metadata: authors: 0: author: Gijón Mancheño, Alejandra
1: author: Vuik,Vincent
2: author: Wesenbeeck,Bregje K. van
3: author: Jonkman,S.N. (Bas)
4: author: Moll,Roelof
5: author: Kazi,Swarna
6: author: Urrutia Duarte,Ignacio M.
7: author: Mathijs Van Ledden
count: Bangladesh
docna: 0: docna: Wave Reduction by Mangroves during Cyclones
            in Bangladesh : Implementing Nature-Based Solutions for
            Coastal Resilience
docty: Policy Research Working Paper
theme: Natural disaster management,Water resource management,Environmental policies and institutions,Other social development,Participation and civic engagement
lndinstr: 0: lndinstr: Investment Project Financing
lndinstr_key: 1353855
majtheme: Social protection and risk management,Social dev/gender/inclusion,Environment and natural resource management
abstracts: cdata!: This paper investigates how mangrove
            foreshores can be integrated into embankment designs in
            Bangladesh. The effect of mangroves on surges has already
            been studied for the design conditions of Bangladesh.
            However, the impact of wave attenuation by mangroves on
            embankment designs is not known. A model is thus developed
            to estimate the wave height reduction by a mangrove forest,
            and how such wave attenuation would influence the design of
            a landward embankment. Model simulations suggest that
            mangrove belts with a width between 100 and 1,000 meters
            (perpendicular to the coast) could provide wave attenuation
            rates between 7 and 55 percent (compared to a situation
            without mangroves) at potential afforestation sites
            identified in previous studies. Such wave attenuation rates
            would reduce the embankment height by 0.09&#x2013;0.30
            meters, diminish the slope revetment thickness by
            13&#x2013;46 percent, and decrease the wave shear
            stresses at the embankment toe up to 25&#x2013;70
            percent. Relatively wider mangrove belts not only cause a
            larger reduction of the embankment design requirements, but
            also host larger biodiversity and are more resilient against
            pests and extreme events. The model results are highly
            sensitive to the mangrove properties, and collecting data on
            the local mangrove species is recommended to reduce
            uncertainty in the predictions. Moreover, the results also
            suggest that trees older than 10&#x2013;20 years might
            collapse during storms. Expanding the mangrove stability
            model, including other pioneer species in the analysis, and
            exploring the option of canopy pruning are thus advised to
            ensure the integrity of any future afforestation efforts.
            Overall, this paper provides a methodology that could be
            applied to design nature-based solutions in Bangladesh.
display_title: Wave Reduction by Mangroves during
            Cyclones in Bangladesh : Implementing Nature-Based Solutions
            for Coastal Resilience
listing_relative_url: /research/2022/11/33946809/wave-reduction-mangroves-during-cyclones-bangladesh-implementing-nature-based-solutions-coastal-resilience
EnvironmentalCategory: A
topicv3: Environmental Engineering m1326003 2197,Flood Protection m1326683 154,Ecosystem-Based Adaptation m1327546 576,Climate Change Adaptation m1327767 574,Tropical Cyclone m1503640 3176
publishtoextweb_dt: 2022-11-29T00:00:00Z
dois: 10.1596/1813-9450-10240
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: NA
3: author_key: NA
4: author_key: NA
5: author_key: 645388
6: author_key: 886060
7: author_key: NA
model_extractions: Policy Research Working Paper 10240 Wave Reduction by Mangroves during Cyclones in Bangladesh Implementing Nature-Based Solutions for Coastal Resilience _Alejandra Gijón Mancheño Vincent Vuik Bregje K. van Wesenbeeck S.N. (Bas) Jonkman Roelof Moll Swarna Kazi Ignacio Urrutia Mathijs van Ledden_ Urban, Disaster Risk Management, Resilience and Land Global Practice November 2022
Policy Research Working Paper 10240

# **Abstract**

This paper investigates how mangrove foreshores can be integrated into embankment designs in Bangladesh. The effect of mangroves on surges has already been studied for the design conditions of Bangladesh. However, the impact of wave attenuation by mangroves on embankment designs is not known. A model is thus developed to estimate the wave height reduction by a mangrove forest, and how such wave attenuation would influence the design of a landward embankment. Model simulations suggest that mangrove belts with a width between 100 and 1,000 meters (perpendicular to the coast) could provide wave attenuation rates between 7 and 55 percent (compared to a situation without mangroves) at potential afforestation sites identified in previous studies. Such wave attenuation rates would reduce the embankment height by 0.09–0.30 meters, diminish the slope revetment thickness by 13–46 percent, and decrease the wave shear stresses at the embankment toe up to 25–70 percent. Relatively wider mangrove belts not only cause a larger reduction of the embankment design requirements, but also host larger biodiversity and are more resilient against pests and extreme events. The model results are highly sensitive to the mangrove properties, and collecting data on the local mangrove species is recommended to reduce uncertainty in the predictions. Moreover, the results also suggest that trees older than 10–20 years might collapse during storms. Expanding the mangrove stability model, including other pioneer species in the analysis, and exploring the option of canopy pruning are thus advised to ensure the integrity of any future afforestation efforts. Overall, this paper provides a methodology that could be applied to design nature-based solutions in Bangladesh.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. This research received financial support from the European Union (EU) in the framework of the EU-SAR Capacity Building for Disaster Risk Management Program, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR). It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at a.gijonmancheno-1@tudelft.nl, mvanledden@worldbank.org, and skazi1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Wave Reduction by Mangroves during Cyclones in Bangladesh**

## **Implementing Nature-Based Solutions for Coastal Resilience**

Alejandra Gijón Mancheño<sup>1</sup> , Vincent Vuik<sup>1,2</sup> , Bregje K. van Wesenbeeck<sup>1,3</sup> , S.N. (Bas) Jonkman<sup>1</sup> , Roelof Moll<sup>1</sup> , Swarna Kazi<sup>4</sup> , Ignacio Urrutia<sup>4</sup> , Mathijs van Ledden<sup>4</sup> 1Delft University of Technology 2Royal Haskoning 3Deltares 4World Bank

**Keywords** : building with nature, mangroves, wave attenuation, flood risk reduction, hybrid flood protection, embankments, climate change, resilience, Bangladesh.

### **JEL Classification** : Q54, Q56, Q57.
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hazards, such as structure reinforcement and afforestation at the seaward side of the embankments (World Bank, 2013). Afforestation plans included both planting of commercial species for economic purposes and mangrove planting for coastal protection. Bangladesh has large mangrove afforestation potential, as it is the home of 60% of the Sundarbans, the largest mangrove forest in the world, and it has considerable experience in mangrove afforestation, with 150,000 ha of planted mangroves since the 1960s (Saenger and Siddiqi, 1993). The modeling work of Dasgupta et al. (2017) showed that mangrove afforestation in Bangladesh would likely have a small effect on surge heights. Nevertheless, mangroves could significantly reduce the flow velocities and erosion near embankments (Dasgupta et al., 2017). Mangrove belts could also provide additional protection to coastal structures by attenuating short waves, and thus reducing the wave loads acting on them and the wave runup. Potential locations for mangrove afforestation seaward from embankments have been identified for Bangladesh (Gijón Mancheño et al., 2021), however, their effect on the wave loads acting on the structures and on wave runup has not been evaluated.

This paper quantifies the potential wave height reduction by a mangrove belt planted on the seaside of embankments, and how such wave attenuation would impact embankment designs. Wave energy attenuation by mangroves is calculated by integrating the energy dissipation caused by the trees, and by solving the wave energy balance as done by Mendez and Losada (2004). The wave model is applied using the hydraulic properties of _Sonneratia apetala_ , which is a mangrove pioneer species in Bangladesh, obtained from the analysis of field pictures and from a literature study. The potential failure of the trees by either overturning or trunk breakage is estimated for the design conditions using empirical expressions developed in agroforestry studies. Finally, the calculated wave heights are used as an input in the design formulas of embankments to evaluate how the presence of a mangrove belt would affect their height, slope protection needs, and the shear stresses at their toe.

