- Open Access
Experimental study on tsunami attenuation by mangrove forest
© The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences; TERRAPUB. 2012
- Received: 2 November 2010
- Accepted: 5 November 2011
- Published: 24 October 2012
Laboratory experiments on the effectiveness of mangroves to reduce tsunami energy were performed. A complex tree structure of Rhizophora sp. was parameterized using the stiff structure assumption (root system and trunk) for different submerged root volume ratios and frontal tree areas. The hydraulic resistance of the prototype and the parameterized models under steady flow conditions was compared and the most appropriate parameterized model in terms of both equivalent flow resistance and practical feasibility was selected for further investigation. The damping performance of the mangrove forest was determined from laboratory tests performed synchronously in a twin-wave flumes (with and without the forest model in 1 and 2 m-wide wave flumes, respectively) for varying incident height of solitary wave, water depth and forest width. The role of the different types of wave evolution modes on wave damping is discussed based on the measurements of the forces exerted on the single tree models along the entire forest width. A new approach for the wave transmission coefficient, which is based on the ratio of the forces exerted on the trees placed in the last and first forest row, is proposed. In the paper, the most important results of the tree parameterization procedure and the wave flume experiments are discussed.
- Tsunami attenuation
- tree parameterization
- laboratory experiments
The capability of coastal forests to reduce the impacts of extreme events such as tsunamis and storm surges has been reasonably considered by engineers and scientists as one of the risk mitigation measure alternatives. In some degree, typical coastal forest vegetation such as mangroves or coastal pines are generally sufficient to withstand extreme winds or storms. However, the capability of the forests to withstand extremely high tsunami has controversially been discussed due to the fact that so far, there are no definite conclusions, particularly on the role of the coastal forests as an effective natural tsunami barrier. Several reports and surveys (field observations, experienced damage, and satellite images) have apparently shown that coastal forests may play an important role as a natural protection against tsunami (Dahdouh-Guebas et al., 2005). These findings are also supported by semi-analytical and empirical approaches based on series of experiments using either physical or numerical models (Istiyanto et al., 2003; Imai and Matsutomi, 2005; Yanagisawa et al., 2009).
On the other hand, many unknown aspects still remain unknown to draw any conclusions on the effectiveness of coastal forest vegetation as a protective green-shield. Field evidence (damage surveys) also showed that coastal forests did not always effectively protect coastal areas from destruction by tsunami (Chatenoux and Peduzzi, 2005). Some tree species did survive tsunami attack, however the villages behind them experienced significant damages (Tanaka et al., 2007). Even in some areas with dense and healthy mangroves (e.g. Ule-lhe, Banda Aceh, Indonesia), the forest did not provide any protection against the 2004 tsunami. Instead, the trees were destroyed, uprooted, and carried kilometres inland, creating more lethal tsunami debris (EJF, 2006). Moreover, tsunami onshore propagation, the effect of complex bathymetry and topography, and relevant vegetation characteristics are some of the poorly understood aspects related to the damping performance of coastal forests.
One of the important aspects associated with the attenuation performance of coastal forest against tsunami is the hydraulic resistance of the vegetation, which determines the overall tsunami attenuation (transmission, reflection and dissipation) by coastal forests. Efforts to derive the hydraulic resistance in terms of drag, inertia or Manning roughness coefficients have been made mostly via series of laboratory experiments (Harada and Imamura, 2000). To provide reliable values of the hydraulic resistance from laboratory tests, the complex tree structure has to be simplified first through a parameterization process, based on the assumption that the hydraulic resistance of the prototype tree and the parameterized tree model is identical. However, the methodologies used to parameterize the vegetations were often not physically-based and contradictory to each other (Husrin and Oumeraci, 2009). To date, there is no general consensus for a proper selection of the hydraulic resistance for typical coastal forest vegetation.
