size for tsunamis
using Green’s function calculation. In this study, bilinear in-
terpolation is performed without changing the average slip,
thus maintaining the magnitude of the moment of the inter-
polated sample. In the formation of the stochastic earthquake
model, several settings were used:
1. The minimum magnitude value used is Mw6.5, sourced
from USGS.
2. The maximum magnitude value used was sourced from
PuSGeN in 2017. The maximum magnitude value used
is different for each megathrust segment. The Sunda
Strait segment has a maximum magnitude of Mw8.8,
West Java–Central Java Mw8.8, East Java Mw8.9, and
Bali Mw9.
Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025 https://doi.org/10.5194/nhess-25-1057-2025
W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras 1061
Figure 4. Stochastic earthquake generation illustration (Mulia et al.,
2020).
3. The earthquake magnitude bin (interval) used in the
modelling is Mw0.1, so the earthquake will be gener-
ated starting from the minimum magnitude to the max-
imum magnitude with a difference of Mw0.1.
4. The rupture area is randomly specified within the
megathrust segment, as done in the PTHA study by
Mori et al. (2017).
Figure 4 shows examples of the resulting slip distribution
from various earthquake magnitudes by Mulia et al. (2020).
We also conducted statistical analyses that show the level
of variability in each magnitude range. This analysis uses
the coefficient of variation (CV) of σ/µ, where σis the
standard deviation, and µis the mean of maximum coastal
tsunami heights at all coastal points. This analysis reflects
the convergence of Monte Carlo samples of tsunami heights
in coastal areas associated with the sources of the identified
active faults so that the number of samples required across
the earthquake magnitude range can be estimated.
We performed this calculation for each increment of
magnitude bin (Mw0.1) of the earthquake in the interval
Mw6.5 to Mw9.0. The maximum magnitude values used
were sourced from PuSGeN in 2017, such as in the Sunda
Strait (Mw8.8), West Java–Central Java (Mw8.8), East Java
(Mw8.9), and Bali (Mw9.0) segments. At each magnitude
interval, we calculated the coefficient of variation with each
increase in the number of samples from 2 to 150.
In Fig. 5 it can be seen that the variability value decreases
as the magnitude increases due to randomisation of the rup-
ture area from smaller earthquakes. Each magnitude shows
different variability. For example, the height of a coastal
tsunami caused by a Mw6.5 earthquake is quite stable if
about 100 samples are used. Therefore, in this study we as-
sume that the minimum number of samples required is 100
for Mw6.5. From the resulting graph, using a visual ap-
proach, we also applied it to all magnitudes by drawing a
Figure 5. Coefficient of variation of maximum coastal tsunami
height for each magnitude across megathrust segments. The blue
line indicates the number of samples required at each magnitude
bin for the PTHA.
straight line (blue-coloured line). This line shows the number
of earthquake scenarios required for each magnitude. Thus,
the resulting number of scenarios is 7019 scenarios, as can be
seen in Table 1. This large number of scenarios is expected
to provide a good explanation of the epistemic uncertainty in
the PTHA results in this study.
2.2.3 Determination of aand bvalue modelling
The values of aand bin PTHA are constants from the empir-
ical formula derived by Beno Gutenberg and Charles Fran-
cis Richter with the following equation (Gutenberg and
Richter, 1954):
logN(M)=a−bM , (14)
where Nis the earthquake frequency, Mis the magnitude,
and aand bare constants.
This equation shows the relationship between earthquake
frequency and magnitude. The values of aand bindicate the
seismic activity of the megathrust segment, which is influ-
enced by the degree of rock fragility, and these values de-
pend on the observation period, the area of observation, and
the seismicity in the area. A larger value of afor a segment
indicates that the segment has high seismic activity; a larger
value of bindicates that the degree of rock fragility in the
segment is higher. In this study, the values of aand bwere
not calculated. Instead, they were sourced from PuSGeN in
2017.
https://doi.org/10.5194/nhess-25-1057-2025 Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025
1062 W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras
Table 1. Total samples at each magnitude bin.
