Modelling the Impacts of Mangrove Vegetation Structure on Wave ...

Journal of Coastal Research

31

2

340–347

Coconut Creek, Florida

March 2015

Modelling the Impacts of Mangrove Vegetation Structure on Wave Dissipation in Ben Tre Province, Vietnam, under Different Climate Change Scenarios Nguyen Thi Kim Cuc†‡*, Tomohiro Suzuki§††‡‡, Erik D. de Ruyter van Steveninck§§, Hoang Hai††† †

‡

Department of Natural Resources Management Faculty of Water Resources Engineering Water Resources University Dong Da, Hanoi, Vietnam

Mangrove Ecosystem Research Division Centre for Natural Resources and Environmental Studies Vietnam National University Hanoi, Vietnam

††

‡‡

Department of Civil Engineering Ghent University Ghent, B-9052, Belgium †††

Faculty of Environment Da Nang University of Technology Da Nang, Vietnam

Environmental Fluid Mechanics Section Faculty of Civil Engineering and Geosciences Delft University of Technology Delft, The Netherlands

§

Flanders Hydraulics Research Antwerp B-2140, Belgium §§

Water Science and Engineering Department UNESCO-IHE Institute for Water Education Delft, The Netherlands

Abstract Cuc, N.T.K.; Suzuki, T.; Ruyter van Steveninck, E.D. de, and Hai, H., 2015. Modelling the impacts of mangrove vegetation structure on wave dissipation in Ben Tre Province, Vietnam, under different climate change scenarios. Journal of Coastal Research, 31(2), 340–347. Coconut Creek (Florida), ISSN 0749-0208. Mangroves are widely distributed along the coastline of Vietnam, where they provide protection against sea waves caused by extreme weather. Impacts of climate change, together with population growth and economic development, are expected to exert pressure on these vulnerable systems. In this study the numerical wave-propagation model SWANVEG (Simulating Waves Nearshore–Vegetation) was used to simulate the possible impacts of climate change on the wave-dissipation capacity of different types of mangrove vegetation. Vegetation characteristics were assessed in planted plots (Rhizophora apiculata and a mix of R. mucronata, Sonneratia caseolaris, Avicennia alba, and Nypa fructicans) and in natural regenerated areas (A. alba and S. caseolaris) in Thanh Phu Natural Reserve, Mekong Delta, Vietnam; these assessments were used as model input. Different sea levels and mangrove vegetation characteristics were used to simulate the potential impacts of climate change. Planted plots with a cover of 70% reduced the height of incoming waves by 60%, compared with 40% for natural regenerated forest. Reducing the vegetation cover in planted plots from 70% to 50%, 35%, and 0% resulted in wave-height reductions of 51%, 42%, and 4%, respectively. A sea level rise (SLR) up to 0.96 m did not change the wave-dissipation potential of R. apiculata planted in the plots. However, an assumed decline in the width of vegetation from 1.5 km to 0.5 km, e.g. as a consequence of coastal erosion, reduced the height of incoming waves 21% (no SLR) and 29% (0.96 m SLR), as compared to 60% and 59%, respectively, without erosion.

ADDITIONAL INDEX WORDS: Mangrove, wave, climate change, SWAN-VEG, Thanh Phu.

INTRODUCTION Tropical coastlines are under great pressure due to a rapid increase in population, changes in land use, and infrastructural developments. Large-scale mismanagement of these coastlines and the inability to cope with events such as cyclones can have devastating long- and short-term effects, especially in developing countries. Densely vegetated mangrove forests contribute to the protection of coasts against severe sea waves caused by storms or tsunamis, which can affect local people living in tropical coastal areas (Alongi, 2008; Kathiresan and Rajendran, 2005). Thus many efforts to plant mangroves have been made based on qualitative observations (Hong and San, DOI: 10.2112/JCOASTRES-D-12-00271.1 received 27 December 2012; accepted in revision 16 April 2013; corrected proofs received 10 June 2013; published pre-print online 15 July 2013. *Corresponding author: [email protected] Ó Coastal Education and Research Foundation, Inc. 2015