Another aspect that has not been fully understood is the distinction between tsunami attenuation by shore topography and coastal forest. The presence of natural complex bathymetry/topography (e.g. natural reefs or dunes) may significantly contribute to the attenuation of tsunami energy in comparison to that of the coastal forest (Chatenoux and Peduzzi, 2005). Strusińska (2011) has concluded that the dissipation of tsunami-like solitary wave by submerged reefs is influenced not only by wave conditions but also by reef geometry and local water depth conditions, affecting generation of wave breaking. Different shore topography (i.e. different beach slopes) has also been examined in laboratory experiments to determine the attenuation of tsunami by forest models (e.g. Istiyanto et al., 2003; Kongko, 2004; Imai and Matsutomi, 2005). However, the distinction between the energy dissipation due to the forest and the shore topography, including the associated physical processes such as inception of wave breaking, was not clearly shown.
In this paper, a concept for the parameterization of mangrove trees (Rhizophora sp.) with stiff structure assumption (only roots and trunk) and their associated hydraulic resistance coefficients will first be discussed, including some aspects not considered so far in the literature and which have been identified based on the recent laboratory tests. Furthermore, the most first results of the large scale model tests on the damping performance of the forest, consisting of individual parameterized mangrove tree models, will be presented.
Parameterization is a simplification process of a complex 3D structure of coastal forest vegetation to a simpler and more organized model with a similar resistance to flow, i.e. the hydraulic losses of the prototype should be similar to the hydraulic losses of the proposed parameterized model. Husrin and Oumeraci (2009) have identified the most important parameters and aspects for the parameterization of typical coastal forest vegetations; mangroves and coastal pines. Not only geometrical aspects should be considered in the vegetation parameterizations, but also other physical and biological aspects such as tree species, age, stiffness, frontal area, canopy density (characterised by the leaf area index LAI), natural frequencies, and behaviour under seasonal changes.
Considering the physical and morphological aspects of the vegetation, three main parts of a tree are addressed in the parameterization: roots, trunk and canopy. Each structural part of a tree contributes simultaneously in blocking/reducing the flow impact. For tsunami height lower than the canopy, the trunk and roots play a dominant role in the flow reduction; this is particularly the case for the complex root system of mangroves (Rhizophora sp.). However, when tsunami reaches the canopy, all parts of the tree become important. The canopy with much higher density may result in higher wave damping, provided the trunk is strong enough (not broken) and the roots are well anchored in the soil (not uprooted). These characteristics (the canopy density, the trunk strength, and root parameters) depend on the vegetation species, age, and environmental aspects. At younger age, any species are vulnerable to damage (breaking or uprooting), while at a mature stage, trees are structurally more stable but still vulnerable to breakage. Therefore, two assumptions have been made for the parameterization process: (i) stiff structure assumption, in which trunk and roots of the tree are considered, (ii) flexible structure assumption which is applicable to all tree components (i.e. trunk, roots, and canopy). In this paper, the parameterization of a mangrove tree (Rhizophora sp.) according to the stiff structure assumption will be discussed in more details, including the derivation of the hydraulic resistance based on results obtained from laboratory experiments. This assumption results from the fact that most of the bottom parts of mature mangroves (Rhizophora sp.) remain intake after being hit by large tsunami (Yanagisawa et al., 2009). Furthermore, for the case where tsunami does not reach the canopy, the roots and the trunk behave relatively stiff.
For each real model, three parameterized models made of a group of cylinders with different diameters were constructed. The different diameters indicate the influence of different frontal area A f , submerged root volume ratio V m /V, and cylinder dimensions on the hydraulic performance of the models. The frontal area A f (or blockage area) is defined as the area perpendicular to the flow direction of the submerged tree model. Hence, the frontal area is determined by taking the picture of the intended side of the model to be subjected by the flow (Fig. 1). The submerged root volume ratio is defined as a ratio of the submerged root volume (V m ) related to the water control volume (V) (Mazda et al., 1997). Both the frontal area and the submerged volume ratio vary as the water level changes. Figure 1 also shows real mangrove models and their counterpart parameterized models with the nomenclature used.
All models were tested in the flume according to the experimental set-up shown in Fig. 2. Different flow velocities ranging from u = 0.2–1.4 m/s (or relative flow discharges q = 0.2–1.4 m3/s/m2) for four water depths (h = 0.05, 0.10, 0.125 and 0.15 m) were employed in the testing programme to investigate the effect of varying frontal area A f and submerged root volume ratio V m /V on the measured hydraulic forces. The flow velocity is measured at the level of at least 0.4 h above the platform which can be considered as the depth-averaged flow velocity. Each model was subjected to a combination of different current velocities and water depths in 17 tests. Therefore, the total number of tests is 204 (17 tests × 12 mangrove models).