No. MwNumber of faults Number of Total samples at
with magnitude samples at each each magnitude
Mw(A) magnitude bin (B) bin =A×B
1. 6.5 4 100 400
2. 6.6 4 98 392
3. 6.7 4 95 380
4. 6.8 4 92 368
5. 6.9 4 90 360
6. 7.0 4 88 352
7. 7.1 4 85 340
8. 7.2 4 83 332
9. 7.3 4 80 320
10. 7.4 4 77 308
11. 7.5 4 75 300
12. 7.6 4 73 292
13. 7.7 4 71 284
14. 7.8 4 68 272
15. 7.9 4 65 260
16. 8.0 4 63 252
17. 8.1 4 61 244
18. 8.2 4 58 232
19. 8.3 4 56 224
20. 8.4 4 53 212
21. 8.5 4 51 204
22. 8.6 4 49 196
23. 8.7 4 47 188
24. 8.8 4 45 180
25. 8.9 2 43 86
26. 9.0 1 41 41
Total scenario 7019
Table 2. General parameter for tsunami modelling.
Parameter Domain
1 2
Simulation time (second) 18 000 18 000
Save time interval (second) 60 60
Reference domain 0 1
Grid size (metre) 1800 600
2.2.4 Probabilistic tsunami hazard analysis (PTHA)
calculation
In this tsunami modelling, the modelling basis is used, which
can be seen in Table 2.
Based on Table 2, the simulation time used for each
tsunami scenario run is 6 h. In addition, earthquake parameter
data sourced from USGS and PuSGeN in 2017 were also de-
termined. Earthquake parameters, such as depth, strike, slip,
and dip, were obtained from the USGS slab 2.0 model, with
the rake angle considered opposite to the direction of plate
movement in the interseismic phase (Hayes et al., 2018). On
the other hand, the parameters of the epicentre and megath-
rust segmentation were obtained from the results of the PuS-
GeN study in 2017.
Probabilistic seismic hazard assessment was introduced by
Cornell (1968), which was then adopted in PTHA to predict
the rate of exceedance of a certain tsunami height (H) rel-
ative to the tsunami height level (h), which in discrete form
can be formulated in Eq. (15):
λ(H≥h)=Xns
i=1viXnm
j=iPH≥h|mjPMi=mj,(15)
where nsis the total number of isources/faults, nmis the
number of magnitudes mconsidered with jintervals, mis
the magnitude bin, υis the occurrence rate of earthquakes
from each common plate, and Pis the probability of tsunami
height.
In this study, a magnitude ranges from the smallest mag-
nitude to the largest magnitude with a magnitude bin of 0.1.
The variable υindicates the occurrence rate of earthquakes
with Mequal to or greater than each fault calculated us-
ing the Gutenberg–Richter frequency magnitude distribution
(Gutenberg and Richter, 1944). The variable υcan be defined
by Eq. (16).
v=10a−bmmin (16)
Next, the probability of tsunami height is calculated. The
probability that the tsunami height Hexceeds any tsunami
height level given the magnitude mcan be expressed as
P(H≥h|m)=1−8ln(h)−ln (H)
β,(17)
where 8is the cumulative standard-normal distribution func-
tion, ln(H ) represents the median logarithmic tsunami height
of all models with a given source and magnitude, and βis the
standard deviation of ln(H ).
Based on the PTHA manual by Thio (2012), a crucial as-
pect of a probabilistic hazard analysis is the incorporation of
uncertainties in both the source and the propagation models
into the final outcome. There are two types of uncertainties:
aleatory and epistemic. These uncertainties belong to the fre-
quency and degree of belief approaches to probability, re-
spectively.
Aleatory uncertainties refer to the inability to predict the
outcome of a process due to its random nature. However, it
may not always be clear whether an uncertainty in the out-
come of a process is a true aleatory uncertainty caused by the
random behaviour of nature or the result of a limited under-
standing of the process itself. In such cases, researchers may
have differing opinions. To express the outcome of a process
that involves aleatory uncertainties, distribution functions are
used rather than single mean or median values. The probabil-
ity of an outcome being within a certain range is then given
by the area under the probability density or distribution func-
tion. We have identified three main sources of aleatory uncer-
tainty in our analysis, which are modelling uncertainty (σA),
uncertainty in geometry (σD), and uncertainty in random slip
distribution (σS).
Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025 https://doi.org/10.5194/nhess-25-1057-2025
W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras 1063
Figure 6. Tsunami hazard curves at each observation point (light
blue), including mean and median value curves in Batukaras.
