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Nat. Hazards Earth Syst. Sci., 18, 2143–2160, 2018
https://doi.org/10.5194/nhess-18-2143-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Development and application of a tsunami fragility
curve of the 2015 tsunami in Coquimbo, Chile
Rafael Aránguiz1,2, Luisa Urra3, Ryo Okuwaki4, and Yuji Yagi5
1Department of Civil Engineering, Universidad Católica de la Santísima Concepción, Concepción, Chile
2National Research Center for Integrated Natural Disaster Management CONICYT/FONDAP/1511007 (CIGIDEN),
Santiago, Chile
3Laboratory of Remote Sensing and Geoinformatics for Disaster Management, International Research Institute of Disaster
Science, Tohoku University, Tohoku, Japan
4Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
5Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
Correspondence: Rafael Aránguiz (raranguiz@ucsc.cl)
Received: 13 October 2017 – Discussion started: 3 November 2017
Revised: 25 June 2018 – Accepted: 8 July 2018 – Published: 10 August 2018
Abstract. The last earthquake that affected the city of Co-
quimbo took place in September 2015 and had a magnitude
of Mw=8.3, resulting in localized damage in low-lying ar-
eas of the city. In addition, another seismic gap north of
the 2015 earthquake rupture area has been identified; there-
fore, a significant earthquake (Mw=8.2 to 8.5) and tsunami
could occur in the near future. The present paper develops a
tsunami fragility curve for the city of Coquimbo based on
field survey data and tsunami numerical simulations. The
inundation depth of the 2015 Chile tsunami in Coquimbo
was estimated by means of numerical simulation with the
Non-hydrostatic Evolution of Ocean WAVEs (NEOWAVE)
model and five nested grids with a maximum grid resolu-
tion of 10 m. The fragility curve exhibited behavior similar
to that of other curves in flat areas in Japan, where little
damage was observed at relatively high inundation depths.
In addition, it was observed that Coquimbo experienced less
damage than Dichato (Chile); in fact, at an inundation depth
of 2 m, Dichato had a ∼75 % probability of damage, while
Coquimbo proved to have only a 20% probability. The new
fragility curve was used to estimate the damage by possible
future tsunamis in the area. The damage assessment showed
that ∼50 % of the structures in the low-lying area of Co-
quimbo have a high probability of damage in the case of a
tsunami generated off the coast of the study area if the city is
rebuilt with the same types of structures.
1 Introduction
On 16 September 2015 a Mw=8.3 earthquake took place off
the coast of the Coquimbo Region (USGS: http://earthquake.
usgs.gov/earthquakes/eventpage/us20003k7a#executive, last
access: 10 July 2018). The earthquake generated a tsunami
that inundated low-lying areas of the city of Coquimbo, with
run-up reaching up to 6.4 m and a penetration distance of
up to 700 m (Aránguiz et al., 2016; Contreras-López et al.,
2016), resulting in reports of significant damage to houses
and public infrastructure (Contreras-López et al., 2016). This
earthquake filled the seismic gap that had existed since at
least the last significant earthquake along the Coquimbo–
Illapel seismic region in 1943 (Melgar et al., 2016; Ye et
al., 2016). However, the region just north of the 2015 rupture
area has not experienced significant seismic activity since the
1922 Mw=8.3 event (Melgar et al., 2016; Ye et al., 2016).
Thus, it is recommended that reconstruction plans and new
tsunami mitigation measures consider potential impacts due
to possible future tsunamis generated north of the 2015 Il-
lapel earthquake rupture zone.
With regard to the assessment of structural damage within
the exposed area against a potential tsunami hazard, two dif-
ferent approaches were identified. Damage can be estimated
deterministically based on the forces acting on a single struc-
ture (Nandasena et al., 2012; Nistor et al., 2009; Shimo-
zono and Sato, 2016; Wei et al., 2015); however, such an
analysis could be extremely time-consuming and impracti-
Published by Copernicus Publications on behalf of the European Geosciences Union.
2144 R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo)
cal for an entire city due to the high-resolution numerical
simulations (∼2 m) that are required. Alternatively, the as-
sessment of structural damage could be performed proba-
bilistically by means of fragility curves (Koshimura et al.,
2009a, b; Suppasri et al., 2011). Tsunami fragility curves rep-
resent the probability of damage to structures in relation to
a tsunami intensity measure, such as the inundation depth,
current velocity or hydrodynamic force Koshimura et al.,
2009a), although a fully probabilistic approach may use a
wide range of possible scenarios; thus, both hazard assess-
ment and damage assessment are probabilistic (Park et al.,
2017). A classical approach uses linear models with ordinary
least-square methods and aggregated data. This methodology
has been applied to obtain empirical tsunami fragility curves
for Banda Aceh in Indonesia (Koshimura et al., 2009b) and
Thailand (Suppasri et al., 2011) after the 2004 Indian Ocean
Tsunami. The same methodology was applied to areas af-
fected by the 2009 Samoa tsunami (Gokon et al., 2014). In
a similar manner, this method was applied in Japan after
the 2011 Tohoku tsunami, allowing several fragility curves
that considered several damage levels and different build-
ing materials to be obtained (Suppasri et al., 2013). After
the 2010 Chile tsunami, Mas et al. (2012) developed the
first tsunami fragility curve in Chile for masonry and mixed
structures in Dichato. In recent years, new methodologies
have been proposed for the development of tsunami fragility
curves that use disaggregated data and different classes of
models such as the generalized linear model, generalized ad-
ditive model and non-parametric model (Charvet et al., 2015,
2017; Macabuag et al., 2016). These new methodologies pro-
pose a more comprehensive analysis in order to select appro-
priate statistical models and identify which tsunami intensity
measure gives the best representation of the observed dam-
age data (Macabuag et al., 2016). Even though the use of
different classes of models could offer an improvement over
the ordinary least-square method, there is no quantifiable as-
sessment of the effect of data aggregation and linear model
assumption violation on the predictive power of a model.
(Macabuag et al., 2016). For example, the fragility curves de-
veloped by Suppasri et al. (2013) have been applied to build-
ing damage estimation in Napier, New Zealand (Fraser et al.,
2014), and both building damage and economic loss estima-
tion in Seaside, Oregon (Wiebe and Cox, 2014). The former
study also applied the fragility curves of Dichato, Chile (Mas
et al., 2012), and American Samoa (Gokon et al., 2014).
Tsunami fragility curves are obtained for a given area un-
der a given scenario; therefore, they may not be applicable to
other areas of interest since the tsunami characteristics and
building materials may differ (Koshimura et al., 2009a; Sup-
pasri et al., 2011). For example, buildings along the Sanriku
ria coast in Japan experienced greater damage than struc-
tures located on the plains of Sendai (Suppasri et al., 2012b,
2013); thus, De Risi et al. (2017) analyzed the influence of
tsunami velocity on structural damage on ria-type and plain-
type coasts. They found that while flow velocity improves the
fragility models, the two coastal typologies should be con-
sidered separately when velocity is included in the analysis.
