Simulation of Tsunami Inundation in Central Peru from
Future Megathrust Earthquake Scenarios

Paper:

Simulation of Tsunami Inundation in Central Peru from
Future Megathrust Earthquake Scenarios
Erick Mas∗1 , Bruno Adriano∗2 , Nelson Pulido∗3 , Cesar Jimenez∗4 , Shunichi Koshimura∗1∗1 Laboratory of Remote Sensing and Geoinformatics for Disaster Management

International Research Institute of Disaster Science, Tohoku University
Aoba 468-1, Aramaki, Aoba-ku, Sendai, Miyagi, Japan
E-mail: mas@irides.tohoku.ac.jp∗2 Graduate School of Engineering, Tohoku University, Miyagi, Japan∗3 National Research Institute for Earth Science and Disaster Prevention (NIED), Ibaraki, Japan∗4 Universidad Nacional Mayor de San Marcos Fenlab., Lima, Peru
[Received July 1, 2014; accepted September 19, 2014]

We estimated, from twelve scenarios of potential
megathrust earthquakes, the tsunami impact on the
Lima-Callao region in Central Peru. In addition, we
conducted hazard mapping using the local envelopeof the maximum inundation simulated in thesescenarios. The deterministic approach is supportedby the decades of geodetic measurements in thisarea that characterize the interseismic strain build
up since historical megathrust earthquakes. The
earthquake scenarios for simulation proposed in [1]introduce spatially correlated short-wavelength slip
heterogeneities to a first slip model in [2] calculated
from the interseismic coupling (ISC) distribution
in Central Peru. The ISC was derived from GPS
monitoring data as well as from historical earthquake
information. The results of strong ground motion
simulations in [1] reported that the slip scenario
with the deepest average peak values along theM w = 8 . 8 6) generates the largest PGA in
strike (Mthe Lima-Callao area. In this study, we found fromtsunami simulation results that the slip model with
M w = 8.87)
the largest peak slip at a shallow depth (Myielded the highest tsunami inundation.
Suchdifferences in maximum scenarios for peak groundacceleration and tsunami height reveal the importanceof a comprehensive assessment of earthquake andtsunami hazards in order to provide plausible worstcase scenarios for disaster risk management andeducation.
Keywords: tsunami simulation, megathrust earthquake,tsunami hazard, Peru

1. Introduction
Megathrust earthquakes are extremely large thrustfault earthquakes, thrust being the upward movementof one side of a fault relative to the other. Megathrustearthquakes, which occurred at plate interfaces along
subduction zones, present magnitudes over Mw 8.5 and
Journal of Disaster Research Vol.9 No.6, 2014

are especially destructive when over Mw 9.0. Such events
have caused great number of casualties in the past. In
the last decade, the 2004 Sumatra (Mw 9.2), 2010 Chile(Mw 8.8) and 2011 Japan (Mw 9.0) events have highlighted
the importance of assessing extreme scenarios of strong
ground motion and tsunami impact. Our study area
is in the Central coast of Peru facing the Peru-Chile
trench, where the subduction of the Nazca Plate under
the South American Plate is responsible for earthquakesof magnitudes (Mw ) greater than 8.2 approximately
every 25 years [3, 4]. In addition, in Central Peru,
the built up areas of the capital city of Lima and thenational port of Callao are located near the coast. Huge
earthquakes triggering tsunami events have occurred in
the past and have been recorded at least since the era of
the Spanish occupation of the Inca empire in the 16th
century. Nowadays, the potential damage to the national
infrastructure exposed in Callao and Lima could yield not
only a large number of casualties and damage but also adramatic breakdown in the Peruvian economy. Thus, therisk of destructive earthquakes and tsunamis is high andmust be addressed by disaster managers.
Accordingly, this paper studies the area of seismic gap
and accumulated stress that correspond to the rupture areaof the 1746 megathrust earthquake. Here, building on theshoulders of previous seismological research [1, 2, 5–7],
we perform tsunami simulations for twelve megathrustearthquake scenarios in order to grasp the tsunami hazardof a potential future disaster. The outcomes of the tsunami
inundation simulations are merged with a point by point
maximum depth envelope to form a deterministic-based
hazard map to be proposed for disaster education andmitigation in Central Peru.

