8CUEE CONFERENCE PROCEEDINGS
8th International Conference on Urban Earthquake Engineering
March 7-8, 2011, Tokyo Institute of Technology, Tokyo, JapanTHE TSUNAMI VULNERABILITY ASSESSMENT IN PERU USING THE INDEX OF
POTENTIAL TSUNAMI EXPOSURE

Hideaki Yanagisawa1), Koshimura Shunichi2), Yuji Yagi3), Yushiro Fujii4), Gaku Shoji5),and Cesar Jimenez6)
1) Tokyo electric power services co., ltd., Japan
2) Associate Professor, Department of Civil Engineering, Tohoku University, Japan
3) Associate professor, Department of Life and Environmental Sciences, University of Tsukuba, Japan
4) International Institute of Seismology and Earthquake Engineering, Japan5) Associate professor, Department of Engineering Mechanics and Energy, University of Tsukuba, Japan
6) Directorate of Hydrography and Navigation, Peruh-yanagi@tepsco.co.jp, koshimura@tsunami2.civil.tohoku.ac.jp, yagi-y@geol.tsukuba.ac.jp, fujii@kenken.go.jp,
gshoji@kz.tsukuba.ac.jp, cesarsud86@hotmail.com
Abstract: Tsunami vulnerability assessment along the coast of Peru was carried out based on the modeling of the 2001
Camana tsunami. We first conducted field survey of the 2001 Canama tsunami to measure tsunami flow depth andtopography of the Camana coast. Based on the field observation, we found that 1 - 3 m of tsunami flow depth inundated
the Camana coast and caused significant damage on houses. We then modeled the 2001 Camana tsunami using nonlinear
shallow-water theory to examine the validation of a tsunami modeling. As a result, our numerical model is consistent withmeasured data. Developing the tsunami hazard scenarios, we examined potential tsunami hazard in Peruvian coast using
the probability of potential tsunami exposure. Consequently, we found that Lima and Callao face a high risk of major
tsunami disaster.

1. INTRODUCTION
Peruvian coast is one of the most important seismologic
regions in the world. In the Peru-Chile border region, thelargest earthquakes with moment magnitude close to 9.0
occurred in 1604, 1868 and 1877. In recent years, large
earthquakes with moment magnitude more than 8.0 occurredin 2001 and 2007. These earthquakes have accompanied
large tsunamis which were responsible for extensive
property and human damage in Peruvian coast. To develop
the effective countermeasure against the tsunamis, the areaprone to be tsunami damage should be determinedconsidering tsunami hazards, population and frequency ofearthquakes. The purpose of this study is to provide
vulnerability assessment to possible tsunami damage for thecoastal area of Peru.
According to Koshimura et al.,(2009), tsunamihydrodynamic feature such as tsunami height is greatly
related to damage ratio of human or buildings. Therefore, theaccurate estimation of tsunami hazards is much required toconduct tsunami vulnerability assessment. We thus estimate
tsunami hazards using numerical simulation to determine thedetail of tsunami hydraulic feature. We first conduct themodeling of 2001 Camana tsunami to validate the numericalmodel. We then conduct vulnerability assessment
developing tsunami hazard scenarios using population data,
the fragility function of human damage and the computedtsunami hazards.

(a)

(b)

(b)

Figure 1 Map of southern coast of Peru showing theepicenter and after shocks of 2001 earthquake. The survey
area is shown in the solid line.
2. FIELD SURVEY OF 2001 CAMANA TSUNAMI
2.1 Study area
On June 2001, a major earthquake of moment magnitude
8.4 generated a destructive tsunami that hit the southerncoast of Peru (Fig. 1). The tsunami killed at least 25 people
with additional 62 missing in the Camana area (Jaffe et al.,2003). Tsunami runup height around Camana area wereestimated to have been approximately 3 - 8 m and thetsunami inundation distance extended more than 1 km inland- 1591 -from coast (Okal et al., 2002). Nine years after the tsunami
damage, some destroyed houses have been left derelict in thecoast of Camana city, which was once the developed beachresort area. To find the evidence of 2001 tsunami, we
conducted field investigation at the coast of Camana city.
2.2 Results of field survey
Although 9 years have passed since the tsunami impact,water marks of the tsunami have been remained on walls ofsome damaged houses. Figure 2 shows the tsunami flow
depth inferred from the water marks on damaged houses andwitness of the tsunami. The tsunami flow depth is themeasuring water depth above the ground. These results showthat 1.6 – 3.1 m tsunami inundated the beach resort area. The
residential area on flat land stretches approximately 0.3－1.0
km inland so that the tsunami could have easily inundated
the overall flat area and caused significant damage to houses(Fig. 2). We also found the trace of damage on thefoundation of the house caused by tsunami wave erosion,
which is mostly the same as it was when the tsunami
attacked (Fig. 3). From field observation 9 years after thetsunami damage in Camana, we found valid data to estimate
the tsunami flow.

