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Academic Editor: Ioanna Triantafyllou
Received: 28 January 2025
Revised: 13 March 2025
Accepted: 26 March 2025
Published: 1 April 2025
Citation: Windupranata, W.;
Nuraghnia, A.; Al Ghifari, M.W.;
Pasaribu, S.K.; Rakhmanisa, W.I.; Vani,
T.; Ginting, K.A.; Aventa, M.B.;
Hayatiningsih, I.; Suwardhi, D.; et al.
Analysis of Tsunami Economic Loss in
Tourism Areas Using High-Resolution
Tsunami Run-Up Model. GeoHazards
2025,6, 18. https://doi.org/
10.3390/geohazards6020018
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Article
Analysis of Tsunami Economic Loss in Tourism Areas Using
High-Resolution Tsunami Run-Up Model
Wiwin Windupranata 1,* , Alqinthara Nuraghnia 1, Muhammad Wahyu Al Ghifari 1, Sonia Kartini Pasaribu 1,
Wiwin Indira Rakhmanisa
1
, Tiara Vani
1
, Kevin Agriva Ginting
1
, Michael Bintang Aventa
1
, Intan Hayatiningsih
1
,
Deni Suwardhi 2, Irwan Meilano 2, Iyan Eka Mulia 1and Albert Kristiawan Lim 1
1Hydrography Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung,
Bandung 40132, Indonesia; nalqinthara@gmail.com (A.N.); alghifariwahyu@gmail.com (M.W.A.G.);
psrbsonia@gmail.com (S.K.P.); windirakhmanisa@gmail.com (W.I.R.); tvani455@gmail.com (T.V.);
kevin.aginting22@gmail.com (K.A.G.); michaelaventa@gmail.com (M.B.A.);
intanhayatiningsih@gmail.com (I.H.); iyan.mulia@itb.ac.id (I.E.M.); 25124059@mahasiswa.itb.ac.id (A.K.L.)
2Spatial System and Cadastre Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi
Bandung, Bandung 40132, Indonesia; deni.suwardhi@itb.ac.id (D.S.); irwanm@itb.ac.id (I.M.)
*Correspondence: w.windupranata@itb.ac.id
Abstract: A tsunami can cause significant economic losses for tourism areas like Batukaras
Village, which is located on the southern coast of Java Island. This paper seeks to elaborate
on the calculation of economic losses in tourism areas due to damage to buildings, loss
of land production, and loss of income, based on high-resolution geospatial data. The
data are derived from UAV photogrammetry surveys and high-resolution tsunami run-up
models. The tsunami worst-case scenario run-off model provides an inundation area of
43 ha with 185 buildings and 24.4 ha of productive land. The estimated losses from the
tsunami disaster amounted to IDR 208.79 billion, consisting of 49.63 billion from building
damage, 6.73 billion from productive land, and 152.43 billion from the tourism sector.
These results show that the tsunami disaster will severely affect tourism areas, because
the tourism sector makes up 73% of the total economic losses. Reductions in the amount
of economic loss, in addition to spatial planning near the coastline to reduce the number
of impacted buildings and productive land, can be achieved by accelerating the recovery
period so that economic activities after the tsunami disaster can be carried out immediately,
including in the tourism sector.
Keywords: run-up model; economic loss; Batukaras; tsunami; tourism; recovery period
1. Introduction
The southern coast of Java, Indonesia, is one of the most tsunami-prone regions in
the world due to its proximity to the highly active tectonic boundary between the Indo-
Australian and Eurasian Plates. This boundary is part of the Pacific Ring of Fire, an area
with frequent seismic activity that generates powerful underwater earthquakes capable
of producing tsunamis. The main source of tsunami events in Indonesia is generally
considered to be shallow earthquakes in the subduction zone or plate boundary due to
the release of large amounts of energy, which cause vertical shifts on the seafloor [
1
]. The
latest research conducted by the authors of [
2
,
3
] showed that there is a surface deformation
of the multi-GNSS receiver data that has the potential to cause a megathrust earthquake
that could cause a tsunami in the southern region of Java Island. Therefore, the southern
region of Java Island is one of the regions in Indonesia that has potential for earthquake
and tsunami disasters.
GeoHazards 2025,6, 18 https://doi.org/10.3390/geohazards6020018
GeoHazards 2025,6, 18 2 of 17
The 2006 Pangandaran earthquake and tsunami serve as a stark reminder of this
region’s vulnerability. In that tragic event, the tsunami waves reached heights of up to
seven metres, causing widespread devastation, including loss of life, destruction of property,
and significant economic impacts [
4
–
6
]. The geography of southern Java, characterized by
steep coastal areas and narrow continental shelves, amplifies the impact of tsunamis [
7
]. The
tsunami severely affected the tourism industry in areas like Pangandaran. Visitor numbers
dropped drastically from about 900,000 annually to just over 250,000 immediately after the
tsunami; furthermore, it also caused extensive damage to buildings and infrastructure [8].
In addition to direct damage, tsunamis can have long-lasting economic consequences
for local industries [
9
]. Coastal economies often rely heavily on tourism, fisheries, and
agriculture, which can be severely affected by such natural disasters. The destruction of
fishing boats, equipment, and marine habitats disrupts fishing communities’ livelihoods,
leading to income loss and increased poverty. Similarly, the tourism industry may suffer
from the destruction of infrastructure and a decline in visitor numbers due to safety
concerns. This was observed after the 2011 Tohoku tsunami in Japan, where the tourism
sector faced significant challenges in recovery, and the closure of Japanese ports following
the 2011 tsunami had global repercussions, affecting industries reliant on Japanese exports
such as the automotive and electronics sectors [10].
Tsunami run-up models are essential tools for predicting the extent of coastal flooding
and the potential impact on coastal communities. These models simulate how far inland
a tsunami wave will travel, known as the “run-up”, by considering wave height, coastal
topography, and bathymetry (the underwater depth of ocean floors). Accurate run-up
models help in assessing risk and planning effective mitigation strategies to protect lives
and property [
11
]. The run-up can be simulated on a 3-D topographic model to obtain
detailed information about the potential damage. A 3-D topographic model of the coastal
area can be derived by integrating UAV photogrammetry and bathymetric survey data [
12
].