Force measurements deviation of parameterized models in comparison to real models.
Force deviation from the real model
Large force deviations, attributed to the parameterized models with larger cylinder diameters (D c = 1.0 and D c = 1.5 cm) may result from the different cylinder geometry used, which affect the magnitude of the flow-induced forces. The models with D c = 1.0 cm are subjected to larger forces (averaged deviation of δF D = 42%) in comparison to the models with the largest cylinder diameter (D c = 1.5 cm) and with clearly smaller forces measured (averaged deviation, δF D = −35%), as shown in Table 1. The weaker flow-induced load on these models is caused by larger spaces among individual cylinders, allowing the flow to pass more freely through the models (see Fig. 1).
The recent study is also comparable to the previous experiments dealing with mangrove models and their hydraulic resistance. Harada and Imamura (2000) conducted laboratory experiments using artificial porous media for parameterized mangrove models and proposed a relationship for C D versus V m /V. Though, the physical basis of the relationship is unclear, incomplete and found to be in the lower envelope of the current study (Husrin and Oumeraci, 2010). The obtained hydraulic resistance from the current study also confirms the laboratory works of Struve et al. (2003) for higher C D values and Imai and Matsutomi (2005) for smaller C D values (C D ~ 1.0).
The relationships shown in Eqs. (6)–(8) and in Fig. 5 explain thoroughly both physical object properties and flow characteristics such as variation of root density V m /V, frontal area A f , and flow regimes. Therefore, the parameterized model, which has been selected in the current study, is physically based and sufficiently verified under steady flow conditions to be further implemented for a larger scale of model experiments on tsunami attenuation by a mangrove forest under a stiff structure assumption in a wave flume, described in the next section.
The forest model was arranged in staggered rows of 12 and 13 tree models, as shown in Fig. 6(b). Thus, the forest density was kept constant and only the forest width was varying (B = 0.75, 1.5, 2.25 and 3.0 m as well as B = 0.0 m corresponding to no forest in the 1 m-wide flume). By varying the water depth in front of the foreshore model (h = 0.415, 0.465, 0.515, 0.565 and 0.615 m), different submergence depths of the forest were achieved, covering the entire tree model height (i.e. up to the top of a trunk in case of the stiff tree structure): the tree models were fully emerged for d r = 0.0 m and fully submerged for d r = 0.2 m (d r : water depth over the horizontal part of the beach platform).
Dimensions of prototype forest and forest model with corresponding water depth and wave conditions (according to Froude’s similitude law with a scale of 1:25).
Height of tree trunk h tr [m]
Forest width B [m]
0.0, 18.75, 37.50, 56.25, 75.00
0.0, 0.75, 1.50, 2.25, 3.00
Water depth h [m]
10.375, 11.625, 12.875, 14.125, 15.375
0.415, 0.465, 0.515, 0.565, 0.615
Forest submergence depth d r [m]
5.0, 3.75, 2.50, 1.25, 0.0
0.20, 0.15, 0.10, 0.05, 0.0
Nominal incident wave height H i,nom [m]
1.0, 2.0, 3.0, 4.0, 5.0
0.04, 0.08, 0.12, 0.16, 0.20
generation of wave breaking,
- (ii)location of incipient wave breaking occurring in four regions along the beach profile as shown in Fig. 8 (i.e. in region 1 over the foreshore slope, in region 2 stretched between the end of the foreshore slope and the beginning of the forest model, in region 3 corresponding to the forest width, in region 4 behind the forest model),
generation of wave fission resulting in a train of solitary waves emerging from a single incident solitary wave (consisting of at least two solitons), caused predominantly by the water depth reduction from h = 0.465–0.615 m in front of the beach profile to d r = 0.05–0.20 m above the horizontal part of the beach model, and also due to the presence of the forest model.