Moreover, Song et al. (2017) used a bivariate intensity mea-
sure to evaluate tsunami losses, such that both flow velocity
and inundation depth are analyzed. They found that flow ve-
locity is important for buildings located less than 1 km from
the coastline. In addition, they found that reinforced concrete
buildings are the most sensitive to the incorporation of veloc-
ity, while wood structures exhibit no sensitivity to this vari-
able.
The Coquimbo area provides a good opportunity to de-
velop a fragility curve and assess potential tsunami impact
since the tsunami in 2015 did not damage all structures and
some of the damaged structures have been repaired or re-
built on their original sites. This study develops an empirical
fragility curve for the Coquimbo area using field survey data
and numerical simulation of the 2015 Chile tsunami. In addi-
tion, we estimated the probability of structural damage for a
deterministic tsunami scenario using the Coquimbo fragility
curve. Section 2 gives a description of the study area, with
a short review of the local seismicity. Section 3 presents the
methodology of the fragility curve development, which in-
cludes a comparison with existing tsunami fragility curves.
Section 4 presents an application of the fragility curves. Fi-
nally, Sect. 5 gives the main conclusions of the present re-
search.
2 Study area
The city of Coquimbo is located on the southern shore of Co-
quimbo Bay (29.96◦S). The Coquimbo area was mentioned
by the conquistadors as a good place for a port and the lo-
cation became important in the 19th century due to the nat-
ural protection it offered against southwest swell waves. Co-
quimbo Bay is open to the northwest and characterized by
a lowland topography with a long, flat, sandy beach (Arán-
guiz et al., 2016), similar to the coastal plains of Sendai. Like
all coastal cities in Chile, Coquimbo is located over the sub-
duction zone of the Nazca plate beneath the South Ameri-
can plate (18–44◦S). The convergence rate of the plates is
68 mm yr−1along the Chile subduction zone and large seis-
mic events take place every 10 years on average (Métois et
al. 2016). In fact, three events over a magnitude of 8.0 have
taken place in the last 6 years, namely, the 2010 Maule (34–
38◦S), 2014 Iquique (19–20◦S) and 2015 Illapel (30–32◦S)
earthquakes.
Figure 1 shows the seismic events recorded in the Co-
quimbo area. The oldest record of a tsunami is that of the
1730 event. This earthquake generated a destructive tsunami
that destroyed Valparaiso and Concepción and flooded low-
lying areas in Japan (Cisternas et al., 2011). The tsunami de-
stroyed several ranches on the shore of Coquimbo (Soloviev
and Go, 1975). Although the 1880 and 1943 earthquakes are
considered to be similar in size (Nishenko, 1985), it is ob-
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R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo) 2145
Figure 1. Seismicity of central Chile. (a) Space–time plot of large earthquakes along central Chile. Red bars are the events along the
Copiapó–Coquimbo region and the red stars represent smaller seismic events. The blue bars are events along the Coquimbo–Illapel seismic
region, while the black lines represent events along the Los Vilos–Constitución segment. The dashed line is the large event of 1730, which
ruptured both the Los Vilos–Constitución and Coquimbo–Illapel segments (Beck et al., 1998; Lomnitz, 2004; Métois et al., 2016; Nishenko,
1985). (b) Map showing the cities and towns mentioned in the text. The yellow star represents the epicenter of the 2015 Illapel earthquake.
The thin black lines are isobaths at water depths of 200, 1000 and 3000 m. The thick black line is the Peru–Chile trench.
served that the behaviors of the tsunamis generated by these
events seem to be different. While the former generated large
columns of water that resulted in the anchor chain of a ship
snapping in Coquimbo (Soloviev and Go, 1975) and a deep
submarine cable breaking off the coast near the mouth of the
Limarí River (Lomnitz, 2004), the latter generated a minor
tsunami that damaged fishing boats in Los Vilos and raised
the water level by 80cm in Valparaiso (Soloviev and Go,
1975), while no tsunami was reported in Coquimbo. Con-
versely, the 2015 tsunami reached up to 4.75 m at the Co-
quimbo tide gauge, with a run-up of 6.4 m (Aránguiz et al.,
2016; Contreras-López et al., 2016). Moreover, a maximum
tsunami amplitude of 2 m was observed at the Valparaiso tide
gauge (Aránguiz et al., 2016). The main reason behind this
is that the 1943 event broke the deepest portion of the sub-
duction interface, while the 2015 Illapel earthquake had a
shallower rupture area and a larger magnitude (Fuentes et
al., 2016; Okuwaki et al., 2016), resulting in a larger initial
tsunami amplitude (Aránguiz et al., 2016).
The largest tsunami ever recorded in Coquimbo took place
in 1922. It arrived in Coquimbo 2h after the earthquake,
with three large waves observed, the third of which was the
largest, with a maximum inundation height of 7 m and an in-
land penetration of 2 km. The part of the city located on the
southern shore of Coquimbo Bay was totally destroyed by
both the water and tsunami debris (Soloviev and Go, 1975).
In a similar manner, the tsunami reached inundation heights
of up to 9 m at Chañaral and 6–7 m at Caldera. The tsunami
was also observed in Japan, with maximum amplitudes rang-
ing from 60 to 70 cm (Carvajal et al., 2017; Soloviev and Go,
1975), which is similar to the amplitudes of the 2015 event
(80 cm), but larger than those of the 1943 event, which were
10–25 cm (Beck et al., 1998). Another significant event was
the 1849 earthquake, which generated a localized tsunami
that mainly affected Coquimbo. The tsunami arrived 10 to
30 min after the earthquake, penetrated 300 m horizontally
and rose 5 m above the high tide mark (Soloviev and Go,
1975).
3 Development of the fragility curve
The development of the fragility functions in the present
work required three main steps: first, data collection re-
garding building damage levels in the Coquimbo area and
tsunami inundation heights for numerical modeling valida-
tion; second, selection of a rupture model of the 2015 Illapel
earthquake and validation of the tsunami inundation heights
for estimation of tsunami inundation depth; and third, GIS
analysis and statistical analysis for correlation between dam-
age level and simulated tsunami inundation depth.
3.1 Building damage and tsunami inundation data
Only 5 to 7 days after the 2015 event, a team surveyed the
affected area and collected more than 40 inundation height,
inundation depth and tsunami run-up measurements in the
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2146 R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo)
Figure 2. Photographs of structures undamaged and masonry houses damaged by the 16 September 2015 tsunami in the Coquimbo area. The
red letter d indicates the observed tsunami inundation depth. All photos were taken on 22 September 2015.
