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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, C06032, doi:10.1029/2009JC005587, 2010

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Tsunami damage reduction performance of a mangrove forest in Banda Aceh, Indonesia inferred from field data and a numerical model H. Yanagisawa,1 S. Koshimura,2 T. Miyagi,3 and F. Imamura2 Received 20 June 2009; revised 4 December 2009; accepted 18 December 2009; published 30 June 2010.

[1] Since the 26 December 2004 Indian Ocean tsunami, the role of mangrove forests as natural defenses protecting coastal communities from tsunami disaster has been highlighted. However, some mangrove forests were destroyed by that tsunami. They are expected to have lost their protective functions. In this study, we develop a fragility function to assess the mangrove trees’ vulnerability, expressed as the damage probability of mangrove trees, based on field surveys and numerical modeling of the 2004 Indian Ocean tsunami in Banda Aceh, Indonesia. Based on the fragility function, we reconstruct a numerical model of tsunami inundation including the performance of mangrove forests in terms of reducing tsunami damage. The model reveals that a 10 year old mangrove forest in a 500 m wide area can reduce a tsunami’s hydrodynamic force by approximately 70% for an incident wave of 3.0 m inundation depth and a wave period of 40 min at the shoreline. The model also shows, for a tsunami inundation depth of greater than 4 m, that a 10 year old mangrove forest would be mostly destroyed and that it would lose its force reduction capacity. Moreover, approximately 80% of a 30 year old mangrove forest would survive a 5 m tsunami and absorb 50% of the tsunami’s hydrodynamic force. Citation: Yanagisawa, H., S. Koshimura, T. Miyagi, and F. Imamura (2010), Tsunami damage reduction performance of a mangrove forest in Banda Aceh, Indonesia inferred from field data and a numerical model, J. Geophys. Res., 115, C06032, doi:10.1029/2009JC005587.

1. Introduction [2] Mangroves are believed to be one of the world’s most productive types of vegetation, providing considerable economic and ecological service to local people [e.g., Saenger, 2002; Mann, 2000]. Mangroves densely vegetate at the confluence of land and sea in tropical and subtropical areas, where they play a special role in protecting human life and property from natural disasters such as storm surges, coastal erosion, and tsunamis [Mazda et al., 1997; Saenger, 2002; Wolanski, 2007]. However, mangrove ecosystems are threatened by deforestation for economic development: globally, one‐third of mangrove habitats have disappeared over the last 50 years [Alongi, 2002]. [3] Since the 2004 Indian Ocean tsunami, which caused the most catastrophic tsunami disaster in recorded history, several observations of mangrove forests’ role in protecting human lives from the disaster have been reported [Danielson et al., 2005; Kathiresan and Rajendran, 2005; Iverson and Prasad, 2007]. Accordingly, governments’ recognition of the role of mangrove forests as coastal pro1

Tokyo Electric Power Services Co., Ltd.,Tokyo, Japan. Graduate School of Engineering, Tohoku University, Sendai, Japan. Department of Regional Management, Tohoku Gakuin University, Sendai, Japan. 2 3

Copyright 2010 by the American Geophysical Union. 0148‐0227/10/2009JC005587

tection and its attendant benefits is increasing; mangrove plantations have become an important part of disaster management planning [Japan International Cooperation Agency (JICA), 2005; Walton et al., 2006; Check, 2005]. Although tsunami mitigation effects of mangrove forest have been emphasized, some studies have indicated analytical weaknesses of the preliminary reports of mangrove forests’ protective role in the 2004 tsunami [Kerr et al., 2006; Vermaat and Thampanya, 2006a; Kerr and Baird, 2007; Bhalla, 2007; Baird and Kerr, 2008; Feagin et al., 2008; Srinivas et al., 2008]. Those studies showed that the role of other coastal features, in particular, bathymetry, ground elevation, topographic profile and distance from the coast, must be considered as variables to explain tsunami damage reduction behind mangrove forests. Furthermore, some researchers have reported that the massive tsunami severely damaged mangrove habitats and that they have lost their protective functions [Dahdouh‐Guebas, 2006; Wolanski, 2007; Yanagisawa et al., 2009a]. Consequently, the vulnerability of mangrove forests to tsunamis has been raised as a problem of assessment of its functions and limitations in use as coastal protection [Kerr et al., 2006; Wolanski, 2007; Asano, 2008; Yanagisawa et al., 2009a]. Since mangrove forests’ protective role has been reported, considerable controversy has arisen surrounding its tsunami defense functions [e.g., Kathiresan and Rajendran, 2005; Kerr et al., 2006; Vermaat and Thampanya, 2006a, 2006b]. To explore these