Non-breaking wave disintegrating into solitons (evolution mode “EM1”). No wave breaking is generated as the wave propagates over the entire foreshore model. However, the wave splits into a number of solitons (see Figs. 8(a) and 9). This evolution mode was observed for two smallest incident wave heights: H i,nom = 0.04 m at water depth h = 0.515–0.615 m and H i,nom = 0.08 m at water depth h = 0.615 m.
Breaking of incident wave over the beach slope (i.e. in region 1) with wave disintegration into solitons (evolution mode “EM2”). The progressive broken wave resembles a turbulent bore (see Figs. 8(b) and 9). Due to the complex shape of the broken wave, recognition of wave fission generation was in some cases not successful and thus a longer travel distance would be required for a full development of the solitons. A promising analysis method for this purpose is the non-linear Fourier Transform proposed by Brühl and Oumeraci (2010). This evolution mode is typical for the smallest water depth h = 0.465 m and relatively high waves H i,nom = 0.12–0.20 m.
Breaking of incident wave in the region between the end of the beach slope and the beginning of the forest model (i.e. in region 2) with wave disintegration into solitons (evolution mode “EM3”) (Figs. 8(c) and 9). This evolution mode was found to be the mostly often observed pattern in the tests. Depending on the inception point of the wave breaking, two submodes can be further distinguished: (i) generation of wave breaking followed by wave fission, provided breaking event was generated very close to the end of the foreshore slope (evolution mode “EM3a”), (ii) wave disintegration into solitons followed by wave breaking occurring for the tests in which the inception point of breaking was shifted towards the beginning of the forest (evolution mode “EM3b”).
Breaking of incident wave in the forest model (i.e. in region 3) with wave disintegration into solitons (evolution mode “EM4”). The process of wave scattering into solitons takes place already in front of the forest. As a result of the increase of the height of the leading soliton, which accompanies wave fission, the first wave becomes unstable and breaks in the forest. Further development of the fission process starts once the breaking event is accomplished (see Figs. 8(d) and 9). This evolution mode was observed for forest width B = 1.5–3.0 m, two highest water levels h = 0.565 and 0.615 m and incident wave height range of H i,nom = 0.08–0.16 m.
Breaking of incident wave behind the forest model (i.e. in region 4) with wave disintegration into solitons (evolution mode “EM5”). Similarly to “EM4”, the incident wave starts to scatter into solitons in front of the forest: The leading soliton becomes unstable due to the wave height amplification associated with the fission, and breaks behind the forest. Further evolution of the solitons can be observed within and behind the forest (Figs. 8(e) and 9). This evolution mode was observed solely for the shortest forest of width B = 0.75 m, for waves of height H i,nom = 0.08 m generated at water depth h = 0.565 m and H i,nom = 0.12 m at h = 0.615 m.
The effect of water depth, incident wave height and forest width on the solitary wave evolution modes observed in the 2 m-wide flume is illustrated in Fig. 9. The change of the two first parameters was found to influence significantly the pattern of wave behaviour, particularly on the transition from the breaking to the non-breaking wave conditions. Generally, smaller waves (H i,nom = 0.04, 0.08 m), generated at the lowest water depth (h = 0.465 m), broke in front of the forest (“EM3”), while higher waves (H i,nom = 0.12–0.20 m) broke earlier, already over the foreshore slope (“EM2”). By increasing the water depth to h = 0.515 m, non-breaking wave conditions (“EM1”) were achieved solely for the smallest wave height (H i,nom = 0.04 m), while waves of height H i,nom = 0.08–0.20 m broke in front of the forest (“EM3”). In this case, waves of height H i,nom = 0.08 m, which were classified as “EM2” for h = 0.465 m, became “EM3”. For higher water level of h = 0.565 m, the same wave behaviour as for h = 0.515 m was observed except for waves of height H i,nom = 0.08 m (breaking behind forest—“EM5” for forest width of B = 0.75 m and breaking in the forest—“EM4” for the other forest widths). In the case of the highest water depth h = 0.615 m, non-breaking wave conditions occurred for two smallest wave heights (H i,nom = 0.04, 0.08 m). In comparison to the previous water depth, waves of height H i,nom = 0.12 m broke behind the forest of width B = 0.75 m and in the forest of width B = 1.5–3.0 m. Except of waves of height H i,nom = 0.08, breaking in the forest of width B = 1.5 and 2.25 m, the evolution modes of the two highest wave heights remained unchanged (i.e. breaking over the foreshore slope—“EM3”).