Coquimbo inundation area. The field measurements followed
established post-tsunami survey procedures (Dengler et al.,
2003; Dominey-Howes et al., 2012; Synolakis and Okal,
2005) and were corrected for tide level at the time of max-
imum inundation. At the same time, 585 structures within
the inundation area were identified and classified as mixed
structures made of wood and masonry (568), reinforced con-
crete buildings of eight or more stories (4) and very light
structures that did not meet minimal building standards (13).
The present analysis considered the mixed structures only;
therefore, the reinforced concrete and light structures were
removed from the fragility curve analysis. Typical structures
within the inundated area of Coquimbo have one story and
are made of masonry, though there are some two-story build-
ings made of both masonry (the first floor) and wood (the
second floor). In order to facilitate the comparison with ex-
isting fragility curves (e.g., Dichato) all data were combined
in a single category: mixed structures. Figure 2 shows typical
mixed structures and inundation depth marks surveyed in Co-
quimbo immediately after the 2015 tsunami. Figure 2a and b
show masonry houses that were not damaged by the tsunami
despite inundation depths that ranged from 1.5 to 2 m.
Meanwhile, Fig. 2c and d show houses with moderate to
major damage, ready for inhabitation again after major re-
pairs. In fact, the house in Fig. 2c was being repaired at the
time of the field survey and the gray wall in the corner had
been built a few days earlier. Meanwhile, the house in Fig. 2d
was abandoned since all interior walls, windows, doors and
the roof were destroyed and major repairs and retrofitting
would be needed. Figure 2e shows a destroyed structure with
its interior walls and roof completely removed, while Fig. 2f
shows the remaining foundation of a washed-away structure.
Even though the damage to the structure could be due to
both the earthquake and tsunami, it was observed that dam-
age due to the earthquake was limited (Candia et al., 2017)
and the structures most affected by the earthquake were made
of adobe (Fernández et al., 2017). In addition, the authors
had the opportunity to compare damage to inundated and
non-inundated houses in Coquimbo in order to verify that
the structural damage to inundated houses was due to the
tsunami. In order to avoid categorizing light damage (due to
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R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo) 2147
Figure 3. (a) Surveyed damage to structures due to the 2015 tsunami; R.C.: reinforced concrete structures; L.S.: light structures. (b) Co-
quimbo inundated area (Aránguiz et al., 2016) and survey data. Red circles represent inundation measures and yellow triangles tsunami
run-up.
the earthquake) as tsunami damage, a two-level damage scale
was used. Thus, the present work assumed that the damage
to flooded structures was due only to the tsunami.
In addition, the two-level damage scale was used due to
the small number of inundated structures (568) and for com-
parison with the existing fragility curve of Dichato (Mas et
al., 2012), which has only two damage levels. The first level,
called “not destroyed,” included structures with no damage
or minor to major damage, corresponding to levels 1–3 given
by Suppasri et al. (2013). These damage levels indicate that
there is slight to severe damage to nonstructural components;
therefore, it would be possible to use the structures after
moderate to major repairs (Fig. 2a–c). The other damage
level, called “destroyed”, included damage levels 4 to 6 ac-
cording to Suppasri et al. (2013), i.e., structures that under-
went severe damage to walls or columns or that had com-
pletely collapsed (Fig. 2d–f).
Previous works carried out damage inspections using
satellite images and field surveys (Koshimura et al., 2009b;
Mas et al., 2012; Suppasri et al., 2011); however, the satel-
lite image method assumes that buildings with intact roofs
are not destroyed (Suppasri et al., 2011), and severe dam-
age to columns or interior walls may not be observed (Mas
et al., 2012), as in the case of the houses shown in Fig. 2c
and d. Therefore, the present work employed damage de-
tection based on field surveys only. Figure 3a shows the
surveyed buildings and the damage levels assigned to the
568 mixed structures. The four reinforced concrete build-
ings (R.C.) and the 13 light structures (L.S.) that did not meet
minimal building standards are also included in the figure.
Figure 3b shows the inundation height and run-up measure-
ments recorded during the field survey. It is observed that
the maximum inundation height was reached in the corner,
where the coastal road and the railway converge. Most of the
damaged structures were identified in that location as well.
3.2 Tsunami inundation depth
Tsunami inundation depth was estimated as the difference
between tsunami inundation height and ground elevation.
Since the inundation heights were measured at a few lo-
cations across the inundation area and there is a lack of
tsunami traces in the wetland, interpolation of tsunami height
may not be suitable; therefore, the tsunami heights were ob-
tained from tsunami numerical simulation of the 2015 event.
We tested four available finite-fault models, namely those of
Li et al. (2016), Ruiz et al. (2016), Okuwaki et al. (2016)
and Shrivastava et al. (2016), and the best fit was selected
according to tide gauges in Coquimbo and Valparaiso and
DART buoy 32402. Once the best slip model was selected,
we used the field measurements of inundation height and run-
up to select an appropriate dry-land roughness coefficient.
The model proposed by Li et al. (2016) is obtained from iter-
ative modeling of teleseismic body waves as well as tsunami
records at DART buoys. Since the magnitude of the proposed
model is Mw=8.21, the slip distribution was multiplied by a
factor of 1.38; thus, all events have the same magnitude: 8.3.
The tsunami initial condition was estimated to be equal to
the seafloor displacement. In addition, the vertical displace-
ment from each subfault was computed using a kinematic so-
lution of the planar fault model of Okada (1985). The numer-
ical simulations were carried out with the Non-hydrostatic
Evolution of Ocean WAVEs (NEOWAVE) model (Yamazaki
et al., 2009, 2011). This model is a staggered finite-difference
model that solves the nonlinear shallow water equation and
uses a vertical velocity term to account for weakly dispersive
waves. The model generates the tsunami initial condition,
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2148 R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo)
Figure 4. Model setting and nested computational grids for Coquimbo.
propagation and inundation by means of several nested grids
of different resolutions. The present research used five nested
grids, as shown in Fig. 4. The level 1 grid describes tsunami
propagation from generation to the continental shelf and to
the Pacific Ocean at a resolution of 2 arcmin (∼3600 m).
This grid was generated from 30 arcmin General Bathymet-
ric Chart of the Oceans (GEBCO) data. The level 2 and level
3 grids were built from nautical charts 4100, 4112, and 4113
and it had a resolution of 30 and 6 arcsec, respectively. The
level 4 grid covered Coquimbo Bay and was built from nauti-
cal chart 4111, and had a resolution of 1 arcsec (∼30 m). Fi-
nally, the level 5 grid had a resolution of 1/3 arcsec (∼10 m)
and was built from bathymetry from nautical chart 4111 and
topography from a digital terrain model (DTM) with contour
lines with a resolution of 2 m provided by the Coquimbo of-
fice of the Ministry of Housing (MINVU). The topography
used high-resolution data; thus, the most important features,
such as the coastal road embankment, railway, river and wet-
land, are well represented (see Fig. 4, grid 5). Numerical sim-
ulations in Valparaiso involved four nested grids with a max-
imum grid resolution of 1 arcsec (∼30 m).