The parameter βin Eq. (17) is included to account
for aleatory uncertainty related to tsunami simulations and
source uncertainties. Thio (2012) and Horspool et al. (2014)
use combined uncertainty with a total βvalue of 0.519,
while Davies et al. (2018) use the square root of the βval-
ues obtained from four historic tsunamis, which results in a
larger βvalue of 0.927. Annaka et al. (2007) and Fukutani et
al. (2015) have also treated βas epistemic uncertainty using a
logic-tree approach. In this approach, βranges from 0.223 to
0.438, corresponding to κvalues of 1.25 to 1.55. The βvalue
can be estimated from the logarithmic standard deviation κ
of Aida (1978), where β=ln(κ), as used in this research. To
obtain the logarithmic standard deviation κ, we compared the
height of historical tsunami waves in Pangandaran in 2006
(Fritz et al., 2007) with the modelling scenario that has the
closest characteristics to the historical tsunami waves. The κ
value is then calculated in accordance with Aida (1978), and
the βvalue can be obtained. Based on the 2006 Pangandaran
tsunami, the value of βis 1.0999.
3 Results and discussion
Based on the PTHA that has been carried out, several prod-
ucts are obtained that can be used as material for analysis to
determine the level of tsunami hazard in Batukaras.
3.1 Tsunami hazard curve
The first product of the PTHA is the tsunami hazard curve.
The hazard curve is a curve that describes the relationship
between the tsunami intensity value and the return period of
an earthquake at an observation point (Grezio et al., 2017).
The tsunami hazard curve for Batukaras can be seen in Fig. 6.
Based on the hazard curve, the level of hazard can be
seen from the probability of tsunami wave heights occur-
ring in Batukaras at each observation point. In this case, 20
observation points were used, spread along the coastline of
Batukaras. In addition, the mean and median values of the
tsunami height can also be seen. As can be seen from Fig. 6,
at an earthquake return period of 100 years, the tsunami
height in Batukaras does not reach 1 m, but as the earthquake
return period increases, the tsunami height will increase to
10 m at an earthquake period of 10 000 years.
The morphological conditions (shape) of the coast in
Batukaras can be categorised into two types, namely steep
and sloping coastal areas. When viewed from the distribu-
tion of observation points (coastal points) scattered along the
coastline of Batukaras, the division of the two coastal mor-
phologies can be seen in Fig. 7.
Based on Fig. 7, it can be seen that the first to ninth ob-
servation points are in the coastal area with steep morphol-
ogy with characteristics of cliff and rocky beach. In contrast,
the 10th to 20th observation points are located on the slop-
ing coastal area in Batukaras. Therefore, in this PTHA in
Batukaras, the tsunami height generated from the modelling
results of the two types of coastal morphology is identified,
which can be seen in Fig. 8.
In the resulting hazard curve, a significant difference in
average tsunami height can be seen between the two differ-
ent coastal morphologies in Batukaras. In the coastal areas
with steep morphology for an earthquake return period of
1000 years, the resulting average tsunami height is still be-
low 10 m (lower than the average tsunami height in Batukaras
presented in Fig. 6). In contrast, in coastal areas with a gen-
tly sloping morphology, the average tsunami height is al-
ready above 10 m (higher than the average tsunami height
in Batukaras presented in Fig. 6). This shows that the mor-
phology of the coast in Batukaras will affect the height of the
tsunami generated.
3.2 Tsunami heights based on earthquake return
period
The second product of the PTHA can also be viewed in
graphical form to identify the tsunami height at each observa-
tion point. In this case, the tsunami height in Batukaras was
identified based on four types of earthquake return periods,
namely 250, 500, 1000, and 2500 years. This graph can be
seen in Fig. 9.
Figure 9 shows a graph of the tsunami height at each earth-
quake return period derived from the tsunami hazard curve
in Fig. 6. In this graph, the difference in tsunami height be-
tween the steep beach (1st to 9th observation points in the
southern area) and sloping beach (10th to 20th observation
points in the northern area) in Batukaras is clearly visible.
Overall, tsunami heights at all observation points located on
steep coastal areas were much lower than those on sloping
coastal areas for each earthquake return period.
https://doi.org/10.5194/nhess-25-1057-2025 Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025
1064 W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras
Figure 7. Beach morphology in Batukaras. Imagery © Google Earth 2022.
