Assessing the role of mangrove forests in coastal...

Assessing the role of mangrove forests in coastal protection

in Soc Trang Province, Vietnam

–

analysis of wave attenuation through long-term wave

measurements

Masterarbeit

Im Ein-Fach-Masterstudiengang

Master of Science „Umweltgeographie und –management“

der Mathematisch-Naturwissenschaftlichen Fakultät

der Christian-Albrechts-Universität zu Kiel

Coastal Risks and Sea-Level Rise

vorgelegt von

Roman Sorgenfrei

Erstprüfer: Prof. Dr. rer. nat. Athanasios Vafeidis

Zweitprüfer: Dr.-Ing. Thorsten Albers

Kirchlauter, im Januar 2015

Assessing the role of mangrove forests in coastal protection

in Soc Trang Province, Vietnam

–

analysis of wave attenuation through long-term wave

measurements

Master’s thesis

in the one subject Master study course

Master of Science ‘Environmental Geography and Management’

Faculty of Mathematics and Natural Sciences

Kiel University

Coastal Risks and Sea-Level Rise

submitted by

Roman Sorgenfrei

First examiner: Prof. Dr. rer. nat. Athanasios Vafeidis

Second examiner: Dr.-Ing. Thorsten Albers

Kirchlauter, January 2015

I

Acknowledgements

The author would like to express his profound gratitude to Prof. Dr. Athanasios Vafeidis and

Dr.-Ing. Thorsten Albers for countless helpful conversations and discussions during this

study.

The Author is deeply grateful to Dr. Klaus Schmitt for giving him the opportunity for an

internship in Vietnam, as well as the chance to gather the information needed and the time for

conducting the field measurements. He expresses his sincere thanks to the whole team of the

GIZ project ‘Management of Natural Resources in the Coastal Zone of Soc Trang Province’.

Paul Bourne, for his help during countless field trips to the study sites, vegetation assessments

and interesting conversations that helped the author to expand his horizons. Ms. Vi for her

help and support during the field work, as well as for translating necessary files. Helpful

translations were provided by Ms. Thuy and Ms. Tu, whom the author would like to thank for

their support. Thanks to Ms. Kieu, without whom the author would have not been able to

work and live in Soc Trang for such a long period of time. She made the impossible possible

on several occasions. Mr. Binh for driving to the study sites numerous times in the early

morning hours to ensure successful work and measurements during low tidal phases. Further

thanks go to his former colleagues Ms. Bianca Schlegel and Mr. Dung for their friendship and

help.

The author would like to express his tremendous gratitude to Mr. Hoang of the Soc Trang

Sub-Department of Forest Protection for his help in identifying mangrove species, providing

background information about mangrove plantations, and setting up sensors along the

transects as well as being a kind friend.

Special and sincere thanks go out to all the people in Soc Trang Province and Vietnam in

general for making the authors time such an unforgettable experience.

Furthermore the author expresses his thanks to Dr. Rolf Gabler-Miek for his suggestions

concerning the challenging levelling on mudflats.

Last but not least, thanks to the authors parents, for their support and for believing in him to

find his own way. He hopes he will be able to return the favour one day. Also thanks to the

rest of his family for accepting the path he chose, even if it means that family meetings are not

frequent.

II

Zusammenfassung

Die Küste des Mekong-Deltas ist gekennzeichnet durch Bereiche der Sedimentation sowie

Bereiche der Erosion. Mit Ausblick auf einen steigenden Meeresspiegel durch ungebremsten

Klimawandel wächst die Bedrohung der im Mekong-Delta lebenden Menschen. Um ihnen

eine nachhaltige Perspektive für die Zukunft zu bieten, ist es nötig, Anpassungsstrategien zu

entwickeln.

Dies wird heutzutage mit numerischen Modellen erreicht, welche bestmöglich die realen

Bedingungen wiederspiegeln und unter Rücksichtnahme der Nachhaltigkeit des Vorhabens

verschiedene Strategien der Anpassung vergleichen. Eine wichtige Eingangsgröße in diese

Modelle ist der wellenmindernde Einfluss, den Mangrovenwälder entlang der Küste nehmen.

Bisherige Studien dazu beinhalteten Messungen über Zeiträume von zumeist nur wenigen

Tagen und sind oft nicht kontinuierlich. Um eine möglichst valide Eingangsgröße zu erhalten

sind jedoch Messungen über einen längeren Zeitraum nötig. Zudem gab es bisher keine

Untersuchungen im Mekong-Delta. Diese Lücken sind Anlass für die vorliegende Arbeit.

Dazu wurden in drei Mangrovenwäldern mit unterschiedlichen Bedingungen

(Mangrovenarten: Sonneratia caseolaris, Rhizophora apiculata; Walddichte; Sandbank)

sowie einer Referenzfläche ohne Bewuchs Messungen vorgenommen. Die

Untersuchungsflächen repräsentieren zugleich die an der Küste vorherrschenden

Küstenprozesse und hatten jeweils eine Länge von 200 m. Für alle Flächen wurden

Korngrößenanalysen der Sedimente angefertigt sowie die Höhenprofile mit einem

Nivelliergerät erfasst. In den drei Mangrovenwäldern wurde zudem die Vegetation

hinsichtlich unterschiedlicher Parameter charakterisiert.

Die Messungen wurden mit bis zu vier Drucksensoren je Transekt durchgeführt. Das

angestrebte Ergebnis, je Transekt Daten für einen Monat in der Regenzeit und einen Monat in

der Trockenzeit zu erhalten, konnte aufgrund unterschiedlicher Probleme nicht komplett

erreicht werden. Dennoch gelang es, Daten über einen längeren Zeitraum zu erfassen und zu

analysieren. In der jungen Rhizophora Plantage wurden die Wellen aufgrund der

Vegetationsdichte komplett reduziert. Dahingegen zeigen die Ergebnisse der

Untersuchungsflächen auf der Insel Cu Lao Dung, dass dort die Setzlinge und jungen Bäume

(Sonneratia) den größten Einfluss hatten. Die Ergebnisse der Referenzfläche waren im

Vergleich zu vorherigen Studien hoch. Abgesehen davon ergänzen die Ergebnisse bestehende

Erkenntnisse. Zudem sind weitere Analysen der nun vorliegenden Datensätze für die Provinz

Soc Trang möglich und werden angestrebt.

III

Summary

Along the coast of the Mekong Delta areas of accretion and erosion can be found. Due to the

anthropogenic climate change, the sea level rises and the threat for the people living in the

Mekong Delta increases immensely. Therefore it is necessary to provide the community with

options for the future by developing adaptation strategies.

Nowadays this is done using various numerical models that simulate real conditions as closely

as possible, while taking the sustainability of possible adaptations into account. An important

input value for such models is the effect of wave attenuation by mangrove forests along the

coasts. Previously conducted studies mostly measured the attenuation of waves only for a

couple of days and often not even continuously. Thus, documenting valid input values of

continuous measurements over a longer period of time is essential. Furthermore, no research

on this particular topic had yet been conducted in the Mekong Delta. These topics are

addressed in the presented thesis.