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Figure 1. (a) The epicenter and rupture area of the 2004 Indian Ocean tsunami [Chlieh et al., 2007]. (b) Map of study sites in Banda Aceh with measured tsunami height [Borrero, 2005; Tsuji et al., 2006]. Satellite images of the coast of Banda Aceh (IKONOS) (c) before and (d) after the 2004 tsunami (18 June 2004 and 29 December 2004, respectively). White dotted lines in Figures 1c and 1d mark the mangrove area before the tsunami event. important debates, a quantitative approach must be taken to assess mangrove forests’ protective capabilities with particular consideration of prior damage to the forests. [4] Field measurements have been done in some studies by uprooting of trees (not mangroves) around river channels or coasts to determine the strength of trees against uprooting force [Imai and Suzuki, 2005; Asano, 2008]. Yanagisawa et al. [2009a] recently proposed a fragility function to estimate the damage probability of mangrove trees associated with bending stress caused by tsunami flow, using observed damage data for Pakarang Cape, Thailand and numerical modeling of the tsunami. Especially in earthquake engineering studies, to estimate the performance of structures against strong ground motion [e.g., Shinozuka et al., 2000], development of vulnerability information in the form of a fragility function is a widely practiced approach when numerous uncertain factors such as the tree characteristics, floating debris, and local behavior of the tsunami flow are involved [Koshimura et al., 2009]. However, these uncertain effects related to the fragility function are not quantified and might differ among regions and be affected by site conditions. For that reason, a comparative study investigating the vulnerability of mangrove trees among regions should be undertaken to assess damage probability and to confirm the model’s applicability.

[5] This study specifically assesses the performance of mangrove forest in reducing tsunami hazard and mangrove forests’ vulnerability against tsunamis using field surveys and numerical modeling of the 2004 Indian Ocean tsunami in Banda Aceh, Indonesia. Based on field data of mangrove damage and numerical model results, we develop a fragility function of mangrove trees and compare it with previous study to examine interregional differences. Finally, we assess the tsunami damage reduction performance of mangrove forests using a numerical model including the proposed fragility function, which can simulate tsunami flow under arbitrary conditions of coastal features such as bathymetry, ground elevation, topographic profile, distance from the coast and presence of mangrove forests.

2. Study Area [6] We conducted a field investigation of a mangrove forest and numerical modeling of tsunami at Banda Aceh, Indonesia (Figure 1). Although the Banda Aceh coast had been widely covered by a mangrove forest in previous decades [Parish and Yiew, 2005], large areas of mangrove forest were exploited for aquaculture as shrimp or fish ponds. Consequently, only small and scattered patches of mangrove forest remained by the time of the 2004 tsunami.

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Figure 2. (a) Sketch of a Rhizophora tree with dense prop root structures. The stem diameter is measured at breast height (DBH; approximately 1.3 m above the top of roots). (b) Mangrove area interpreted from posttsunami satellite imagery (190 ha). Mangrove trees photographed at (c) study site 2, (d) study site 4, and (e) study site 5 (Figure 1b). Figure 2c shows surviving trees with more than 20 cm DBH. Figure 2d shows surviving and destroyed trees; although two mangrove trees in Figure 2d exhibited a similar condition (e.g. tree characteristics, location, and ground condition), one had survived and the other had been destroyed. Figure 2e shows the completely destroyed mangrove area. Unfortunately, the massive tsunami on 26 December 2004 caused serious damage not only to the delicate mangrove ecosystems but also to the residential areas inland. The tsunami inundated 4 km inland, causing human damage estimated at more than 70,000 in Banda Aceh [JICA, 2005]. Tsunami heights above tide level when the tsunami attacked Banda Aceh around this area were estimated to have been approximately 3–12 m (Figure 1b) [Borrero, 2005; Tsuji et al., 2006].

3. Methodology to Investigate Mangrove Damage and Hydrodynamic Features of Tsunami

sp. with dense prop root structures (Figure 2a). To investigate the specific dimensions of mangrove trees and the damage by the 2004 tsunami, we measured the stem diameter at breast height (DBH; approximately 1.3 m above the top of the roots), stem diameter at the top of roots, and the position of each tree using a Global Positioning System (GPS) device for adult trees whose DBH was greater than 5 cm. In this study, we defined surviving trees as those that withstood the tsunami and which did not suffer severe damage such as breakage or washing away. In all, 697 trees were sampled throughout the mangrove forest; various stem diameters and locations were selected.