1993). As argued by Lewis and Streever (2000), however, the function of the ecosystem and the hydrology at individual sites need to be understood quantitatively in order to determine what is needed in terms of vegetation density and width to provide the required level of protection. Unfortunately, very little knowledge has been accumulated in this regard. Studies on the wave-dissipation capacity of mangrove vegetation are limited compared to those on salt marshes (e.g. Knutson, 1988) or sea grass beds (e.g. Fonseca and Cahalan, 1992). Based on field observations, Mazda, Wolanski, et al. (1997) have shown the quantitative effects of two mangrove species, Rhizophora stylosa and Kandelia candel, on reduction of the impact of sea waves. Massel, Furukawa, and Brinkman (1999) have discussed the wavedissipation capacity of Rhizophora spp. and Sonneratia spp. based on a mathematical model. A predictive model of wave propagation through a nonuniform forest in water of changing depth in the mangrove forest of Can Gio, Vietnam, has been

Modelling the Impacts of Mangrove Vegetation Structure on Wave Dissipation

Figure 1. Study area in Thanh Phu Natural Reserve

developed by Vo-Luong and Massel (2008). Recently, Mendez and Losada (2004) and Suzuki et al. (2011) have incorporated a full frequency-direction wave spectrum in the numerical wave model SWAN (Simulating Waves Nearshore) and additionally included a layer-wise implementation of vegetation characteristics. Their results, however, cannot be applied directly to other regions or species, as each mangrove species has a unique configuration of trunks, prop roots, and pneumatophores that produces a different drag force (Wolanski et al., 2001) and therefore results in a different reduction rate of sea waves. For example, with regard to Bruguiera spp., Sonneratia spp., Avicennia spp., and Nypa fruticans, no information on their wave-dissipation capacity exists, either on their quantitative hydrological functions or on their physical impact. Accordingly, for useful and effective mangrove planting, there is a need for quantitative knowledge of the physical impact of individual mangrove species in relation to their wave-dissipation capacity. Vietnam is located in the tropical region of Asia. With its long coastlines and high concentration of population and economic activities in coastal areas, its coastal ecosystems and communities could potentially be dramatically impacted by a rise in sea level (CCFSC, 2001). According to Bates et al. (2008), changes in the hydrological cycle are expected to be the most significant aspect of climate change to affect the Mekong Delta. These include changes in sea level, precipitation patterns, and monsoon cycles, both frequency and magnitude of these climatic events. Climate change could also increase the frequency and severity of typhoons (Byrnes et al., 2011). Coastal erosion and accretion (sedimentation) are both part of the natural, dynamic interaction between shorelines and the sea. Erosion along the East Sea coast of the Mekong Delta appears to be mainly structural erosion caused by waves and longshore sediment transport, which strips sediment from the coast and deposits it at the confluence of the East sea current and the circular pattern of water flow from upper streams. Changes in sediment inflow to the coastal zone from rivers in the Mekong Delta could also be involved (Fabrice and Claudia,

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2012). Any rise in sea level or increase in the frequency and intensity of coastal storms is likely to accelerate the rate of coastal erosion and could lead to changes in its spatial pattern. Sections of the coastline that are now eroding could become areas of accretion; conversely, sections that are now accreting may become areas of erosion in the future. Coastlines of the Mekong Delta and Thanh Phu Natural Reserve are facing serious erosion and accretion in different areas. Mangroves have been a key element in reducing the rate of erosion and provide a protective barrier along the shoreline. Mangroves alone cannot provide complete protection against erosion in all situations, as illustrated by the current situation on the Thanh Phu coastline, where there are reasonably welldeveloped mangrove stands. Arguably, however, the rate of erosion would have been much faster without them (Clough and Phan, 2010). Mangroves are widely distributed in coastal areas in Vietnam, particularly in the south (Maurand, 1943). However, deforestation due to war activities and conversion to agricultural land and (more recently) shrimp ponds (Hong, 1999) has resulted in a decline from 408,500 ha in 1943 (Maurand, 1943) to 290,000 ha in 1962 (Rollet, 1981), 252,000 ha in 1982 (FIPI, 1982), 156,608 ha in 2001 (FIPI, 2001), 209,741 ha in 2006 (FIPI, 2007), and 139,955 ha in 2010 (MARD, 2011). In this paper, the SWAN model has been used to analyze the impact of different mangrove species and densities on wavedissipation capacity and wave-energy dissipation characteristics in Thanh Phu Natural Reserve, Mekong Delta. The SWAN model is a third-generation wave model developed at Delft University of Technology, the Netherlands (TU Delft, 2011). The SWAN model computes the processes of wave generation by wind, quadruplet wave-wave interactions, white-capping, bottom friction, depth-induced breaking interactions, and triad wave-wave interactions; it is thus capable of estimating wave propagation from offshore to nearshore. In addition to the fundamental features of waves, the effects of vegetation have been recently incorporated into the model. Using data about the present mangrove vegetation in Thanh Phu Natural Reserve, a comparison has been made between a planted area dominated by Rhizophora sp. and a naturally regenerated area dominated by Avicennia sp. To analyse the impacts of tree density, sea level rise (SLR) associated with climate change, and coastal erosion, only data about the planted forest have been used.