Figure 2 (a) Tsunami flow depth at the coast of Camana
city (b) Cross-section view of the ground level with thetsunami heights along the Line A(a)(b)

Figure 3 Damaged house and destruction of the foundationin (a) 2001 (Dengler, 2001) and (b) 2010

3. NUMERICAL MODELING OF 2001 CAMANATSUNAMI
3.1 Fault parameter
The 2001 earthquake ruptured a portion of the plateboundary between the Pacific and Nazca Plates. According
to Harvard CMT catalog, the magnitude of 2001 earthquake
is estimated to be Mw8.4 ( M 0 = 4.67 × 10 21 N / m ). Todetermine the fault length and width, we applied theempirical relationships between moment magnitude Mw,
fault length L and width W obtained by Papazachos et al(2004) as equation (1) and (2).

Log ( L) = 0.5Mw − 2.19
Log (W ) = 0.31Mw − 0.63

(1)
(2)

Using the fault area A (=L×W) and scalar moment M 0 , thefault displacement D is determined by equation (3).

M 0 = µDA

(3)

where μ is the rigidity. Table 1 shows the determined fault
parameters for 2001 earthquake. We applied the fault depth,
angle of strike, dip and slip acquired by Harvard University.
For the initial conditions of the tsunami modeling, weestimated the vertical seismic deformation of the land andsea bottom using the theory of Manshinha and Smylie(1971). We assumed instantaneous displacement of the sea
surface identical to the vertical seafloor displacement.
3.2 Modeling of tsunami propagation and inundation
To validate a tsunami modeling, we performed numericalsimulation of the 2001 Camana tsunami. Tsunami
propagation in coastal areas was simulated based on thenonlinear shallow-water theory, which includes the bottom
friction in the form of Manning’s formula. For tsunamimodeling,
wemade1350
mgrid
digitalbathymetry/topography data, interpolated from the data
provided by the General Bathymetry Chart of the Oceans(GEBCO) Digital Atlas. In addition to GEBCO, we
compiled local bathymetric/topography data of southern
Peru provided by Directorate of Hydrography andNavigation (DHN), Shuttle Radar Topography Mission(SRTM) data and our measured data to construct mergedbathymetry/topography data. The grid size varies from 1350
m to 50 m, constructing a nested grid system.
We compared measured tsunami runup heights withcomputed values. The local tsunami runup height is theresult of measuring the water marks above the astronomicaltide level when the tsunami arrived. According to Okal et al.
(2002) and this study, totally 25 points of local tsunami

Table 1 Fault parameters for the 2001 Camana tsunami
Mw
8.4

Origin of the fault (°) Length

Width

Dislocation

Dip

Slip

StrikeDepth

Latitude Longitude

(km)

(km)

(m)

(°)

(°)

(°)

(km)

260

95

1863

310

-18.40-72.30- 1592 -

4.1

29.6

runup height and inundation flow depth were obtained in thestudy area. The inundation flow depth (above ground level)is changed to the tsunami runup height using the compiled
topography data acquired by this study. The validation of themodel is performed by K and κ proposed by Aida (1978) asequations (4), (5) and (6).
1 nlog K in i =1∑(4)log κ =

1 n(log K i )2 − (log K ) 2n i =1(5)∑K i = Ri / H i(6)where Ri and H i are the measured and modeled valuesof tsunami runup height at point i. JSCE (2002) empiricallyprovides the guideline for a tsunami numerical modelingsuggesting that 0.95 < K < 1.05 and κ < 1.45 as thethreshold of valid tsunami source to develop the tsunami
hazard assessment.