2. Background2.1. Megathrust Earthquakes in Peru
The rapid convergence of the oceanic Nazca Platewith the South American Plate ( 60 − 70 mm yr−1 ) has
produced large subduction earthquakes listed in several
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Mas, E. et al.

catalogues and research papers of historical events in
Peru [3, 4, 8–12]. The largest earthquake to occur inCentral Peru so far has been the October 28, 1746 event.
Its magnitude has been estimated to have been fromMw 8.6 [5, 9, 13] to even Mw 9.0 [14]. The 1746 LimaCallao earthquake claimed 5,941 lives. In central Lima
1,141 people were killed by the strong motion, and in
Callao only 221 out of the 5,000 inhabitants survived thetsunami [15–17]. Surprisingly, in a recent paleotsunami
study conducted by Spiske et al. [4], no trace of the 1746
tsunami was found in the areas sampled along the coastof Peru, possibly due to urban sprawl or eolian processes
that have disturbed tsunami sediments (Spiske, personalcommunication, 2014).
During the 18th and 19th centuries large megathrustearthquakes and tsunamis were triggered north of the
Nazca Ridge, in 1746, and south of it, in 1868. It haslong been suggested that the Nazca Ridge may act asa barrier to the rupture propagation of earthquakes inCentral Peru [12, 18]. Similarly, the north edge of the1746 rupture area is bordered with the Mendaña fracturezone (FZ), another possible natural boundary for Central
Peru earthquake rupture propagation. Consequently, it isbelieved that the 1746 earthquake rupture area propagatedfrom approximately the Mendaña FZ to the Nazca Ridge(> 500 km). Within this area, four large earthquakes
have occurred with Mw 8.0 in 1940, 1966, 1974 and2007, representing only a 23% of the moment deficit
accumulated since 1746. Thus, a significant amount ofseismic moment is still to be released in Central Peru [5].

2.2. Previous Studies and Data for Simulation
Chlieh et al. [5] have derived, from two decadesof geodetic measurements in Peru and Chile, theinterseismic strain build up along the subduction zone
from Lima, Peru to Antofagasta, Chile (Fig. 1). The
areas of high seismic slip agreed with the portions ofhigh inter seismic coupling (ISC), proving effective theassessment of location, size, and magnitude of future
large megathrust earthquakes in subduction zones. In
the case of Central Peru, the area ruptured after 1746
(Mw 8.6-8.8) released some of the elastic strain built up
to the moment. However, it resulted in a small amountof the expected accumulated elastic strain [5]. Thus, it
is possible for a megathrust earthquake of magnitude
Mw > 8.5 to occur in this area of high ISC (Fig. 1).
Based on this model of ISC distribution, two areas
strongly coupled are off the coasts of the cities of Lima
and Pisco (Fig. 1). Assuming the interseismic period of
265 years from the last megathrust earthquake in 1746
to 2010, Pulido et al. [2] obtained a slip deficit model
equivalent to a magnitude Mw 8.9 earthquake (Fig. 2).
The tsunami impact of this source model was studied byAdriano et al. [7].
In addition, for strong motion simulations it wasnecessary to add short wavelength heterogeneities to thesource slip to simulate high frequency ground motions:long wavelength asperities were already present within
the original megathrust model. Subsequently, Pulido et962

Fig. 1. Iterseismic Coupling (ISC). Distribution of asperities
and regional accumulated stress reported by [5]. The
rectangular inset corresponds to the Lima-Callao area inCentral Peru.

Fig. 2.
Slip Deficit Model and Domains for tsunami
simulation. The slip deficit model developed by [2] (left)
and tsunami simulation result reported in [7] (right). In thisstudy, we used the same nested grid domains to simulate the
12 megathrust earthquake scenarios shown in Fig. 3.

al. [1] calculated twelve different slip distributions fromthe original slip deficit source by combining the geodetic
slip model (GSM) with several short wavelength slip
distributions. Details of the strong motion simulation canbe found in [6]1 . Fig. 3 shows the 12 mega-earthquake
scenarios. Strong motion simulation results in [6]suggest that the slip with the deepest average peak value
along the strike (Model 5) generates the largest PGA in
Lima-Callao (≈ 1000 cm/s2 ). Consequently, to grasp
the tsunami risk posed by such mega-earthquakes in
1. For the 12 slip deficit models, the original source of Fig. 2 was reducedin length. Patches on the north and south edges with small slip values
were eliminated.