The roughness coefficient was defined as n=0.025 on the
seabed, as recommended for tsunamis (Bricker et al., 2015;
Kotani et al., 1998); however, we tested several roughness
coefficient values in coastal, wetland and urban areas in or-
der to obtain the best fit of tsunami inundation height. The
validation of the numerical simulation was performed us-
ing the root mean square error and the parameters Kand κ
given by Eqs. (1) and (2) (Aida, 1978). The variable Kiis de-
fined as Ki=xi/yi, where xiand yiare recorded and com-
puted tsunami heights, respectively. The Japan Society of
Civil Engineers provides guidelines, which recommend that
0.95 < K < 1.05 and κ < 1.45 for there to be good agree-
ment (Aida, 1978; Gokon et al., 2014).
logK=1
n
n
X
i=1
logKi(1)
logκ=v
u
u
t
1
n
n
X
i=1
(logKi)2−(logK )2(2)
Figure 5 shows the tsunami initial conditions of the four
slip models and the tsunami waveforms over an elapsed
time of 4 h at three selected gauges, namely Coquimbo, Val-
paraiso and DART buoy 32402. Even though the modified
Li et al. (2016) model overestimates the maximum ampli-
tude at the DART buoy, the simulation exhibits a very good
agreement with the tsunami record in Coquimbo. When the
Mw=8.3 models proposed by Ruiz et al. (2016) and Shri-
vastava et al. (2016) were analyzed, it was possible to ob-
serve a good agreement at the DART buoy and Valparaiso
tide gauge, although the amplitude in Coquimbo is underes-
timated by more than a meter. The Okuwaki et al. (2016)
model overestimates both the DART buoy and Valparaiso
tide gauge, despite the second tsunami wave reaching a sim-
ilar amplitude in Coquimbo. Nevertheless, the maximum
tsunami amplitude is underestimated. Therefore, the modi-
fied Li et al. (2016) model was selected to assess the suitable
Manning roughness coefficient.
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R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo) 2149
Figure 5. Tsunami initial conditions of four source models and comparison of tsunami records with simulated tsunami waveforms at
DART 32402, Coquimbo and Valparaiso.
Figure 6. Tsunami inundation heights obtained with the modified Li et al. (2016) source model and four different Manning coefficients,
n=0.025, 0.04, 0.05 and 0.06. The parameters of root mean square error, Kand κarea are also shown.
Figure 6 shows the inundation area and tsunami inunda-
tion height results obtained from the numerical simulations
of the Li et al. (2016) model, with four different roughness
coefficients. The tested coefficients are n=0.025 for coastal
and riverine areas, 0.04 and 0.05 for low-density urban ar-
eas, and 0.06 for medium-density urban areas (Bricker et
al., 2015; Kotani et al., 1998). From the figure, it is possible
to observe that the best fit is obtained for n=0.025, which
resulted in K=1.05 and κ < 1.45, corresponding to good
agreement. For higher roughness coefficients, the tsunami in-
undation heights are underestimated. In addition, the larger
the coefficient, the smaller the inundation area. This behav-
ior could be explained by the fact that a significant part of
the flooded area is a wetland and the developed area is rather
small, with a low-density residential distribution. Thus, the
inundation depth is computed from the inundation area given
by the modified Li et al. (2016) slip model, with a roughness
coefficient of n=0.025.
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2150 R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo)
Figure 7. Results of tsunami numerical simulations for each intensity measure. (a) Inundation depth, (b) flow velocity and (c) hydrodynamic
force.
3.3 Fragility curve
The construction of a fragility curve requires a correlation
between the structural damage level and a tsunami inten-
sity measure, such as the inundation depth, current veloc-
ity or hydrodynamic force. To this end, we used the clas-
sical approach with aggregated data and a least-square fit
(Koshimura et al., 2009a), in which a sample size is defined
such that each range of the tsunami intensity measure in-
cludes the defined number of structures. Then the damage
probability is calculated by counting the number of destroyed
or not-destroyed structures for each range of the intensity
measure. Finally, the fragility function is developed through
regression analysis of the discrete set of damage probabilities
and the tsunami intensity measure. Therefore, it is assumed
that the cumulative probability Pof damage follows the stan-
dardized normal or lognormal distribution function given in
Eq. (3). 8is the distribution function, xis the hydrodynamic
feature of the tsunami, and µand σare the mean and stan-
dard deviation of x, respectively. The values of µand σare
calculated by means of least-square fitting of xand the in-
verse of 8, (8−1) on normal paper given by Eq. (4).
P (x) =8x−µ
σ(3)
x=σ 8−1+µ(4)
The hydrodynamic force per unit width (kN m−1) acting on
a structure is computed as the drag force given by Eq. (5),
where the drag coefficient is assumed to be CD=1.0 for sim-
plicity, ρis the density of sea water (1025kgm−3), Uis the
flow velocity (ms−1) and his the inundation depth (m).
F=1
2CDρhU 2(5)
Figure 7 shows the results of the simulated tsunami inten-
sity measures. It can be observed that the topography plays
an important role in tsunami inundation, as the maximum in-
undation depth values (Fig. 7a) occur at the beach and wet-
land, while developed areas behind the railway and areas dis-
tant from the shore present low inundation depths. In a sim-
ilar manner, high velocities occur close to the sites of rapid
topographic changes (Fig. 7b), such as the lee side of the
Table 1. Statistical parameters for developed fragility curves ob-
tained from a normal distribution.
Tsunami intensity measure µ σ R2
Inundation depth (m) 2.4395 0.5537 0.8524
Flow velocity (ms−1) 2.5268 0.6421 0.8580
Hydrodynamic force (kN m−1) 4.2564 1.7055 0.7512
coastal road, while low velocities are observed within the
developed area under analysis (<3 m s−1). Since hydrody-
namic force is a combination of both inundation depth and
flow velocity (Fig. 7c), the developed area behind the rail-
way presents low force as well. Figure 8 shows the results
of the tsunami fragility curves of Coquimbo for inundation
depth, flow velocity and hydrodynamic force. The sample
size was defined to be 40 structures; thus, 15 ranges were
used. Figure 8a shows the histogram, while Fig. 8c shows
the relationship between damage probability and inundation
depth (upper panel), flow velocity (central panel) and hydro-
dynamic force (lower panel), with the solid line representing
the best-fit curve of the plot. The fragility curves were esti-
mated by means of regression analysis, as shown in Fig. 8b.