Three-dimensional tsunami inundation mapping provides more spatially accurate infor-
mation, precisely representing how tsunami waves interact with coastal topographies and
coastal buildings so that damage estimates for affected buildings can also be calculated
more precisely. In addition, by conducting 3D tsunami inundation mapping, office holders
can make better decisions and plan better in their areas to increase infrastructure resilience
and the resilience of the surrounding community to tsunami disasters.
Improving the ability to predict and mitigate tsunami disasters is very important to
reduce the impact of future tsunamis. One way to achieve this is to map the potential
area of a high-resolution three-dimensional tsunami submersion. Unlike 2D models, 3D
models can provide more accurate and realistic results to overcome the potential dangers
of tsunami immersion. With 3D models, the main characteristics of the immersion process
can be better described [
13
]. The 3D tsunami submersion potential map can also be useful
in selecting tsunami evacuation buildings, planning evacuation routes, and planning
mitigation measures to strengthen buildings to minimize potential human and economic
losses [14].
In this research, the tsunami economic loss analysis will be applied for one tourism
area in Southern Coast of West Java Province, in the area known as Batukaras Village.
The village is part of Pangandaran Regency, one of the top tourist destinations in West
Java. Every year, the village attracts national and international tourists to Batukaras
beach, famous as a surfing destination [
15
]. According to data from the Tourism Office
of Pangandaran Regency, in 2023, the village attracted 475,988 national and international
tourists, an increase from 449,629 in the previous year. The village is located in a closed bay
and faces the Indian Ocean; as such, Batukaras beach is popular among surfers. For those
reasons, tourism is the main economic pillar, alongside fisheries and agriculture [15].
GeoHazards 2025,6, 18 3 of 17
2. Materials and Methods
Tsunami economic loss analyses use geospatial data that are mainly produced from
UAV (Unmanned Aerial Vehicle) Photogrammetric Surveys and Tsunami Wave Modelling
(Figure 1). A UAV Photogrammetric Survey was conducted in May 2023 and was intended
to conduct three-dimensional mapping with the level of detail of objects on the Earth’s
surface up to Level of Detail (LOD)-2. The mapping uses UAVs that can take off and land
vertically (VTOL—Vertical Take Off and Landing) with GNSS RTK positioning systems [
16
].
The Digital Elevation Model (DEM) and LOD-2 model of the Batukaras Village area are
derived from aerial photographs produced at an altitude of 300 m with a data resolution of
10–12 cm or with a point density of 100 ppm (points per m
2
). To improve the accuracy of
the horizontal and vertical position of DEM and LOD-2, the aerial photos are georeferenced
using 22 Ground Control Points (GCP) and Independent Check Points (ICP).
GeoHazards 2025, 6, x FOR PEER REVIEW 4 of 18
Figure 1. Methodology Flowchart.
Figure 2. Tsunami economic loss estimation methodology flow charts.
2.1. Methods of Run-Up Model
The tsunami run-up is obtained by modelling with Delft3D using the numerical mod-
elling method. The Delft3D-FLOW numerical hydrodynamic modelling system solves
problems using non-linear shallow water equations derived from equations based on the
basic principles of continuity and momentum equations expressed through the Navier–
Stokes equations to describe fluid flow in shallow waters where the water depth is shallow
compared to the wavelength [20]. These equations assume that the fluid flow has a con-
stant density.
The continuity equation in the hydrodynamic model ensures that the mass of water
remains constant in the system, that no water is lost or suddenly appears, and that the
flow conditions in the system increase or decrease due to the flow from and to other
places, while the momentum equation describes how the velocity of water changes due to
various forces acting on the system.
A few parameters need to be set to build a tsunami run-up model. The most im-
portant is the generator source of the tsunami and topographic and bathymetric data as
the model domain.
Figure 1. Methodology Flowchart.
The dentification and mapping of buildings with LOD-2 detail was provided by UAV
aerial photo data processing and field surveys for sampling the uses of the buildings.
These buildings were then classified based on the type of building (refer to [
17
]) for the
vulnerability study of the building.
The land cover and land use map is derived from UAV aerial photo data; refer to
the [
18
] for concerns regarding the national standard for Land Cover Classification. To
complement and assist in interpreting and identifying land cover from this UAV aerial
photo, existing land cover maps from the Indonesian Geospatial Agency, or any other
sources, were used. The results of this land cover mapping will be verified through a field
survey for accuracy test analysis.
Potential tsunami hazards related to tsunami wave height, time of arrival, and inun-
dation area are simulated using a hydrodynamic model. The simulation starts from the
formation of the initial wave according to the worst possible scenario from the megath-
rust segments in the Indian Ocean, to the spread of tsunami waves from their source to
the southern coastal area of Java Island, and to the run-up of the tsunami waves on the
mainland in the Pangandaran coastal tourist area. The scenario of the tsunami simulation
was based on the worst-case scenario reported in [
19
]. Modelling the formation of waves,
the propagation of tsunami waves to coastal areas and the runoff from the coast to the
mainland, along with the speed of wave propagation, are modelled using Delft3D software
version 4.05 [
20
]. The tsunami wave run-up simulation on the coastal area uses the DEM
from UAV Photogrammetry.
GeoHazards 2025,6, 18 4 of 17
The economic loss (in IDR or Indonesian Rupiah) is calculated based on three factors
(Figure 2):
1. Buildings, estimated from the damage ratio and the value of the buildings.
2.
Land use/land cover, estimated from the loss of crop products for a period of one year.
3.
Tourism, estimated from the loss of visitors who engage in tourism activities (surfing,
eating, buying souvenirs), buy entrance tickets, and stay in hotels, for a period of one
year. One year is the length of time in which the economy slowly begins to return to
normal. This happened in the 2004 Indian Ocean Tsunami, where the victims began
to live normal lives again, children started going to school, those who lost their jobs
started to go back to work, most of the damaged fishing boats were replaced, and the
farmland was cleared and replanted [21,22].
GeoHazards 2025, 6, x FOR PEER REVIEW 4 of 18
Figure 1. Methodology Flowchart.
Figure 2. Tsunami economic loss estimation methodology flow charts.
2.1. Methods of Run-Up Model
The tsunami run-up is obtained by modelling with Delft3D using the numerical mod-
elling method. The Delft3D-FLOW numerical hydrodynamic modelling system solves
problems using non-linear shallow water equations derived from equations based on the
basic principles of continuity and momentum equations expressed through the Navier–
Stokes equations to describe fluid flow in shallow waters where the water depth is shallow
compared to the wavelength [20]. These equations assume that the fluid flow has a con-
stant density.