MATERIALS AND METHODS Study Area The study was carried out in Thanh Phu Natural Reserve, Ben Tre Province, Vietnam (Figure 1). Ben Tre is a coastal province in the Mekong Delta. The area is characterised by a tropical monsoon climate (Vu, 1994); average air temperature is about 268C 288C. In the channels inside Thanh Phu Natural Reserve, the average water temperature is about 288C. Normally the rainy season extends from May to November, with maximum monthly rainfall from June to August. Monthly maximum river flows occur from August to October. About 9% of the delta area is occupied by channels 20–30 m long. The tidal regime is semidiurnal with a tidal amplitude of 1.5–3.75 m.

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Tides can propagate 50–100 km inland with velocities of 0.75– 1.80 m s1. Salinity in the estuaries varies from 0%–12% (June– November) to 12%–30% (November–May) (Sub-FIPI II, 2003). Mangroves concentrated in the districts of Binh Dai, Ba Tri, and Thanh Phu covered 13,153 ha, or 33%, of the province in 1994 (Sub-FIPI II, 1995). In 2000 and 2007, coverage was estimated at 3413 and 3520 ha, respectively (Ben Tre DARD, 2010; Ngo, 2004). The coastal landscape at Thanh Phu consists of sandy belts, tidal mudflats, saline tidal swamps, and toxic acid-sulphate swamps. Thanh Phu Natural Reserve contains a narrow strip (about 0.8–5.0 km) of mangroves along the coastline between two of the mouths of the Mekong River: the Co Chien and Ham Luong estuaries (Figure 1). As is the case with other sites on the eastern coastline of the Mekong Delta, Thanh Phu Natural Reserve is strongly affected by both erosion as well as accretion (Sub-FIPI II, 1998, 2003). In 2009, Thanh Phu had a population of 127,574 inhabitants. The district covers an area of 44,350 ha, of which 17,300 ha is used for aquaculture and 15,000 ha as paddy fields. Another 2000–3000 ha is used for industrial crops, fruit farming, etc. (Ben Tre Statistics Office, 2010).

results in less wave energy behind the vegetation field and thus a lower wave height. Vegetation is modelled as cylindrical obstacles; vegetation characteristics such as height, width, density, and drag coefficient are used to determine the magnitude of the dissipation term. In addition, wave characteristics like significant wave height and peak period influence the energy-dissipation term (de Oude et al., 2010). The SWANVEG module for wave dissipation by vegetation (Suzuki et al., 2011) is based on Mendez and Losada (2004) and de Oude et al. (2010). The model assumes a group of cylinders as representation of vegetation. It includes vertical-layer schematization, making it possible to calculate multilayer structures such as mangroves. The energy dissipation term in SWAN-VEG is described by Suzuki et al. (2011):

Mangrove Vegetation Structure

Sds;veg;i

In the study area, planted Rhizophora apiculata is the predominant species, representing more than 80% of the mangrove vegetation. Three transects perpendicular to the coastline were laid out with a length of 1–2 km, depending on width of the vegetation. In each transect three 10 3 10 m plots were established—one close to the shore, one in the middle, and one at the end of the transect—according to accessibility. In each plot all trees were counted, while a separate 1 3 1 m subplot was established within each plot to count seedlings (less than 1.0 m in height) and saplings (1.0–4 m in height). Trees taller than 4 m were identified to the species level, and for these the following parameters were measured as described by English et al. (1994): (1) (2) (3) (4)

Tree and still root densities Root diameter and height Tree diameter at a height of 1.3 m Height from stratum to height of the first branch (h branch) (5) Height from stratum to height of the first leaf (h leaf) (6) Height from stratum to the top of the tree (h top). One similar transect with three plots was set up within the section of natural regenerated vegetation in the study area and was analysed in the same way. For the model input, possible zonation patterns were neglected, and the mean values of the measured parameters per vegetation type, i.e. planted and natural regenerated, were used.