# 2. Theoretical background

2.1. Coastal protection by mangroves Mangroves reduce coastal erosion and flooding through several mechanisms. During storms, strong winds push the sea water towards the coast, causing an increase of water level 2
<!-- Start of picture text --> (a)<br>Wave run-up wind.<br>Fo —. 8 WaveSurge <——a<br>(b) Mangroves have Mangroves reduce waves<br>limited effect and wave run-up<br>on surges <—ee eS<br><!-- End of picture text -->
<!-- Start of picture text -->
[autor |W Atenation per m (6) | Wave height (m) | Wave periods) [Species [Height (m) [Density fe7m)<br>PMardaetai a7 | oreo | S| Kandeliacandel 02 |<br> Mazdaeta(i997 | 0032 —*(| SiS —*dCKandeliacandel | 4 | ge<br>[Mazda etal-(2000) | 020060 | 011036 | 810 | Sommeratiasp. |_| 0.08:<br>| Brinkman (2006 | 028050 | ooroor | 2 «i SS Med SY<br>| Brinnan (2006) | 03-188 | ogeoas | 2 —~*+|~—Bnguierasp | =| SS<br>Brinkman (2006) | 2.25280 ~(| oorogs | 6 S«dYSSSsMed S|<br>| Horstmanet al. (2014) | 0,140 | o01020_| 255 | _Mmed_—<[ =~ SS<br>[Sancher-Nufer et al. (2070)| 015045 | 000020 |__| _Rhivophora mangle [|<br>Bést et al. {2022) 0,20-0,32 0,00-0,70 45 Mixed 0,07-13 007<br><!-- End of picture text -->
even further, as they decrease the maximum waves that can propagate towards the embankment without breaking.

Although the previous functions result in sheltering of the coast from extreme events, applying mangroves to reduce the wave loads on dikes requires considering several limiting factors. Firstly, mangroves have specific habitat requirements, and they may not grow at every location. Mangroves grow at intertidal areas with low wave action, and sufficient freshwater and sediment input (Alongi, 2002), and different mangrove species have specific requirements in terms of temperature, inundation, and salinity levels that they can tolerate. Potential sites for mangrove establishment have been mapped in Bangladesh (Gijón Mancheño et al., 2021), indicating locations where mangroves could grow naturally, and those where erosion mitigation would be necessary for mangrove colonization. However, the potential wave load reduction by mangroves at such sites has not been investigated, which hinders accounting for the effect of mangroves on embankment designs in future CEIP programs.

2.2. Mangrove geometry Wave reduction by mangroves is highly dependent on the location of the trees on the tidal profile and on their geometry. Mangrove species often grow in distinct bands between mean sea level (MSL) and mean high water (MHW), depending on their relative tolerance to inundation (Snedaker, 1982). Some species, denoted as pioneers, have a relatively higher tolerance to salinity and are the first to colonize at locations near MSL. Within the salttolerant species found in Bangladesh, _Sonneratia apetala_ (locally known as Keora) and _Avicennia officinalis_ (locally known as Baen) are the pioneers with highest planting survival (Saenger and Siddiqi, 1993). Since _S. apetala_ is the most planted species, for which there is also most available information about its geometrical and structural properties, this species is chosen for further analysis.

At sites where mangroves were not present before, such as on newly accreted land, mangrove trees will need time to colonize and grow. Ecologic studies suggest that mangroves can reach their full height in 10 years (Wang et al., 2021), and their wave attenuation capacity will increase as the surface area of the tree enlarges by growth of the trunk diameter, and by increasing the size and number of branches (Maza et al., 2021).

5
<!-- Start of picture text --> Height (h.) 39 20<br>(a) (b) © Planted trees from China * 18 (c) °<br>25 © Planted trees from Bangladesh ><br>» Natural trees from Bangladesh ° 16<br>20 ° ° x 14 © 0° °<br>Canopy te ° _" "00 2 6 5<br>s15 > cS) ° = —+ 10 oe<br>a e0° ad al [ono]<br>0) oy ° 8 PH o<br>fear? x s| f° °<br>Diameter at °<br>Trunk —_> f— ---: 4<br>Roots [| breast heightcertda (d,.,,) Weleda 1 3 77m 50 . 2+ol f? °<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>age (years) age (years)<br><!-- End of picture text -->
~~<u>se ss es GO Re es sses</u> ssse~~ **~~e~~** ~~sDDO~~ (a) b, = crown width VOUT tia (b) b, = total branch width (c) b, = trunk width <!-- Start of picture text --> VOU tia<br><!-- End of picture text --> <!-- Start of picture text --> VOUT ti<br><!-- End of picture text -->
over by wind and/or waves (van Hespen et al., 2021). Including the full leaf area in predictions most likely overpredicts the effect of trees during extreme events. vv bb Conversely, some authors completely neglect the effect of canopy leaves and branches and assume that the tree area above the root system is given by the area of the trunk (Massel et al., 1999), as shown in Figure 2.3 (c). This assumption may hold for measurements taken during calm conditions, where the water levels remain below the canopy, and/or for very tall trees for which the canopy is never submerged. However, canopies may be the largest contributor to wave attenuation for relatively higher water levels (van Wesenbeeck et al., 2022) and for shorter trees. Under such conditions, using the trunk width as the equivalent width would likely underestimate the tree area, and the wave dissipation by a tree. vv bb The effective canopy width for wave attenuation purposes is likely to correspond with an intermediate situation (Figure 2.3, b). For willow trees, tree trunk and branch models were developed and successfully used to predict wave attenuation by Kalloe et al. (2022). However, the vertical distribution of the canopy branch area has not been quantified for mangroves.

2.3. Mangrove stability against storms <mark>Although mangroves can protect the coast from storm events, they can also be degraded</mark> by the impact of winds and waves. Hurricanes and cyclones can directly damage mangroves by defoliating them, by breaking their trunk or branches, or by overturning them (Figure 2.4). Extreme events can also damage mangroves by altering their habitat, for instance, by <mark>changing the local hydrology, topography, sediment conditions, or the forest structure</mark> (Herrera-Silveira et al., 2022). In Bangladesh the cyclone Sidr (2007) damaged between 11% (Carrol et al., 2019) and 45% (Bhowmik and Cabral, 2011) of the total area of the <mark>Sundarbans. Even though mangrove vegetation can recover over time (Carrol et al., 2019), its degradation implies that the hinterland is less sheltered by the vegetation (Pendleton et al.,</mark> 2012). Several studies have investigated the potential effect of restored mangrove foreshores <mark>on flood risk (Menéndez et al., 2020; van Zelst et al., 2021), but they often assume the</mark> vegetation fully grown and/or neglect vegetation failure.

8
<!-- Start of picture text --> (a) Root failure (b) Trunk failure (c) Partial damage<br>(d) Soil failure (e) Erosion<br>EY oe<br>—_<br><!-- End of picture text -->
# 3. Methodology

This section describes the potential mangrove sites (Section 3.1), the method developed to quantify the effect of mangroves on embankment designs (Section 3.2), and the modeling scenarios (Section 3.3).

## 3.1. Potential mangrove sites in Bangladesh

The coastal system of Bangladesh is located on the north of the Bay of Bengal, on the Ganges-Brahmaputra-Meghna (GBM) delta. The western side of the coastal area is covered by 60% of the Sundarbans, the largest continuous mangrove forest in the world (Figure 1.1). The central part of the system consists of low-lying polder areas, whereas the east coast of the country is formed by smaller polders located on steeper ground. The climate of Bangladesh is characterized by several seasons: the hot pre-monsoon between March and May, the rainy monsoon between June and August, the post-monsoon between OctoberNovember, and the cool and dry winter between November and February. Cyclones occur yearly in Bangladesh, and severe cyclones with wind speeds varying between 90-119 km/hour hit the coastline on average every 3 years (Dasgupta et al., 2017). Surge heights often reach 3-5 m (Karim and Mimura, 2008; Zaman and Mondal, 2020), and observations suggest that 10 m water levels (consisting of surge plus tides) occur with a frequency of once in every 20 years (Dasgupta et al., 2014).