The statistical parameters of the developed fragility func-
tions are shown in Table 1. In Fig. 8 it is possible to ob-
serve that inundation depths lower than 1.5m did not gener-
ate damage to the surveyed structures and the damage prob-
ability of the curve is less than 10 %. Moreover, the fragility
curve shows that inundation depths higher than 4m could re-
sult in a 100 % probability of severe damage to mixed struc-
tures in Coquimbo. With regard to the flow velocity, it is ob-
served that most of the simulated data are in the range of 0 to
2.5 m s−1, with a damage probability of less than 40 %. In a
similar manner, a hydrodynamic force lower than 2.5kNm−1
proves to result in a damage probability of less than 20%.
Since the 2015 tsunami had a moderate impact, with low
inundation depths and flow velocities in developed areas, it
becomes very important to assess the tsunami damage due to
possible events taking place in the same rupture area as that
of the 1922 earthquake since large inundation depths were
reported there (see Sect. 2).
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R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo) 2151
Figure 8. Developing the tsunami fragility curve. (a) Histogram of the number of destroyed and not-destroyed structures in terms of the
tsunami intensity measures within the inundation area. (b) Data plotted on normal probability paper and least-square fit. (c) Fragility function
for building damage in terms of the tsunami intensity measures; the solid line is the best-fit curve of the plot (circles show the distribution of
damage probability).
3.4 Comparison with existing fragility curves
This section compares the fragility curve obtained in Co-
quimbo with curves obtained in other places after recent
events. The statistical parameters of existing fragility curves
are shown in Table 2. One curve is that of Okushiri, Japan,
which was obtained for wooden structures after the 1993
tsunami event. The analysis included 523 houses and a range
of approximately 50 structures (Suppasri et al., 2012a). In
a similar manner, the fragility curve of Dichato, Chile, in-
volved 915 mixed-material structures and a range of 50 struc-
tures after the 2010 Chile tsunami (Mas et al., 2012). A more
comprehensive analysis was conducted in Banda Aceh, In-
donesia, after the 2004 Indian Ocean tsunami (Koshimura
et al., 2009b). This case involved 48 910 structures made of
wood, timber and lightly reinforced concrete constructions,
with a range of 1000 structures. The proposed curves were
constructed for inundation depth, flow velocity and hydrody-
namic force. After the 2009 Samoa event, Gokon et al. (2014)
developed a fragility curve for mixed structures, which in-
cluded wood, masonry and reinforced concrete, for the same
three tsunami intensity measures as in the previously men-
tioned study. Similarly, the fragility curves of Thailand were
developed for two provinces, namely, Phang Nga and Phuket,
with 2508 and 1033 structures, respectively. In addition, all
data were combined in order to develop a fragility curve for
mixed-material structures and inundation depth (Suppasri et
al., 2011). Figure 9a shows a comparison of the Coquimbo
fragility curve with two-level damage curves of Dichato,
Okushiri, Banda Aceh, American Samoa and Thailand. It is
seen that Coquimbo experienced less damage than Dichato
and Okushiri at inundation depths lower than 3 m. In fact,
at an inundation depth of 2 m, Dichato and Okushiri have a
68 %–75 % probability of damage, while in Coquimbo the
probability is only 20 %. The high probability of damage in
Dichato and Okushiri could be due to the large number of
structures made of wood and lightweight materials with little
ability to withstand tsunami flows (Mas et al., 2012). Even
though the building materials in Coquimbo are similar, it is
observed in Fig. 7b that distance from the shore and the rail-
way embankment decrease flow velocity and thus tsunami
energy; therefore, the same inundation depth generates less
damage to structures. In a similar manner, the fragility curve
for mixed-material structures in Thailand shows a high prob-
ability of damage at an inundation depth of 2 m (∼50 %),
but a 100 % probability of damage is reached at inundation
depths higher than 8 m. In the case of Banda Aceh, the curve
shows a low probability of damage (<20%) at an inunda-
tion depth of 2 m, which is comparable to Coquimbo; how-
ever, the damage probability in Coquimbo increases rapidly
as the inundation depth increases, reaching 100 % at an inun-
dation depth of only 4 m, which could be a result of most of
the houses having only one or two stories (see Fig. 2).
In addition, it was observed in Banda Aceh that struc-
tures were quite vulnerable when flow velocity exceeded
2.5 m s−1, with a damage probability of 60 % and a 100 %
probability of damage at velocities larger than 4ms−1
(Koshimura et al., 2009b). These results are in good agree-
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2152 R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo)
Figure 9. Tsunami fragility curves for damage probability developed for other locations and different damage levels. (a) Two levels of
damage obtained for three different cities in Chile, Japan and Indonesia. (b) Six damage levels for wooden structures given by Suppasri et
al. (2013). (c) Six damage levels for mixed-material structures by Suppasri et al. (2013). (d) Four damage levels for wooden houses given by
Suppasri et al. (2012b). (e) Four damage levels for mixed-material structures given by Suppasri et al. (2012b).
ment with the Coquimbo fragility curve. Moreover, the to-
pography of Banda Aceh is characterized by low land with
an elevation of around 3m, which is also similar to Co-
quimbo. With regard to American Samoa, the curve shows
a low probability of damage at inundation depths lower than
2 m; it begins to increase to up to 80 % when the inundation
depth reaches 6 m. It is important to mention that the Samoa
fragility curves were developed considering different types
of structures, including wood, brick and reinforced concrete.
In addition, the fragility curve as a function of flow velocity
shows significant damage (∼50 %) at velocities of 2 m s−1,
and only an 80 % probability of damage at velocities as high
as 8 m s−1(Gokon et al., 2014). Since all types of struc-
tures are analyzed in a single curve, it is believed that low
velocities would easily cause damage to wooden structures,
while damage to reinforced concrete structures would require
higher inundation depths and flow velocities. The relatively
high damage probability at low inundation depths could also
be due to the ria-type coast of American Samoa (Gokon et
al., 2014).
Figure 9b and c show the comparison of the Coquimbo
fragility curve with the curves given by Suppasri et al. (2013)
for wooden and mixed-material structures in Japan, respec-
tively. The study considered more than 250 000 damaged
buildings surveyed after the 2011 Tohoku tsunami and made
it possible to analyze different damage levels and build-
ing materials. In general, it is seen that wooden and mixed
structures in Japan have similar behavior. If damage level 4
(complete destruction) is analyzed, the damage probability
is higher than in Coquimbo at an inundation depth lower
than 2 m. Wooden and mixed structures in Japan present a
relatively high probability of complete destruction (level 4),
ranging from 50 % to 60 %, while in Coquimbo it is only
20 %.