3.1. Field Survey [7] Field surveys of the mangrove forest were conducted during 12–16 Dec 2006. We selected five study sites of mangrove forests, which were mainly dominated by Rhizophora

3.2. Modeling of the 2004 Indian Ocean Tsunami [8] Recent developments of numerical models of a tsunami enable us to determine detailed hydrodynamic features of tsunami flow [Koshimura and Yanagisawa, 2007].

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Table 1. Dimensions of Fault and Tsunami Source Parameters for the 2004 Earthquake and Tsunami Eventa Segment N

H (km)

L (km)

W (km)

Strike (°)

Dip (°)

Slip (°)

D (m)

1 2 3 4 5 6

10 10 10 10 10 10

200 125 180 145 125 380

150 150 150 150 150 150

323 335 340 340 345 7

15 15 15 15 15 15

90 90 90 90 90 90

11 20 15 15 8 8

a As modified from Oie et al. [2006] and Koshimura et al. [2009]. N is the number of fault segments, H is the depth of the upper edge of each fault segment, L is the strike length of the fault, W is the downdip width, and D is the fault displacement.

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merged bathymetry/topography data [Koshimura et al., 2009]. The grid size varies from 1860 m to 23 m from Indian Ocean to the coast and land of Banda Aceh, constructing a nested grid system [Koshimura et al., 2009]. For bottom friction, we used the constant roughness coefficient n in the form of Manning’s formula, depending on land use conditions: 0.02 for bare ground and grass, 0.025 for sea and rivers, and 0.05 for vegetation other than grass [e.g., Latief and Hadi, 2007]. For friction in mangrove and populated areas, we used the variable roughness coefficient estimated by the equivalent roughness model, considering the occupancy ratio of trees (or houses) in a control volume/area [cf., Yanagisawa et al., 2009a; Koshimura et al., 2009]

Synolakis and Bernard [2006], for example, modeled the 1993 Okushiri tsunami in Japan using Method of Splitting Tsunami (MOST) code based on the nonlinear shallow water wave equation. To examine the hydrodynamic features of tsunami inundation flow in mangrove forest, we performed numerical modeling of the 2004 Indian Ocean tsunami. We used a nested model to integrate two different tsunami codes: for transoceanic propagation (linear shallow water wave equation) and for nearshore propagation and coastal inundation (nonlinear shallow water wave equation) [Goto et al., 1997]. The coastal inundation model includes the bottom friction term in the form of Manning’s roughness formula [Goto et al., 1997].

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi D4=3 n0 ¼ ð CD AÞ þ n20 ; 2gV

ð4Þ

where n′ is the variable roughness coefficient (equal to n in equations (2) and (3)), D is the tsunami inundation depth (water level above the ground surface), g denotes the acceleration of gravity, V is the volume of water per unit area on the bottom floor (m−2) and n0 is the bottom roughness coefficient without trees or houses, A is the projected vertical sectional area of trees/houses per unit area on the bottom floor (m−2). We determine the projected area of trees per unit area by multiplying DBH by both the tree density and tsunami inundation depth [Yanagisawa et al., 2009a]. We then disregard the number of destroyed trees to determine the resistance of the mangrove forest because the consumption of tsunami energy during the tree destruction @ @M @N þ þ ¼ 0; ð1Þ process is considered to be minor: the tsunami has a long @t @x @y wave period and penetrates continuously across the forest area long after the trees have been destroyed [Yanagisawa et

 al., 2009a]. The tree densities of surviving mangrove areas @M @ M2 @ MN @ gn2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þ þ gD þ 7=3 M M 2 þ N 2 ¼ 0; were determined from satellite images after the 2004 tsuþ @t @x D @y D @x D nami (Figure 1d) by estimating the forest area and number ð2Þ of trees. The drag coefficients C for trees and houses were D determined following Yanagisawa et al. [2009a] and