Figure 8. Tsunami hazard curves based on earthquake return pe-
riod. (a) Observation points 1 to 9; (b) observation points 10 to 20.
The average tsunami heights for sloping coastal areas are
0.58, 1.1, 1.98, and 3.75 m for each earthquake return period
of 250, 500, 1000, and 2500 years, respectively. In contrast,
the mean tsunami heights for steep coastal areas are 1.04,
2.05, 3.77, and 7.29 m for each of the 250-, 500-, 1000-, and
2500-year return periods, respectively. Based on these val-
ues, it can be seen that there is a twofold difference in the av-
erage tsunami height between the sloping coastal areas and
the steep coastal areas at each earthquake return period.
3.3 Results of disaggregation of hazard in Batukaras
Disaggregation of earthquake hazard is the process of iden-
tifying which earthquake sources are most likely to affect an
area, with a focus on magnitude and distance. This process
yields the most significant average magnitude and distance
combinations of earthquakes that are likely to have a large
impact on the location (Kwong et al., 2015). Based on the re-
sults of this PTHA, the hazard contribution of each megath-
rust segment that can have an influence on tsunami events in
Batukaras can be known. This disaggregation value can be
seen at each observation point used in this modelling.
Overall, if the hazard disaggregation values are averaged
from all observation points scattered along the coastline of
Batukaras, the tsunami hazard disaggregation in Batukaras
can be obtained. The hazard disaggregation values of each of
these megathrust segments for tsunami events can be seen in
Fig. 10.
Based on the map, the disaggregation of each megathrust
segment based on the return period of an earthquake can be
seen. In each earthquake return period, the disaggregation
numbers for each megathrust segment are shown in Table 3.
The table shows that the Sunda Strait megathrust segment
has the smallest disaggregation for each earthquake return
period. This is because the geographical location of this seg-
ment is in the western part of Batukaras. Thus, tsunami waves
generated by an earthquake on this segment would have dif-
ficulty travelling to Batukaras since they do not directly face
Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025 https://doi.org/10.5194/nhess-25-1057-2025
W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras 1065
Figure 9. Visualisation of tsunami height based on earthquake return period at each observation point.
Figure 10. Disaggregation map of each megathrust segment based on earthquake return period. Imagery © Google Earth 2022.
the Sunda Strait megathrust. In contrast, the disaggregation
of the West Java–Central Java megathrust segment increases
with each advancement of the earthquake return period. Ge-
ographically, this is because this segment is closest to and
directly faces Batukaras. Therefore, if an earthquake occurs
with an epicentre originating from this segment, the potential
for the tsunami to reach Batukaras is quite high.
3.4 Results of annual probability of tsunami
occurrence at earthquake return periods
The final PTHA product is the annual probability value of a
tsunami occurring in Batukaras for each observation point.
Based on this, the PTHA results show the annual tsunami
probability values for each observation point for tsunami
heights greater than 0.5, 1.5, and 3 m. These probability val-
ues are shown in Fig. 11.
Based on the map, it can be seen that the probability
of a tsunami in Batukaras is less than 0.1 % in any given
year. This probability value indicates a very small number of
tsunami events in Batukaras. Such a result aligns with histor-
ical data, in which tsunami disasters are rare in Pangandaran
Regency and its surroundings.
The probability for tsunami heights greater than 0.5 m in
a given year, with a probability value greater than 0.04 %, is
only found at observation points located in sloping coastal
https://doi.org/10.5194/nhess-25-1057-2025 Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025
1066 W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras
Table 3. Disaggregation of each megathrust segment based on earthquake return period.
Segments 250 years 500 years 1000 years 2500 years Description
Sunda Strait 4.54 % 1.97 % 0.53 % 0.05 % Decrease
West Java–Central Java 57.05 % 59.96 % 62.29 % 64.75 % Increase
East Java 23.89 % 22.07 % 19.24 % 13.11 % Decrease
Bali 14.53 % 16 % 17.94 % 22.09 % Increase
Figure 11. Annual probability of tsunami occurrence in Batukaras based on tsunami heights of (a) >0.5 m, (b) >1.5 m, and (c) >3 m.
areas. The probabilities for tsunami heights of more than 1.5
and 3 m in a given year are less than 0.02 % and 0.001 % for
all observation points, respectively. However, these probabil-
ity values should not be used as the main reference because,
like earthquakes, tsunamis can occur at any time.