For this, measurements in three mangrove forests with different conditions (mangrove

species: Sonneratia caseolaris, Rhizophora apiculata; forest density; sandbank) were

obtained and a non-vegetated site chosen as reference. The study sites represent the typical

coastal processes along the coast of Soc Trang Province and had a length of 200 m each. For

each individual study site, sediment grain size distribution was evaluated and elevation

changes along the profile calculated using a levelling instrument. In addition, native

vegetation was assessed according to multiple characteristics.

Per transect, up to four pressure transducers were used for the measurements. The initial

target to collect data for each of the four transects over a period of one month (once in the

rainy season and once in the dry season) could not be achieved completely because of several

issues. However, data could be collected and analysed over a longer period of time.

At the Rhizophora planting site the dense vegetation attenuated the waves completely,

whereas on the study sites on Cu Lao Dung Island, the seedlings and saplings of young trees

(Sonneratia) damped the waves most effectively. The results of the reference site were higher

than in previous studies. Beside this, the results confirm existing knowledge about wave

attenuation and create potential for further analysis of the now available data from Soc Trang

Province.

V

Table of Contents

Acknowledgements .............................................................................................................. I

Zusammenfassung .............................................................................................................. II

Summary ........................................................................................................................... III

Table of Contents ............................................................................................................... V

List of Figures ................................................................................................................. VII

List of Tables ..................................................................................................................... IX

1. Introduction ....................................................................................................................... 1

2. Methods .............................................................................................................................. 6

2.1. State of existing research ....................................................................................... 6

2.1.1. Mangrove species in Soc Trang Province as bio-shields ..................................... 6

2.1.2. Factors affecting wave attenuation in mangroves ................................................ 8

2.1.3. Existing studies about wave reduction in mangrove forests .............................. 10

2.2. Coastal processes at the shoreline of Soc Trang Province ................................ 15

2.3. Wind and waves along the coast of Soc Trang Province .................................. 18

2.4. Study areas ............................................................................................................ 21

2.4.1. Location of the study transects ........................................................................... 22

2.4.2. Height profiles of the transects ........................................................................... 29

2.4.3. Sediment grain size distributions ........................................................................ 33

2.4.4. Vegetation assessments ...................................................................................... 34

2.4.4.1. Cu Lao Dung north and south (CLD_n and CLD_s) ................................................ 35

2.4.4.2. Vinh Chau (VC) ........................................................................................................ 40

2.5. Measurements of wave attenuation .................................................................... 41

2.5.1. Pressure transducers ........................................................................................... 43

2.5.2. Schedule and adjustments of measurements ...................................................... 45

2.5.3. Data processing and analysis .............................................................................. 48

2.5.3.1. Data processing ......................................................................................................... 48

2.5.3.2. Data analysis ............................................................................................................. 51

2.5.3.3. Parallel measurements to relate data ......................................................................... 52

3. Results .............................................................................................................................. 55

3.1. Overview ............................................................................................................... 55

3.2. Comparison between CLD_n, CLD_s and LH .................................................. 59

3.3. Cu Lao Dung north (CLD_n) .............................................................................. 62

3.4. Cu Lao Dung south (CLD_s) ............................................................................... 67

3.5. Lai Hoa (LH)......................................................................................................... 69

VI

4. Discussion ........................................................................................................................ 71

4.1. Wave attenuation at the study sites .................................................................... 71

4.1.1. Transect VC ....................................................................................................... 71

4.1.2. Transect LH........................................................................................................ 72

4.1.3. Transect CLD_n ................................................................................................. 73

4.1.4. Transect CLD_s ................................................................................................. 77

4.1.5. Comparison between CLD_n and CLD_s ......................................................... 78

4.2. Comparison with previous studies ..................................................................... 80

4.3. Limitations............................................................................................................ 83

4.4. Recommendations ................................................................................................ 84

5. Conclusion ....................................................................................................................... 87

References ............................................................................................................................... 89

Appendices ............................................................................................................................ A-1

Appendix I – The three main mangrove species in Soc Trang Province

Appendix II – Wave attenuation with concurrent water depth in previous studies

Appendix III – Coordinates of sensor locations and overview of transect CLD_s 2006

Appendix IV – Times of successful sensor measurements per sensor location

Appendix V – Parallel measurements of CLD_n and LH

Appendix VI – Parallel measurements of CLD_s and LH

Appendix VII – Correlations between Hs and r200 at transects CLD_s and LH

Appendix VIII – LH comparison of 2nd

- and 3rd

-order poly. best-fit line

Appendix IX – CLD_n measurement results for Tm and Tp

Appendix X – Reduction of Hs per m (r) against water depth for CLD_n

Appendix XI – CLD_s measurement results for Tm and Tp

Appendix XII – Reduction of Hs per m (r) against water depth for CLD_s

Appendix XIII – LH measurement results for Tm and Tp

VII

List of Figures

Figure 1-1: Work flow from preparations over field works, data processing and analysis to the finished

master’s thesis. ............................................................................................................................. 5

Figure 2-1: Factors affecting wave attenuation in mangroves. ............................................................... 8

Figure 2-2: The Mekong Delta in Vietnam, Soc Trang Province with details of the coastal zone, the

location of the maps shown in Figure 2-4 and locations of the study transects ......................... 15

Figure 2-3: Idealised situation along the coast of Soc Trang Province ................................................. 16

Figure 2-4: Shoreline changes in Soc Trang Province from 1904 till 2012 .......................................... 17

Figure 2-5: Wind and wave directions at Con Dao Island .................................................................... 18

Figure 2-6: Wind direction distribution in percent during part of the rainy season 2013 on Con Dao

Island (21.07.2013 - 12.09.2013) ............................................................................................... 19

Figure 2-7: Distribution of the predicted high water levels and absolute frequency for various

predicted high water levels during the year 2013 for the VN hydrological station My Thanh,

Soc Trang Province. ................................................................................................................... 21

Figure 2-8: Locations of sensors, sediment samples and vegetation assessments along each transect. 24

Figure 2-9: Overview of transect CLD_n with sensor locations and spots of vegetation assessments

inside the Sonneratia caseolaris forest. ..................................................................................... 24

Figure 2-10: Sandbank seaward of transect CLD_s. ............................................................................. 25

Figure 2-11: Overview of transect CLD_s with sensor locations and spots of vegetation assessments

inside the Sonneratia caseolaris forest. ..................................................................................... 26

Figure 2-12: View of the area in front of CLD_s 1 with small saplings growing in the western adjacent

area closer to the sea .................................................................................................................. 26

Figure 2-13: Overview of transect VC with sensor locations and spots of vegetation assessments ..... 27

Figure 2-14: Overview of the sensor location at the reference transect LH without mangrove

vegetation. .................................................................................................................................. 28

Figure 2-15: Levelling in the mangrove forests .................................................................................... 29

Figure 2-16: Elevation profiles and slope gradients of the four study transects ................................... 30

Figure 2-17: Time gap between measurement results for Hs (significant wave height) of the coastal

sensors of the two transects on Cu Lao Dung Island on the 20.08.2013 ................................... 32

Figure 2-18: Impressions of transect CLD_n ........................................................................................ 32

Figure 2-19: Grain size distribution by weight of sediment samples taken along each transect ........... 33

Figure 2-20: Self-built clinometer for tree height measurements on Cu Lao Dung Island and sample

frame to assess pneumatophores in 1 m2. .................................................................................. 35