Due to the amplification of the height of the leading soliton accompanying the fission process, the transmitted wave height (measured at the end of the forest) was in some cases higher than the incident wave height (measured at the beginning of the forest), particularly for non-breaking waves propagating over the shortest forest, as shown in Fig. 8(a). Therefore, the wave-induced forces on the single mangrove models were found to be the most appropriate indicators of the forest capability to attenuate solitary wave, in contrast to the measurements of water free surface elevation.
For example, the force reduction achieved for forest width of B = 0.75 m and water level of h = 0.465 m varies from ca. 20% to 33%, while for B = 3.0 m it is between ca. 66% and 86% for the incident wave height range H i,nom = 0.04 and 0.20 m.
Generally, there is a trend of decreasing of wave transmission with the increasing relative forest width, which is particularly noticeable when comparing the transmission coefficients for the smallest (B = 0.75 m) and the largest forest widths (B = 3.0 m). For example, for water depth h = 0.515 m and incident wave height H i,nom = 0.16 m the transmission coefficient yields K t = 0.763 in case of forest width B = 0.75 m and it is reduced to K t = 0.488 in case of forest width B = 3.0 m. Such a high rate of wave attenuation in case of breaking waves results also from the fact that the measurements of the forces exerted on single tree models were always performed over the entire considered forest width (see exemplary the configuration of the force transducers for single tree models in Figs. 10 and 11). Thus, a much longer wave propagation distance was covered by the force measurements for the widest forest, so that more significant wave energy reduction took place as compared to the shortest forest. Additionally, transmission coefficient may vary even by a factor of 3 for a similar ratio of relative forest width B/L i,gen , depending on the evolution mode (as indicated by the lower and the upper envelope in Fig. 12).
The calculated values of the transmission coefficient are provided in Table A.1 in Appendix A for each of the performed tests.
The transmission coefficients presented in this paper are comparable to the previously reported values from laboratory experiments, particularly for the range of K t values. Harada et al. (2000) reported K t values for porous media ranges from 1.0–0.5, while Kongko (2004) obtained the K t in the range between 0.95 and 0.65 for mangrove models made of a group of cylinders. Using wires-made mangrove models, Istiyanto et al. (2003) obtained a wider range of K t values (0.95–0.20). It should be noted here that those K t values were derived based on different parameterized tree models as well as different foreshore models. Moreover, their models also did not take into account the influence of breaking wave conditions. For breaking and non-breaking conditions, however, Augustin et al. (2008) reported K t values for a group of dowels subjected by irregular waves in the range of 0.99–0.65. Additionally, the highest transmission coefficient was determined for non-breaking wave conditions, which is in agreement with the results of the present study.
Two aspects of the damping performance of the mangrove forest have been investigated: (i) the development of the parameterization procedure to determine the tree model with an identical hydraulic resistance as its actual counterpart, which could be further used in the large scale model tests, (ii) the determination of the effect of the incident wave parameters, water depth conditions and forest width on wave reduction by the forest. The successful application of the parameterization procedure to a mature Rhizophora tree (the roots and the trunk according to the stiff structure assumption) revealed that the hydraulic resistance of the tree (drag coefficient C D ), tested under steady flow conditions, is influenced by submerged root volume ratio V m /V and tree frontal area A f . A new relationship between the drag coefficient based on effective length L e and Reynolds number R e is also provided.
The capability of the mangrove forest to damp tsunami was determined on the basis of large scale experiments with and without the forest model, which consisted of the selected parameterized tree models for different incident solitary wave conditions, water depths and forest widths. The rate of wave attenuation, analysed in terms of the forces exerted on single tree models, was found to be governed by the observed wave evolution modes, generally classified as non-breaking and breaking conditions. The highest wave energy reduction by forest model was achieved for breaking waves propagating over the widest forest (transmission coefficient K t = 0.2).