The continuity equation in the hydrodynamic model ensures that the mass of water
remains constant in the system, that no water is lost or suddenly appears, and that the
flow conditions in the system increase or decrease due to the flow from and to other
places, while the momentum equation describes how the velocity of water changes due to
various forces acting on the system.
A few parameters need to be set to build a tsunami run-up model. The most im-
portant is the generator source of the tsunami and topographic and bathymetric data as
the model domain.
Figure 2. Tsunami economic loss estimation methodology flow charts.
2.1. Methods of Run-Up Model
The tsunami run-up is obtained by modelling with Delft3D using the numerical mod-
elling method. The Delft3D-FLOW numerical hydrodynamic modelling system solves prob-
lems using non-linear shallow water equations derived from equations based on the basic
principles of continuity and momentum equations expressed through the Navier–Stokes equa-
tions to describe fluid flow in shallow waters where the water depth is shallow compared
to the wavelength [20]. These equations assume that the fluid flow has a constant density.
The continuity equation in the hydrodynamic model ensures that the mass of water
remains constant in the system, that no water is lost or suddenly appears, and that the
flow conditions in the system increase or decrease due to the flow from and to other places,
while the momentum equation describes how the velocity of water changes due to various
forces acting on the system.
A few parameters need to be set to build a tsunami run-up model. The most impor-
tant is the generator source of the tsunami and topographic and bathymetric data as the
model domain.
The tsunami generator source used in this model is the worst scenario which has
affected the Pangandaran District [
18
]. The Jabar-Jateng megathrust segment generates
this worst scenario. This megathrust segment is located in the southern part of Java Island,
directly opposite the Pangandaran District (Figure 3). This megathrust segment is the
segment that has the longest size among the other segments in the southern region of Java
Island, with the highest potential magnitude. There are other segments with the same
GeoHazards 2025,6, 18 5 of 17
potential magnitude but provide different tsunami potential results due to differences in
other parameters and location. Based on the model conducted by (2020) [
18
], the Jabar-
Jateng megathrust segment generates a tsunami with the worst results for the Cijulang
sub-district, Pangandaran District, where the research area of this paper is located. This
megathrust segment generates a tsunami with a maximum inundation height of 15.9 m, an
arrival time of 28 min, and a maximum inundation range of 2.215 km. In comparison, the
other segments have inundation height results of no more than 10 m, arrival times of no
faster than 30 min, and an inundation range of no more than 1.1 km.
GeoHazards 2025, 6, x FOR PEER REVIEW 5 of 18
The tsunami generator source used in this model is the worst scenario which has
affected the Pangandaran District [18]. The Jabar-Jateng megathrust segment generates
this worst scenario. This megathrust segment is located in the southern part of Java Island,
directly opposite the Pangandaran District (Figure 3). This megathrust segment is the seg-
ment that has the longest size among the other segments in the southern region of Java
Island, with the highest potential magnitude. There are other segments with the same po-
tential magnitude but provide different tsunami potential results due to differences in
other parameters and location. Based on the model conducted by (2020) [18], the Jabar-
Jateng megathrust segment generates a tsunami with the worst results for the Cijulang
sub-district, Pangandaran District, where the research area of this paper is located. This
megathrust segment generates a tsunami with a maximum inundation height of 15.9 m,
an arrival time of 28 min, and a maximum inundation range of 2.215 km. In comparison,
the other segments have inundation height results of no more than 10 m, arrival times of
no faster than 30 min, and an inundation range of no more than 1.1 km.
Figure 3. Megathrust segment as tsunami generation source.
The megathrust parameters in Table 1 are used to generate tsunamis in a larger model
that includes the segment sizes. This model generates the initial wave of the tsunami that
enters the study area. The initial waves will be input to the open boundaries of the model
domain by nesting from the larger model.
Table 1. Megathrust parameters.
Scenarios 4 5
Location Seg. Selat Sunda Seg. Jabar-Jateng
Mw 8.7 8.7
Depth (km) 34 20.188
Length (km) 340 460
Width (km) 180 160
Max. Disp (m) 15.926 21.678
Strike (o) 297 296.753
Dip (o) 18 9.962
Slip (m) 8 6.5
Figure 3. Megathrust segment as tsunami generation source.
The megathrust parameters in Table 1are used to generate tsunamis in a larger model
that includes the segment sizes. This model generates the initial wave of the tsunami that
enters the study area. The initial waves will be input to the open boundaries of the model
domain by nesting from the larger model.
Table 1. Megathrust parameters.
Scenarios 4 5
Location Seg. Selat Sunda Seg. Jabar-Jateng
Mw 8.7 8.7
Depth (km) 34 20.188
Length (km) 340 460
Width (km) 180 160
Max. Disp (m) 15.926 21.678
Strike (◦) 297 296.753
Dip (◦) 18 9.962
Slip (m) 8 6.5
Rake (◦) 104 104
Lat;Long (◦)−7.239; 104.435 −8.92; 107.88
The model domain is built by integrating the topographic and bathymetric data. Each
of the data have a different resolution and vertical reference. Therefore, the data must
first be aligned regarding vertical reference and resolution before being integrated. The
resolution is based on the desired model resolution, which is 5.5 m.
The run-up model parameter settings can be seen in Table 2.
GeoHazards 2025,6, 18 6 of 17
Table 2. Model parameters settings.
Parameters Value
Grid resolution (m) 5.5
Timestep (second) 0.5
Threshold Depth (m) 0.5
Reflection Parameter Alpha 400
Roughness Coefficient Manning, the values vary depending on the land cover
Settings for the roughness coefficient parameter use varying values depending on the
land cover in the study area. The roughness coefficients are summarized from [
23
–
25
] and
can be seen in Table 3.
Table 3. Manning’s n value for roughness coefficient parameters (summarized from [23–25]).