Figure 2-21: Vertical configuration of Sonneratia caseolaris .............................................................. 36

Figure 2-22: Growth of young branches in lower heights of the Sonneratia trees at CLD_n observed

during the dry season and dense pneumatophores which secure the sediments. ....................... 38

Figure 2-23: Dead Sonneratia caseolaris trees lying on the forest ground in the south of CLD. ......... 39

Figure 2-24: Well developed Rhizophora apiculata tree inside the big sample frame of Veg_1 at the

Transect VC. .............................................................................................................................. 40

Figure 2-25: Impressions of the dense vegetation pattern of planted Rhizophora apiculata trees at

transect VC. ............................................................................................................................... 41

Figure 2-26: Vertical profile of an idealised (monochromatic) ocean wave ......................................... 42

VIII

Figure 2-27: Bamboo poles with pressure sensors 20 cm above ground and Vietnamese flag at transect

CLD_s. ....................................................................................................................................... 44

Figure 2-28: Sensor attached to Rhizophora plant at VC 1 before and after disguising it with plastic

bags. ........................................................................................................................................... 46

Figure 2-29: Example of barometric output file with wrong pressure values ....................................... 48

Figure 2-30: Recorded pressure values of the seaward and landward sensors at transect LH .............. 50

Figure 2-31: Reduction of the significant wave height per m between the seaward and the landward

sensors of the transects CLD_n, CLD_s and LH plotted against water depth for the rainy and

dry season ................................................................................................................................... 53

Figure 3-1: Water depth above sensor membrane at locations VC 1 (seaward) and VC 2 (landward)

derived from the measurements of the pressure transducers ...................................................... 56

Figure 3-2: Recorded significant wave heights (Hs) at the seaward and landward sensors of the

transects CLD_n, CLD_s and LH .............................................................................................. 57

Figure 3-3: Comparison of the reduction of significant wave heights after crossing through the

mangrove forest along the whole transect (r200) and per m (r) between the transects CLD_n,

CLD_s and LH ........................................................................................................................... 58

Figure 3-4: Correlation between the initial significant wave height (Hs) at the coastal sensor and the

rate of wave height reduction at the landward sensor 200 m further inland (r200) during the rainy

and dry season at the transect CLD_n ........................................................................................ 59

Figure 3-5: Reduction of the significant wave height between the seaward and the landward sensors of

the transects CLD_n and LH plotted against water depth for all assessed data ......................... 60


the transects CLD_s and LH plotted against water depth for all assessed data ......................... 61


the transects CLD_n and CLD_s plotted against water depth for all assessed data ................... 62

Figure 3-8: Comparison of the sensor measurements of Hs at transect CLD_n during the dry season

and rainy season ......................................................................................................................... 63

Figure 3-9: Sensor measurements of the significant wave height Hs at transect CLD_n during the rainy

season ......................................................................................................................................... 64

Figure 3-10: Reduction of wave heights at the sensor locations at CLD_n during the rainy season in

total after x meters and per m ..................................................................................................... 64

Figure 3-11: Reduction of the significant wave heights per m between the sensor positions of transect

CLD_n during the rainy season .................................................................................................. 65

Figure 3-12: Reduction of the significant wave height between the seaward sensor CLD_n 1 and the

three landward sensors of the transect plotted against water depth during rainy season ........... 66

Figure 3-13: Sensor measurements of the significant wave height Hs at transect CLD_s during the

rainy season ................................................................................................................................ 67

Figure 3-14: Reduction of wave heights at the sensor locations at CLD_s during the rainy season in

total after x meters and per m for the distances between the sensor locations as well as the

whole transect ............................................................................................................................ 68

Figure 3-15: Reduction of the significant wave height between the seaward sensor CLD_s 1 and the

two landward sensors of the transect plotted against water depth during rainy season ............. 69

Figure 3-16: Sensor measurements of the significant wave height Hs and wave reduction after crossing

through the mangrove forest along the whole transect (r200) and per m (r) at transect LH during

the rainy season .......................................................................................................................... 70

Figure 4-1: Impact of a high floodplain on wave energy dissipation .................................................... 72

IX

Figure 4-2: Reduction of the significant wave height per m between the seaward sensors of CLD_n

and CLD_s and the landward sensors of the respective transect plotted against water depth

during rainy season .................................................................................................................... 75

Figure 4-3: Reduction of the significant wave height between the seaward sensors of CLD_n and

CLD_s and the landward sensors of the respective transect plotted against water depth during

rainy season ............................................................................................................................... 76

Figure 4-4: Comparison of wave reduction per meter at transects CLD_n and CLD_s for distances

between sensor locations as well as the whole transect ............................................................. 79


sensors of transect LH plotted against water depth for all assessed data ................................... 84

Figure 4-6: View into an older monocultural Rhizophora apiculata plantation at the southwest of the

coast of Soc Trang Province. ..................................................................................................... 85

List of Tables

Table 1-1: SLR in cm at the coast of Vietnam according to national climate change scenarios.. .......... 1

Table 2-1: Overview of previous studies into wave attenuation in mangroves..................................... 11

Table 2-2: Distribution of recorded wind data at Con Dao Island regarding wind speed classes. ........ 20

Table 2-3: Vegetation characteristics of Sonneratia caseolaris along the transects CLD_s and CLD_n

on Cu Lao Dung Island. ............................................................................................................. 37

Table 2-4: Vegetation characteristics of Rhizophora apiculata along the transect VC. ....................... 41

Table 2-5: Planned time schedule for sensor measurements and sensor coding. .................................. 45

Table 2-6: Overview of successful measurements for each transect during the wet season and dry

season ......................................................................................................................................... 47

Table 2-7: Successful measurement times and number of tides used for analysis. ............................... 47

Table 2-8: Results of PressMea software for the maximum value of the wave parameter Hs for the

aggregation periods of 5 and 15 minutes. .................................................................................. 49

Table 3-1: Summary of the measured incoming wave characteristics Hs and Tm at the seaward sensors

as well as the rate of wave height reduction r200 and r for all transects during the rainy season

and dry season derived from the 15-min-period data. ............................................................... 55

1 Introduction

1

1. Introduction

Sea-level rise (SLR) due to climate change is a serious threat to countries with heavy

concentrations of population and economic activity in coastal regions (DASGUPTA et al. 2009).

The Intergovernmental Panel on Climate Change (IPCC) SREX report (IPCC 2012) has

highlighted with high confidence that in the absence of adaptation, “locations currently

experiencing adverse impacts such as coastal erosion and inundation will continue to do so in

the future due to increasing sea levels”.

Recently the IPCC AR5 (IPCC 2013) announced the projections of sea level rise (SLR) based

on two different approaches, namely process-based projections (PBP) and semi-empirical

projections (SEP). They estimate the median values of projected SLR for the year 2100 to be

between 0.44 and 0.74 m by PBP and between 0.32 and 1.24 m by SEP, respectively. It

should be mentioned that many semi-empirical model projections of global mean SLR are

higher than process-based model projections, but there is little agreement between semi-

empirical model projections and no consensus about their reliability (IPCC 2013).