Sds;veg ¼

ð1Þ

Sds;veg;i ;

i¼1

where Sds,veg is the total energy dissipation due to vegetation, n is the number of vegetation layers, i is the layer under consideration, and Sds,veg, i is the energy dissipation for layer i. The energy dissipation term for a given layer i therefore becomes rffiffiffi ¯ 3 k 2 2 ¼ g CD bv Nv 2r p

¯ i h sinh3 ka ¯ i1 hÞ þ 3ðsinh3 ka ¯ i h sinh3 ka ¯ i1 hÞ ðsinh3 ka 3 ¯ ¯ 3k cosh kh pffiffiffiffiffiffiffiffi 3 Etot Eðr; hÞ; ð2Þ 3

where g is gravitational acceleration, CD is the bulk drag coefficient, bv the cylinder diameter, Nv is the cylinder density, aih is the plant height, and E(r,h) is the wave-energy density. The bulk drag coefficient value 1.0 is used for simplicity in this study as per de Oude et al. (2010) and Narayan et al. (2010). As far as we are concerned, the bulk drag coefficient value of mangrove forest has not been very clear up to now due to the complexity of the vortex effect around cylindrical structures (e.g. stems and roots) under wave motion. For instance, different cylinder densities and arrays, which generate different vortex patterns, give different bulk drag coefficient values under waves. Furthermore, even drag coefficient values of a single cylinder range from 0.5 to 2.5 (Sarpkaya and Michael, 1981) with changes in Keulegan–Carpenter (KC) value and beta value. The ¯ and the total wave mean frequency r, ¯ the mean wave number k, energy Etot are defined as follows (Wamdi Group, 1988): r¯ ¼

k¯ ¼

E1 tot ¼

E1 tot

SWAN-VEG Model The SWAN-VEG model consists of the original SWAN model with a vegetation module added. This module consists of a variable for energy dissipation due to vegetation. The dissipated energy is subtracted from the incoming wave energy, which

n X

E1 tot

Z 0

2P

Z

2P 0

Z

‘

0

2P

0

Z

Z

Z

‘ 0

1 Eðr; hÞdrdh r

1

1 pffiffiffi Eðr; hÞdrdh k

ð3Þ

2 ð4Þ

‘

Eðr; hÞdrdh

ð5Þ

0

The wave-dissipation term associated with vegetation,



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Table 1. Summary of data used as input for the SWAN-VEG model to analyse the impact of mangrove type (planted vs. natural regenerated) and coverage, SLR, and coastal erosion with and without SLR. Vegetation

Vegetation type and density

SLR

Erosion and SLR

Code

Type

% Coverage

Width (km)

A1, A3 A4 A5 A6 B1 B2 B3 C1 C2 C3 C4

P, N P P P P P P P P P P

70 0 50 35 70

1.5 1.5

70

1.0 0.5 1.0 0.5

70

WL (m)

Hs (m)

Tp (s)

4.1 4.1

0.9 0.9

9.0 9.0

0.39 0.65 0.96 0

4.49 4.75 5.06 4.1

0.9

9.0

0.9

9.0

0.96

5.06

0.9

9.0

SLR (m) 0 0

1.5

Abbreviations: Width ¼ width of mangrove forest, SLR ¼ possible sea level–rise scenarios based on MONRE (2010), WL ¼ water level, Hs ¼ significant wave height, Tp ¼ wave peak period, P ¼ planted mangrove type, N ¼ natural regenerated mangrove type. The simulation codes are also used in Figures 3–6. Note: mangroves coverage(0–70% cover); sea level rise (0.39-0.96 m) and coastal erosion (from 1.5-0.5 km width) with/without sea level rise (0.96 m).