Another group of fragility curves for wooden and mixed
structures – shown in Fig. 9d and e, respectively – were ob-
tained from survey data of the 2011 Japan tsunami in the
Sendai and Ishinomaki plains (Suppasri et al., 2012b). The
curves show that structures located in flat areas were less im-
pacted by the tsunami despite significant inundation depths,
in contrast to what happened in areas with ria topography,
such as the Sanriku coast (Suppasri et al., 2012a, 2013), and
semi-closed bays such as Dichato (Mas et al., 2012). This
behavior is in good agreement with damage observed in the
Coquimbo area, where the flat nature of the area and dis-
tance from the shore could decrease tsunami impact. Thus,
based on the influence of inundation depth and flow veloc-
ity on tsunami damage, De Risi et al. (2017) proposed the
development of vulnerability models related to specific topo-
graphic contexts, such as plain-type or ria-type coasts. They
found that ria-type coasts experience greater damage proba-
bility than plain-type coasts at the same inundation depth.
It is noteworthy that the Coquimbo fragility curve for
destruction or complete damage overlaps with the minor-
damage-level curve for wood and mixed-material houses in
flat areas in Japan (Fig. 9d and e). A possible explanation is
that houses in Japan are relatively new and built according to
strict construction standards (Suppasri et al., 2012b), in con-
trast to what was observed in Coquimbo, where old houses
are found (see Fig. 2), although it could also be due to the lo-
cal topographic features of Coquimbo. This finding suggests
that both topography and structure quality should be consid-
ered in tsunami damage estimation.
4 Application of fragility curve to tsunami damage
estimation
This section presents an example of the use of fragility curves
to estimate tsunami damage through a deterministic tsunami
scenario in Coquimbo. We first define a tsunami scenario,
then we run the numerical simulation to obtain the inunda-
tion depth and, finally, we estimate the tsunami damage in
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R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo) 2153
Table 2. Summary of statistical parameters and damage levels for empirical fragility curves (Mas et al., 2012; Suppasri et al., 2012b, 2013)
including the current case of Coquimbo. µand σare statistical parameters for normal distribution, while µ0and σ0are the same parameters
for lognormal distribution. R.C. indicates reinforced concrete structures.
Event Location Structure Damage level No. of structures µ σ µ0σ0R2
type inspected
Chile Dichato – Wood, Not destroyed/ 915 0.092 1.272 0.86
(2010) Chile masonry, destroyed
mixed
Japan Okushiri – Wood Not destroyed/ 523 0.216 0.736 0.82
(2011) Japan destroyed
Indian Banda Aceh – Wood, R.C. Not destroyed/ 48 910 2.985 1.117 0.99
Ocean Indonesia destroyed
(2004)
Japan Ishinomaki and Mixed Not destroyed/ 3541 0.747 0.984 0.88
(2011) Sendai plains destroyed
Samoa American Wood, brick Not destroyed/ 1.17 0.69 0.89
(2009) Samoa and R.C. destroyed
Japan Hokkaido,
Wood
Level 1 −2.1216 1.2261 0.98
(2011) Aomori, Level 2 −0.9338 0.9144 0.98
Iwate, Level 3 251 000 −0.040 0.7276 0.98
Miyagi, Level 4 (total) 0.6721 0.4985 0.98
Fukushima, Level 5 0.7825 0.5559 0.98
Ibaraki, Level 6 1.2094 0.5247 0.97
Chiba
Japan Hokkaido,
Mixed
Level 1 −2.4562 1.4874 0.99
(2011) Aomori, Level 2 −1.1373 1.115 0.96
Iwate, Level 3 251 000 −0.0756 0.8277 0.97
Miyagi, Level 4 (total) 0.5316 0.6235 0.91
Fukushima, Level 5 0.8336 0.6077 0.97
Ibaraki, Level 6 1.2244 0.5723 0.98
Chiba
Japan Ishinomaki
Wood
Minor
150
2.4409 0.6409 0.95
(2011) and Sendai Moderate 2.9028 0.6777 0.94
plains Major 3.8458 0.8516 0.95
Complete 4.2243 1.0159 0.80
Japan Ishinomaki
Mixed
Minor
189
2.4954 0.8249 0.81
(2011) and Sendai Moderate 3.2550 1.0647 0.80
plains Major 4.4355 1.3068 0.83
Complete 5.0620 1.4872 0.84
Coquimbo. Since earthquake damage in the Coquimbo Re-
gion was limited in 2015 (Candia et al., 2017; Fernández et
al., 2017), it is assumed that the damage to structures is due
exclusively to the tsunami.
4.1 Tsunami source model
Based on Fig. 1, three possible segments can be defined,
namely, the Copiapó–Coquimbo, Coquimbo–Illapel and
Illapel–Constitución regions. However, events in the Illapel–
Constitución region, including those of 1822 and 1906,
have never generated a tsunami in Coquimbo (Soloviev
and Go, 1975), and only the 1730 event, which ruptured
the Coquimbo–Illapel segment, generated a tsunami in the
area of interest (Cisternas et al., 2011); therefore, possible
tsunamis generated in the Valparaiso segment were not con-
sidered in the present analysis. In a similar manner, earth-
quakes on the Coquimbo–Illapel segment were not consid-
ered because the 2015 Illapel earthquake filled the seismic
gap that had existed since the last major earthquake in 1943
or earlier events (Ye et al., 2016); thus, no significant earth-
quakes that generate significant tsunamis could take place
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2154 R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo)
Figure 10. Upper panels show slip distributions along scenario source models. The gray rectangles outline each scenario source segment.
The moment magnitude for each scenario source model is denoted in the top left of the corresponding panel. Lower panels show the inter-
seismic coupling (ISC) model from Métois et al. (2016) (left panel), Global Centroid Moment Tensor (GCMT) solutions (center panel) and
the inverted slip model from Okuwaki et al. (2016) (right panel), which were used to construct the scenario source models. The star denotes
the epicenter of the 2015 Illapel earthquake determined by the National Seismological Center (CSN, for its initials in Spanish). The blue
contours delimit the inverted slip distribution every 2.08 m for the 2015 Illapel earthquake (Okuwaki et al., 2016).
there in the near future. Conversely, the northern segment
has presented no relevant seismic activity since 1922, i.e.,
95 years before 2017 (see Fig. 1); moreover, the previous
significant event took place in 1819 (73 years before the
1922 event). Therefore, the Copiapó–Coquimbo segment is
of particular interest regarding possible future earthquakes
and tsunamis in Coquimbo.
It is important to note that the small event in 1849 (mag-
nitude 7.5, according to Lomnitz, 2004) generated a 5 m
tsunami in Coquimbo. Despite the small earthquake mag-
nitude and large tsunami run-up of the event, there is no
scientific evidence that a tsunami–earthquake occurred. In
addition, the 1922 Atacama event had a complex source of
three time-clustered shocks (Beck et al., 1998). Therefore,
it seemed reasonable to separate the northern segments into
two different seismic regions, with one segment covering
Copiapó to Punta Choros (Fig. 10b) and the second segment
from Punta Choros to Ovalle (Fig. 10a), which also coin-
cides with the estimated rupture length of the 1849 event (see
Fig. 1).