According to Vietnam’s national climate change and SLR scenarios, for the low-emission

scenario (B1), medium-emission scenario (B2), and high-emission scenario (A1FI), the mean

temperature could increase by about 3° C while the rainfall could increase by 5-10% by the

end of this century (MONRE 2012). The projected mean SLR ranges from 51 to 99 cm (see

Table 1-1).

Table 1-1: SLR in cm at the coast of Vietnam according to national climate change scenarios. SLR given

in comparison to the period 1980 – 1999 (MONRE 2012).

2020 2030 2050 2070 2100 low emission (B1) 8-9 11-13 22-26 37-42 51-66

medium emission (B2) 8-9 12-14 23-27 37-44 59-75

high emission (A1FI) 8-9 13-14 26-30 45-53 79-99

Vietnam is considered to be one of the most vulnerable countries to the effects of climate

change, particularly to floods, storms, and SLR (ISPONRE 2009). This is especially true for

the Mekong Delta because of its low elevation, dense population, and economic importance.

Recently several papers have been published on the negative influence of SLR and the

accompanying salinity intrusion on agro-ecology in the Mekong Delta. The future prospects

of Pangasius fisheries, other aquaculture, and the rice industry were found to be threatened by

the salinity intrusion (CHEN et al. 2002, KOTERA et al. 2008, NGUYEN et al. 2014, RENAUD et

al. 2014, TRAN & NGUYEN 2014).

1 Introduction

2

DASGUPTA et al. (2009) assessed the consequences of continued SLR for 84 coastal

developing countries in 5 regions using 6 indicators: land, population, gross domestic product

(GDP), urban area, agricultural land, and wetlands. Under the assumption of a 1 m SLR

scenario, wetlands are especially impacted by SLR in developing countries. In the ranking of

the top ten impacted countries by population, Vietnam would be the most affected. Around

10% of the population would be displaced and 10% of the urban areas would be inundated by

a 1 m SLR. Estimations indicate that affected areas would account for 10% of the country’s

GDP. In addition, nearly 28% of the wetlands in Vietnam would be flooded by a 1 m SLR.

For all of the indicators used in the study of DASGUPTA et al. (2009), Vietnam ranks among

the top five most impacted countries, four times as the most impacted and twice as the second

most affected (land area and agricultural land). It should be noted that their method has

multiple limitations. The most important of these are a lack of predictions for future

development of the indicators and the use of a 1 m scaled digital elevation model in the

analysis, which prevents sub-meter SLR modelling. Nevertheless, their results clearly show it

is necessary to take action in Vietnam to cope with the climate change induced SLR.

BOATENG (2012) confirms the vulnerability of the coastal zone of Vietnam, which provides a

diverse range of natural resources (wetlands, minerals, and fertile agricultural land) and

favourable conditions for social and economic development (fisheries, aquaculture,

agriculture, tourism, transportation, urbanization). The “physical character of the coast of

Vietnam and the effects of human development and over-exploitation of the coastal resources

are among the causes of increased vulnerability of the coastal zone to climate change and sea

level rise” (BOATENG 2012).

Without action, SLR by 1 m would cause an estimated loss of 17,423 km2 (5.3%) of Vietnams

total land area (IMHEN 2010a, qtd. in UN 2012). In particular it would threaten the Mekong

Delta (39%), the Red River Delta (10%), the Central Coast provinces (over 2.5%), and more

than 20% of Ho Chi Minh City (MONRE 2012).

Of all 63 provinces and municipalities in Vietnam, 33 are threatened by inundation. Among

them, the four Mekong Delta provinces Kien Giang: Ca Mau, Hau Giang and Soc Trang will

be affected most (CAREW-REID 2007). SLR of 1 m would threaten 62.5% (3,896 km2) of the

land in Kien Giang, 52.7% (2,733 km2) of that in Ca Mau, 49.6% (1,620 km

2) of that in Soc

Trang and 86.5% (1,397 km2) of that in Hau Giang (IMHEN 2010a, qtd. in UN 2012).

The projections for increased sea level by the end of the century indicate that the people living

in the Mekong Delta and in other low lying areas need to find ways to deal with these

challenges to their livelihood. There are numerous strategies on how to handle this situation

1 Introduction

3

(e.g. in ALBERS et al. 2013). In general, the first decision that has to be made is which coastal

protection strategy shall be followed: retreat, defence, adaptation, or expansion.

In Vietnam, retreating is an unpopular strategy, while defence and expansion are more

popular (SOC TRANG SUB-FPD 2013). In order to achieve protection by defence, it is

necessary to manage the coastal areas. Different elements can be applied to defend the

coastline (ALBERS et al. 2013). Depending on the site-specific circumstances like wave

climate, shape of the coastline, and the bathymetry, structures built into the ocean can have

negative effects downstream. This should be avoided.

Numerical models are used for the management of coastal zones and for planning suitable and

cost effective coastal protective measures (SCHMITT et al. 2013). These models need various

input data to make them as accurate and reliable as possible. To model coastal processes, data

about wind (direction and strength), wave climate, bathymetry, coastal elevation, sediments,

and sediment transport are needed. The shape of the coast also needs to be assessed.

Furthermore, in tropical regions, the influence of mangrove forests as wave dampening

obstacles along the coast needs to be taken into account. They act as a natural coastal

protection, primarily through the prevention of erosion, the collecting of sediment, and the

reduction of the wave climate (CLOUGH 2013). Understanding these key processes is

important for the preservation of the mangrove forests themselves, but also for the often

highly populated areas behind them. To make the numerical models more accurate, drag

coefficients or rates of wave attenuation through mangrove forests must be known

(BRINKMAN 2006).

Several studies have already been conducted to provide input values about the wave

dampening effect of mangrove forests. Due to the inaccessibility of mangrove forests, the

number of field studies is limited (see chapter 2.1). Conducted in Vietnam, Australia, Japan

and Thailand, these studies accurately quantified the hydrodynamic conditions. However, they

often span only a few tides and therefore cover only a limited range of possible conditions

(HORSTMAN et al. 2014).

For more accurate numerical models, data assessed over a longer span of time is more

reliable. In addition, many tropical and subtropical regions where mangroves grow along the

coast have a wave climate that is highly influenced by the monsoon wind fields and changing

seasons (tidal range varies during the year, winter is both, dry season and stormy season in the

Mekong Delta). These changing patterns also need to be taken into account in numerical

models, making data from both monsoon seasons necessary (ANDERSON et al. 2011).

1 Introduction

4

Besides the relative brevity of most previous studies on wave attenuation rates, none was

conducted in the Mekong Delta. As presented above, the Mekong Delta is expected to be

threatened most by SLR within Vietnam and also in comparison to other countries. These

points shall be addressed in this thesis.

The purpose of this thesis is to assess long-term data about the wave dampening effect of

mangrove forests in the Mekong Delta. For this thesis, data was collected along the coast of

the Soc Trang Province of Vietnam, one of the provinces that is expected to be most severely

threatened by SLR. The aim was to gather data in different mangrove forests over the course

of both, the southwest and the northeast monsoon season. To measure the dampening effect,

sensors with pressure transducers were used. Depending on the number of sensors available,

four study sites were identified to give information about various mangrove forests, including

one reference site. For each of the four sites, data for one month in the rainy season (RS) and

one month in the dry season (DS) was aimed to be measured. The study sites were described

by their elevation changes along their profile, their sediment grain size distribution, and their

vegetation characteristics (if vegetated).