Parameters Manning’s n Value
Barren land/mud, sand, beach, roads 0.031
Grassland 0.036
Scrubland 0.038
Other plantation 0.043
Coconut plantation 0.0458
Open area 0.055
Other forest 0.085
Mangrove forest 0.0951
Buildings, non-resistant 0.09
Buildings, resistant 0.4
Paddy field 0.05
Aquaculture 0.045
River 0.025
Ocean 0.02
Land in general 0.05
2.2. Buildings
An analysis of the potential damage to buildings and land cover is conducted in
tsunami inundation areas based on the height of the tsunami waves. The calculation of
potential economic losses due to building damage is carried out using the type of building
and the tsunami hazard (height of the tsunami wave) that inundates the building. The
buildings are classified into 3 types, as follows: (1) type 1, Timber/Traditional Block
(1-storey); (2) type 2, Traditional Brick with RC (1-storey); and (3) type 3, RC Column
with Brick Infill (2+ storeys) based on [
17
]. Once the inundation height of each building
is identified, the damage ratio of the building is estimated based on the vulnerability
curve modified and extrapolated from [
17
] (Figure 4). The vulnerability curve from [
17
] is
carefully selected after a previous analysis of the curves from several studies [
26
–
34
]. The
vulnerability curve from [
17
] is the most relevant to this study, since the study areas are
very close, both are tourism areas, and the building types have the same characteristics.
In [
26
–
29
], the vulnerability curves were analyzed based on the impact of earthquake-
generated tsunamis in various areas in Indonesia. However, the building characteristics
are different due to different cultures and not being a tourism area. As vulnerability curves
from [
30
–
34
] were analyzed from other countries, they are also not applicable to this study.
The economic loss for each building is estimated based on the damage ratio multiplied by
the area of the building and the value per unit area (Equation (1)). The value is analyzed
based on interviews with local residents and the government.
GeoHazards 2025,6, 18 7 of 17
GeoHazards 2025, 6, x FOR PEER REVIEW 7 of 18
close, both are tourism areas, and the building types have the same characteristics. In [26–
29], the vulnerability curves were analyzed based on the impact of earthquake-generated
tsunamis in various areas in Indonesia. However, the building characteristics are different
due to different cultures and not being a tourism area. As vulnerability curves from [30–
34] were analyzed from other countries, they are also not applicable to this study. The
economic loss for each building is estimated based on the damage ratio multiplied by the
area of the building and the value per unit area (Equation (1)). The value is analyzed based
on interviews with local residents and the government.
Figure 4. Building vulnerability curve (modified from [17]).
𝐸𝐿=
𝐴
×𝐵
×𝐷
(1)
where 𝐸𝐿 = economic loss in buildings (in IDR)
𝑛 = number of inundated buildings of type 𝑗 (where 𝑗 is 1, 2 or 3)
𝐴 = area of inundated building number 𝑖 (in m
2
)
𝐵 = value of building of type 𝑗 (IDR per m
2
)
𝐷
= damage ratio of building of type 𝑗 number 𝑖 (in %)
2.3. Land Use/Land Cover
The potential loss of crop products from certain land use/land cover areas after a
tsunami event is estimated based on the crop production per year per hectare. The inun-
dated land use/land cover area (in hectares) is multiplied by the crop production (in kg
per year per hectare) and multiplied by the price of the crop production per kg (Equation
(2)). The five following types of land use/land cover are inundated from the tsunami mod-
elling result: (1) coconut plantation, (2) paddy field, (3) fishpond, (4) shrimp pond, and (5)
mixed plantation.
𝐸𝐿=
𝐴
×𝐶
×𝑃
(2)
where 𝐸𝐿 = economic loss in land use/land cover (in IDR)
𝑛 = number of inundated land use/land cover areas of type 𝑗 (where 𝑗 is 1–5)
𝐴 = area of inundated land use/land cover number 𝑖 (in m
2
)
𝐶 = price of crop production of land use/land cover of type 𝑗 (IDR per kg)
𝑃
= Crop production in land use/land cover of type 𝑗 number 𝑖 (in kg/ha/yr)
Figure 4. Building vulnerability curve (modified from [17]).
ELB=
3
∑
j=1
nj
∑
i=1
Ai×Bj×Dji(1)
where ELB= economic loss in buildings (in IDR)
nj= number of inundated buildings of type j(where jis 1, 2 or 3)
Ai= area of inundated building number i(in m2)
Bj= value of building of type j(IDR per m2)
Dji= damage ratio of building of type jnumber i(in %)
2.3. Land Use/Land Cover
The potential loss of crop products from certain land use/land cover areas after
a tsunami event is estimated based on the crop production per year per hectare. The
inundated land use/land cover area (in hectares) is multiplied by the crop production
(in kg per year per hectare) and multiplied by the price of the crop production per kg
(Equation (2)). The five following types of land use/land cover are inundated from the
tsunami modelling result: (1) coconut plantation, (2) paddy field, (3) fishpond, (4) shrimp
pond, and (5) mixed plantation.
ELL=
5
∑
j=1
nj
∑
i=1
Ai×Cj×Pji(2)
where ELL= economic loss in land use/land cover (in IDR)
nj= number of inundated land use/land cover areas of type j(where jis 1–5)
Ai= area of inundated land use/land cover number i(in m2)
Cj= price of crop production of land use/land cover of type j(IDR per kg)
Pji= Crop production in land use/land cover of type jnumber i(in kg/ha/yr)
2.4. Tourism
The economic loss in tourism is estimated based on the loss of visitors who spend
their money (on food, activities, and souvenirs), buy entrance tickets, and stay in hotels
for a period of one year (Equation (3)). The estimated number of visitors is assumed to
be equal to the number in 2023. Characteristics of domestic and international visitors are
identified from the interviews to estimate the average tourist spending and average period
of stay for both types of visitors.
ELT=
2
∑
i=1"(Fi×Vi×Li)+(ACi×Vi×Li×SFAC i)
+(SOi×Vi×Li×SFSOi)+(TP ×Vi)#+
nh
∑
j=1"Rj×RDj×OD j×W D
+Rj×REj×OEj×WE#(3)
GeoHazards 2025,6, 18 8 of 17
where
ELT= economic loss in tourism (in IDR);
Fi
= average expenditure of money on food for visitor type
i
(domestic or international);
Vi= the number of visitor type i;
Li= average length of stay for visitor type i(days);
ACi= average expenditure of money for activities for visitor type i(IDR);
SFACi
= ratio of the values for visitors’ expenditures on activities for visitor type
i
(%);
SOi= average expenditure of money for souvenirs for visitor type i(IDR);
SFSOi
= average ratio of values for visitors who spend money on souvenirs, of visitor
type i(%);
TP = entrance ticket price (IDR);
nh= the number of hotels in the inundation area;
Rj= the number of rooms in hotel j;
RDj= average weekday room rate of hotel j(IDR);
ODj= weekday occupancy rate of hotel j(%);
WDj= the number of weekdays in a year;
REj= average weekend room rate of hotel j(IDR);
OEj= weekend occupancy rate of hotel j(%);
WEj= the number of weekend day in a year.