4 Conclusion
The PTHA in Batukaras resulted in tsunami heights of 0.84,
1.63, 2.97, and 5.7 m for each earthquake return period of
250, 500, 1000, and 2500 years, respectively. The results of
the tsunami hazard disaggregation in Batukaras show that
the largest contribution of earthquake sources that can gener-
ate tsunamis in Batukaras comes from the West Java–Central
Java megathrust segment, with a contribution value of more
than 57 % for each earthquake return period. This can serve
as a tsunami warning for Batukaras in the event of a high-
magnitude earthquake centred on the West Java–Central Java
segment. In contrast, the annual probability value of tsunami
occurrence in Batukaras with a height of 0.5, 1.5, and 3 m
has a probability smaller than 0.1 % in any given year.
The results of the PTHA can then be analysed in more
detail by reviewing the tsunami height at each observation
point. In the results of the hazard curve, different coastal
morphologies produce different tsunami heights. The max-
imum tsunami height occurs in coastal areas with sloping
morphology. This sloping coastal area is very vulnerable be-
cause it is dominated by lodging places and restaurants, mak-
ing it vulnerable to tsunami disasters. Apart from the slop-
ing coastal factor, the vulnerability of the Batukaras coast to
tsunamis is also influenced by the social aspect, where the
population in this village is quite dense, reaching 393 inhab-
itants per km−2. Apart from being crowded with residents,
the Batukaras coast is also a famous tourist attraction for
foreign tourists. This contributes to the vulnerability of the
Batukaras coast to tsunami disasters. The coastal area is also
Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025 https://doi.org/10.5194/nhess-25-1057-2025
W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras 1067
vulnerable from an economic perspective because this coast
is dominated by lodging, restaurants, and also agricultural
land and plantations, which contribute to the village’s re-
gional income. The large amount of land that may be affected
by the tsunami could cause significant economic losses for
the surrounding community. The results of this PTHA mod-
elling can be a reference for developing disaster mitigation
strategies and scenarios in Batukaras, especially for sloping
beach areas. This is particularly important considering that
Batukaras is a tourist villages that is visited by many local
and foreign tourists.
The results of this study can be used as a reference to
conduct further research to calculate economic losses from
buildings, land use, and other economic factors from tsunami
disasters. In mitigation activities, the results of this research
can be used to support assessment components in preparing
Batukaras to become a tsunami-ready village, as published
by IOC-UNESCO, considering that Batukaras is currently in
the recognition process.
Data availability. The bathymetry data were obtained from
National Bathymetry (BATNAS) provided by the Indone-
sian Geospatial Information Agency (BIG) and are avail-
able at https://sibatnas.big.go.id/seamlessbatnas (login required;
Geospatial Information Agency, 2025). The earthquake gener-
ation parameter data were obtained from the National Cen-
tre for Earthquake Studies (PuSGeN) in 2017 from https://
klop.pu.go.id/knowledge/peta-sumber-dan- bahaya-gempa-1 (Pus-
Gen, 2017) and the United States Geological Survey (USGS)
(https://doi.org/10.5066/F7PV6JNV, Hayes, 2018), and Hayes et
al. (2018).
Author contributions. WW: writing – original draft, methodology,
investigation, conceptualisation, supervision, funding acquisition.
MWAG: writing – review and editing, data curation, formal anal-
ysis, visualisation. CADSN: writing – review and editing, data cu-
ration, supervision, validation. MS – data curation, formal analysis,
visualisation. IH: writing – review and editing, supervision, valida-
tion. IEM: writing – review and editing, methodology, supervision.
AN: writing – review and editing, validation.
Competing interests. The contact author has declared that none of
the authors has any competing interests.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims made in the text, pub-
lished maps, institutional affiliations, or any other geographical rep-
resentation in this paper. While Copernicus Publications makes ev-
ery effort to include appropriate place names, the final responsibility
lies with the authors.
Special issue statement. This article is part of the special issue
“Strengthening climate-resilient development through adaptation,
disaster risk reduction, and reconstruction after extreme events”. It
is not associated with a conference.
Acknowledgements. We acknowledge the support of the Faculty of
Earth Sciences and Technology, Institut Teknologi Bandung, in the
financial and administrative aspects of the research. We would also
like to acknowledge the local government of Batukaras in Pangan-
daran Regency, Indonesia, for their support during the data acquisi-
tion.