Sds,veg, is implemented in the source term Stot, as shown in Equation (6): Stot ¼ Sin þ Snl3 þ Snl4 þ Sds;b þ Sds;wc þ Sds;br

ð6Þ

where Sds,b is bottom friction, Sds,wc is white-capping, Sds,br is depth-induced breaking, Sin is wave growth due to wind input, Snl4 and (Snl3) are energy transfer within the spectrum due to nonlinear wave-wave interactions such as quadruplets (Snl4) and triads (Snl3). These six processes contribute to the source term Stot. The evolution of the wave spectrum is described by the spectral action balance equation, which for Cartesian coordinates is given by (e.g. Hasselmann et al., 1973) d d d d d Stot N þ cx N þ cy N þ cr N þ ch N ¼ ; dt dx dy dr dh r

depth at 1 m is 5.1 m). This value was used as the extremeevent condition in the simulation.

Vegetation Type and Density The local forest management system allows households to harvest or convert up to 30% of mangrove areas that are

ð7Þ

where the first term represents the local rate of change in N over time and the second and third terms represent propagation along the x and y directions with velocities cx and cy. Thus the model is capable of calculating wave dissipation associated with vegetation along with other source terms. As shown in Equation (7), not only one-dimensional but also two-dimensional calculations are feasible by distributing the vegetation factors in space.

Model Inputs Table 1 summarises the parameters used as input data in the SWAN-VEG model to analyse the impacts of (1) vegetation type and coverage, (2) water level as a consequence of SLR, and (3) the width of vegetation on the wave-dissipation capacity of mangroves.

Water Level, Wave Height and Wave Peak Period According to the Centre for Meteorology Forecast in Ben Tre and the Vietnam Institute of Meteorology, Hydrology, and Environment, the maximum recorded value of significant wave height on the coast of Ben Tre during the past 100 years was 0.9 m at a point 2.5 km from the shoreline with a depth of1 m. The shoreline refers to the line behind the 1.5 km wide mangrove vegetation (see Figure 2). The peak periods range from 7 s to 10 s. This value occurred when the water level was at 4.1 m (total

Figure 2. Spatial variation of significant wave height for simulations A1, A3, A4, A5, and A6: vegetation type and density (see Table 1 footnote for explanation of simulation codes). Wave: Hs denotes significant wave height; Tp denotes wave peak period; WL denotes water level. The dotted arrow shows an SLR of 0.96 m (scenario 3); the dashed arrow shows an SLR of 0.65 m (senario 2); the dotted and dashed arrow shows an SLR of 0.39 m (senario 1).


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Table 2. Mean values (significant difference) of mangrove vegetation characteristics in both planted Rhizophora (n ¼ 9) and natural regenerated (n ¼ 3) plots in Thanh Phu Natural Reserve. Parameter 2

Tree density (m ) Diameter at 1.3 m (m) Height branch (m) Height leaf (m) Height top (m) Stilt roots Density (m2) Diameter (m) Height root (m)

Planted Rhizophora 0.16 0.12 6.67 7.20 12.73

(0.07) (0.04) (1.83) (2.04) (1.77)

3.2 (0.4) 0.04 (0.01) 1.42 (0.64)

Natural Regenerated 0.24 0.11 1.90 4.13 7.63

(0.10) (0.08) (1.63) (1.92) (2.04) — — —

managed by them to water surface for aquaculture activities. Thus coverage of 70% of the actual measured figures was used to compare the impact of planted forests with naturally regenerated forests (codes A1 and A3, respectively; see Table 1). The impact of vegetation density was analysed by reducing the coverage in the planted forest from 70% (A1) to 50% (A5), 35% (A6), and 0% (A4) (Table 1).

Sea Level Rise Sea level rise scenarios for Vietnam have been developed based on different emission scenarios: low (B1), medium (B2), and high (A1F1) (MONRE, 2009). MONRE (2010) recalculated the expected water levels for several specific coastal locations in Vietnam. This resulted in predicted increases in water levels of 0.39 m (scenario B1), 0.65 m (B2), and 0.96 m (A1F1) in Ham Luong (Ben Tre) in the year 2050, 2075, and 2100, respectively. In this study, these values have been added on top of the extreme water level of 4.1 m (comparisons A1, B1, B2, and B3; see Table 1).

Coastal Erosion The expected SLR will affect the hydrodynamic regime of the Vietnam East Sea through general circulation, tidal amplitude, and tidal coastal flooding. In addition, factors affecting hydrodynamic regimes such as wind, storms, and air temperature will be impacted by climate change. Changes in these parameters are expected to lead to changes in erosion and accretion in the region (Ben Tre DARD, 2010). Therefore, in this study, future erosion scenarios simulation a reduction in the width of mangrove forests to 0.5 and 1.0 km have been included (comparison of A1 with C1 and C2; comparison of B3 with C3 and C4; see Table 1). Although a simplification, it was assumed that erosion did not change the shore topography, and thus a narrower mangrove forest resulted in the ocean floor rising less steeply.