 Koshimura et al. [2009] (CD = 0.7–1.2 for trees and CD = @N @ N2 @ NM @ gn2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þ þ þ gD þ 7=3 N M 2 þ N 2 ¼ 0: 2.0 for houses). Furthermore, for the prop roots of Rhizo@t @y D @x D @y D phora sp. which are apparently highly resistant to tsunami ð3Þ inundation flow, we assumed n0 = 0.04 as the bottom fric[9] Therein, h and h respectively represent the water level tion [Latief, 2000; Yanagisawa et al., 2009a]. and the still water depth, M and N respectively signify discharge fluxes in the x and y directions, D is the total water 4. Damage and Tsunami Impact on Mangrove depth (= h + h), g denotes gravitational acceleration, and n is the Manning’s roughness. The staggered leapfrog finite Trees difference method was used to discretize the equations 4.1. Result of Field Survey numerically [Goto et al., 1997]. As initial conditions of the [10] Figure 2b depicts the area of mangrove forest before model, we calculate the vertical seismic deformation of the the tsunami extending approximately 190 hectares along the land and sea bottom using the theory presented by coast of Banda Aceh, as inferred from visual inspection of Manshinha and Smylie [1971]. The tsunami source para- IKONOS satellite imagery acquired on 18 June 2004. The meters of the 2004 Indian Ocean tsunami were determined 2004 tsunami severely damaged mangrove forests (Figures 1c from Oie et al. [2006] and Koshimura et al. [2009] (Table 1). and 1d): trees with DBH less than 10 cm were mostly To create computational grids for tsunami propagation, we destroyed (Figures 2 and 3). From satellite images obtained used 1 arc min grid (approximately 1860 m) of digital after 2004 tsunami, we determined tree densities of the bathymetry and topography data (GEBCO) published by the surviving mangrove areas in study sites as 0.005–0.009 (m−2). British Oceanographic Data Centre [1997]. In addition to Additionally, we found that there were few surviving manGEBCO data, we compiled local bathymetric charts of grove trees other than Rhizophora sp.: trees of other genera northern Sumatra (1:125,000) and digital photogrammetric such as Bruguiera sp. and Sonneratia sp. were completely mapping of the Banda Aceh coastal region to construct destroyed in the study area, probably because prop roots of a 4 of 11

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tsunami inundation depth (tsunami height from ground level) whereas the tsunami inundation depths at other sites were approximately 3–6 m [Borrero, 2005]. Based on these results, we found that more than 50% of mangrove trees with 20–25 cm stem diameter could survive a tsunami of less than 6–7 m, although mangrove trees were destroyed completely under 7–9 m or higher tsunamis. 4.2. Validation of the Numerical Modeling of the 2004 Indian Ocean Tsunami [11] The numerical model results were validated through comparison with observed tsunami waveforms in tide gauge record, the measured tsunami heights and the extent of inundation zone. We first compared the tsunami record observed in the tide gauge at Sibolga (Figure 1a) with the modeled tsunami waveform (Figure 4a). According to the measured data [Rabinovich and Thomson, 2007], the first tsunami with approximately 120 min wave period reached Sibolga at 107 min after the earthquake. The computed arrival time, wave period and height of the tsunami were consistent with the observed waveform (Figure 4a). Figures 4b and 4c portray the measured tsunami heights [Borrero, 2005; Tsuji et al., 2006] and the extent of the inundation zone [JICA, 2005] versus the results from our numerical model. The model results are also consistent with measured data in terms of the tsunami height (RSME = ±1.2, Figure 4b) and the extent of inundation zone (approximately 94% agreement). Through the model validation above, we conclude that our numerical model is useful with confidence to estimate the hydrodynamic features of the tsunami which attacked the mangrove forest at Banda Aceh. According to our numerical model results, the tsunami front arrived at the coast of Banda Aceh approximately 80 min after the earthquake and inundated the city up to 4.0 km inland (Figure 4c). The computed variable roughness coefficients n′ ranged from 0.04 to 0.05 in mangrove areas.

5. Vulnerability of Mangrove Trees Against a Tsunami

Figure 3. Frequency distributions of surviving and destroyed mangroves at (a) study site 1, (b) study site 2, (c) study site 3, (d) study site 4, and (e) study site 5.

Rhizophora tree, forming a dense structure and extending all around (Figures 2a and 2c), contribute to resistance of tsunami flow even in the soft ground of tidal flat; other genera without prop roots were uprooted easily. Figure 3 portrays the frequency of destroyed and surviving trees sampled at each study site (Figure 1b). At study sites 1–4, the survival rate increases concomitantly with increasing stem diameter, indicating that the bold trees are stronger generally. However, when we compare the survival rates of sites with regard to the same stem diameter, the survival rate seems to decrease westward (from 1 to 4). Mangroves were completely destroyed at the site 5 (Figure 3e) because the west coast of Banda Aceh was attacked by more than 7–9 m