Either a probabilistic or deterministic approach could be
used for the tsunami hazard assessment and damage estima-
tion. While the former takes into account many uncertainties
related to generation, propagation and inundation (Cheung et
al., 2011; Geist and Parsons, 2006; Heidarzadeh and Kijko,
2011; Horspool et al., 2014; Park and Cox, 2016), the latter
uses credible worst-case scenarios based on historical events
(Aránguiz et al., 2014; Mitsoudis et al., 2012; Wijetunge,
2012). However, the coupling coefficient could be used to
assess the shape of possible future deterministic earthquakes
(Métois et al., 2016; Pulido et al., 2015) since reasonable het-
erogeneous slip models could be predicted by the degree of
interseismic locking (Calisto et al., 2016; Gonzalez-Carrasco
et al., 2015). Thus, the slip distribution Sat arbitrary space ξ
is represented as given by Eq. (6):
S(ξ ) =
t1
Z
t0
C(ξ ,t )V (ξ)dt−X
jsj(ξ ) +pj(ξ),(6)
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R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo) 2155
Figure 11. Results of tsunami numerical simulations for case 1 and the three scenarios, S1, S2 and S1+S2. Left column panels show vertical
seafloor displacement. Central column panels show maximum inundation depth; the asterisk indicates the location of the tide gauge and
the thin black lines represent the contour lines every 2m. Right column panels show tsunami waveform over an elapsed time of 4h at the
Coquimbo tide gauge G.
where Cis the interseismic coupling, ranging from 0 to 1.
The interseismic coupling model adopted in this study is
from Métois et al. (2016), which is derived from invert-
ing Global Positioning System (GPS) measurements along
the Chilean margin (18–38◦S) that have been made by in-
ternational teams since the early 1990s (see Métois et al.,
2016, and references therein). It provides a reasonable es-
timate of the degree of locking between the Nazca and the
South American plates, indicating strong coupling along
the scenario source regions (see Fig. 10d to f). Vis the
plate convergence rate at ξ, derived from the NNR-NUVEL-
1A model (DeMets et al., 1994), and t0and t1delimit
the interseismic period for integration. sjis the slip of the
small event (4.8≤Mw≤7.9) at the jth location, which is
listed in the Global Centroid Moment Tensor (GCMT) cata-
log (http://www.globalcmt.org/CMTsearch.html, last access:
10 July 2018; see Fig. 10e), and pjis the post-seismic slip
following sj. Each amount of slip sjis calculated based on
the seismic moment obtained by the GCMT and the empiri-
cal relationship between rupture area and the moment mag-
nitude introduced by Wells and Coppersmith (1994). The
rigidity modulus for the calculation of moment magnitude
of each sjis computed with the layered, near-source struc-
ture adopted in the source study by Okuwaki et al. (2016).
We eliminated the Mw=8.3 2015 Illapel earthquake from
the GCMT list and instead considered its contribution to the
scenario source models with the inverted slip model devel-
oped by Okuwaki et al. (2016) in Eq. (6) (Fig. 10). The slip
motion of Sis assumed to be pure thrust against the subduct-
ing plate motion. Note that Cis constant against time and
the post-seismic slip pjis not considered in the present anal-
ysis; thus, it is possible that the scenario source models will
slightly overestimate S.
The variable slip distribution was obtained from the het-
erogeneous interseismic coupling C. Time intervals for the
integral of Eq. (6) are assumed to be 94 years (1922 to 2016).
Each segment is subdivided into 10km ×10km subspace
knots for 150 ×160 and 180 ×160 km2source areas for S1
and S2, respectively. While the magnitude of the event re-
lated to segment S1 is Mw=8.2, the magnitude of the
S2 event is Mw=8.4. If both segments are considered to-
gether (S3 =S1 +S2), the total magnitude is Mw=8.5. The
strike and dip angles for the scenario source geometry are
assumed to be constant based on the subducting slab ge-
ometry of the Slab 1.0 model (Hayes et al., 2012): (strike,
dip)=(2.7, 15.0◦) for S1 and (strike, dip)=(16.0, 15.0◦)
for S2. The fault geometry and characteristic source parame-
ters, as well as complete model parameters for each scenario
source model, are available from the authors upon request.
4.2 Numerical simulation of proposed tsunami scenario
The computation covered an elapsed time of 6h with out-
put intervals of 1 min. Figure 11 shows the main results and
the three different tsunami scenario combinations. The upper
row shows the results for segment S1 (Mw=8.2) and the
middle row shows the results for segment S2 (Mw=8.4),
while the lower row shows the results for the combined sce-
nario of S1 and S2 (Mw=8.5). In addition, the left column
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2156 R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo)
Figure 12. Results of tsunami numerical simulation of the S1 event (Mw=8.2). (a) Inundation depth, (b) flow velocity, (c) hydrodynamic
force and (d) increase in inundation height compared to the 2015 Coquimbo tsunami. (e) Increase in flow velocity compared to the 2015 Co-
quimbo tsunami. (f) Increase in hydrodynamic force compared to the 2015 Coquimbo tsunami.
shows the vertical displacement of the seafloor, the mid-
dle column shows the maximum inundation depth and the
right column shows the tsunami wave form at the Coquimbo
tide gauge over an elapsed time of 4h (240 min). It is ob-
served that segment S2 (Mw=8.4) generated lower inun-
dation depths than segment S1 (Mw=8.2), which can be
explained by the fact that the strike angle and the coastal
morphology cause the tsunami to be propagated toward the
north and not directly toward Coquimbo Bay. Meanwhile, the
tsunami generated by segment S1, the second wave of which
is the largest, propagates directly toward Coquimbo Bay. It
is possible to observe that the maximum inundation depths
reached up to 5 m in developed areas and along the coast-
line. Moreover, it is interesting that the Mw=8.5 event, as
a combination of S1 and S2 (lower row in Fig. 11), gener-
ated lower inundation depths than segment S1 alone. This
can be explained by the fact that the maximum tsunami am-
plitude of each individual event does not occur at the same
time; thus, the segment S2 tsunami decreases the maximum
amplitude of the segment S1 tsunami. Larger tsunami ampli-
tudes could result from a time gap between the segment S1
and S2 events such that the maximum tsunami waves coin-
cide. Nevertheless, this analysis is beyond the scope of the
present paper.
4.3 Damage to structures
The previous section demonstrated that the combination
of S1 and S2 rupturing at the same time generated lower
inundation heights than the S1 event alone; therefore, the
damage to structures is assessed for segment S1 only, i.e., a
tsunami generated by a Mw=8.2 earthquake off the coast of
Coquimbo that generates inundation heights lower than 5 m.