10
<!-- Start of picture text --> cove orooe ceooe<br>4 y 4<br> . . »<br>Z € | a C bs isa)<br>A i tdwy f:<br>g 4 f:) 6 J Exposed $ 3<br>Ee ; 5 area ne<br>ug ) Ay! od 6 with low Q x<br>epee tJ ‘ e wy 9 presence<br>@ Soe : al ® » 3 4 Fes<br>e Relatively > st‘ 2 ‘@A S F. , 7<br>higher ground bi 2<br>me! Ze|!<br>== Expanding foreshore IRIDHAA OWIZA = BB.<br>Retreating foreshore Ml Areas below MSL in 2100 (+2 m RSLR) QSaebes<br>I Polder areas I Areas below MSL in 2050 (+1 m RSLR) 1On— wee kn ‘<br>Mangrove areas Areas below MSL in 2050 (+0.3 m RSLR) a<br>S000 s100'E S200F :<br><!-- End of picture text -->
2018). It should be noted that van Berchum et al. (2020) applied one design condition per polder, whereas different parts of the coastline may have varying wave exposure in terms of fetch, water depth, and coastline angle with respect to the dominant wave direction.

_Table 3. Design conditions (surge levels and incoming significant wave heights, Hm0) from IWM (2018) and associated embankment crest height from van Berchum et al. (2020) at sites with potential for developing mangrove belts seaward from embankments._

|Site|Polder|Location|Surge [mPWD]|Hm0[m]|Required crest height [m]|
|---|---|---|---|---|---|
|number|||2-M|2-M|2-M|
|1|55/4|Galachipa|4,9|3,7|6,3|
|2|55/3|Galachipa|5,1|2,3|5,9|
|3|56/57|Bhola|5|3,0|6,1|
|4|73/2|Hatiya South|4,9|3,4|6,2|
|5|66/3|Khangona|4,8|2,1|5,5|
|6|69|Boro Moheshkhali|4,4|2,7|5,4|

- 3.2. Modeling approach

The method to include the effect of mangroves on embankment designs is schematized in Figure 3.2, and consists of the following steps:

- Propagating waves from the offshore boundary to the toe of the structure (Equations 4-6 of Appendix B)

- Estimating potential vegetation failure (Equations 2 and 11-14 of Appendix B)

- Designing embankment using propagated waves as input, specifically:

- The crest height of the embankment (Equation 7 of Appendix B), and

- The size of the blocks of the slope (Equation 8 of Appendix B)

- Estimating the shear stresses at the toe of the structure (Equation 9-10 of Appendix B).

12
<!-- Start of picture text --> Hydraulic loads Integrating mangroves Dike design<br>on dike designs<br>\ Wave _<br> Neo,<br>Slope<br>— i NTN! protection<br>ttt )\<br>Surge, tide =<br>L surge, tie \ ([ Breaking Collapse<br>P. @ NS<br>UL, =<br><!-- End of picture text -->
<!-- Start of picture text --> 20 0.04<br>Canopy (a) Canopy —— Roots (b)<br>18 ——— Trunk 0035 | «Trunk = —— Total<br>16 —— Roots<br>0.03<br>14 €<br>5<br>42 = 0.025<br>=£10 2S002s<br>N 8 2an<br>SD 0015<br>6 ss >2<br>4 Surge level wiY 001<br>2 0.005<br>0 10 20 30 40 50 0ie) 10 20.‘30‘‘40‘:50<br>age (years) age (years)<br><!-- End of picture text -->
4.2. Wave attenuation through a mangrove belt 4.2.1. Effect of wave height on wave attenuation The effect of the incoming wave height on wave attenuation by a 100 m belt with a slope of 1/100 is evaluated for different forest ages in Figure 4.2 for = 0.7 (Figure 4.2 b) and for = 1.3 (Figure 4.2 c). Wave attenuation is here defined as the reduction in wave DD,ww cc height at the toe of an embankment with respect to a situation without mangroves: DD,ww cc (1) W W a (%) = 100<sup>HHn</sup> n v<sup>v</sup><sup><u>v</u>−HHv</sup><sup><u>v</u></sup> WWa WWa WWa vv HH where n v is the wave height at the embankment toe without mangrove vegetation, and v is the wave height at the same location considering the presence of a mangrove vv HH foreshore. Without mangroves, waves experience dissipation due to breaking across the bare vv HH foreshore. In the presence of mangroves, waves experience additional dissipation due to the resistance exerted by the trees. As it can be seen in Figure 4.2 (b)-(c), young trees below 5- years-old induce attenuation rates smaller than 4% in all scenarios, and wave attenuation increases as trees grow older.

Wave reduction by mangroves also varies with the height of the incoming waves, as the wave height influences both wave breaking and wave dissipation by the vegetation. Higher waves experience more dissipation due to breaking both seaward from and inside the forest. Conversely, smaller waves penetrate further into the forest and experience proportionally more wave attenuation by the vegetation (compared to how much they decrease due to wave breaking). Although the model results provide an indication of the potential wave attenuation provided by the mangroves, the choice of drag coefficient can significantly affect the predictions. For instance, the maximum wave attenuation by a 50-year-old forests increases from 16% to 25% when the drag coefficient is increased from = 0.7 to = 1.3. Wave reduction by mangroves is also very dependent on the vegetation properties, water DD,ww DD,ww cc cc levels, and profile slope; additional sensitivity analyses are provided in Appendix E.