5.1. Strength Indicator of a Mangrove Tree [12] According to Yanagisawa et al. [2009a], damage to mangrove trees can be categorized into the following five patterns: (1) broken at stem or prop roots, (2) uprooted and inclined, (3) uprooted and fallen down, (4) uprooted and swept up by tsunami flow, and (5) lost bearing capacity because of ground erosion. However, Rhizophora trees were destroyed only slightly in the patterns resembling patterns (2)–(4) because the prop roots of Rhizophora trees are sufficiently thick to resist the tsunami flow [Yanagisawa et al., 2009a]. When we are not concerned with pattern (5) related to ground erosion, pattern (1) is mostly applicable as the damage pattern of Rhizophora trees (Figure 2c). To discuss the mangroves’ strength, we use a general strength indicator of timber [e.g., Zhang, 1994], i.e. the bending strength of a tree is estimated using the bending stress when a tree is broken. Therefore, bending stress, especially maximum bending stress by tsunami during its inundation, is useful as a representative factor of the strength of a mangrove tree against tsunami flow [Yanagisawa et al., 2009a]. Based on the linear elastic theory, the relation between the bending

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Figure 4. (a) Comparison of measured data to the modeled water level at Sibolga [Rabinovich and Thomson, 2007]. (b) Comparison between measured tsunami heights and modeled maximum tsunami heights [Borrero, 2005; Tsuji et al., 2006]. Circluar and triangular dotted data show data measured by Borrero [2005] and Tsuji et al. [2006], respectively. Dotted lines in Figure 4b show the range of root‐ mean‐ square error (RSME = ±1.2) (c) Comparison of the computed inundation area to the extent of tsunami inundation area surveyed by (JICA) [2005]. moment Md and the bending stress s was estimated using the following equation: Md ¼ W :

ð5Þ

[13] In that equation, W is a section modulus of a circle (W = pd3/32) and d is DBH in a sampled tree. Because the tsunami is a shallow water wave, the tsunami current velocity is assumed to be vertically uniform. The tsunami bending moment to estimate the tsunami bending stress is calculable using the following equation [Yanagisawa et al., 2009a]: 1 Md ¼ CD d ð D  Hb Þ2 u2 : 4

ð6Þ

[14] Therein, u is the computed current velocity, d is DBH, r is water density, D is the tsunami inundation depth, and Hb is the height at a broken point. The drag coefficient CD is determined from Coastal Engineering Research Center [1984] (CD = 0.7–1.2). Here, we assumed that trees were vertically rotated when the tsunami force broke them. Then, the broken point is defined as the center of rotation. In Rhizophora sp., Hb is determined approximately at the joint between the stem and prop roots because most Rhizophora trees were destroyed at this joint, which is inferred as the most fragile point (Figure 2c). In this study, Hb = 1.47 m is represented by taking the mean value of the height at broken points from 697 sampled trees. [15] We used a Geographical Information System (ArcGIS) to integrate both field data (DBH and damage) and tsunami hazard data (computed current velocity and inun-

dation depth). Using GIS, we showed each location of mangrove trees as point data sampled in the field survey, and overlaid its locations with the spatial distribution of tsunami hazard data. For numerical modeling, combined with the field data, the tsunami bending moment on each tree was calculated sequentially during the tsunami inundation. The maximum bending moment was determined from the time series. We then determined the maximum bending stress st as a strength indicator of mangrove destruction, substituting the maximum bending moment for equation (5). 5.2. Developing the Fragility Function of a Mangrove Forest [16] Integration of the numerical model, field measurements, and GIS analysis reveals a relation between the damage probability of mangrove trees and the maximum bending stress in the form of a fragility function [Yamaguchi and Yamazaki, 2001; Koshimura et al., 2009]. The flow to obtain the fragility function is determined from our previous approach [Koshimura et al., 2009; Yanagisawa et al., 2009a]. To estimate the damage probability in terms of maximum bending stress, 50 sample data points of surviving and destroyed trees are grouped. Furthermore, the damage probability in each group is defined as the number of destroyed trees divided by the total number in each group (50 sampled trees in each). Consequently, we obtained the histogram presented in Figure 5a as a discrete set of the number of destroyed and surviving trees and the maximum bending stress. The maximum bending stress in Figure 5a is determined by taking the median value within a range that includes 50 trees in it. We can see from Figure 5a that the ratio of the number of destroyed trees to surviving ones increases as the

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involved in the fragility functions might not be large, at least in these two cases. We further determined parameters of the fragility function, combining them with damage data of mangrove trees in Banda Aceh and Pakarang Cape [Yanagisawa et al., 2009a], and determined the parameters l, z, to be, respectively, 2.57, 0.92 (Figure 6b; r2 = 0.94). These proposed fragility functions are useful as preliminary information to assess the vulnerability of mangrove trees in areas prone to tsunami damage, when a tsunami bending stress is assumed.