The work on this thesis was accompanied by an eight-month internship and four months of

consulting for the GIZ ‘Management of Natural Resources in the Coastal Zone of Soc Trang

Province’ project. This had two main influences on the study: (1) it was possible to conduct

long-term measurements in the rainy and the dry season, and (2) it caused a delay of the

thesis’ workflow caused by discontinuity.

Figure 1-1 presents the workflow of this master’s thesis. After initial preparations, several

months of fieldwork followed. The assessed data was then processed before analysis.

1 Introduction

5

Figure 1-1: Work flow from preparations over field works, data processing and analysis to the finished

master’s thesis.

master's thesis

data analysis (08. - 10.2014)

calculating and analysing wave height reductions in MS Excel

data processing (05. - 08.2014)

raw data in MATLAB further processing in PressMea post-processing in MS Excel

field work (07. - 08.2013 & 12.2013 - 01.2014)

sensor measurements in rainy season

vegetation assessments, levelling and sediment samples

sensor measurements in dry season

preparations (06.2013)

reviewing background literature field trips to identify study-sites collection of background data

(wind, tides, ...)

2 Methods 2.1 State of existing research

6

2. Methods

In the following chapter, available information based on a literature review will be presented.

Afterwards, descriptions of the shoreline changes and the wind and wave patterns along the

coast of Soc Trang Province are presented, followed by the sites chosen for this study. They

and the methods used for their characterisation are described in more detail in chapters about

their elevation profiles, sediment grain size distribution and vegetation structure. Finally, the

methods used to assess the wave dampening effect on these sites are presented. Maps

published in this thesis are generally north-oriented. Therefore no north arrows are included in

the map layouts.

2.1. State of existing research

2.1.1. Mangrove species in Soc Trang Province as bio-shields

Mangrove forests can act as bio-shields for the protection of people and their economic values

from erosion and storms. Several studies presented in chapter 2.1.3, p. 10, have measured the

attenuation of waves in mangroves and found a reduction in wave height as they pass through

mangroves. However, the effectiveness depends on many variables and it should be noted that

mangroves do not provide an effective protection against hazards like extremely large tsunami

waves (WOLANSKI 2006, GEDAN et al. 2011).

“Coastal erosion is a very complex and dynamic process. The extent to which mangroves can

prevent or help to reduce erosion tends to be fairly site specific. It depends amongst other

things on wave energy, tidal range, coastal currents and the shape of the coastline and

offshore mud or sand banks. In some places where erosional forces are weak, the presence of

mangroves is sufficient to prevent erosion more or less completely; in others where erosional

forces are stronger, mangroves may help to reduce the rate of erosion significantly; but along

high energy coastlines, mangroves may provide only minimal or no protection against coastal

erosion” (CLOUGH 2013).

The mangrove environment is a tough and difficult habitat for plants. Soft, unstable soils that

are usually highly saline, more or less permanently flooded, and generally anaerobic (anoxic

or lacking in oxygen) are characteristic (CLOUGH 2013). The various mangrove species have

different ecological requirements. The main factors are: temperature, salinity and rainfall,

duration and depth of inundation, wave action and exposure, connectivity and exchange of

water currents, and the development of the soil (DUKE 1998). These factors interact with each

other and determine the mangroves distribution patterns. While Avicennia spp. and


7

Sonneratia spp. grow best on sites inside the zone of daily tidal inundation, Rhizophora spp.

are able to grow on higher surfaces with less flooding (PHAM et al. 2011). And while

Sonneratia spp. only grow in brackish water conditions, Avicennia spp. can survive in more

saline waters (CLOUGH 2013). This natural pattern can also be seen in Soc Trang Province,

even though all the mangrove forests growing along the coast of the province are the result of

plantation programs (JOFFRE 2010, PHAM et al. 2011).

The main types of species that can be found in Soc Trang Province are Sonneratia caseolaris,

Avicennia marina, Rhizophora apiculata, Ceriops tagal and Bruguiera cylindrical (SPELCHAN

& NICOLL 2011). The most common, which are also growing along the study transects of this

thesis, are Sonneratia, Avicennia and Rhizophora. For these three species a brief overview,

including their habitats, is presented in Appendix I, App. 1.

These species each provide different protective functions. Mangrove species with

pneumatophores, like Avicennia and Sonneratia, grow naturally at the forest edge facing the

sea. They secure sediments with their massive underground root system preventing erosion.

However, further inland, the above-ground prop (stilt) root system of Rhizophora attenuates

waves more effectively (CLOUGH 2013).

The mangroves aerial root system that provides them with air is of special interest for this

study, because it is one of the main factors affecting wave attenuation (MCIVOR et al. 2012a).

Sonneratia spp. and Avicennia spp. have characteristic pneumatophores (see Figure 2-22, p.

38 from the vegetation assessment for an impression of pneumatophores). These are aerial

roots that protrude out of the sediments (CLOUGH 2013). While the aerial roots of Avicennia

are commonly narrow and reach up to between 20 to 30 cm above ground, the

pneumatophores of Sonneratia develop secondary thickening (MCIVOR et al. 2012a). This

makes their morphology more cone-shaped and allows some species to reach over a metre in

height.

In Soc Trang, Sonneratia caseolaris has pneumatophores that are typically 50-90 cm in height

and 7 cm in diameter (SPELCHAN & NICOLL 2011). They grow in brackish estuarine areas

where inundation of less than 1 m in depth occurs for 6-12 hrs/day. The pneumatophores of

Avicennia marina are typically 10-15 cm in height and grow in mudflats far from the Hau

River mouth, where flooding of no more than 1 m occurs for 6-18 hrs/day.

In contrary, Rhizophora spp. have prop or stilt roots that grow from the stem and reach

through the air into the sediments (see Figure 2-24, p. 40 for an impression of prop roots at

one of the study transects). Their aerial root system is mainly above the substrate. They grow

in sheltered areas where inundation occurs for approx. 6 hrs/day (SPELCHAN & NICOLL 2011).


8

2.1.2. Factors affecting wave attenuation in mangroves

MCIVOR et al. (2012a) provided a comprehensive overview of the factors affecting wave

attenuation in mangroves based on existing studies. Known factors are water depth (a function

of topography/bathymetry and tidal phase), wave height, and mangrove structure. The latter

depends on their species, age and size. Figure 2-1 presents the factors in more detail. They can

be further described according to the following specifications: distance travelled through the

mangrove forest, water depth relative to the structure of the mangrove trees (root system;

trunks, branches and leaves; age of trees), shore slope and topography/bathymetry, wave

height and period, and factors affecting wave energy dissipation like the density and spacing

of the mangrove trees.

Figure 2-1: Factors affecting wave attenuation in mangroves (MCIVOR et al. 2012a).

The main factors determining the rate of wave attenuation with distance into the mangrove

forest are the water depth (related to the tidal phase) and the mangrove morphology.

Combined they define the nature of obstacles which attenuate the waves on their way through

the forest (MCIVOR et al. 2012a). The dampening effect depends on the density and shape of

the obstacles: primarily their height relative to the water depth. As long as the obstacles are

taller than the water depth, their resistance increases together with an increasing water depth.