List of Symbols
- A f :::
frontal area of mangrove model [m2]
width of mangrove forest model [m]
wave celerity [m/s]
- C D ::
drag coefficient [-]
- d r ::
submergence depth of mangrove forest [m]
- D t ::
cylinder diameter for the trunk model [m]
- D c ::
cylinder diameter for the root model [m]
total energy per unit crest width [J/m]
- E k ::
kinetic energy per unit crest width [J/m]
- E p ::
potential energy per unit crest width [J/m]
measured force [N]
- F D ::
measured drag force [N]
measured force of parameterized mangrove model [N]
measured force of real mangrove model [N]
maximum measured force on three model [N]
water level [m]
- H i,nom ::
nominal incident wave height [m]
- H i,gen ::
generated incident wave height [m]
- h tr ::
height of tree trunk [m]
- K t ::
wave transmission coefficient [-]
leave area index [-]
wave length [m]
- L e ::
effective length [m]
- L i,nom ::
nominal incident wave length [m]
- L i,gen ::
generated incident wave length [m]
wave-induced pressure [kPa]
relative flow discharge [m3/s/m2]
- R e ::
Reynolds number [-]
current flow velocity (depth-averaged velocity) [m/s]
- u o ::
particle velocity in the direction of wave propagation [m/s]
particle velocity perpendicular to the direction of wave propagation [m/s]
control volume of water [m3]
- V m ::
volume of submerged roots [m3]
horizontal distance of force transducers within the forest model [m]
- δF D ::
deviation of measured force [%]
water free surface elevation [m]
kinematic viscosity of water (ν = 1.004 × 10−6 m2/s for water temperature of 20°C)
water density [kg/m3]
This study was performed in the framework of the project “Tsunami Attenuation Performance of Coastal Forests” (TAPFOR), a subproject of the project “Tracing Tsunami impacts on- and offshore in the Andaman Sea Region” (TRIAS), founded by the Deutsche Forschungsgemeinschaft (DFG) and the Office of the Research Council of Thailand (NRCT). The first author is also supported by the DFG within the Graduate College of TU Braunschweig (GRK 802) in “Risk Management of Natural & Civilization Hazards on Buildings & Infrastructure”. The authors would like to thank students H. Brodersen, D. Schubert and A. Syukri for their support in conducting of the experiments.
- Augustin, L. N., J. L. Irish, G. Balsmeier, and J. Kaihatu, Laboratory measurments of wave attenuation and wave setup by vegetation, Proceedings of the 31st International Conference on Coastal on Coastal Engineering ICCE, Hamburg, German, 324–330, 2008.Google Scholar
- Brühl, M. and H. Oumeraci, Analysis of soliton fission over a submerged structure using Non-linear Fourier Transform, Proceedings of the 32nd International Conference on Coastal on Coastal Engineering, Shanghai, China, 12 pp, 2010.Google Scholar
- Chatenoux, B. and P. Peduzzi, Analysis of the role of bathymetry and other environmental parameters in the impacts from the 2004 Indian Ocean Tsunami, UNEP/GRID-Europe, 25 pp., 2005.Google Scholar
- Daenecke, K., Solitary wave attenuation anlysis by mangrrove forest using energy dissipation approach, Master thesis, Leichtweiss Institute for Hydraulic Engineering and Water Resources, TU Braunschweig, Germany, 121 pp., 2010.Google Scholar
- Dahdouh-Guebas, F., L. P. Jayatissa, D. Di Nitto, J. O. Bosire, D. Lo Seen, and N. Koedam, How effective were mangroves as a defence against the recent tsunami?, Curr. Biol., 15(12), 443–447, 2005.View ArticleGoogle Scholar
- Environmental Justice Foundation (EJF), Mangroves: Nature’s defence against tsunamis—A report on the impact of mangrove loss and shrimp farm development on coastal defences, Environmental Justice Foundation (EJF) Report, London, UK, 30 pp., 2006.Google Scholar