The total tsunami economic loss (
EL
) can be calculated as the sum of the economic
losses in building, land use, and tourism sectors (Equation (4))
EL =ELB+ELL+ELT(4)
3. Results
The LOD2 Model resulting from the UAV photogrammetry survey is presented in
Figure 5. The model is capable of displaying individual buildings along with the heights
of the buildings. The buildings, in LOD-2, are classified based on [
17
] and presented
in Figure 6. Another derivation product of UAV photogrammetry mapping is the land
use/land cover map presented in Figure 7. Figures 8and 9show the tsunami inundation
area as the result of the tsunami run-up model. Figure 9shows the results of a more
detailed, high-resolution tsunami model, where it can be seen that the tsunami waves
entering the research area entered through gaps in buildings or trees and were held back
by several taller infrastructures. Impacted buildings and land uses/land covers are shown
in Tables 4and 5.
Table 4. Affected buildings and estimation of economic loss.
No. Building Classification Number of Building Economic Loss (IDR)
1 Timber/Traditional Brick 105 26,519,735,811
2 Traditional Brick with RC Columns (1 storey) 46 18,207,773,702
3 RC Columns with Brick Infill (2 storeys) 14 4,905,853,654
Total Loss of Building (IDR) 165 49,622,363,168
Estimating tsunami economic loss as described in Equations (1)–(4) requires some
data and assumptions. The economic loss in buildings (
ELB
in Equation (1)) requires the
number of inundated buildings, as well as their area (
A
), type, damage ratio (
D
), and value
(
B
). The number, type, and area of the buildings can be derived from the LoD-2 model
(Figure 5) as presented in Table 4, while the damage ratio is estimated based on [
17
] and
the inundation height of the buildings. The value of the building per m
2
(
B
) is analyzed
from the interviews, and determined as IDR 3 million/m
2
for a one-storey building and
GeoHazards 2025,6, 18 9 of 17
IDR 6 million/m
2
for a two-storey building. Table 4presents the estimated economic loss
from buildings.
GeoHazards 2025, 6, x FOR PEER REVIEW 9 of 18
Figure 5. Examples of LOD-2 3D model screenshots.
Figure 6. Building Classification Map.
Figure 5. Examples of LOD-2 3D model screenshots.
GeoHazards 2025, 6, x FOR PEER REVIEW 9 of 18
Figure 5. Examples of LOD-2 3D model screenshots.
Figure 6. Building Classification Map.
Figure 6. Building Classification Map.
GeoHazards 2025,6, 18 10 of 17
GeoHazards 2025, 6, x FOR PEER REVIEW 10 of 18
Figure 7. Land use/Land Cover Map for the whole village of Batukaras.
Figure 8. Tsunami run-up model result.
Figure 7. Land use/Land Cover Map for the whole village of Batukaras.
GeoHazards 2025, 6, x FOR PEER REVIEW 10 of 18
Figure 7. Land use/Land Cover Map for the whole village of Batukaras.
Figure 8. Tsunami run-up model result.
Figure 8. Tsunami run-up model result.
Related to economic loss in productive land (
ELL
in Equation (2)), five types of land
use were identified, as shown in Table 5. The economic value/price of crop products
(
Cj
) and the production per hectare (
Pij
) for each land use type were analyzed based on
interviews with local farmers and the government.
GeoHazards 2025,6, 18 11 of 17
GeoHazards 2025, 6, x FOR PEER REVIEW 11 of 18
(a) (b)
Figure 9. Results of high-resolution model. (a) Northern part of the study area; (b) middle part of
the study area.
Estimating tsunami economic loss as described in Equations (1)–(4) requires some
data and assumptions. The economic loss in buildings (𝐸𝐿 in Equation (1)) requires the
number of inundated buildings, as well as their area (𝐴), type, damage ratio (𝐷), and value
(𝐵). The number, type, and area of the buildings can be derived from the LoD-2 model
(Figure 5) as presented in Table 4, while the damage ratio is estimated based on [17] and
the inundation height of the buildings. The value of the building per m
2
(𝐵) is analyzed
from the interviews, and determined as IDR 3 million/m
2
for a one-storey building and
IDR 6 million/m
2
for a two-storey building. Table 4 presents the estimated economic loss
from buildings.
Table 4. Affected buildings and estimation of economic loss.
No. Building Classification Number of Building Economic Loss (IDR)
1 Timber/Traditional Brick 105 26,519,735,811
2 Traditional Brick with RC Columns (1 storey) 46 18,207,773,702
3 RC Columns with Brick Infill (2 storeys) 14 4,905,853,654
Total Loss of Building (IDR) 165 49,622,363,168
Related to economic loss in productive land (𝐸𝐿𝐿 in Equation (2)), five types of land
use were identified, as shown in Table 5. The economic value/price of crop products (𝐶𝑗)
and the production per hectare (𝑃𝑖𝑗) for each land use type were analyzed based on inter-
views with local farmers and the government.
Table 5. Affected land use/land cover and estimation of economic losses.
Land Cover Value of Crop Production per Hectare (IDR) Area (Ha) Economic Loss (IDR)
Coconut Plantation 57,846,500 2.6991 156,133,488
Paddy Field 21,000,000 16.2133 340,479,300
Fishpond 525,000,000 0.1882 98,805,000
Shrimp Pond 960,000,000 0.2419 232,224,000
Mixed Plantation 1,153,923,250 5.1166 5,904,163,701
Total Loss of Land use (IDR) 6,731,805,489
Economic loss in the tourism sector is estimated based on the loss of visitors, which
will impact income from tickets, hotels, and other touristic expenses (Equation (3)). Table
Figure 9. Results of high-resolution model. (a) Northern part of the study area; (b) middle part of the
study area.
Table 5. Affected land use/land cover and estimation of economic losses.