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Simulation of Tsunami Inundation in Central Peru from
Future Megathrust Earthquake Scenarios

Table 1. Source parameters of 12 megathrust earthquake sources [1] used for tsunami modeling in this study and the maximum
inundation depth resulting from simulation.
Model
1
2
3
4
5
6
7
8
9
10
11
12

Lon
(deg)∗-77.80-78.30-78.31-78.32-78.04-78.08-76.43-77.86-78.00-76.86-78.00-76.54Lat(deg)∗-11.88-11.58-11.83-11.96-11.48-12.23-13.57-11.81-11.92-13.35-12.05-13.31

Depth∗
32.03
26.85
21.68
19.09
34.62
19.09
29.44
32.03
26.85
24.26
24.26
32.03

Slip
(m)∗
17.61
17.86
17.30
19.92
18.24
18.86
17.32
15.64
16.85
15.66
16.32
16.50

Rake
(deg)
55.70
55.70
55.70
55.70
55.70
55.70
55.70
55.70
55.70
55.70
55.70
55.70

Strike(deg)
319.00319.00319.00319.00319.00319.00319.00319.00319.00319.00319.00319.00

Dip
(deg)
15.00
15.00
15.00
15.00
15.00
15.00
15.00
15.00
15.00
15.00
15.00
15.00

Width
(km)
480.00480.00480.00480.00480.00480.00480.00480.00480.00480.00480.00480.00Length(km)160.00160.00160.00160.00160.00160.00160.00160.00160.00160.00160.00160.00Max. Inund. Depth
(m)
6.87
7.82
6.46
7.98
6.09
7.55
6.93
6.21
7.00
6.15
7.86
6.59

∗ These data correspond to the subfault with the maximum slip.

3. Tsunami Numerical ModelingFig. 3. Mega-earthquake scenarios. Slip distribution for
12 scenarios of slip deficit in Central Peru [19]. The
small rectangle shows the area of study for the tsunami
simulations.

Lima-Callao, we evaluate the tsunami impact and thedifferences in the spatial features of slip sources as they
relate to tsunami inundation.
Journal of Disaster Research Vol.9 No.6, 2014

In this study, the Tohoku University Numerical
Analysis Model for Investigation of Near-field tsunami
No.2 (TUNAMI-N2) was used for the simulation.
Nonlinear shallow water equations were discretized using
a staggered leap-frog finite difference scheme [20]. The
computational region was divided into five domains
represented by nested grids, as seen in the rectangularinsets in Fig. 2. The bathymetry data was originallyobtained from GEBCO2 at a 30 arc-sec resolution
and was resampled for the first and second domains
into 405 m and 135 m, respectively. In the case of
the third to fifth domains, nautical charts and echosounder bathymetry data were provided by DHN3 froma 2014 new bathymetry acquisition campaign in thearea. Topography data was obtained from the Regional
Government of Callao as contour lines. A 5 m grid
resolution was used in the smallest domain of simulation.
In general, detailed digital urban information, such asbuilding outlines and building heights, were limited inthis area. In addition, building heights could not be
included as part of a topographic model, except for asmall district located in the peninsula called La Punta,
where data on building heights were included in the
topography. An instantaneous displacement of the sea
surface, identical to the vertical sea floor displacement,
is assumed in the tsunami source model by means of the
Okada’s analytical formula for the static deformation ofelastic half-space induced by a uniform co-seismic slip
on rectangular faults [21]. Each scenario of slip deficit
consists of 768 subfaults of 10 × 10 km located offshore
from Lima. Table 1 shows the source parameters of thetwelve megathrust earthquake scenarios for numericaltsunami simulation. Due to the large number of subfaults
per model, parameters shown in the table were limited tothe subfault with maximum slip value. A total of 3 hours
of computation time was set for each model with a time
step of 0.1 s. Inundation was calculated for the smallest
2. General Bathymetric Chart of the Oceans. (www.gebco.net)
3. Direccion de Hidrografia y Navegacion de la Marina de Guerra del Peru

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domain using a 5 m grid size domain with building height
information included in the topography (topographic
model) for the area of La Punta, the peninsula of LimaCallao. For other areas and domains, a constant Manningroughness value of 0.025 was used.