If the water depth is overtopping the obstacles, most of the incoming waves are unaffected

and little attenuation occurs (MCIVOR et al. 2012a).

The mangrove morphology depends on the species. Beside the trunk, the branches and leaves,

mangrove species often have a far spreading root system (CLOUGH 2013). This is of

significant importance for the rate of wave attenuation on shallow slopes and for low water

heights. The three main root systems after CLOUGH (2013) are: prop or stilt roots (e.g.

Rhizophora spp.), knee roots (e.g. Bruguiera spp.), pneumatophores (e.g. Sonneratia spp. and


9

Avicennia spp.) and buttress or plank roots (e.g. Heritiera littoralis). These aerial root systems

act as obstacles to wave motion when the water depths are shallow. Above these aerial root

systems the trunks present less resistance to the flow of water so that waves pass through

more easily (MCIVOR et al. 2012a). This results in high wave dampening at shallow water

depths, but when the water becomes deeper causes a reduction of the wave attenuation. There

are also mangroves like Kandelia candel and Nypa fruticans which have no aerial roots. Their

effect on waves is determined only by their trunks, branches and leaves (MCIVOR et al.

2012a).

Along the coast of Soc Trang Province mainly Sonneratia caseolaris, Avicennia marina and

Rhizophora apiculata can be found (PHAM 2011). These are also the species along the study

transects of this study. In chapter 2.1.1, p. 6, a brief overview was given for the species

relevant for this thesis. For further details, also for other species, MCIVOR et al. (2012a)

presents a good overview and CLOUGH (2013) presents a detailed summary.

Beside the obstacles, the shore slope and the topography are important for the energy

dissipation in waves. They influence the water depth and thereby causes shoaling and

breaking (MCIVOR et al. 2012a). With a decrease in water depth the wave height is increasing.

Without the breaking of waves this results in a temporary increase in wave energy.

Roughness coefficients represent the resistance to flood flows in channels and flood plains.

For calculations of wave attenuation and current speeds formulas including the Manning’s

coefficient n, which is expressed by a value between 0 and 1, is often used. The higher its

value the bigger the effect of obstacles in wave direction is. In river sciences many studies

were conducted to estimate the influences on wave attenuation by different obstacles in

channels (rivers) and floodplains (ARCEMENT & SCHNEIDER 1989). According to ARCEMENT

& SCHNEIDER (1989) the value of n may be computed by:

𝑛 = ( 𝑛𝑏 + 𝑛1 + 𝑛2 + 𝑛3 + 𝑛4 ) × 𝑚

where:

nb = a base value of n for a straight, uniform, smooth channel in natural materials,

n1 = a correction factor for the effect of surface irregularities,

n2 = a value for variations in shape and size of the channel cross section,

n3 = a value for obstructions,

n4 = a value for vegetation and flow conditions, and

m = a correction factor for meandering of the channel.


10

The formula shows that the importance of the sediment grain sizes can, in comparison to the

vegetation, be neglected. Manning’s roughness coefficient n is used to quantify the resistance

to flow in studies about flow in channels, floodplains, and areas affected by storm surges

(MCIVOR et al. 2012b). Less though in studies about the attenuation of wind and swell waves

by mangroves (MCIVOR et al. 2012a).

2.1.3. Existing studies about wave reduction in mangrove forests

For numerical models the measurement, calculation, or approximation of the drag coefficient

is an essential element (MCIVOR et al 2012a). ANDERSON et al. (2011) compiled an overview

of several attempts (both field and laboratory studies) to calculate the drag coefficient of

coastal vegetation using parameters which are easier to measure, such as vegetation

characteristics. As mentioned above, wave attenuation is highly dependent on the mangrove

morphology and wave characteristics. Therefore, wave attenuation shows a high variability

that makes the generalisation of vegetation-wave behaviour extremely difficult (ANDERSON et

al. 2011).

Wave reduction by obstacles can be calculated and expressed using different formulas and

transmission coefficients or factors (ALBERS et al. 2013, MASSEL et al. 1999, MAZDA et al.

1997a, MAZDA et al. 2006, TANAKA et al. 2007). Observations taken in the field during

previous studies have parameterised the wave attenuation by mangroves calculating bulk

roughness parameters that include both, vegetation induced drag forces and bottom friction

(HORSTMAN et al. 2014).

In Table 2-2 (two pages) an overview of previous studies into wave attenuation in mangroves

is given. Where necessary, the wave attenuation parameters were calculated with figures

given in the literature to be able to compare the existing studies. The chosen parameters for

wave attenuation are presented in further detail in chapter 2.5.3.2, p. 51. The existing studies

were conducted in Vietnam, Australia, Japan and Thailand, but although they accurately

quantified the hydrodynamic conditions, they often span only a few tides, and therefore cover

only a limited range of possible conditions (HORSTMAN et al. 2014).


11

Table 2-1: Overview of previous studies into wave attenuation in mangroves. Where necessary the wave attenuation parameters were calculated with figures given in the literature.

Location - Mangrove setting

Conditions (slope, measurement time, etc.)

Vegetation Incident wave height H & period T

Wave attenuation parameters* r [m-1] rx

Tong King Delta, Vietnam (N) - Fringing mangroves (MAZDA et al. 1997a)**

slope: 0.5/1000, 4 days (two sites with each 2 days), max. water depth 100 cm, in total 1.5 km wide mangrove belt

Sparse Kandelia candel seedlings (1/2 year-old), planted Dense 2-3 year-old Kandelia candel, up to 0.5 m high, planted Dense 5-6 year-old Kandelia candel, up to 1 m high, planted

H= – T = 5–8 s H= – T = 5–8 s H= – T = 5–8 s

r = 0.0001–0.0010 r = 0.0008–0.0015 r = 0.0015–0.0022

r100 = 0.01–0.10 r100 = 0.08–0.15 r100 = 0.15–0.22

Vinh Quang, Vietnam (N) - Fringing mangroves (MAZDA et al. 2006)**

slope 1/1000, 1 day, measurement during typhoon, max. water depth 90 cm, 100 m wide belt

Sonneratia sp. 25 cm high pneumatophores, canopy starts 60 cm above bed, planted No vegetation

H = 0.11–0.16 m T = 8–10 s H = 0.11–0.16 m T = 8–10 s

r = 0.002–0.006 r = 0.001–0.002

r100 = 0.2–0.6 r100 = 0.1–0.2

Can Gio, Vietnam (S) - Riverine mangroves (VO-LUONG & MASSEL 2006, VO-LUONG & MASSEL 2008)

16 days, not continuous, cliff within the first 10 m of the transect which influences the wave height

Mixed Avicennia sp. and Rhizophora sp. (190 cm water depth, 11 datasets) Mixed Avicennia sp. and Rhizophora sp. (210 cm water depth, 10 datasets) Mixed Avicennia sp. and Rhizophora sp. (250 cm water depth, 4 datasets)