Wiwin Windupranata is financially supported by Penelitian Ko-
laboratif (2023) (letter no. 1965/IT1.C01/SK-TA.00/2023), man-
aged by the Faculty of Earth Sciences and Technology, Institut
Teknologi Bandung (ITB).
Financial support. This research has been supported by the
Lembaga Penelitian dan Pengabdian Kepada Masyarakat (grant
no. FITB.PPMI-1-42-2023).
Review statement. This paper was edited by Marvin Ravan and re-
viewed by two anonymous referees.
References
Aida, I.: Reliability of a tsunami source model derived
from fault parameters, J. Phys. Earth., 26, 57–73,
https://doi.org/10.4294/jpe1952.26.57, 1978.
Annaka, T., Satake, K., Sakakiyama, T., Yanagisawa, K., and Shuto,
N.: Logic-tree Approach for Probabilistic Tsunami Hazard Anal-
ysis and its Applications to the Japanese Coasts, Pure Appl. Geo-
phys., 164, 577–592, https://doi.org/10.1007/s00024-006-0174-
3, 2007.
Batukaras Village Profile: Batukaras Village Profile, Pangandaran
Regency Government, Pangandaran, 2022.
Cornell, C. A.: Engineering seismic risk analysis, B. Seismol. Soc.
Am., 58, 1583–1606, 1968.
Davies, G., Griffin, J., Løvholt, F., Glimsdal, S., Harbitz, C., Thio,
H. K., Lorito, S., Basili, R., Selva, J., Geist, E., and Bap-
tista, M. A.: A global probabilistic tsunami hazard assessment
from earthquake sources, Geol. Soc. Spec. Publ., 456, 219–244,
https://doi.org/10.1144/SP456.5, 2018.
Fritz, H. M., Kongko, W., Moore, A., McAdoo, B., Goff, J., Harb-
itz, C., Uslu, B., Kalligeris, N., Suteja, D., Kalsum, K., Titov, V.,
Gusman, A., Latief, H., Santoso, E., Sujoko, S., Djulkarnaen, D.,
Sunendar, H., and Synolakis, C.: Extreme runup from the 17 July
2006 Java tsunami, Geophys. Res. Lett., 34, 2007GL029404,
https://doi.org/10.1029/2007GL029404, 2007.
Fujii, Y. and Satake, K.: Source of the July 2006 West Java tsunami
estimated from tide gauge records, Geophys. Res. Lett., 33,
2006GL028049, https://doi.org/10.1029/2006GL028049, 2006.
Fukutani, Y., Suppasri, A., and Imamura, F.: Stochastic analysis
and uncertainty assessment of tsunami wave height using a ran-
dom source parameter model that targets a Tohoku-type earth-
https://doi.org/10.5194/nhess-25-1057-2025 Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025
1068 W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras
quake fault, Stoch. Environ. Res. Risk. Assess., 29, 1763–1779,
https://doi.org/10.1007/s00477-014-0966-4, 2015.
Geospatial Information Agency (BIG): SIBATNAS, Geospatial In-
formation Agency (BIG) [data set], https://sibatnas.big.go.id/
seamlessbatnas, last access: 19 February 2025.
Grezio, A., Babeyko, A., Baptista, M. A., Behrens, J., Costa, A.,
Davies, G., Geist, E. L., Glimsdal, S., González, F. I., Griffin, J.,
Harbitz, C. B., LeVeque, R. J., Lorito, S., Løvholt, F., Omira, R.,
Mueller, C., Paris, R., Parsons, T., Polet, J., Power, W., Selva, J.,
Sørensen, M. B., and Thio, H. K.: Probabilistic Tsunami Hazard
Analysis: Multiple Sources and Global Applications, Rev. Geo-
phys., 55, 1158–1198, https://doi.org/10.1002/2017RG000579,
2017.
Gunawan, E., Meilano, I., Abidin, H. Z., Hanifa, N. R., and
Susilo: Investigation of the best coseismic fault model of
the 2006 Java tsunami earthquake based on mechanisms of
postseismic deformation, J. Asian Earth. Sci., 117, 64–72,
https://doi.org/10.1016/j.jseaes.2015.12.003, 2016.