4. Results and Discussion
The outputs of the numerical tsunami simulations of
the 12 mega-earthquake scenarios are shown in Fig. 4. In
addition, the maximum inundation depth resulting fromeach model simulation is shown in the right column ofTable 1.
Results show that the area of inundation does not varysignificantly (Fig. 4), and the local maximum inundation
depth can differ up to 1.9 m across the scenarios(Table 1). In addition, according to [1, 6], Model 5 isthe scenario with the maximum values in strong motion
for Lima-Callao. However, it yields a lower run-up,
depth, and area of inundation (≈ 16.3 km2 ) than does
Model 4, which has the overall maximum run-up, depth,
and area of inundation (≈ 18.4 km2 ). Such differencesin the worst-case scenario for strong motion simulationand tsunami simulation may be explained by the spatial
distribution and values of maximum slip from eachsource. For example, in Table 1, we can observe that
Model 5 presents the third highest slip value (18.24 m),
after Model 4 (19.92 m) and Model 6 (18.86 m), but
it has a much greater maximum slip depth than do theother models (34.62 m). These spatial distribution andmaximum slip values can also be confirmed in Fig. 3.
Therefore, it is observed that the source with the deepest
along-strike slip, which is at the same time closer to thecoast of Central Peru, results in the strongest groundmotion in the study area, as suggested before by [1].
This condition is enhanced if the rupture nucleation
point is in the shallow area with directivity towards
Lima. Conversely, Model 4, the worst-case scenario of
tsunami inundation, is triggered by the model with thelargest peak slip in a shallow region. It is importantfor such differences in the worst case scenarios forstrong ground motion and tsunami inundation to be
noticed and communicated to the layperson for theirdisaster prevention education, since tsunami awarenessand self-evacuation behavior in areas such as Peru andChile are based mostly on people’s own risk perception
triggered by the earthquake intensity [22]. There is alocal saying in these coastal areas that “the first warningis the earthquake itself, and whenever you cannot standup during an earthquake, a tsunami might soon follow.”
While this statement does not lack truth, it can resultin dangerous behavior when, for example, tsunamiearthquakes occur [13, 23], since, as shown in this paper,
although it is a non tsunami-earthquake mechanism,
the strongest motion (Model 5, PGA> 1000 cm/s2 ,Max. Inund. ≈ 6 m) does not yield the highest tsunami
inundation (Model 4, PGA< 500 cm/s2 , Max. Inund.
≈ 8 m).

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Fig. 4. Tsunami Inundation results. Tsunami inundationin 12 mega-earthquake scenarios in Central Peru. The
maximum value corresponds to the maximum inundation
depth within the entire study area. Model 4 results in thehighest level of tsunami hazard.

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Simulation of Tsunami Inundation in Central Peru from
Future Megathrust Earthquake Scenarios

Fig. 5. Model frequency. Spatial distribution of maximum
inundation at each point from the output of the comparisonof 12 models. The percentage denotes the frequency ofpresence from the model in the plot, based on the numberof grids per model with respect to the total number of grids
in the envelope.

5. Hazard Mapping
A previous hazard mapping effort was conductedby [7] using local bathymetry data lower in resolution
and accuracy than the data gathered in 2014 in a new
bathymetry campaign by DHN-Peru for this study. In
addition, the topographic model presented in [7] assumesa height of two-stories at a block scale for buildings in
the entire computational domain, including areas where
no building height data was available. Due to the highuncertainty of the potential tsunami damage to buildings,
as suggested by [24], we decided to avoid this assumptionthat yields lower areas and depths of inundation andprepare hazard maps with constant roughness model
results. Simulations of the tsunami inundation resulting
from future megathrust earthquake scenarios in Central
Peru show areas at risk of inundations approximately
Journal of Disaster Research Vol.9 No.6, 2014

Fig. 6.
Maximum of the maximum inundation from
12 scenarios. An envelope of inundation depths showing the
maximum value of the outputs from the 12 models at eachgrid cell.