Figure 12 shows the results for each tsunami intensity mea-
sure, namely inundation depth, flow velocity and hydrody-
namic force (upper row panels). In addition, the lower row in
Fig. 12 shows the difference between the maximum tsunami
intensity measures given by the S1 scenario and those of the
simulated 2015 tsunami event (Fig. 7). This figure allows ar-
eas with a greater increase in tsunami intensity measure and
therefore higher damage probability to be identified.
In order to determine a high or low probability of damage
to a given structure, first latitude and longitude coordinates
are assigned to each structure within the inundation area, and
the maximum inundation depths given by the tsunami nu-
merical simulation at the location of each structure are ex-
ported to GIS. Second, the inundation depth database is di-
vided into several ranges, with 40 samples in each range, and
the mean value of each range is intersected with the fragility
curve given in Fig. 8c in order to define the damage proba-
bility for each range. For simplicity, and similar to previous
studies (Fraser et al., 2014; Wiebe and Cox, 2014), we used
only the fragility curve generated as a function of the inun-
dation depth. Third, the damage probability given in the pre-
vious step is assumed to be equal to the percentage of struc-
tures with a high probability of damage within each range.
To make this determination, the inundation depths for each
range are arranged in descending order and the structures
outside of that percentage (with the lowest inundation depth
within the range) are assumed to have a low probability of
damage.
Figure 13a shows the low-lying area of the city of Co-
quimbo and the computed inundation depth given by the nu-
merical simulation of scenario S1. A total of 646 mixed-
material structures were identified within the inundation
area, and they are colored according to inundation depth
level. Figure 13b shows the result of the damage estima-
tion. It was found that 321 structures, i.e., 49.6 % of the
flooded structures, have a high probability of damage, a fig-
ure that is much higher than the 20 % surveyed right after
the 2015 tsunami. As expected, the structures behind the rail-
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R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo) 2157
Figure 13. (a) Tsunami inundation map and inundation depth on structures. (b) Tsunami inundation map and low and high probabilities of
damage to the flooded structures.
way embankment and wetland would experience less damage
than those located close to the shore.
Due to the high probability of damage to houses located
near the shore, it is recommended that any reconstruction
plan or future tsunami mitigation measures consider the fact
that high tsunami inundation depths (5–8 m) could be gener-
ated in this area. After the 2011 Japan tsunami, it has been
demonstrated that comprehensive urban planning is the key
point for avoiding future disasters, such that the best ap-
proach to decrease tsunami risk is an integration of struc-
tural and nonstructural means of coastal protection and land-
use management as a strategy with multiple lines of defense
(Strusi´
nska-Correia, 2017). In addition, the most important
lessons from the 2011 Japan tsunami include methods to
strengthen coastal defense structures, evacuation buildings
and coastal forests (Suppasri et al., 2016). Thus, Coquimbo
seems to be an interesting case study since the coastal road,
wetland and railway partly fulfill the structural requirements
of a multilayer tsunami countermeasure, and it would be
necessary to implement more comprehensive nonstructural
countermeasures in the future. In a local context, Khew et
al. (2015) found that the tsunami countermeasures imple-
mented in the Greater Concepción area after the 2010 Chile
tsunami, such as hard infrastructure, contributed positively
to the recovery of economic and social resilience, although
it was found that new elevated housing decreased social re-
silience. Moreover, it is recommended that governmental and
business structures be effectively decentralized such that lo-
cal conditions are successfully incorporated into the design
of hard infrastructure for tsunami mitigation (Khew et al.,
2015). Finally, it was also found that tsunami mitigation mea-
sures implemented in Dichato after the 2010 Chile tsunami
did not decrease tsunami risk, as some vulnerability vari-
ables (housing conditions, low household incomes and lim-
ited knowledge of tsunami events) are still at the same level
(Martínez et al., 2017). Therefore, nonstructural mitigation
measures should play an important role in effectively de-
creasing tsunami risk in the future.
5 Conclusions
Numerical simulations of the 2015 Chile tsunami proved to
be in good agreement with field survey data in Coquimbo. A
Coquimbo fragility curve was developed with two-level clas-
sification of structural damage, namely, not destroyed and
destroyed. The Coquimbo fragility curve shows a low proba-
bility of damage, 20 %, at a relatively high inundation depth
(2 m), in contrast to what was observed in another Chilean
town, Dichato, where a 68 % probability of damage resulted
from the same inundation depth. This result is in good agree-
ment with fragility curves for the Sendai and Ishinomaki
plains in Japan, in that tsunami energy decreased and less
damage was observed.
The fragility curve may be used to estimate possible future
tsunami damage in the Coquimbo area and other places with
similar topography and building materials. In Coquimbo, it
was found that a magnitude Mw=8.2 earthquake off the
coast of the city could generate a destructive tsunami with
inundation depths of up to 5 m. The assessment of tsunami
damage with the fragility curve demonstrated that ∼50 %
of the assessed structures have a high probability of dam-
age if reconstruction is carried out with the same types of
structures, which is greater than the damage caused by the
2015 tsunami (20 %). Therefore, tsunami mitigation mea-
sures and the reconstruction plan should consider potential
tsunami damage due to a future earthquake off the coast of
Coquimbo. It is recommended that new land-use policies be
implemented in order to regulate the types of structures be-
ing built in the inundation area. In addition, based on previ-
ous experience in Japan and Chile, new tsunami mitigation
www.nat-hazards-earth-syst-sci.net/18/2143/2018/ Nat. Hazards Earth Syst. Sci., 18, 2143–2160, 2018
2158 R. Aránguiz et al.: Development and application of a tsunami fragility curve (2015 tsunami in Coquimbo)
measures must consider a combination of both structural and
nonstructural tsunami countermeasures in order to effectively
decrease tsunami risk in Coquimbo in the future.
Data availability. Data sets are available upon request by contact-
ing the corresponding author.
Author contributions. The idea was conceived by LU and RA. The
field survey of damaged structures was carried out by LU, while
the field survey of tsunami inundation heights and run-ups was car-
ried out by RA and LU. All numerical simulations were performed
by RA. Tsunami fragility curves were developed by RA and LU,
while RO and YY proposed the tsunami source model for the ap-
plication of fragility curves. LU assessed damage to structures and
RA prepared the first manuscript; thus all authors contributed to
editing the final version of the article.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. The authors would like to thank CON-
ICYT (Chile) for its FONDAP 15110017 and FONDE-
CYT 11140424 grants, as well as the Research and Innovation
Department (Dirección de Investigación e Innovación) of the
Universidad Católica Ssma. Concepción. Special thanks to those
who contributed to the collection of field data: Enrique Muñoz and
Evelyn Pedrero, Evans Aravena, and Diego Espinoza. Thanks to
the Ministry of Housing for providing us with topography data.
Finally, thanks to the two anonymous reviewers, who significantly
helped us improve the paper.
Edited by: Thomas Glade
Reviewed by: two anonymous referees
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