6. Performance of Mangrove Forest and its Capacity in Tsunami Hazard Reduction

Figure 5. (a) Histogram of the number of surviving and destroyed trees related to maximum bending stress for groups of 50 data points. The value of the maximum bending stress on the horizontal axis is the median value in each group. (b) The fragility function for mangrove trees in Banda Aceh, Indonesia. maximum bending stress increases; mangrove trees are severely destroyed by more than 42 Nmm−2 of maximum bending stress. [17] Using least squares fitting of the discrete set of damage data (black circles in Figure 5b) assuming the following lognormal distribution function [Yamaguchi and Yamazaki, 2001], we obtain the fragility function (fragility curve), which indicates the damage probability Pd as a function of the maximum bending stress st Pd ðt Þ ¼ ½ðlnt  Þ= :

6.1. Method to Simulate the Reduction of Tsunamis in Mangrove Forest [19] We simulate tsunami reduction in mangrove forest using a numerical model including the resistance law of a mangrove forest and its destruction [Yanagisawa et al., 2009a]. The numerical model for tsunami propagation and coastal inundation is based on the leapfrog finite difference method of the nonlinear shallow water wave equation. We employed equivalent roughness formula (equation(4)) to calculate the resistance of mangrove forest. In the roughness formula, the damage probability (fragility function) is included as functions of bending stress by tsunami flow. Consequently, at each time step in the numerical model of tsunami inundation, Manning’s roughness n is recalculated using the flow condition (inundation depth and current velocity) and the probability of tree destruction (equation(7)). However, the stumps including prop roots of destroyed trees

ð7Þ

[18] In that equation, l and z respectively denote the mean and standard deviation of lnx, and F is the cumulative distribution function of the standard normal distribution. The fragility function for the damage probability of Rhizophora trees is obtained when the two parameters of equation (7), l and z are determined using least squares fitting on a lognormal probability plot. Results show that the parameters of l and z were determined respectively as 2.54 and 0.90 (Figure 5b; coefficient of determination r2 = 0.87). According to Yanagisawa et al. [2009a], l and z, which were determined from damage data in Pakarang Cape, Thailand, were, respectively, 2.50 and 0.94. Although a fragility function might include the local uncertain conditions such as tree characteristics, floating debris, and local behavior of tsunami flow, fragility functions determined from damage data in Banda Aceh, Indonesia closely resemble that of Pakarang Cape, Thailand (Figure 6a, |Error| < 0.023). This fact indicates that the difference of uncertain effects

Figure 6. (a) Comparison of fragility functions between Banda Aceh and Pakarang Cape (|Error| < 0.023). (b) Fragility function combining damage data of mangrove trees in Banda Aceh and Pakarang Cape [Yanagisawa et al., 2009a].

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grove forest. Here, we define the simplified form of hydrodynamic force [e.g., Hatori, 1964] as an indicator F ¼ u2 D:

ð9Þ

[21] The constant parts are deducted from drag force F F ¼ 1=2CD u2 A:

ð10Þ

Therein, r is the water density, CD is the drag coefficient, u is the current velocity, and A is the projected area. Here, we consider a simple model condition related to the ground level and mangrove forest as a preliminary test. For the coastal landform, we simplify the nearshore bathymetry and topography of Banda Aceh as an example to discuss the tsunami reduction performance of a mangrove forest (Figure 7a). The question of the protective role of mangrove forest is whether the tsunami damage is reduced by the presence of a mangrove forest when conditions of other coastal features such as ground elevation, topographic profile, and distance from coast are the same in the scenarios with and without mangroves. Therefore, testing of two model scenarios (with and without mangrove forest) is performed given equivalent conditions of other coastal features. For the mangrove forest, we assume 10 year old Rhizophora trees (tree density of 0.6 trees m−2 and DBH = 7 cm), 20 year old Rhizophora trees (tree density of 0.2 trees m−2 and DBH = 15 cm) and 30 year old Rhizophora trees (tree density of 0.1 trees m−2 and a DBH = 20 cm) [Alongi, 2002]. The offshore incident wave condition in the numerical model test is assumed as one‐sided sine wave of 40 min period, which is dominant period of the 2004 Indian Ocean tsunami [Abe, 2006]. Figure 7. (a) Numerical conditions; the incident wave’s period is assumed to be 40 min, and the coastal landform was simplified from the nearshore of Banda Aceh. The (b) maximum tsunami inundation depth and (c) hydrodynamic force with and without mangrove forest.