RESULTS Mangrove Vegetation Structure Vegetation in the planted site consisted of Rhizophora apiculata (.90%) and a mix of R. mucronata, Sonneratia caseolaris, Avicennia alba and Nypa fruticans. Sonneratia caseolaris and A. alba were mainly found on the alluvial coastal and river mud flats, while N. fruticans was planted in the inland brackish aquaculture ponds. In the planted area, the tree density was 0.16 trees m2, with a mean tree height of 12.7

Figure 3. Root structures for the different tree species: stilt roots for Rhizophora sp. and pneumatophores for Avicennia sp. and Sonneratia sp. (Hong and San, 1993).

m and a diameter (measured at a height of 1.3 m) of 0.12 m (Table 2). Vegetation in the naturally regenerated site consisted mainly of A. alba and S. caseolaris. Avicennia alba grows naturally on mud flats with average tidal levels of 1.0 m to 1.5 m and is directly exposed to waves and wind from the sea. In some areas, A. officinalis appears landwards from the A. alba zone, protected from the direct impact of waves and wind. Sonneratia caseolaris develops on soft mud at depths of 1.0–1.5 m, affected by tides, waves and wind, and a mixture of seawater and freshwater from rivers. Although tree density in these plots was higher than in the planted plots (0.24 trees m2), their average size was smaller (3.9 m high and a diameter of 0.11 m). Besides these differences, another major difference between the planted and naturally regenerated sites that could affect wave-dissipation capacity is caused by the different root structures for the various tree species, i.e. stilt roots for Rhizophora and pneumatophores for Avicennia (Figure 3). Based on the characteristics of the planted Rhizophora trees (h top, 12.73 m; h leaf, 7.20 m; h branch, 6.67 m) and the maximum water level (5.06 m), it was assumed that the stems and roots of the trees will play a major role in wave dissipation; therefore the influence of the canopy was not considered.

Model Analyses Vegetation Type and Density The wave-simulation model started 2.5 km seaward from the shoreline with a wave of 0.9 m high. After passing the 1.5 km wide mangrove forest, the wave height was reduced by 60% in the planted forest dominated by R. apiculata with stilt roots (70% coverage, A1) compared to 40% by the natural regenerated forest (A3), which is characterised by A. alba and S. caseolaris and their associated numerous, but small (10–15 cm), pneumatophores (Figure 2, Table 3). Decreasing the mangrove cover in the planted Rhizophora forest to 50% and 35% resulted in a reduction of wave height of 51% and 42%, respectively (A5 and A6; see Figure 2, Table 3). When the vegetation was removed completely (A4), the wave height even slightly increased after passing the 1.5 km of no forest (now cleared) (Figure 2, Table 3).

Sea Level Rise Figure 4 shows the transmitted wave heights in the planted forest for three different sea levels (B1, 4.49 m; B2, 4.75 m; and B3, 5.06 m). Although the water levels increased, the impact of the mangrove forest on the incoming wave height was the



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Table 3. Summarised output of the SWAN-VEG model showing percent reduction of the incoming wave height after passage through the mangrove vegetation (see Table 1 footnote for explanation of simulation codes). Parameter Vegetation type A1 A3 Density A1 A5 A6 A4 SLR A1 B1 B2 B3 Erosion A1 C1 C2 Erosion and SLR B3 C3 C4

% Reduction 60 40 60 51 42 4 60 59 59 59 60 46 21 59 46 29

same, i.e. a 59% reduction, which is comparable to the 60% observed in case A1 (Table 3).

Coastal Erosion The impacts of coastal erosion, in combination with a more gradually increasing ocean floor (C1 and C2) and an SLR of 0.96 m (C3 and C4), are illustrated in Figure 5. The impact of wave dissipation by vegetation (planted forest with 70% cover) shifts landwards when the width of the mangrove forest is diminished to 1.0 km and 0.5 km. This resulted in a clear impact on the reduction of wave height: 46% and 21% reduction by the 1.0 km and 0.5 km forests, respectively (compared to 60% by the 1.5 km forest with the sea level at 4.1 m); and 46% and 29% reduction by the 1.0 km and 0.5 km forests, respectively (compared to 59% by the 1.5 km forest with the sea level at 5.06 m; see Table 3).