- Fritz, H. M., J. C. Borrero, C. E. Synolakis, and J. Yoo, 2004 Indian Ocean tsunami flow velocity measurements from survivor videos, Geophys. Res. Lett., 33(24), 5 pp., 2006.View ArticleGoogle Scholar
- Grant, P. F. and W. G. Nickling, Direct field measurement of wind drag on vegetation for application to windbreak design and modeling, Land Degradation and Development, 9, 57–66, 1998.View ArticleGoogle Scholar
- Harada, K. and F. Imamura, Experimental study on the resistance by mangrove under the unsteady flow, Proceedings of the 1st Congress of the Asian and Pacific Coastal Engineering, Dalian, 975–984, 2000.Google Scholar
- Harada, K., H. Latief, and F Imamura, Study on the mangrove control forest to reduce tsunami impact, Proceedings of the 12th Congress of the IAHRAPD, Bangkok, Thailand, 10 pp., 2000.Google Scholar
- Husrin, S. and H. Oumeraci, Parameterization of coastal forest vegetation and hydraulic resistance coefficients for tsunami modelling, Proceedings of the 4th Annual International Workshop and Expo on Sumatra Tsunami Disaster and Recovery, Banda Aceh, Indonesia, 78–86, 2009.Google Scholar
- Husrin, S. and H. Oumeraci, Mangrove parameterization with stiff strucutre assumption A hydraulic model study, Progress Report, Leichtweiss Institute for Hydraulic Engineering and Water Resources, TU Braunschweig, Germany, 46 pp, 2010.Google Scholar
- Husrin, S., A. Strusińska, A. Hoque, and H. Oumeraci, Storm wave attenuation and damping performance of mangrove forest, e-Proceedings of the Joint Convention Makassar, Exploring Eastern Indonesia, 16 pp, 2011.Google Scholar
- Imai, K. and H. Matsutomi, Fluid force on vegetation due to the tsunami flow on sand spit, in Tsunamis: Case Studies and Recent Developments, edited by K. Satake, 343 pp, Springer, The Netherlands, 2005.Google Scholar
- Istiyanto, D. C., K. S. Utomo, and Suranto, Pengaruh Rumpun Bakau Terhadap Perambatan Tsunami di Pantai, Proceeding of Reducing the Impact of Tsunami Seminar, BPPT JICA, Yogyakarta, Indonesia, 316– 326, 2003 (in Indonesian).Google Scholar
- Kongko, W., Study on tsunami energy dissipation in mangrove forest, 43 pp., Master Thesis Report, Iwate University, Japan, 2004.Google Scholar
- Liu, P. L.-F and Y. Cheng, A numerical study of the evolution of a solitary wave over a shelf, Phys. Fluids, 13(6), 1660–1667, 2001.View ArticleGoogle Scholar
- Longuet-Higgins, M. S. and J. D. Fenton, On the mass, momentum, energy and circulation of a solitary wave, Proc. R. Soc. Lond., A, 337, 1–13, 1974.View ArticleGoogle Scholar
- Mazda, Y., E. J. Wolanski, B. A. King, A. Sase, D. Ohtsuka, and M. Magi, Drag force due to vegetation in mangrove swamps, Mangroves and Salt Marshes, 1(3), 193–199, 1997.View ArticleGoogle Scholar
- Munk, W., The solitary wave theory and its application to surf problems, Ann. NY Acad. Sci., 51, 376–424, 1949.View ArticleGoogle Scholar
- Strusinska, A., Hydraulic Performance of an Impermeable Submerged Structure for Tsunami Damping, 228 pp, Ibidem-Verlag, Germany, 2011.Google Scholar
- Struve, J., R. A. Falconer, and Y. Wu, Influence of model mangrove trees on the hydrodynamics in a flume, Estuar. Coast. Shelf Sci., 58,163–171, 2003.View ArticleGoogle Scholar
- Tanaka, N., Y. Sasaki, M. I. M. Mowjood, K. B. S. N. Jinadasa, and S. Homchuen, Coastal vegetation structures and their functions in tsunami protection: Experience of the recent Indian Ocean tsunami, Landsc. Ecol. Eng., 3, 33–45, 2007.View ArticleGoogle Scholar
- Yanagisawa, H., S. Koshimura, K. Goto, T. Miyagi, F Imamura, A. Ruangrassamee, and C. Tanavud, The reduction effects of mangrove forest on a tsunami based on field surveys at Pakarang Cape, Thailand and numerical analysis, Estuar. Coast. Self Sci., 81, 27–37, 2009.View ArticleGoogle Scholar