6 m to 8 m deep. The maximum inundation depth andinundated area was found to be that in Model 4, which
presents the maximum slip value. However, there is asmall correlation between maximum slip and maximum
tsunami height in these scenarios (r = 0.53, R2 = 0.28)
and in general [25]. Therefore, instead of relying only
on Model 4 as the worst-case scenario for disastermitigation activities, another approach would be to use
the maximum local inundation depth at each grid cell of
the study area from all scenarios. Fig. 5 illustrates thespatial distribution of maximum inundation depths at eachpoint in the 12 model results with their corresponding
frequency calculated by the percentage of grids from eachmodel with respect to the total number of grids in theenvelope of tsunami inundation. Consequently, 57% of
grids in the inundation envelope correspond to Model 4
tsunami numerical simulation results, while 21% and
13% correspond to Model 10 and Model 2, respectively.
Thus, we propose Fig. 6 as a tsunami hazard map for965

Mas, E. et al.

short and mid-term tsunami disaster mitigation activities
in the study area.

6. Conclusions
We have conducted the tsunami inundation simulationof 12 mega-earthquake scenarios in Central Peru topropose a short to mid-term tsunami hazard map usefulfor tsunami mitigation activities in the study area of
Lima-Callao, Peru. In addition, worst-case scenarios forstrong ground motion and tsunami inundation simulations
resulted from different settings of slip deficit models.
While the strongest motion is generated by the slip
distribution where the highest value is in the deep
region and closer to the study area the highest tsunami
inundation is generated by the slip distribution with thelargest peak slip value in a shallower area. Therefore,
the earthquake intensity expected from the worst-casescenario of tsunami inundation is not necessarily the
maximum intensity produced by the worst-case scenario
of strong motion. Since residents’ evacuation behavior inthese regions relies on cues from earthquake intensities, it
is important to educate the population as to the possibilityof smaller intensities and higher tsunami inundations,such as the case studied here, or to the possibility oftsunami-earthquakes on the Peruvian coast.
Acknowledgements
This study was carried out under the framework of the SATREPS
project Enhancement of Earthquake and Tsunami Disaster
Mitigation Technology in Peru, sponsored by the Japan
International Cooperation Agency (JICA) and the Japan Scienceand Technology Agency (JST). Our appreciation goes to the
Ministry of Education, Culture, Sports, Science, and Technology(MEXT); Tohoku University; the National Research Institutefor Earth Science and Disaster Prevention (NIED), and theInternational Research Institute of Disaster Science (IRIDeS) for
their support. We would like to thank Dr. Mohamed Chlieh forhis support and for kindly providing the map included as Fig. 1 inthis paper.

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Simulation of Tsunami Inundation in Central Peru from
Future Megathrust Earthquake Scenarios

Name:
Erick Mas Samanez

Affiliation:
Assistant Professor, Laboratory of Remote
Sensing and Geoinformatics for Disaster
Management (ReGiD), International ResearchInstitute of Disaster Science (IRIDeS), Tohoku
University

Address:
Aoba 468-1, Aramaki, Aoba-ku, Sendai, Miyagi, Japan

Brief Career:
1999-2004 B.S. Civil Engineering, National University of Engineering,Perú
2006-2009 M.Sc. Disaster Risk Management, National University ofEngineering, Perú
2009-2012 Ph.D. Civil Engineering, Tsunami Engineering, Tohoku
University, Japan
2012- Assistant Professor, ReGiD, IRIDeS, Tohoku University, Japan

Selected Publications:• E. Mas, B. Adriano, S. Koshimura, F. Imamura, H. J. Kuroiwa, F.
Yamazaki, ,C. Zavala, M. Estrada, “Identifying Evacuee’s Demand of
Tsunami Shelters using Agent Based Simulation,” Published on “Tsunami
Events and Lessons Learned,” Y. Kontar, V. Santiago-Fandino, T.
Takahashi (Eds.), Springer Netherlands, 2014.
• E. Mas, A. Suppasri, F. Imamura, and S. Koshimura, “Agent Basedsimulation of the 2011 Great East Japan Earthquake Tsunami evacuation.
An integrated model of tsunami inundation and evacuation,” Journal of
Natural Disaster Science, Vol.34, Issue 1, pp. 41-57, 2012.

Academic Societies & Scientific Organizations:• Peruvian Engineering College• Japan Society of Civil Engineers (JSCE)• Japan Geoscience Union (JpGU)• American Geophysical Union (AGU)• European Geosciences Union (EGU)

Journal of Disaster Research Vol.9 No.6, 2014967