DISCUSSION AND CONCLUSIONS An analysis of the output of the SWAN-VEG model confirms the potential role of mangroves in dissipating incoming wave energy. A simulated reduction in mangrove vegetation in

Figure 4. Spatial variation of significant wave height for simulations B1, B2, and B3 according to SLR (see Table 1 footnote for explanation of simulation codes).

Figure 5. Spatial variation of significant wave height in erosion simulations C1/C3 and C2/C4 (see Table 1 footnote for explanation of simulation codes).

planted R. apiculata–dominated forests from 70% to 0% resulted in an increase in wave height from about 0.4 m to more than 0.9 m. Similar conclusions were made by Mazda, Magi, et al. (1997), Mazda, Wolanski, et al. (1997), and Quartel et al. (2007) in the field when measuring surface wave propagation in mangrove forests of Kandelia candel (in the Tong King Delta, Vietnam) and Bruguiera gymnorrhiza (in Nakama-Gawa in Iriomote Island, Japan) and R. stylosa and K. candel (in the Red River Delta, Vietnam), respectively. Comparable results were also described by Massel, Furukawa, and Brinkman (1999) in a study combining theoretical analysis and a field experiment with Rhizophora sp. Rhizophora apiculata is characterized by very dense aboveground stilt roots. This probably explains the difference in wave dissipation capacity between the planted Rhizophora forest and the naturally regenerated plot (0.4 and 0.5 m, respectively). The latter consists mainly of Avicennia sp. and Sonneratia sp., both species with pneumatophores. A similar difference in wave-energy reduction between species has also been observed by Mazda, Magi, et al. (1997) and Mazda et al. (2006) when comparing vegetation dominated by Sonneratia sp. and K. candel, respectively. Besides reducing the height of incoming waves, mangrove forests probably also reduce wind-driven and tidal currents due to their dense network of stems, branches, and aboveground roots (Quartel et al., 2007). The mangrove trees in the study area rise quite high above the water level (12.7 m on average) and might efficiently reduce wind energy. Further studies on this role of mangroves should be carried out, especially in the Mekong Delta area, where the windy season has a strong influence on the coastline. Increasing the sea level in the model up to 0.96 m, thus anticipating a possible SLR due to climate change, did not reduce the wave-energy dissipation potential of the planted Rhizophora forest. The resulting wave height of almost 0.4 m is not different in the four tested water depths. This probably could be explained by the high density of stems and aboveground roots distributed throughout the whole water depth, even with a rise in sea level. The width of the vegetation, however, seems to have more impact on the wave-dissipation capacity of the planted mangrove forest. Although a reduction from 1.5 km to 1.0 km resulted in a slight increase in wave height from 0.4 m to 0.5 m, further reducing the forest width to 0.5 km resulted in a wave height of 0.7 m. The actual sea level (present level and an increase of 0.96 m) did not affect these results. In addition to vegetation density, the width of the area to be planted is an


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important factor in wave attenuation for protecting tropical coastlines. Quartel et al. (2007) found that wave heights were depth limited. Small water depths corresponded with small wave heights and large water depths with higher incident waves. With the expected SLR as a consequence of climate change, coastal areas will face the impact of higher wave height. The existence of mangrove forests in coastal areas in general, and in the study area in particular, will thus be very important in terms of protecting the coastline from the impacts of sea waves. Further investigations are needed to quantify the function of mangroves in wind-driven and tidal currents in protecting the coastal zone.

ACKNOWLEDGMENTS We thank the Department of Agriculture and Rural Development of Ben Tre Province and the Management Board of Thanh Phu Natural Reserve for their unstinting support for data collection and field survey. The work reported here was undertaken as part of the research programme ‘‘PRoACC—Postdoctoral Research Programme on Climate Change Adaptation in the Mekong River Basin’’. The project is funded by the Netherlands Ministry of Development Cooperation (DGIS) through the UNESCOIHE Partnership Research Fund. This research project is a joint initiative of UNESCO-IHE Institute for Water Education and many partner institutions in the Lower Mekong countries and China.

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