15
<!-- Start of picture text --> (a) 1<br><anmensrove bet Incoming wave<br>Ao<br>Wave attenuationARETE NAT E T ](%) \ DT<br>(b) Lower mangrove resistance (c) Higher mangrove resistance<br>3 25 3 25<br>28 28<br>26 20 26 20<br>24 24<br>E 22 ssss£ 8 56<br>2.2 © °—£ 2 fe)<br>x o x a<br>1.8 we 18 e<br>= 0 3<br>16 | o 1.6 °<br>14 Lie a 5 zs 5 &<br>1 I\ Lill 16% BP 1 25%<br>5 10 15 20 25 30 35 40 45 50 5 10 15 20 2 30 35 40 45 50<br>age (years) age (years)<br><!-- End of picture text -->
<!-- Start of picture text --> (a)<br>Wave attenuationutero A (%)TT nu Tn A<br>(b) Lower mangrove resistance (c) Higher mangrove resistance<br>1000 55 1000 55<br>900 30% 50 900 55% 50<br>800 45 800 | | “<br>= oF 2 0 =<br>=€ 700 35 oO< —€ 700 | 35 ©<<br>= 600 2 = 600 2<br>> 30 & a } 30 @<br>c Ss & 3<br>® 500 Cc 2 500 c<br>va 2 5 ra 2 5<br>B 400 2 2 400 2 g<br>S 2 =<br>300 15 300 15 3<br>200 10 200 10<br>100 5 100 5<br>0 Ts<br>5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50<br>age (years) age (years)<br><!-- End of picture text -->
<!-- Start of picture text --> 20<br>(b) jm 50 years-old<br>18 mm 20 years-old<br>| 10 years-old<br>16 Mmmm 3 years-old<br>14<br>_ 2<br>£<br>~ 10<br>z 8<br>=6 wind loads 4<br>\<br>a<br>vu ie fh 4 wave loads | Surge level<br>el 0 0 0.5 1.0 1.5 2.0 2.5 3 35<br>dF [N]<br>1g £10" 1g X108<br>16 (c) === Resistance against trunk breakage 16<br>w= Resistance against overturning<br>14 =—— Wind load 14<br>—— Wave load<br>12 12<br>E 10 £ 10<br>=8=8<br>6. 6 A<br>4 4<br>0 ) ie10 20 30 40 50 00 10di20 30 40 50<br>age (years) age (years)<br><!-- End of picture text -->
<!-- Start of picture text --> 52 5.65<br>(a) Effect of mangroves on embankment height (b) Effect of mangrove age 56 (c) Effect of forest length<br>54 |<br>5.55<br>LO LA Wy AN 5 E 55<br>"re" Lower E DB 5.45<br>ve’ clestheloiti Ss= 49 reduction by 4 oO= 54<br>= Oo mangrove belt of 100 m 8<br>mn % 48 with 1/100 slope 5 5.35<br>itt ooS47 53<br>Mangrove belt 46 —= no mangroves 525) no mangroves<br>— mangroves 52+ =< mangroves<br>45 : . 5185400200 300 400 500 600 700 800 900 1000<br>5 10 15 20 2 30 35 40 45 50<br>age (years) forest length (m)<br><!-- End of picture text -->
<!-- Start of picture text --> (a) Reduction of revetment weight by mangroves (b) Effect of forest length<br>0.25<br>wave attenuation om NO Mangroves<br>/\_ PZby mangroves 02 mangroves<br>=~ E<br>Thinner a<br>mCi in! htt NIT p\ revetment( D) 0.15<br>0.1<br>0 100 200 300 400 500 600 700 800 900 1000<br>forest length (m)<br><!-- End of picture text -->
<!-- Start of picture text --> (a) Shear stress reduction by mangroves (b) Effect of forest length<br>wave attenuation 0.7 om NO Mangroves<br>by mangroves == mangroves<br>0.6<br>0.5<br>Papeete J os<br>quia \ eS<br>erosion mitigation 02<br>(V7) 04<br>0<br>0 100 200 300 400 500 600 700 800 900 1000<br>forest length (m)<br><!-- End of picture text -->
_Table 4. Estimated effect of mangroves at the potential afforestation sites summarized in Table 3. For each case, the effect of a 100-m wide mangrove belt with a slope of 1/100 is evaluated in terms of (1) embankment height reduction, (2) reduction of the thickness needed for slope protection, and (3) reduction of the shear stresses at the toe of the embankment. The estimates are obtained for_ = 1.3 _,_ = 0.1 _trees/m_<sup>_2_</sup> _, and a profile slope of 1/100._ DD,ww vv cc NN

|Site|Polder|Location|Surge<br>[mPWD]|Hm0<br>[m]|100 m of m<br>|angroves with slope o<br>|f 1/100<br>|
|---|---|---|---|---|---|---|---|
|number|||2-M|2-M|Crest height<br>reduction[m]|Slope protection<br>reduction[%]|Stress<br>reduction[%]|
|1|55/4|Galachipa|4,9|3,7|0.08|8|15|
|2|55/3|Galachipa|5,1|2,3|0.09|11|21|
|3|56/57|Bhola|5|3|0.08|9|17|
|4|73/2|Hatiya South|4,9|3,4|0.08|8|16|
|5|66/3|Khangona|4,8|2,1|0.09|12|23|
|6|69|Boro<br>Moheshkhali|4,4|2,7|0.06|8|16|

A wider belt of 500 m over a shallower profile with a slope of 1/1000 would have a more pronounced effect on embankment designs (Table 5) with a crest height reduction of 0.130.19 m, a slope protection thickness reduction of 23%-32%, and a shear stress reduction of 41%-54%. It should be noted that these predictions are indicative of the potential effect of afforesting mangrove belts, but they are highly sensitive to the choice of vegetation properties and drag coefficient. More accurate predictions could be obtained by using the geometric and mechanical properties of the local mangrove species (see Section 5).

_Table 5. Estimated effect of mangroves at the potential afforestation sites summarized in Table 3. For each case, the effect of a 500-m wide mangrove belt with a slope of 1/1000 is evaluated in terms of (1) embankment height reduction, (2) reduction of the thickness needed for slope protection, and (3) reduction of the shear stresses at the toe of the embankment. The estimates were obtained for_ = 1.3 _,_ = 0.1 _trees/m_<sup>_2_</sup> _, and a profile slope of 1/1000._ DD,ww vv cc NN

|Site|Polder|Location|Surge<br>[mPWD]|Hm0<br>[m]|500 m of m<br>|angroves with slope o<br>|f 1/1000<br>|
|---|---|---|---|---|---|---|---|
|number|||2-M|2-M|Crest height<br>reduction[m]|Slope protection<br>reduction[%]|Stress<br>reduction[%]|
|1|55/4|Galachipa|4,9|3,7|0.16|23|41|
|2|55/3|Galachipa|5,1|2,3|0.19|31|53|
|3|56/57|Bhola|5|3|0.17|26|45|
|4|73/2|Hatiya<br>South|4,9|3,4|0.16|24|42|
|5|66/3|Khangona|4,8|2,1|0.18|32|54|
|6|69|Boro<br>Moheshkhali|4,4|2,7|0.13|24|42|

22
# 5. Discussion

## **Model limitations**

The geometric tree model applied in this study is a conservative representation of real trees, especially regarding the total canopy surface. Since the canopy area is determined from photographs, overlapping branches are not included in the area estimates. Moreover, small branches are neglected in Section 4, and the effect of leaves is also disregarded in the analysis under the assumption that leaves are quite flexible and easily detachable for _S. apetala_ trees (van Hespen et al., 2021). Despite their flexibility, these elements likely contribute to some extent to wave dissipation by the trees.

Besides the underestimation of the tree area, in most runs the drag coefficients are not increased above = 1.3 even though the drag coefficient estimates of van Hespen et al. (2021) for _S. apetala_ DD,ww branches reach values between of = 2 − 6. As discussed in cc Appendix D, values above 2.5 exceed the expected drag range for cylindrical elements, DD,ww cc and such overprediction could be due to a combination of scale effects and due to testing DD,ww cc single branches in isolation. The values between = 0.7-1.3 used in most runs are relatively more conservative and would be representative of situations with sheltering and/or DD,ww cc where plant flexibility reduces wave dissipation by the trees. Values larger than = 1.3 would also be possible in the field, and scenarios with = 2 show almost 50% more DD,ww cc wave attenuation than those obtained with = 0.7 (Figure 4.3 b-c). Such large DD,ww cc differences suggest that good predictions of DD,ww are key for precise assessments of the effect cc of mangroves. DD,ww cc With respect to vegetation damage, only two failure mechanisms (overturning and trunk breakage) are considered in this paper, whereas trees can die due to other causes such as pests, massive leaf loss, burial, uprooting, and hyper salinity. For tree overturning, maximum resistance values for terrestrial trees were used, whereas the maximum bending moment is likely to depend on the local soil and mangrove properties. The wave attenuation predictions are not updated to account for tree failure, which implies that the wave attenuation rates may be overpredicted for the most extreme wave conditions.

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# **Implementing mangroves in embankment designs**

Although the present study indicates that 100-m belts of _S. apetala_ could have an appreciable effect on embankment designs, a narrow forest with a single mangrove species would be highly vulnerable to pests and have an overall lower resilience against extreme events. Our modeling work suggests that wind loads could exceed the tree resistance of _S. apetala_ mangroves for wind speeds of 120 km/h, which on average hit the coast of Bangladesh every 3 years (Dasgupta et al., 2017). Tree pruning could be a potential method to reduce the loads acting on the trees, and this option could be investigated in future studies. Moreover, other pioneer species like _A. officinalis_ could also be relatively more stable than _S. apetala_ trees for potential afforestation sites.