remained after the tsunami in the study areas (Figure 2c). We therefore assumed that the friction of prop roots (n = 0.04) has remained even if mangrove trees had been broken by the tsunami flow. This numerical model can simulate tsunami reduction effects of mangrove forest and their limitations under arbitrary conditions of coastal features such as bathymetry, ground elevation, topographic profile, and distance from the coast. [20] The tsunami reduction effect of mangrove forest is estimated using the numerical model results of two model scenarios, i.e. with or without the mangrove forest, in terms of the following reduction index: Rd ¼

a0  a : a0

ð8Þ

In the above equation, a is the value of tsunami inundation depth D or hydrodynamic force with the mangrove forest considering its destruction, and a′ is the value of tsunami inundation depth or hydrodynamic force without the man-

6.2. Results and Discussion [22] Figures 7b and 7c show the modeled maximum water level and maximum hydrodynamic force (u2D) of the tsunami in the area with and without mangrove forest (500 m width). The tsunami yields 3 m inundation depth at the shoreline in the case where mangroves do not exist. The results indicate that the maximum water level and hydrodynamic force behind the mangrove forest are greatly reduced by mangrove’s resistance, although the tsunami height was temporarily increased at the front of the mangrove forest (Figure 7b) because the mangrove forest blocked the tsunami current and the water mass accumulated in front of it. Moreover, the maximum hydrodynamic force was increased at offshore areas (Figure 7c) because mangrove forests reflected part of tsunami wave and generated a high‐ velocity current backward to offshore areas. Performing numerical model tests under several scenarios of incident wave height of 40 min period, Figure 8 depicts a summary of the reduction rate in terms of the inundation depth, hydrodynamic force and inundation distance from shoreline. Comparison of the performance of tsunami reduction rate between the tree ages of forests shows that the 10 year old mangrove forest contributes the most effectively to reducing the tsunami energy in the case of a 3 m inundation depth: the model shows that a 500 m wide mangrove area reduces the maximum tsunami inundation depth by 38% and the maximum hydrodynamic force by approximately 70% in areas behind the mangrove forest (Figures 8a and 8b). On the

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nami inundation depth or hydrodynamic force rather than inundation distance [Hatori, 1964; Koshimura et al., 2009]. In Banda Aceh, the houses were destroyed to a considerable degree by the greater than a 20 m3/s2 hydrodynamic force of the 2004 Indian Ocean tsunami, although they remained at 20% in 6 m 3/s 2 hydrodynamic force [Koshimura et al., 2009]. The model results imply that the assumed mangrove forest would have reduced a 20 m3/s2 hydrodynamic force to about 6–8 m3/s2. The assumed mangrove forest might mitigate 60% of structural damage. These results underscore that the mangrove forest can mitigate tsunami disasters, although they can not completely stop the tsunami flow. As presented in Figures 8a and 8b, the tsunami reduction performance of 20 year old or 30 year old mangrove forest is more effective than 10 year old when the tsunami inundation depth exceeds approximately 4 m because 10 year old mangroves were largely destroyed by the more than 4 m tsunami and its reduction performance decreased (Figure 9). Table 2 presents a summary of the reduction rate of hydrodynamic force versus the tsunami inundation depth of the incident wave and forest width. Table 2 shows that we can determine the performance of the mangrove forest in terms of its growth, width and the inundation depth of the incident tsunami. The numerical model results underscore the necessity of preserving a forest over a long period (>20 years) to mitigate a large tsunami disaster effectively: a young mangrove forest such as 10 year old forest would lose its protective capability against a large tsunami impact (>4 m). However, a forest’s protective role should not be overestimated when a tsunami inundation depth is greater than 6–9 m, even when a mangrove forest that is more than 30 years old of 500 m width is assumed.

7. Concluding Remarks Figure 8. Reduction rate of (a) inundation depth, (b) hydrodynamic force, and (c) inundation distance versus inundation depth at shoreline in 10 year old, 20 year old, and 30 year old mangrove forest. other hand, the model results show that mangrove forests are mildly effective for reducing inundation distance (Figure 8c) because the forest is permeable to a body of water. However, the extent of tsunami damage is correlated with tsu-

[23] Combined use of damage data related to mangroves and numerical results of the 2004 tsunami produced a fragility function to assess mangrove forest vulnerability. The proposed fragility function is useful for preliminary assessment of potential mangrove damage in a tsunami‐ prone area. Based on the fragility function, we developed a numerical model to assess the tsunami reduction effect of a mangrove forest. Our numerical results, as a preliminary test, showed that mangrove forests can be a reasonable countermeasure when used as a bioshield for tsunami mitigation as long as catastrophic damage to the mangrove Table 2. Reduction Rate of Hydrodynamic Force by the Assumed Mangrove Foresta

10 year old forest 20 year old forest 30 year old forest

Figure 9. Destruction rate of mangrove forest with 500 m width versus inundation depth at shoreline in 10 year old, 20 year old, and 30 year old mangrove forest.