Land Cover Value of Crop Production
per Hectare (IDR) Area (Ha) Economic Loss (IDR)
Coconut Plantation 57,846,500 2.6991 156,133,488
Paddy Field 21,000,000 16.2133 340,479,300
Fishpond 525,000,000 0.1882 98,805,000
Shrimp Pond 960,000,000 0.2419 232,224,000
Mixed Plantation 1,153,923,250 5.1166 5,904,163,701
Total Loss of Land use (IDR) 6,731,805,489
Economic loss in the tourism sector is estimated based on the loss of visitors, which
will impact income from tickets, hotels, and other touristic expenses (Equation (3)). Table 6
presents the number of visitors to Batukaras Village from 2019 to 2023. The loss from ticket
income can be calculated directly using the ticket price per person (
TP
) as IDR 15,000. The
number of visitors in 2023 is assumed to be the number of visitors that were lost during the
recovery period in this analysis (Vi).
Table 6. Number of Visitors (2019–2023) and income from tickets in 2023.
Visitor 2019 2020 2021 2022 2023
International
1456 237 0 123 276
Domestic 519,468 349,041 380,577 449,506 475,988
TOTAL 520,924 349,278 380,577 449,629 476,264
Ticket (IDR)
7,143,960,000
Table 7presents some assumptions for tourist spending on food (
Fi
), activities (
ACi
),
and souvenirs (
SOi
), as well as the estimated average length of stay (
Li
). Using part of
Equation (3), the estimation of economic loss in tourist spending is presented in Table 8.
In this estimation, the value of the ratio of visitors who spend money on activities for
domestic and international visitors to those who do not (
SFACi
) is 0.5, which means only
half of the domestic and international visitors who spend money on activities (rent a bike
or a surfboard). Another assumption involves the ratio of visitors who spend money
on souvenirs, regarding both domestic and international visitors, to those who do not
GeoHazards 2025,6, 18 12 of 17
(
SFSOi
), which was determined to be 0.5. Tourists are assumed to spend money on food
and activities every day during their stay in Batukaras Village, but only once on a souvenir.
Table 7. Assumption of tourists’ lengths of stay and spending.
Visitors Tourist Spending (IDR) Length of Stay
(Days)
Food Activity Souvenir
Domestic 100,000 50,000 50,000 2
International 300,000 250,000 200,000 10
Table 8. Estimation of economic loss from tourist spending factors.
Visitors Estimated Economic Loss (IDR) Economic Loss (IDR)
Food Activity Souvenir
Domestic 95,142,400,000 23,785,600,000 11,892,800,000 130,820,800,000
International 828,000,000 345,000,00 27,600,000 1,200,600,000
Total Loss (IDR) 132,021,400,000
Table 9presents the estimation of the annual incomes of all hotels that are located in
the inundation area based on the number of rooms (
Rj
), weekday rate (
RDj
), and weekend
rate (
REj
). In this estimation, based on interviews, the occupation rate during weekday
(
ODj
) is determined as 0.5, and the occupation rate during the weekend (
OEj
) is 1. The
number of weekdays (
WDj
) in a year is 260, while the number of weekend days (
WEj
) in a
year is 104.
Table 9. Estimation of the annual incomes of hotels that are located in the inundation area.
Nr. Type of
Hotel Name Type of
Building
Number
of Room
Weekday
Rate (IDR)
Weekend
Rate (IDR)
Weekday
Income
(IDR)
Weekend
Income
(IDR)
Total Income
(IDR)
1 Homestay Pondok
Maranti 1 5 140,000 200,000 91,000,000 104,000,000 195,000,000
2 Homestay Laut Biru 1 6 210,000 300,000 163,800,000 187,200,000 351,000,000
3 Homestay Agus
Homestay 1 1 630,000 900,000 81,900,000 93,600,000 175,500,000
4Hotel The SO Hotel 2 4 420,000 600,000 218,400,000 249,600,000 468,000,000
5Hotel The SO Hotel 2 5 420,000 600,000 273,000,000 312,000,000 585,000,000
6Villa The Beach
House 2 1 3,150,000 4,500,000 409,500,000 468,000,000 877,500,000
7Hotel Sunrise 2 2 350,000 500,000 91,000,000 104,000,000 195,000,000
8Hotel Sunrise 2 5 350,000 500,000 227,500,000 260,000,000 487,500,000
9Hotel Sunrise 2 5 350,000 500,000 227,500,000 260,000,000 487,500,000
10 Villa D’Jarwo 2 1 196,000 280,000 25,480,000 29,120,000 54,600,000
11 Hotel Balekarang 2 6 455,000 650,000 354,900,000 405,600,000 760,500,000
12 Hotel Balekarang 2 2 455,000 650,000 118,300,000 135,200,000 253,500,000
13 Homestay 8 Wolu
Homestay 2 3 315,000 450,000 122,850,000 140,400,000 263,250,000
14 Homestay Buana
Homestay 2 2 140,000 200,000 36,400,000 41,600,000 78,000,000
15 Homestay Buana
Homestay 2 2 140,000 200,000 36,400,000 41,600,000 78,000,000
16 Hotel Wirton
Batukaras 3 26 420,000 600,000
1,419,600,000
1,622,400,000 3,042,000,000
17 Hotel Sunrise 3 5 350,000 500,000 227,500,000 260,000,000 487,500,000
18 Homestay Pondok Putri 3 14 336,000 480,000 611,520,000 698,880,000 1,310,400,000
19 Hotel Le Pari 3 2 2,100,000 3,000,000 546,000,000 624,000,000 1,170,000,000
20 Hotel Le Pari 3 1 2,100,000 3,000,000 273,000,000 312,000,000 585,000,000
21 Hotel Le Pari 3 1 2,100,000 3,000,000 273,000,000 312,000,000 585,000,000
22 Hotel Le Pari 3 1 2,100,000 3,000,000 273,000,000 312,000,000 585,000,000
23 Homestay Bintang
Labuan 3 5 140,000 200,000 91,000,000 104,000,000 195,000,000
Total Economic Loss (IDR) 13,269,750,000
GeoHazards 2025,6, 18 13 of 17
4. Discussion
In total, 3276 buildings are identified from the LOD-2 model, of which most of the
buildings (1874) are in the type 2 class (Traditional Brick with RC Columns, one storey),
and only 24 buildings are classified as type 3 (RC Columns with Brick Infill, two storey or
more). The rest (1378) are classified as Timber/Traditional Brick buildings; beyond this, the
land use/land cover map shows that most of the area in the Batukaras Village (90.9%) is
covered by vegetation (forest and plantations). Only 2.1% of the area is used for residential
purposes, and 5% for paddy fields.