Since different mangrove species grow at different ground elevations with respect to MSL, a more biodiverse forest requires a wider extension than a plantation with a single species. Wider forests would also provide an additional buffer during situations in which part of the forest is damaged. The choice of the optimal mangrove width is site-specific and requires balancing several factors such as the ecological requirements for mangrove establishment and survival, the potential flood risk reduction by the vegetation, and the costs and benefits of other alternatives (such using the land for other purposes). Climate change may be another important consideration for species selection, as increasing inundation and salinity may reduce the long-term survival of some species (Dasgupta et al., 2017b; Mukhopadhyay et al., 2018).

Another important consideration for design implementations is that mangroves need time to grow, and their wave attenuation efficiency increases over time. However, embankments also experience increasing design water levels and wave loads with time due to sea level rise. The embankment height can also progressively reduce due to processes like subsidence or compaction of the earth filling. Assuming that maintenance works must be done when the height of a structure reaches a minimum threshold value (due to ground sinking or 24
<!-- Start of picture text --> eet (a) eee (b)<br>no mangroves = mangroves reduction of initial height<br>h - (—_ — h -- -<br>i<br>| effect of mangroves<br>time time<br><!-- End of picture text -->
Mangroves could also affect the morphology of the foreshore, further reducing wave attack on the embankments. Such processes could be investigated with morphodynamic models, and, as previously suggested, be monitored over the life of the structures.

# 6. Conclusions

This paper investigates the potential wave load reduction by mangroves on coastal embankments in Bangladesh. The main conclusions of this study are listed below:

- Mangrove belts with widths between 100-1000 m could attenuate waves up to 7%-55% with respect to the situation without mangroves (for a forest age of 50 years-old).

- Such wave attenuation rates would reduce embankment heights in approximately 0.09-0.30 m, decrease the thickness of the slope protection by 13%-46% and reduce the shear stresses at the toe of the embankment by 25%-70% for the design conditions of the embankments.

- Although 100-m belts of _S. apetala_ mangroves already show an appreciable effect on embankment designs, such narrow belts would be quite vulnerable to extreme conditions and could be largely damaged by pests. Wider forests with higher biodiversity are recommended to provide extra safety against extreme events and to increase their resilience.

# 7. Recommendations

To reduce the uncertainty in the modeling results, the following steps are recommended:

- Collecting data of the local mangrove pioneers, particularly of their geometry and stability as a function of tree age.

- Measuring wave attenuation through mangrove belts in Bangladesh, to collect validation data for the wave model.

- Quantifying morphodynamic effects of the mangrove vegetation on the coastal profile.

- Conducting and monitoring pilot studies where mangroves are planted seaward from embankment and collecting data for the optimization of future designs.

26
27
# 8. References

Alongi, D.M. (2002). Present state and future of the world’s mangrove forests. Environ. Conserv. 29, 331–349.

Awal, M.A.; Islam, A.F.M. (2020). Water Logging in south-western coastal region of Bangladesh: Causes and consequences and people’s response. Asian J. Geogr. Res, 3, 9–28.

Best, Ü.S.N., Van der Wegen, M., Dijkstra, J., Willemsen, P.W.J.M., Borsje, B.W., Roelvink, D.J.A., (2018). Do salt marshes survive sea level rise? Modelling wave action, morphodynamics and vegetation dynamics, Environmental Modelling & Software 109: 152-166, Bao, T. (2011). Effect of mangrove forest structures on wave attenuation in coastal Vietnam. Oceanologia. 76. 10.5697/oc.53-3.807.

Bhowmik, A., Cabral, P. (2013). Cyclone Sidr Impacts on the Sundarbans Floristic Diversity. Earth Science Research. 2. 62-79. 10.5539/esr.v2n2p62.

Bricheno, L.M., Wolf, J., Islam, S., (2016). Tidal intrusion within a mega delta: An unstructured grid modelling approach. Estuarine, Coastal and Shelf Science 182, 12-26.

Carrol, C.A., Bunting, P., Hardy, A., Bell, G. (2019). Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region. Remote Sens. 2019, 11(23), 2833; <u>https://doi.org/10.3390/rs11232833</u> Dasgupta, S., Huq, M., Khan, Z.H., Zahid Ahmed, M.M, Mukherjee, N., Khan, M.F., Pandey, K. (2014). Cyclones in a changing climate: the case of Bangladesh, Climate and Development, 6:2, 96-110, DOI: 10.1080/17565529.2013.868335 Dasgupta, S., Islam, M.S., Huq, M., Khan, Z.H., Hasib, M.R. (2017). Mangroves as Protection from Storm Surges in Bangladesh. Policy Research Working Paper 8251.

Dasgupta, S., Sobhan, I., & Wheeler, D. (2017b). Impact of Climate Change and Aquatic Salinization on Mangrove Species in the Bangladesh Sundarbans. Vol. 46, No. 6, 680-694.

Eckstein, D., Künzel, V., Schäfer, L., Winges, M. (2020). Global Climate Risk Index 2019. Germanwatch e.V.

Etminan, V., Lowe, R., Ghisalberti, M., 2019. Canopy resistance on oscillatory flows. Coast. Eng. 152 https://doi.org/10.1016/j.coastaleng.2019.04.014.

28
Gardiner, B. A., G. R. Stacey, R. E. Belcher, and Wood, C. J. (1997). Field and wind-tunnel assessment of the implications of respacing and thinning on tree stability. Forestry 70:233-252.

Gardiner, B.A., Berry, P., Moulia, B. (2016). Review: Wind impacts on plant growth, mechanics and damage. Plant Science 245.

Gijón Mancheño, A., Herman, P.M.J., Jonkman, S.N., Kazi, S., Urrutia, I., van Ledden, M. (2021). Mapping mangrove opportunities with open access data: A case study for Bangladesh. Sustainability 13.

Global Forest Watch (2020). https://www.globalforestwatch.org Government of Bangladesh (2012). Detailed design of 5 polders. Volume I. Design report.

Herrera-Silveira, J. A., Teutli-Hernandez, C., Secaira-Fajardo, F., Braun, R., Bowman, J., Geselbracht, L., Musgrove, M., Rogers, M., Schmidt, J., Robles-Toral, P. J., Canul-Cabrera, J. A., & Guerra-Cano, L. (2022). Hurricane Damages to Mangrove Forests and Post-Storm Restoration Techniques and Costs. The Nature Conservancy, Arlington, VA.

Horstman, E., Dohmen-Janssen, M., Narra, P., Van den Berg, N.J., Siemerink, Martijn., Hulscher, S. (2014). Wave attenuation in mangroves: A quantitative approach to field observations. Coastal Engineering. 94. 47–62. 10.1016/j.coastaleng.2014.08.005.

Iftekhar, M.S., Islam, M.R. (2004) Degeneration of Bangladesh Sundarbans mangroves: a management issue. Int Forest Rev 6:123–135 Iftekhar, M.S., Saenger, P. (2008). Vegetation dynamics in the Bangladesh Sundarbans mangroves: a review of forest inventories. Wetlands Ecol Manage 16: 291–312.

IPCC (2007). Climate Change 2007: Impacts, Adaptation and Vulnerability: Summary for Policymakers. Working Group II Contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report. IPCC, Geneva.

Islam, S.A., Bala, S.K., Hussain, M.A., Rahman, M.M. (2011). Performance of Coastal Structures during Cyclone Sidr. Natural Hazards Review 12(3).

Islam, S.A., Miah, S.A.Q., Habib, M.A., Alam, M.M. (2016). Growth and yield of Sonneratia apetala (keora) plantations raised from different seed sources in the central coastal belt of Bangladesh. Journal of Bioscience and Agriculture Research 6.

29
IWM (2018). Technical Report on Storm Surge, Wave, Hydrodynamic Modelling and Design Parameters on Drainage System and Embankment Crest Level. Volume-II: Package 2 (Appendix B: Storm Surge). Bangladesh Water Development Board, Ministry of Water Resources.