Gutenberg, B. and Richter, C. F.: Frequency of earthquakes in Cali-
fornia, B. Seismol. Soc. Am., 34, 185–188, 1944.
Gutenberg, B. and Richter, C. F.: Seismicity of the earth and associ-
ated phenomena, Princeton University Press, New Jersey, USA,
https://doi.org/10.1126/science.121.3146.562.c, 1954.
Hamzah, L., Puspito, N. T., and Imamura, F.: Tsunami Cata-
log and Zones in Indonesia., J. Nat. Disaster Sci., 22, 25–43,
https://doi.org/10.2328/jnds.22.25, 2000.
Hanifa, N. R., Sagiya, T., Kimata, F., Efendi, J., Abidin, H.
Z., and Meilano, I.: Interplate coupling model off the south-
western coast of Java, Indonesia, based on continuous GPS
data in 2008–2010, Earth Planet. Sci. Lett., 401, 159–171,
https://doi.org/10.1016/j.epsl.2014.06.010, 2014.
Harris, R., Meservy, W., Stuart, K., Deng, M., Emmett, C., Su-
liaman, H., Prasetyadi, C., Setiadi, G., Yulianto, E., Rafliana,
I., Putra, P., Hall, S., Bunds, M., Arendt, A., and Horns, D.:
Seismic and tsunami risk of the Java Trench and implementa-
tion of risk reduction strategies, in: SEG Technical Program Ex-
panded Abstracts 2019, SEG Technical Program Expanded Ab-
stracts 2019, 15–20 September 2019, SEG19 International Ex-
position and 89th Annual Meeting, San Antonio, Texas, 4786–
4789, https://doi.org/10.1190/segam2019-3215203.1, 2019.
Hayes, G.: Slab2 – A Comprehensive Subduction Zone Geom-
etry Model, U.S. Geological Survey data release [data set],
https://doi.org/10.5066/F7PV6JNV, 2018.
Hayes, G. P., Moore, G. L., Portner, D. E., Hearne, M., Flamme,
H., Furtney, M., and Smoczyk, G. M.: Slab2, a comprehen-
sive subduction zone geometry model, Science, 362, 58–61,
https://doi.org/10.1126/science.aat4723, 2018.
Horspool, N., Pranantyo, I., Griffin, J., Latief, H., Natawidjaja,
D. H., Kongko, W., Cipta, A., Bustaman, B., Anugrah, S. D.,
and Thio, H. K.: A probabilistic tsunami hazard assessment
for Indonesia, Nat. Hazards Earth Syst. Sci., 14, 3105–3122,
https://doi.org/10.5194/nhess-14-3105-2014, 2014.
Koswara, D. V., Windupranata, W., Meilano, I., Hayatiningsih, I.,
and Hanifa, N. R.: Characteristics of Potential Tsunami Evac-
uee and Evacuation Infrastucture in Pangandaran Beach, In-
donesia, IOP Conf. Ser.: Earth Environ. Sci., 925, 012036,
https://doi.org/10.1088/1755-1315/925/1/012036, 2021.
Kwong, N. S., Chopra, A. K., and McGuire, R. K.: A frame-
work for the evaluation of ground motion selection and modi-
fication procedures, Earthq. Engng. Struct. Dyn., 44, 795–815,
https://doi.org/10.1002/eqe.2502, 2015.
Mai, P. M. and Beroza, G. C.: A spatial random field model to char-
acterize complexity in earthquake slip, J. Geophys. Res., 107,
2308, https://doi.org/10.1029/2001JB000588, 2002.
Mori, N., Muhammad, A., Goda, K., Yasuda, T., and Ruiz-
Angulo, A.: Probabilistic Tsunami Hazard Analysis of the
Pacific Coast of Mexico: Case Study Based on the 1995
Colima Earthquake Tsunami, Front. Built Environ., 3, 34,
https://doi.org/10.3389/fbuil.2017.00034, 2017.
Mulia, I. E., Gusman, A. R., Williamson, A. L., and Satake, K.:
An Optimized Array Configuration of Tsunami Observation Net-
work Off Southern Java, Indonesia, J. Geophys. Res.-Sol. Ea.,
124, 9622–9637, https://doi.org/10.1029/2019JB017600, 2019.