The tsunami run-up impacted an area of 43.311 ha, with 165 buildings (Table 4), 23
of which are hotels/guest houses. This inundation area also affected five economically
productive land use types, with a total area of 24.46 ha (Table 5).
Timber/traditional brick class buildings dominate the inundation area, as can be seen
in Table 4, with about 64%, or 105 buildings, potentially contributing about IDR 26 billion
in economic losses. In total, the economic loss from the buildings is about IDR 49.6 billion.
The impacted/inundated area (
Ai
) is calculated from the overlay of the inundation
map and land use map. It can be seen that most of the run-up covered paddy field area;
however, most of the economic loss comes from the mixed plantation land, with a value of
IDR 5.9 billion, which contributes about 87.7% of the economic loss from productive lands.
Table 6presents the potential economic loss from entrance ticket sales to the beach.
In 2020 and 2021, the number of visitors dropped, particularly international ones, due
to the COVID-19 pandemic. However, since then, the number of visitors has increased.
About IDR 7 billion is estimated to be lost from entrance ticket income due to the tsunami
event. Another potential economic loss may come from tourists’ spending during their
visits. Table 8presents the estimation of this loss, and it gives a value for the loss of about
IDR 132 billion. Using part of Equation (3), the annual total income for hotel rooms can be
estimated at IDR 13.27 billion (Table 9), and this is assumed to be the potential economic
loss during a year-long recovery period after a tsunami disaster.
The combination of potential economic losses from tourism in tickets, hotels, and other
spending gives a total value of IDR 152.43 billion (Table 6+ Table 8+ Table 9), with tourist
spending contributing the most, at IDR 132.02 billion, or about 86.61%. Tourist spending
on food is the highest among the other expenditures, with a value of IDR 95.97 billion, or
about 62.96% the of economic loss from the tourism sector. This is understandable, since all
visitors need food every day, for as long as they stay in the area.
In total, the potential economic loss due to the tsunami disaster is IDR 208.79 billion;
the detailed summary is presented in Table 10. The table shows that IDR 152.43 billion,
or about 73% of the total potential economic loss, comes from tourism factors (Figure 10).
Therefore, it is clear that tsunami disasters will severely affect tourism areas.
Table 10. Summary of tsunami economic loss estimation in the tourism area of Batukaras Village.
Sector Estimated Economic Loss (IDR) Percentage (%)
Buildings 49,633,363,168 23.8
Land Use 6,731,805,489 3.2
Tourism 152,430,970,000 73.0
Total (IDR) 208,796,138,657 100
The significant economic losses from the tourism sector highlight the long-term chal-
lenges faced by tsunami-affected areas. Even after infrastructure is rebuilt, the fear and
negative perception of safety among potential visitors can prolong the decline in tourist
arrivals, delaying economic recovery. Tourism recovery after a tsunami event depends on
policy makers’ initiatives, infrastructure rebuilding, media influence, and tourist safety
perceptions. Quick responses, such as financial aid, marketing campaigns, and regulatory
GeoHazards 2025,6, 18 14 of 17
changes, are known to be effective in restoring tourists’ confidence and attracting visitors
back to affected areas [35–38].
GeoHazards 2025, 6, x FOR PEER REVIEW 15 of 18
Figure 10. Percentage of the contribution of building, land use, and tourism factors to total potential
economic loss in Batukaras Village.
Figure 11. Comparison of total economic losses with various recovery periods.
5. Conclusions
An estimation of the potential economic loss following a tsunami disaster in a tour-
ism area on the southern coast of Java Island was successfully carried out. The estimation
is based on geospatial data derived from a UAV photogrammetry survey and a tsunami
run-up model. An LoD-2 3D model, Digital Elevation Model, and land use/land Cover
Map of Batukaras Village were derived from UAV aerial photos and later used in the tsu-
nami wave run-up model. The economic loss is calculated from the following three fac-
tors: (1) buildings, estimated from the damage ratio and the value of the buildings; (2)
land use/land cover, estimated from the loss of crop products for a period of one year; and
(3) tourism, estimated from the loss of visitors who do tourism activities (surfing, eating,
buying souvenirs), buy entrance tickets, and stay in hotels, also for a period of one year.
One year is assumed as the recovery period for crop products and tourism activities fol-
lowing the tsunami event. The total potential economic loss from the tsunami disaster is
IDR 208.79 billion, 73% of which comes from tourism factors. One of the efforts to reduce
this economic loss is to accelerate the recovery period so that economic activities can be
carried out immediately after a tsunami disaster, including in the tourism sector. A 6-
month recovery period reduction could save about IDR 80 billion in economic loss, and
the savings can be used to compensate and repair the damage in buildings and productive
lands as well as to reactivate tourism and public facilities and infrastructure. Another op-
tion is to implement a spatial planning strategy (e.g., reduction in development near the
Figure 10. Percentage of the contribution of building, land use, and tourism factors to total potential
economic loss in Batukaras Village.
It was stated before that all calculations of estimated economic losses in
Equations (1)–(4)
are assumed for a one-year recovery period. A calculation using a shorter recovery period
would reduce the estimated economic losses, particularly in land use and tourism factors.
Different recovery periods are simulated, and the results are presented in Figure 11. It
compares estimated economic losses using various recovery periods (0.5, 1, 1.5, and 2 years)
from all three sectors. From the simulation, a half-year difference in the recovery period
will give a difference of IDR 80 billion in the total economic loss. By reducing the recovery
period to 6 months, savings of IDR 80 billion can be used to compensate and rebuild the
damage in buildings and productive lands as well as to reactivate the tourism and public
facilities and infrastructure.
GeoHazards 2025, 6, x FOR PEER REVIEW 15 of 18
Figure 10. Percentage of the contribution of building, land use, and tourism factors to total potential
economic loss in Batukaras Village.
Figure 11. Comparison of total economic losses with various recovery periods.