Jakobsen, F., Zeaul Hoque, A.K.M., Paudyal, G.N., Bhuiyan, Md. S., (2005) Evaluation of the ShortTerm Processes Forcing the Monsoon River Floods in Bangladesh, Water International, 30:3, 389-399, DOI: 10.1080/02508060508691880 Jiang, Z., Guan, W., Xiong, Y., Li, M., Chen, Y., Liao, B., (2019). Interactive Effects of Intertidal Elevation and Light Level on Early Growth of Five Mangrove Species under Sonneratia apetala Buch. Hamplantation Canopy: Turning Monocultures to Mixed Forests. Forests, 10(2), 83.

Kaimal, J.C., Finnigan, J.J. (1994). Atmospheric Boundary Layer Flows: Their Structure and Measurement. 1st ed., Oxford University Press, New York.

Kalloe, S.A., Hofland, B., Antolínez, J.A., van Wesenbeeck, B.K. (2022). Quantifying FrontalSurface Area of Woody Vegetation: A Crucial Parameter for Wave Attenuation. Frontiers in Marine Science 23.

Kathiresan, K., Rajendran, N. (2005). Coastal mangrove forests mitigated tsunami. <u>Estuarine, Coastal and Shelf Science. Volume 65, Issue 3, November 2005, Pages 601-606</u> Keulegan, G.H., Carpenter, L.H., (1958). Forces on cylinders and plates in an oscillating fluid. J. Res. Natl. Bur. Stand. 60, 423–440 Khan, R.S. (1992). Cyclone Hazard in Bangladesh. Community Development library, Dhaka, 86– 102. National States Geographic Information Council. Available online: <u>www.adpc.net.</u> Krauss, K.W.; Doyle, T.W.; Doyle, T.J.; Swarzenski, C.M.; From, A.S.; Day, R.H.; Conner, W.H. (2009). Water level observations in mangrove swamps during two hurricanes in Florida. Wetlands, 29, 142–149.

Lewis III, R.R. (2009). Methods and criteria for successful mangrove forest restoration. In: Gerardo M. E. Perillo, Eric Wolanski, Donald R. Cahoon, Mark M. Brinson, editors, Coastal Wetlands: An Integrated Ecosystem Approach. Elsevier, 2009, p. 787. ISBN: 978-0-444-53103-2 Lewis, R.R., Brown, B. (2014) Ecological mangrove rehabilitation – a field manual for practitioners. Version 3. Mangrove Action Project Indonesia, Blue Forests, Canadian International Development Agency, and OXFAM. p 275.

30
Lopez-Portillo, J.L., Lewis III, R.R., Saenger, P., Rovai, A.S (2017). Mangrove Forest Restoration and Rehabilitation. Mangrove Ecosystems: A Global Biogeographic Perspective. Springer, Cham. <u>https://doi.org/10.1007/978-3-319-62206-4_10.</u> Lovelock, C., Cahoon, D., Friess, D., Guntenspergen, G., Krauss, K., Rogers, R., et al. (2015). The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526 Massel, S.R., Furukawa, K., and Brinkman, R.M. (1999). Surface wave propagation in mangrove forests. Fluid Dynamics Research 24 219.

Maza, M., Lara, J.L., Losada, I. (2021). Predicting the evolution of coastal protection service with mangrove forest age. Coastal Engineering, 168.

Mazda, Y., Wolanski, E., King, B. (1997). Drag force due to vegetation in mangrove swamps. Mangroves and Salt Marshes 1, 193–199. <u>https://doi.org/10.1023/A:1009949411068</u> McIvor, A., Möller, I., Spencer, T., Spalding, M. (2012) Reduction of wind and swell waves by mangroves. Natural coastal protection series: Report 1. Cambridge coastal research unit working paper 40. The nature conservancy, Arlington, USA/Wetlands International, Wageningen, Netherlands, pp 27 McKee, K. and Vervaeke, W. (2018). Will fluctuations in salt marsh–mangrove dominance alter vulnerability of a subtropical wetland to sea-level rise? Global Change Biology 28 Menéndez, P., Losada, I.J., Torres-Ortega, S., Narayan, S., Beck, M. (2020). The global flood protection benefits of mangroves. Scientific Reports, 10.

Méndez, F.J., Losada, I.J. (2004). An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields. Coastal Engineering 51(2):103-118 Montgomery, J.M.; Bryan, K.R.; Horstman, E.M.; Mullarney, J.C. (2018) Attenuation of Tides and Surges by Mangroves: Contrasting Case Studies from New Zealand. Water, 10, 1119.

Morison, J. R., Johnson, J.W., O’Brien, M.P. (1950). The force exerted by surface waves on piles. Petroleum Trans. 189, pp. 149–157.

Mukhopadhyay, A., Wheeler, D., Dasgupta, S., Dey, A., Sobhan, I. (2018). Aquatic Salinization and Mangrove Species in a Changing Climate: Impact in the Indian Sundarban. World Bank Policy Research Working Paper No. 8532.

31
Naz, F.; Buisson, M.C. (2015). Multiple actors, conflicting roles and perverse incentives: The case of poor operation and maintenance of coastal polders in Bangladesh. In Proceedings of the CPWF, GBDC, WLE Conference on Revitalizing the Ganges Coastal Zone: Turning Science into Policy and Practices, Dhaka, Bangladesh, 21–23; pp. 147–161.

Nicholls, R.J., Hoozemans, F.M.J, Marchand, M., (1999). Increasing flood risk and wetland losses due to global sea-level rise: regional and global analyses. Global Environmental Change 9: 69-87.

Nicoll, B.C., Gardiner, B.A., Rayner, B., Peace, A.J. (2006) Anchorage of coniferous trees in relation to species soil type, and rooting depth, Can. J. For. Res. 36. Pp. 1871–1883.

Nishat, A., (1988). Review of present activities and state of art of the coastal areas of Bangladesh. Coastal area resource development and management Part II, pp. 23-35. Dhaka, Coastal Area Resource Development and Management Association (CARDMA).

Pendleton, L., Donato, D. C., Murray, B. C., Crooks, S., Jenkins, W. A., Sifleet, S., Craft, C., Fourqurean J., Kauffman, J., Marbà, N., Megonigal, P.,Pidgeon, E., Herr, D., Gordon, D., and Baldera, A. (2012). Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS ONE 7(9): e43542.

Pilarczyk, K.W., (editor) (1990). Coastal Protection, Published by A.A. Balkema, Rotterdam.

Pilarczyk, K.W., (editor) (1998). Dikes and revetments, Published by A.A Balkema, Rotterdam.

Quartel, S., Kroon, A., Augustinus, P., Santen, P., Tri, N.H. (2007). Wave attenuation in coastal mangroves in the Red River Delta, Vietnam. Journal of Asian Earth Sciences. 29. 576-584. 10.1016/j.jseaes.2006.05.008.

Quine, P. and Gardiner, B.A. (2007). Understanding how the interaction of wind and trees results in windthrow, stem breakage and canopy gap formation. Plant Disturbance Ecology.

Rahman, M., Sass-Klaassen, U., Zuidema, P.A., Chowdhury, M.Q., Beeckman, H. (2020). Salinity drives growth dynamics of the mangrove tree Sonneratia apetala Buch. -Ham. in the Sundarbans, Bangladesh. Dendrochronologia, 62.

Rahman, M., Donoghue, D.N.M., Bracken, L.J., Mahmood, H. (2021). Biomass estimation in mangrove forests: a comparison of allometric models incorporating species and structural information. Environmental Research Letters 16.

Rennel, J. (1786). Bengal, Bahar, Oude, Allahabad. General Atlas.