Figure 5 Comparison between measured tsunami runup
height and computed maximum tsunami heights. The
black outline square presents the higher value resulting
from water splashing (Okal et al., 2002)

3.3 Numerical results of 2001 Camana tsunami
To validate our numerical results, we compared measured
tsunami runup heights with maximum tsunami runup heights
computed from our model. Figure 4 shows the special
distribution of the computed maximum tsunami runupheight. The tsunami run up averagely 5 meters along thecoast of Camana. Figure 5 shows the comparison between
measured and computed tsunami runup height. The
numerical results were approximately consistent with themeasured data, excluding the higher values resulting fromwater splashing. Our model is sufficiently satisfied with thevalue of JSCE (2002) (K=0.98, κ=1.34). This finding
confirms that our numerical model can be used withconfidence to develop the tsunami hazard scenarios.

8Measured (m)log K =

64K=0.98
κ=1.34

2002468Computed (m)Figure 4 Computed maximum tsunami runup height

4. VULNERABILITY ASSESSMENT INPERUVIAN COAST
4.1 Development of tsunami hazard scenario
During the past 430 years, the 11 largest earthquakes(from Mw 8.0 to 8.8) with accompanying large tsunamis
have occurred along the plate boundary between the Pacificand Nazca Plates near the Peruvian coast (e.g., Dorbath et al.,
1990; Kulikov et al., 2005). To estimate tsunami hazards, we
assumed 53 earthquake scenarios with Mw8.0, 8.2, 8.4, 8.6,
8.8 along the plate boundary (Fig. 6). Based on the modelingof 2001 tsunami, the fault length, width and displacement ofeach moment magnitude were determined by equations (1),(2) and (3). The remaining parameters were determinedusing example of 2001 earthquake (table 2). Tsunami
propagation was simulated based on the nonlinear
shallow-water theory using spherical coordinate system,
which includes the bottom friction in the form of Manning’s
formula. Here, we used 0.5 sec grid digitalbathymetry/topography data provided by GEBCO.
To estimate potential human damage, we used the- 1593 -Figure 6
Scenario earthquakes for tsunami
vulnerability assessment in the case of Mw8.4

following fragility function, which is the relationshipbetween the computed maximum tsunami runup height ηand the ratio of human damage α (η ) , which was acquiredby the analysis of damage of the 2004 Indian Ocean tsunami(Koshimura et al, 2009).

α (η ) = Φ[(η − µ ) / σ ]Table 2 Fault parameters for scenario earthquakes
Mw(7)where Φ is the cumulative density function of a standard
normal distribution, µ and σ are the mean and standard
deviation of η（µ = 5.37, σ = 0.72）. Using the Geographic
Information System (GIS), we estimated the Potential
Tsunami Exposure (PTE) multiplying the human damage
ratio α (η ) by global grid population data at shoreline（OrkRidge National Laboratory, 2006) . We further estimate the tyear probability of exceedance using Poisson process asequation (8),P[PTE ≥ a; t ] = 1 − exp(−λt )Length Width Dislocation Dip
(m)

Slip

Depth(km)

(km)

(°)

(°)

(km)

8.0

16070

2.4

189030

8.2

21080

3.3

189030

8.4

27095

4.3

189030

8.6

350

110

5.7

1890

30

8.8

450

125

7.7

189030(8)where P[PTE ≥ a; t ] is the probability that PTE will
exceed a value at least once in the 50-year, and λ is annual
frequency of occurrence. We simply estimated λ by thefollowing equation,

λ=N [PTE ≥ a ] 1 1× ×Nmn Tm =1n∑(9)where N [PTE ≥ a ] is the number of scenarios in eachmagnitude that PTE exceeds a value, N m is the number ofscenarios in each magnitude, n is the number of magnitude(=5) and T is the average return period. Here, we assumed
that the frequency of occurrence of earthquakes in eachmagnitude is constant because Peruvian coast is lacking incertain data of the largest earthquakes.
4.2 Results and Discussion
Figure 7 shows the 50 year probability of exceedance
when PTE will exceed 100 people. This result indicated thatthe coast of Lima and Callao face a high risk of major
tsunami disaster (P > 10%). These results provide basic
information for the tsunami disaster prevention planning.
However, this study is preliminary assessment to detect area
prone to be tsunami damage. Thus, additional studies are
required to conduct a detailed assessment of the possible
tsunami damage in the highest tsunami-risk areas.
Acknowledgements:
The authors acknowledge support from M. Estrada and H.
Yanagisawa. This research was supported in part by the JST-JICA
project of Science and Technology Research Partnership for
Sustainable Development (SATREPS).
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