H = 0.33–0.43 m T = – H = 0.35–0.42 m T = – H = 0.37–0.42 m T = –

r = 0.025–0.035 r = 0.025–0.035 r = 0.0125

r20 = 0.5–0.7 r20 = 0.5–0.7 r40 = 0.5

Do Son, Vietnam (N) - Fringing mangroves (QUARTEL et al. 2007)** ***

26 days, not continuous (every 1 hr for 17’04’’), muddy mangrove forest behind sandy mudflat

Mainly Kandelia candel bushes and small trees (31.8 m long, slightly negative slope) Non-vegetated beach plain (314.5 m long, positive slope of 0.19%)

H = 0.15–0.25 m T = 4–6 s H = 0.15–0.25 m T = 4–6 s

r = 0.004–0.012 r = 0.0005–0.002

r31.8 = 0.13–0.38 r314.5 = 0.16–0.63

Red River Delta, Vietnam (N) - Fringing (?) mangroves (TRAN 2011)*****

2–10 manual measurements per transect, 4 mangrove locations each 4 measurements

Mixed vegetation, mainly planted H = 0.15–0.27 m T = –

r = 0.0041–0.0065**** r100 = 0.41–0.65****

Can Gio, Vietnam (S) - Fringing (?) mangroves (TRAN 2011)*****

2–10 manual measurements per transect, 18 mangrove plots

Mixed vegetation, mainly planted H = 0.55 m T = –

r = 0.0082**** r100 = 0.82****

Cacoa Creek, Australia - Fringing mangroves (BRINKMAN 2006, MASSEL et al. 1999)

3 days with 3 high tides, 280 m wide forest band, measured over 260 m,

Zonation: Rhizophora stylosa (front 180 m), Aegiceras spp. (60 m wide), Ceriops spp. (back 60 m)

H = 0.03–0.05 m T ~ 2 s

r = 0.0003–0.003 (mean: 0.0019)

r260 = 0.5

Iriomote, Japan - Riverine mangroves (BRINKMAN 2006, MASSEL et al. 1999)

3 days with 4 high tides, 50 m wide forest band, measured over 40 m,

Bruguieria gymnorrhiza, 20-30 cm high knee roots

H = 0.08–0.15 m T ~ 2 s

r = 0.008–0.022 (mean: 0.013)

r40 = 0.54

Table continues on the following page.


12

Location - Mangrove setting

Conditions (slope, measurement time, etc.)

Vegetation Incident wave height H & period T

Wave attenuation parameters* r [m-1] rx

Oonoonba, Australia - Fringing mangroves (BRINKMAN 2006)

3 days with 3 high tides, 130 m wide forest band, measured over 40 m inside the Rhizophora sp. forest

Zonation: Sonneratia sp. (front 75 m) and Rhizophora sp. (back 55 m)

H = 0.04–0.25 m T ~ 6 s

r = 0.015–0.024 (mean: 0.019)

r40 = 0.75

Palian, Thailand - Fringing forest (HORSTMAN et al. 2014)**

slope: 6.0(±7.6)/1000, 98 m, measurements for periods of 2 to 6 weeks between 25.11.2010-19.01.2011 and 17.04-02.05.2011

Non-vegetated mudflat Mixed Avicennia sp. and Sonneratia sp. (sparsely vegetated forest front) Rhizophora sp. (very dense forest in the back)

H = 4.4–11.3 cm T = 2.8-4.1 s H = 4.4–11.3 cm T = 2.8-4.1 s H = 4.4–11.3 cm T = 2.8-4.1 s

r = 0.0019 r = 0.0032 r =0.012

r98 = 0.30–0.43 (= for the whole transect, r = 0.003–0.0043)

Kantang, Thailand - Fringing forest (HORSTMAN et al. 2014)**

slope: 3.3(±2.6)/1000, 246 m measurements for periods of 2 to 6 weeks between 26.01.-14.04.2011, including 1 storm event

Non-vegetated mudflat Mixed Avicennia sp. and Sonneratia sp. Rhizophora sp. (dense forest in the back)

H = 5.5–10.6 cm T = 2.9–6.4 s H = 5.5–10.6 cm T = 2.9–6.4 s H = 5.5–10.6 cm T = 2.9–6.4 s

r = 0.002 r = 0.0024 r = 0.0061

r246 = 0.42–0.47 (= for the whole transect, r = 0.0019–0.0017)

Quang Binh, Vietnam (central) - Fringing (?) mangroves (NGO et al. 2005, data from VU 2005)

1 day with 1 high tide, 920 m wide forest band, measurements on more days, but just one presented

Dense 8-9 year-old Sonneratia caseolaris (120 m into the forest) Dense 8-9 year-old Sonneratia caseolaris (320 m into the forest) Dense 8-9 year-old Sonneratia caseolaris (520 m into the forest) Dense 8-9 year-old Sonneratia caseolaris (720 m into the forest) Dense 8-9 year-old Sonneratia caseolaris (920 m into the forest)

H = 0.55–0.72 m T = – H = 0.55–0.72 m T = – H = 0.55–0.72 m T = – H = 0.55–0.72 m T = – H = 0.55–0.72 m T = –

r = 0.0033 r = 0.0018 r = 0.0014 r = 0.0011 r = 0.0010

r120 = 0.39 r320 = 0.58 r520 = 0.71 r720 = 0.79 r920 = 0.88

* Wave attenuation is quantified by two reduction coefficients: rx = (Hs - Hl) / Hs (based on MAZDA et al. 1997a) where Hs is the wave height before entering the forest and Hl is the wave height at the monitoring points. The values for r are based on the equation r = rx / Δx where Δx is the distance travelled by a wave.

for MAZDA et al. (1997a), VO-LUONG & MASSEL (2006, 2008), and NGO et al. (2005), r was calculated based on rx given in the literature; for BRINKMAN (2006) and MASSEL et al. (1999) r and rx were calculated based on figures of significant water height reduction

** the wave attenuation with concurrent water depth is shown in Appendix II: App. 2 (HORSTMAN et al. 2014), App. 3 (MAZDA et al. 1999, 2006) and App. 4 (QUARTEL et al. 2007) *** r and rx were calculated using data from graphs in QUARTEL et al. (2007) **** incorrect cited in other literature as BAO (2011) ***** r and rx were calculated using data from graphs in TRAN (2011)


13

MAZDA et al. (1997a, 2006), MASSEL et al. (1999), BRINKMAN (2006) and NGO et al. (2005)

measured only for a few tides spread over a couple of days. VO-LUONG & MASSEL (2006)

made good measurements over a time of 16 days, but a cliff right at the beginning of their

study transect caused for all analysed water depths the maximum wave attenuation. Within

the first 20 or 40 m of the transect most of the dampening occurred. After this initial drop the

water height continued to decrease only slightly. TRAN (2011) measured the wave attenuation

in 32 different mangrove plots in the Red River Delta in the north of Vietnam and the Can

Gio Mangrove Biosphere Reserve close to Ho Chi Minh City. This study contained the most

variety in assessed forests. However, for each site, just 2 to 10 repetitive measurements were

conducted which were recorded manually by six people spread along each transect.