32
Rivera-Monroy, V.H., Twilley R.R., Mancera-Pineda J.E., Madden C.J., Alcantara-Eguren A., Moser, Jonsson, B.F., Castañeda-Moya, E., Casas-Monroy, O., Reyes-Forero, P., and Restrepo, J. (2011). Salinity and Chlorophyll a as Performance Measures to Rehabilitate a Mangrove-Dominated Deltaic Coastal Region: the Ciénaga Grande de Santa Marta–Pajarales Lagoon Complex, Colombia. Estuaries and Coasts 34, pp. 1-19.

Rudnicki, M., Mitchell, S.J., Novak, M.D. (2004). Wind tunnel measurements of crown streamlining and drag relationships for three conifer species, Can. J. For. Res. 34. Pp. 666–676.

Saintilan, N., Khan, N.S., Ashe, E., Kelleway, J.J., Rogers, K., Woodroffe, C.D., Horton, B.P. (2020). Thresholds of mangrove survival under rapid sea level rise. Science 368.

Saenger, P., Siddiqi, N.A. (1993). Land from the sea: The mangrove afforestation program of Bangladesh. Ocean and Coastal Management, vol. 20, no. 1, pp. 23-39.

Sasmito, S., Murdiyarso, D., Friess, D., and Kurnianto, S. (2016). Wetlands Ecology and Management Simard, M., Fatoyinbo, L., Smetanka, C. et al. (2019). Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience 12, 40–45 Snedaker, S. C. 1982. Mangrove species zonation: Why?, Tasks for Vegetation Science, Vol. 2., p. 111-125.

Tanino, Y., Nepf, H. M. Laboratory investigation of mean drag in a random array of rigid, emergent cylinders. J. Hydraul. Eng. 134, 34–41. https://doi.org/10.1061/(asce)0733-9429(2008)134:1(34) (2008).

Trampanya, U. (2006). Mangroves and sediment Dynamics along the Coasts of Southern Thailand. Ph.D. Thesis, Wageningen University and Research, Wageningen, The Netherlands.

Uddin, M., Hossain, M.K. (2013). Growth performance of coastal plantations and land stabilization in an offshore Island of Hatiya, Noakhali, Bangladesh. Bangladesh Journal of Forest Science 32.

Van der Meer, J. W., Allsop, N. W. H., Bruce, T., De Rouck, J., Kortenhaus, A., Pullen, T., Schuttrumpf, H., Troch, P. & Zanuttigh, B. 2018. Manual on wave overtopping of sea defences and related structures. An overtopping manual largely based on European resreach, but for worldwide application. www.overtopping-manual.com: EurOtop.

33
Van Hespen, R., Hu, Z., Peng, Y., Borsje, B.W., Kleinhans, M., Ysebaert, T., Bouma, T.J. (2021). Analysis of coastal storm damage resistance in successional mangrove species. Limnology and Oceanography 66.

Van Rooijen, A. A., McCall, R. T., van Thiel de Vries, J. S. M., van Dongeren, A. R., Reniers, A. J. H. M., Roelvink, J. A. (2016), Modeling the effect of wave-vegetation interaction on wave setup, J. Geophys. Res. Oceans, 121, 4341–4359, doi:10.1002/ 2015JC011392.

Van Wesenbeeck, B.K., Wolters, G., Antolínez, J.A., Kalloe, S.A., Hofland, B., de Boer, W.P, Çete, C., Bouma, T.J. (2021). Wave attenuation through forests under extreme conditions. Scientific Reports.

Van Zelst, V.T.M., Dijkstra, J.T., van Wesenbeeck, B.K. et al. (2021). Cutting the costs of coastal protection by integrating vegetation in flood defences. Nature Communications 12.

Vo Luong, P., Massel, S. (2008). Energy dissipation in non-uniform mangrove forests of arbitrary depth. Journal of Marine Systems. 74. 603-622. 10.1016/j.jmarsys.2008.05.004.

Vuik, V., Jonkman, S.N., Borsje, B.W., Suzuki, T. (2016). Nature-based flood protection: The efficiency of vegetated foreshores for reducing wave loads on coastal dikes. Coastal Engineering 116: 4256.

Vuik V., van Vuren S., Borsje B.W., van Wesenbeeck B.K., Jonkman S.N. (2018) Assessing safety of nature-based flood defenses: Dealing with extremes and uncertainties. Coastal Engineering 39, Pages 47–64 Vuik V., Borsje B.W., Willemsen P.W.J.M., Jonkman S.N. (2019) Salt marshes for flood risk reduction: Quantifying long-term effectiveness and life-cycle costs. Ocean and Coastal Management 171. Pages 96-110 Wang, G., Zhang, Y., Guan, D., Xiao, L., Singh, M. (2021). The potential of mature Sonneratia apetala plantations to enhance carbon stocks in the Zhanjiang Mangrove National Nature Reserve. Ecological Indicators, 133.

White, F.M., (1991). Viscous Fluid Flow, 2nd ed. McGraw-Hill, New York.

Woodroffe, C., Rogers, K., McKee, K., Lovelock, I., C.E.and Mendelssohn, and Saintilan, N. (2016). Mangrove Sedimentation and Response to Relative Sea-Level Rise. Annual Review of Marine Sciences 8 World Bank (2013). Project Appraisal Document of People's Republic of Bangladesh Coastal Embankment Improvement Project-Phase 1 (CEIP-1) (P128276). Report No.: PAD432 34
Zhang, Y., Yang, Y., Yang, K., Tan, X., Sun, X., Leng, B., Zhou, C., Zhu, B. (2021). Non-linear wave attenuation quantification model improves the estimation of wave attenuation efficiency of mangroves. Estuarine, Coastal and Shelf Science 245.

Zhu, D., Hui, D., Wang, M. et al. (2021). Allometric growth and carbon storage in the mangrove Sonneratia apetala. Wetlands Ecol Manage 29, 129–141 35
# Appendix A: Mangrove tree geometry

## A.1. Methodology for estimating tree area from images

Predicting wave attenuation by mangroves requires describing the frontal tree area, including roots, trunk, and canopy. A literature study (Section 2) provided information about the trunk and roots of mangrove trees, but no information was found for the canopy. The canopy area of _S. apetala_ trees is thus obtained by digitizing images collected by Rosanna van Hespen in China. The images are scaled assuming a pneumatophore height of 15 cm, based on personal communication with Rosanna. The contour of the trunk and branches is digitized in Photoshop and read as a binary image in MATLAB. Over the height of the tree, the number of pixels corresponding with the tree area are summed to obtain the vertical distribution of the exposed surface. The total tree area is divided by the tree height to calculate an equivalent tree width , which is normalized using the B of the tree ( B ). For a given tree age, the height and vv B are obtained from a fit to the data of _S._ bb dd _apetala_ trees from Bangladesh shown in Figure 2.2. The equivalent tree width is obtained by vv bb /dd dd multiplying the normalized value by the B . vv A.2. Results of tree area bb dd The images provided by Rosanna van Hespen (Figure A1 and Figure A2) were taken from different perspectives and lighting, which hindered programming algorithms to detect the tree canopy area. Overexposure and shadowing resulted in branches and leaves being partly colored and partly black and white, which made it difficult to define color thresholds for the different tree parts. Moreover, due to low resolution in some images, the smallest branches (with diameters approximately below 1 cm) cannot always be distinguished in the pictures. The analysis thus focuses on the image with the best resolution and color homogeneity (Figure A1), and the results are compared with a second specimen photographed with less quality (Figure A2).