5. Conclusions
An estimation of the potential economic loss following a tsunami disaster in a tour-
ism area on the southern coast of Java Island was successfully carried out. The estimation
is based on geospatial data derived from a UAV photogrammetry survey and a tsunami
run-up model. An LoD-2 3D model, Digital Elevation Model, and land use/land Cover
Map of Batukaras Village were derived from UAV aerial photos and later used in the tsu-
nami wave run-up model. The economic loss is calculated from the following three fac-
tors: (1) buildings, estimated from the damage ratio and the value of the buildings; (2)
land use/land cover, estimated from the loss of crop products for a period of one year; and
(3) tourism, estimated from the loss of visitors who do tourism activities (surfing, eating,
buying souvenirs), buy entrance tickets, and stay in hotels, also for a period of one year.
One year is assumed as the recovery period for crop products and tourism activities fol-
lowing the tsunami event. The total potential economic loss from the tsunami disaster is
IDR 208.79 billion, 73% of which comes from tourism factors. One of the efforts to reduce
this economic loss is to accelerate the recovery period so that economic activities can be
carried out immediately after a tsunami disaster, including in the tourism sector. A 6-
month recovery period reduction could save about IDR 80 billion in economic loss, and
the savings can be used to compensate and repair the damage in buildings and productive
lands as well as to reactivate tourism and public facilities and infrastructure. Another op-
tion is to implement a spatial planning strategy (e.g., reduction in development near the
Figure 11. Comparison of total economic losses with various recovery periods.
The other strategy to reduce economic loss is to apply spatial planning to limit devel-
opment near the coastline, as has been performed in several areas in Indonesia that were
affected by the tsunami, like Aceh and Palu. On the other hand, relocating some buildings
and productive lands from the inundation area to the safe area can reduce population
density in vulnerable areas. Improvement incentives and financial mechanisms, such as
insurance planning for buildings in vulnerable areas and subsidies for tsunami-resistant
buildings, are also essential to apply. However, these strategies are more challenging,
and may produce more problems. To avoid more problems, several factors should be
considered when a spatial planning strategy is applied, including the implementation of
GeoHazards 2025,6, 18 15 of 17
multi-faceted approaches, strengthening institutional frameworks, improving data collec-
tion and sharing, enhancing community engagement, and securing adequate funding [
39
].
Some other recommendations on a broader scale are found in [
40
,
41
], related to reducing
economic losses by way of enhancement of early warning systems, investing in research
and development, regional cooperation and information sharing, adapting infrastructure
and urban planning, technological upgrades for disaster monitoring, and improving urban
infrastructure, along with other methods. These strategies can be implemented either at
the local or national level.
5. Conclusions
An estimation of the potential economic loss following a tsunami disaster in a tourism
area on the southern coast of Java Island was successfully carried out. The estimation
is based on geospatial data derived from a UAV photogrammetry survey and a tsunami
run-up model. An LoD-2 3D model, Digital Elevation Model, and land use/land Cover
Map of Batukaras Village were derived from UAV aerial photos and later used in the
tsunami wave run-up model. The economic loss is calculated from the following three
factors: (1) buildings, estimated from the damage ratio and the value of the buildings;
(2) land use/land cover, estimated from the loss of crop products for a period of one year;
and (3) tourism, estimated from the loss of visitors who do tourism activities (surfing,
eating, buying souvenirs), buy entrance tickets, and stay in hotels, also for a period of one
year. One year is assumed as the recovery period for crop products and tourism activities
following the tsunami event. The total potential economic loss from the tsunami disaster is
IDR 208.79 billion, 73% of which comes from tourism factors. One of the efforts to reduce
this economic loss is to accelerate the recovery period so that economic activities can be
carried out immediately after a tsunami disaster, including in the tourism sector. A 6-month
recovery period reduction could save about IDR 80 billion in economic loss, and the savings
can be used to compensate and repair the damage in buildings and productive lands as
well as to reactivate tourism and public facilities and infrastructure. Another option is to
implement a spatial planning strategy (e.g., reduction in development near the coastline,
relocation of buildings and productive lands, use of building insurance, and offerings of
subsidies to tsunami resistant buildings), which is more complex to apply since it should
consider a multi-faceted approach, strengthening institutional frameworks, improving data
collection and sharing, enhancing community engagement, and securing adequate funding.
Author Contributions: W.W.: writing—original draft, methodology, investigation, conceptualization,
supervision, and funding acquisition. A.N.: writing—review and editing, data curation, formal
analysis, and visualization. M.W.A.G.: writing—review and editing, data curation, supervision,
and validation. S.K.P.: data curation, formal analysis, and visualization. W.I.R.: data curation,
formal analysis, and visualization. T.V.: data curation, formal analysis, and visualization. K.A.G.:
data curation, formal analysis, and visualization. M.B.A.: data curation, formal analysis, and
visualization. I.H.: writing—review and editing, supervision, and validation. D.S.: writing—review
and editing, methodology, and supervision. I.M.: writing—review and editing, and supervision.
I.E.M.:
writing—review
and editing, methodology, and supervision. A.K.L.: writing—review and
editing, validation, and visualization. All authors have read and agreed to the published version of
the manuscript.
Funding: WW is financially supported by Penelitian Kolaboratif (2024) (Letter Nr. 57B/IT1.C01/SK-
TA/2024), managed by the Faculty of Earth Sciences and Technology, Institut Teknologi Ban-
dung (ITB).
GeoHazards 2025,6, 18 16 of 17
Data Availability Statement: The bathymetry data were obtained from BATNAS (National
Bathymetry), provided by the Indonesian Geospatial Information Agency (BIG) at https://sibatnas.
big.go.id/ (accessed on 25 March 2025). The earthquake generation parameter data were obtained
from the National Centre for Earthquake Studies (PuSGEN) in 2017 from https://goo.gl/wR9yP2
(accessed on 25 March 2025) and the United States Geological Survey (USGS) https://earthquake.
usgs.gov/earthquakes/search/ (accessed on 25 March 2025).
Acknowledgments: We acknowledge the support of the Faculty of Earth Sciences and Technology,
Insitut Teknologi Bandung, in the financial and administrative aspects of the research. We also would
like to acknowledge the local government of Batukaras Village, Pangandaran Regency, Indonesia, for
their support in data acquisition.
Conflicts of Interest: The authors declare no conflict of interest.
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