Tsunami Inundation Depth (m)

Width (m)

1

2

3

4

5

6

7

8

100 300 500 100 300 500 100 300 500

0.40 0.60 0.76 0.33 0.52 0.69 0.30 0.48 0.65

0.42 0.60 0.71 0.34 0.51 0.63 0.28 0.46 0.58

0.35 0.58 0.69 0.33 0.52 0.62 0.26 0.45 0.56

0.12 0.30 0.45 0.26 0.48 0.59 0.23 0.43 0.54

0.05 0.11 0.12 0.13 0.30 0.42 0.19 0.37 0.48

0.04 0.08 0.10 0.07 0.15 0.19 0.13 0.28 0.37

0.04 0.07 0.09 0.04 0.09 0.11 0.07 0.17 0.23

0.03 0.07 0.09 0.03 0.07 0.09 0.05 0.10 0.13

a Wave period of incident wave is 40 min. Values with more than 70% of destruction rate are shown in bold.

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forest does not occur beyond its threshold level. Consequently, reforesting or preserving mangrove forests in deforested areas such as Banda Aceh might mitigate future tsunami disasters. However, it is important to note that mangrove barriers can not completely halt tsunami flow. For that reason, it is necessary to integrate several countermeasures such as warning systems, education for disaster prevention, and mangrove forests to prevent a widespread tsunami disaster. [24] The protective role of coastal trees as a tsunami defense has been discussed for more than 100 years [e.g., Tanaka, 2009], with detailed preliminary reports appearing after the 2004 tsunami [e.g., Danielson et al., 2005; Kathiresan and Rajendran, 2005; Iverson and Prasad, 2007]. However, the effectiveness of coastal trees observed in the field survey might be affected by the role of other coastal features such as bathymetry, ground elevation, and distance from the coast [e.g., Kerr et al., 2006]. There is little empirical support for the protective role of coastal trees based on field observations. Therefore, our proposed numerical model can play a role in exploring the considerable controversy surrounding the protective capacity of coastal trees, in particular, mangrove forests. Additional studies are necessary to verify mangrove forests’ protective role, as reported in a preliminary report, using a quantitative approach such as our proposed model. However, our study requires the following improvements. [25] First, this study was performed in local areas, which might mean that the proposed fragility functions are not universal measures to assess the vulnerability of mangrove forests, although the fragility functions between Banda Aceh, Indonesia and Pakarang Cape, Thailand are almost identical. As described above, the fragility function should include numerous uncertain effects, which might be influenced by interregional conditions such as tree characteristics, local tsunami behavior, and floating materials. Therefore, future studies must be undertaken to support a wider investigation of the vulnerability of mangrove forests and to examine differences between fragility functions in various cases. [26] Second, the capacity of mangroves as a tsunami defense could differ depending on local conditions such as the wave period, bathymetry and mangrove forest conditions, although this study examined the reduction effect of mangrove forest under simple conditions. In particular, the tsunami wave period might influence the capacity of mangroves as a tsunami defense because the tsunami with a long wave period tends to penetrate mangrove forests [Harada and Kawata, 2004]. Moreover, tsunami flows at a gap between two mangrove forests become faster as the gap becomes smaller [Thuy et al., 2009]. Especially, mangrove trees are prone to be damaged at gaps such as small channels [Yanagisawa et al., 2009b], possibly because of higher tsunami current velocity in the gap. Therefore, additional studies must be undertaken to examine the reduction effect of mangrove forests under various conditions in the model to conduct detailed estimation of the capacity of mangrove forest for tsunami defense. [27] Acknowledgments. The authors gratefully acknowledge M. Affan, J. Takashima, and H. Sato for the support they provided during our field

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survey and K. Goto and H. Nakamura for providing usuful comments. This study was financially supported by a Grants‐in‐Aid for Scientific Research from the Japan Society for the Promotion of Science (18201033 and 19681019) and Industrial Technology Research Grant Program in 2008 (08E52010a) from the New Energy and Industrial Technology Development Organization (NEDO) and International Society for Mangrove Ecosystems (ISME).

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