36
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# Appendix B: Wave and embankment design model

B1. Wave reduction by mangroves Predicting the effect of mangroves on coastal embankments requires estimating wave propagation through the forest. Wave attenuation by mangrove trees is directly proportional to the wave loads acting on the vegetation. Wave-driven forces are often represented using the Morison equation (Morison et al., 1950): where is the force acting on the trees per unit area (N/m<sup>2</sup> ), which consists of a drag term dependent on the flow velocity squared (left term) and an inertial term dependent on the flow ww FF acceleration (right term). is the water density (kg/m<sup>3</sup> ), is the (empirical) drag coefficient for waves (-), is the frontal tree area perpendicular to the wave direction (mDD,ww<sup>2</sup> ), ρρ cc is the number of trees per unit m<sup>2</sup> , and is the wave-driven water velocity relative to the AA tree motion (m/s). If a tree is rigid, the relative velocity will be equal to the velocity of the vv NN aa water particles, and the exposed tree area will remain constant over time. If a tree moves with the waves, its relative velocity and area will change. For trees that do not oscillate but bend to more streamlined shapes, the exposed area reduces under wave loads. In the inertial force component, is an empirical inertia coefficient (-), is the volume of the tree (m<sup>3</sup> ), and is the wave-driven flow acceleration (m/s<sup>2</sup> ). MM cc VV ddaa/ddaa where h is the vegetation height (m) and is the wave period (s). Mendez and Losada (2004) developed an expression for the wave dissipation vv assuming negligible plant TT vv εε 40
motion, that the wave-driven flow can be defined using linear wave theory, and that waves are irregular and follow a Rayleigh distribution, resulting in Equation 4: The wave height reduction across a vegetation field can then be calculated by including the expression for (Equation 3 if the vegetation properties vary over the vertical, or Equation 4 for depth-uniform vegetation) in the wave energy balance: vv εε d <u>v</u> (5) d cc vv bb = −εε −εε where is the wave energy flux, equal to the product of the wave energy (J/m<sup>2</sup> ) times the wave group celerity vv (m/s), and is the distance in the cross-shore direction (m). ddcc dd According to Equation 5, any wave energy losses across the profile are due to dissipation by vv cc dd the vegetation ( ) or due to dissipation by wave breaking ( ), as the effect of bottom friction is assumed negligible in comparison with (Mendez and Losada, 2004). vv bb εε εε vv εε Energy dissipation due to wave breaking is calculated using the method of Thornton and Guza (1983): where is the peak frequency (s<sup>-1</sup> ), and (-) and (-) are empirical coefficients that are set to the default values used by Mendez and Losada (2004): cc bbrr and = 0.6. ff BB γγ bb 41 BB= 1 γγ 41
~~—lI~~ n ~~(—)~~ <u>(2.7193</u> ~~<u>—)</u>~~ <u>— 0.061) + (-1.273</u> ~~<u>(7)</u>~~ <u>+ 0.9701) (1.1127</u> ~~<u>(7)</u>~~ <u>+ 0.0311</u> (0.68 ~~(—~~ - 0.0385) + (-0.68 ~~(2~~ + 0.4785) (1.7239 ~~(2~~ + 0.0316)
where is the modulus of rupture of the wood tissue (N/m<sup>2</sup> ), B is the trunk diameter at breast height (m), and uu is a factor that reduces the wood strength due to the presence of σσ dd knots (-), usually between bbn oo = 0.8-1. Van Hespen et al. (2021) measured values 37±7 ff N/mm<sup>2</sup> for _S. apetala_ branches. bbn oo uu ff σσ Overturning, which is often modeled combining mechanisms (a) and (d) of Figure 2.4, can also be estimated by comparing the bending moment acting on the tree with the critical overturning moment , measured in tree pulling experiments (Nicoll et al., 2006): nnv rr MM (16) nnv rr rr MM = CC WW where is a dimensional regression constant varying between = 60-200 m<sup>2</sup> /s<sup>2</sup> , and is the tree trunk weight in kg. rr rr CC CC WW B.7. Modeling mangrove failure against wind and wave loads At every grid point the bending moment is calculated and compared with the maximum threshold for trunk breakage and tree overturning. The bending moment acting on the tree is calculated for wind loads using Equation 11 and for wave loads using Equation 2, by multiplying the total force of each segment times its distance to the ground. Vegetation motion is neglected for both wave and wind loads.

For wind loads, we assume that the input wind speed is measured 10 m above the ground at a location without vegetation (Figure B1). Since the velocity profile changes between sites with and without vegetation, we calculate the corresponding velocity 200 m from the ground using Equation 12 with 0 = 0.02 (Gardiner et al., 2016). We assume the wind has the same speed at 200 m from the ground for vegetated and unvegetated areas, and that the wind dd velocity differs between both sites below this elevation. The velocity on top of the forest is calculated using Equation 12, assuming a rough boundary layer with a roughness height of 0 = h /30, where h is the tree height. The velocity between the trees is then calculated using an exponential velocity decay (Equation 13) with a reduction factor of = 1. This vv vv dd decay is applied inside the forest, but not at the seaward edge. vv υυ 45
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# Appendix C: Validation of the wave model

The wave transformation model is validated against field wave measurements collected at a salt marsh fringe by Vuik et al. (2016). Wave propagation through the salt marshes is modeled for a significant wave height of = 0.6 m and a peak period of = 3.5 s at the offshore boundary. The bathymetry varies between an offshore water depth of 2 m, and a mm0 cc HH TT nearshore water depth of 0.5 m. The salt marshes extend over a length of 55 m and have varying vegetation properties across the profile, but uniform properties over the vertical coordinate. The vegetation properties are summarized in Table C1. A drag coefficient of = 0.4 is used in the simulations, which is equal to the value calibrated by Vuik et al. (2016). DD,ww cc _Table C1. Salt marsh properties used for the validation from Vuik et al. (2016), consisting of the mean vegetation height hv, the mean stem density Nv, and the mean stem width bv. The location of the sections across the profile is indicated in Vuik et al. (2016)._

|Section|hv,mean (m)|Nv (plants/m<sup>2</sup>)|bv (m)|
|---|---|---|---|
|S1-S2|0,2|944|0,030|
|S2-S3|0,29|1136|0,034|
|S3-S4|0,27|1520|0,037|

The wave propagation model shows good agreement with the model predictions and field measurements of Vuik et al. (2016), as shown in Figure C1. The agreement is good both for a situation without vegetation, and with vegetation, which indicates that both wave breaking and the vegetation module are well implemented.

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# Appendix D: Drag coefficient for mangroves in Bangladesh

Wave attenuation largely depends on the value of the drag coefficient. The drag coefficient of _S. apetala_ branches was estimated in a set of flume experiments by van Hespen et al. (2021). In their tests, tree branches were exposed to waves and currents, and forces and flow velocities were measured to fit . Drag coefficient measurements from experiments with waves vary between = 2 −6DD,ww, and from experiments with a combination of waves cc and currents between = 2.8 −4.3DD,ww . These values exceed the drag coefficient for rigid cc single cylinders, which tend to DD,ww = 1 in currents (White, 1991), = 1 −2.5 in waves cc (Keulegan and Carpenter, 1958). Van Hespen et al. (2021) reported that their measurements DD,cc DD,ww cc cc probably overpredict the drag coefficients expected in the field, as in reality the branches of a tree may shelter each other from the flow. Their drag values could also be larger than for rigid isolated cylinders due to flow acceleration effects between the branches (Tanino and Nepf, 2008; Etminan et al., 2019), but also due to scaling effects. The latter is a likely contributing factor as large-scale flume experiments with real-scale willow trees provided drag coefficients between = 0.5 −2.5 (van Wesenbeeck et al., 2022), where the largest values showed good agreement with Keulegan and Carpenter (1958). The smallest DD,ww cc values for willows were obtained for the highest DD,ww values, where represents the ratio of DD,ww cc cc wave excursion to branch diameter. Those wave conditions were associated with the largest KKCC KKCC tree motion, which resulted in smaller , values compared to rigid elements. Drag reduction due to plant motion will most likely play a role for young mangrove specimens, DD,ww cc with thinner and more flexible branches compared to mature trees.

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