Mulia, I. E., Ishibe, T., Satake, K., Gusman, A. R., and Murotani,
S.: Regional probabilistic tsunami hazard assessment associated
with active faults along the eastern margin of the Sea of Japan,
Earth Planets Space, 72, 123, https://doi.org/10.1186/s40623-
020-01256-5, 2020.
Mustafida, R. P., Veronica, N., Karima, A. Q., De Silva Nusan-
tara, C. A., and Windupranata, W.: Identification of the IOC-
UNESCO Tsunami Ready Indicator to Improve Coastal Com-
munity Preparedness for Tsunami Disaster in Batukaras Village,
Pangandaran Regency, Indonesia, in: 2022 IEEE Asia-Pacific
Conference on Geoscience, Electronics and Remote Sensing
Technology (AGERS), 2022 IEEE Asia-Pacific Conference
on Geoscience, Electronics and Remote Sensing Technology
(AGERS), 21–22 December 2022, Surabaya, Indonesia, 41–47,
https://doi.org/10.1109/AGERS56232.2022.10093500, 2022.
Nijman, V.: Tourism Developments Increase Tsunami Disaster Risk
in Pangandaran, West Java, Indonesia, Int. J. Disaster Risk.
Sci., 12, 764–769, https://doi.org/10.1007/s13753-021-00365-3,
2021.
PusGen: Map of Earthquake Sources and Hazards Year
2017, Pusat Penelitian dan Pengembangan Perumahan dan
Pemukiman, Bandung, https://klop.pu.go.id/knowledge/
peta-sumber-dan-bahaya-gempa-1 (last access: 19 Febru-
ary 2025), ISBN 978-602-5489-01-3, 2017.
Salmanidou, D. M., Heidarzadeh, M., and Guillas, S.: Probabilistic
Landslide-Generated Tsunamis in the Indus Canyon, NW Indian
Ocean, Using Statistical Emulation, Pure Appl. Geophys., 176,
3099–3114, https://doi.org/10.1007/s00024-019-02187-3, 2019.
Supendi, P., Widiyantoro, S., Rawlinson, N., Yatimantoro, T.,
Muhari, A., Hanifa, N. R., Gunawan, E., Shiddiqi, H. A., Im-
ran, I., Anugrah, S. D., Daryono, D., Prayitno, B. S., Adi, S. P.,
Karnawati, D., Faizal, L., and Damanik, R.: On the potential for
megathrust earthquakes and tsunamis off the southern coast of
West Java and southeast Sumatra, Indonesia, Nat. Hazards, 116,
1315–1328, https://doi.org/10.1007/s11069-022-05696-y, 2023.
Thio, H.: Urs probabilistic tsunami hazard system: A
user manual, Technical Report URS Corporation,
San Francisco, CA, https://www.dropbox.com/scl/fi/
3h1u723642zuv6s506a4c/Thio2012-PTHA-Manual-v1.00.
pdf-dl-0?rlkey=odnj6kowfbln711f5zc8qylah&e=1 (last access:
19 February 2025), 2012.
Thio, H. K., Somerville, P., and Ichinose, G.: Probablis-
tic Analysis of Strong Ground Motion and Tsunami Haz-
ards in Southeast Asia, J. Earthq. Tsunami, 1, 119–137,
https://doi.org/10.1142/S1793431107000080, 2007.
Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025 https://doi.org/10.5194/nhess-25-1057-2025
W. Windupranata et al.: Probabilistic tsunami hazard analysis of Batukaras 1069
United States Geological Survey (USGS): Slab2 – A Comprehen-
sive Subduction Zone Geometry Model, https://www.usgs.gov/
data/slab2-a-comprehensive-subduction-zone-geometry- model,
last access: 19 February 2025.
Wang, X.: User Manual for COMCOT Version 1.7 (First Draft),
https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&
doi=401de93588d6c28d0c3984044ad1f95b75dadab0 (last
access: 19 February 2025), 2009.
Windupranata, W., Hanifa, N. R., Nusantara, C. A. D. S.,
Aristawati, G., and Arifianto, M. R.: Analysis of tsunami
hazard in the Southern Coast of West Java Province – In-
donesia, IOP Conf. Ser.: Earth Environ. Sci., 618, 012026,
https://doi.org/10.1088/1755-1315/618/1/012026, 2020.
https://doi.org/10.5194/nhess-25-1057-2025 Nat. Hazards Earth Syst. Sci., 25, 1057–1069, 2025