QUARTEL et al. (2007) measured for a time of 26 days within a Kandelia candel forest (no

aerial root system). The assessments of MAZDA et al. (1997a) (also Kandelia candel) and

QUARTEL et al. (2007) were both in mangroves forests with dwarfed trees. Both research

groups obtained exponentially increasing drag coefficients for increasing water depths, due to

the young structure of the forests (HORSTMAN et al. 2014). Because of the missing aerial root

system in the forest assessed by QUARTEL et al. (2007), the rate of wave reduction increased

with water depth. In MAZDA et al (1997a) the wave attenuation in the oldest forest assessed

(5-6 year-old) was almost constant with variable water depths, while all other studies

assessing mangroves with aerial root systems that correlated wave reduction with concurrent

water depth showed decreasing attenuation values with increasing water depths (see Appendix

II, App. 2, App. 3, App. 4).

MAZDA et al. (2006) observed a Sonneratia spp. forest and found that the initial decrease of

wave reduction with increasing water depths was due to the inundated pneumatophores. With

even higher depths up to a level where the waves passed through the branches and leaves of

the trees, the wave attenuation increased again (see Appendix II, App. 3, right side).

BRINKMAN (2006) measured on three study sites with different mangrove species and aerial

root systems (Cocoa Creek, Australia, Rhizophora stylosa, prop roots; Iriomote Island, Japan,

Bruguieria gymnorrhiza, knee roots; Oonoonba, Australia, Sonneratia sp. and Rhizophora sp.,

pneumatophores and stilt roots). On all sites with increasing water depth the wave reduction

decreased, which was due to the root systems. The measurements presented in NGO et al.

(2005) were limited in time and repetitions along a 920 m wide Sonneratia caseolaris forest

in central Vietnam. They obtained 88% wave reduction after 920 m forest (r920 = 0.88). Most

of the previous studies were conducted in the north of Vietnam (Red River Delta), none in the

Mekong Delta.


14

Recently HORSTMAN et al. (2014) published their results of their measurements along two

transects in mangrove forests in Thailand. They criticise the general description of vegetation

in former studies as insufficient and quantified the volume of submerged mangrove biomass

in more detail, based on MAZDA et al. (1997b). Their study was also the first to obtain

continuous measurements over several months (see Table 2-1). The two study sites assessed

had forests of zones that contained sparse mixed Avicennia sp. and Sonneratia sp. at the

seaward side followed by dense Rhizophora sp. in the landward part of the forests. The wave

attenuation rates plotted against water depth for both transects are shown in Appendix II, App.

2. They also found decreasing wave attenuation rates with increasing water depths in the

Rhizophora zones, when the higher vegetation densities at lower depths were submerged. The

independence of water depths in the Avicennia zones was explained by limited vertical

variability of the vegetation structure. The slight increase of wave reduction with increasing

water depths in the Avicennia zone of transect Palian “could have been caused by

submergence of the canopy of the lower trees at the highest tides” (HORSTMAN et al. 2014).

All the “studies are not directly comparable because (…) environmental parameters differed

(e.g. incoming wave height, wave period, bottom slope (and thus shoaling effects), water

depth)” (MCIVOR et al. 2012a). MAZDA et al. (2006) and HORSTMAN et al. (2014) were the

only ones to measure during a storm event when much larger waves than usual can occur (Hs

≥ 0.30 m at Kantang transect of HORSTMAN et al. 2014). This is also the time when protection

from waves is most important. Most of the previous studies are also limited in the measured

water depths, mostly less than 70 cm in height (MCIVOR et al. 2012a). Therefore some used

extrapolation to estimate how the wave reduction would be changing in case of higher water

depths (MAZDA et al. 2006).

ANDERSON et al. (2011) state for the review of field and laboratory studies on wave

dissipation by vegetation that “existing studies (…) provide a range of analytical, empirical,

and numerical models, but current methods require calibration and application within the

narrow range of available lab and field data. Future studies need to expand the range of the

data as well as generalize model formulations” (ANDERSON et al. 2011).

2 Methods 2.2 Coastal processes at the shoreline of Soc Trang Province

15

2.2. Coastal processes at the shoreline of Soc Trang Province

Soc Trang Province is one of 13 provinces in the Mekong Delta region and is located south of

the Hau River, which is the southern-most arm of the Mekong. The province covers a total

area of 3,311 km2 and had a population of 1,310,292 in the year 2013 (DPI 2014). The

coastline of Soc Trang Province has a length of 72 km along which areas of both accretion

and erosion can be found (SCHMITT et al. 2013).

Figure 2-2 presents the coastline of Soc Trang Province as well as its location within the

Mekong Delta and within Vietnam. The locations of the study areas are marked with red stars

while the red squares are shown in more detail in Figure 2-4, p. 17.

Figure 2-2: The Mekong Delta in Vietnam, Soc Trang Province with details of the coastal zone, the

location of the maps shown in Figure 2-4 and locations of the study transects (SCHMITT & ALBERS

2014, changed).

Usually the coastline of Soc Trang Province features an earthen dyke along the whole coast

that protects the hinterland directly and with it the people and their farmland behind the dyke

from inundation. On the seaward side of the dyke, where erosion has not yet destroyed them,

mangroves are growing on the floodplains. Figure 2-3 depicts this idealised situation.

Figure 2-4

Study transects


16

Figure 2-3: Idealised situation along the coast of Soc Trang Province (ALBERS et al. 2013, changed).

As previously mentioned, this is not the case everywhere. A wide band of mangrove forest

can be found along the whole coast of the province except the southwestern part, where

erosion endangers the remaining mangrove belt. The coastline of the province is characterised

by a dynamic process of accretion and erosion created by the flow regime of the Mekong

River and its sediment load, the tidal regime of the South China Sea (Vietnamese East Sea)

and coastal long-shore currents driven by prevailing monsoon winds (see also chapter 2.3, p.

21).

At the present time, accretion is taking place along the coast of Cu Lao Dung Island (CLD),

especially at its southern tip where a sandbank is forming and stretching seawards for several

kilometres (SCHMITT et al. 2013). While the opposite side of the river in the west is stable,

parts of Vinh Hai suffer from erosion, particularly in recent years (recorded by P. Bourne

during shoreline monitoring for the GIZ project Soc Trang). Southwest of Vinh Hai, the

coastline is experiencing accretion up to a point between the sluice gates 3 and 4.

GoogleEarth background satellite images from April 2014 show where the accretion reaches

currently, approximately 1 km into the southwest from sluice gate 4. The remaining 6

kilometres from there to the border with the neighbouring province, Bac Lieu, are presently

still eroding. This was confirmed during numerous field trips. In several places along this

stretch of coast, the dyke is already endangered by severe erosion because the former

mangrove belt on the seaward side has been completely eroded away. A total of around 300 m

of mangrove forest was destroyed in several spots (SCHMITT et al. 2013).

However, the processes along the coastline of Soc Trang Province have not always been like

that. They are not mono-directional, but instead are often highly dynamic with accretion and

retreating occurring cyclically (SCHMITT & ALBERS 2014). This was found in a digital

analysis of shoreline changes for the coastal zone of Soc Trang Province. It was carried out by

the author for the GIZ project ‘Management of Natural Resources in the Coastal Zone of Soc


17

Trang Province’ using topographic maps from 1904 and 1965 as well as IKONOS satellite

images from 2012. For this analysis the coastline was defined as being the seaward mangrove

forest edge.

The main findings of the analysis are presented in the maps in Figure 2-4 and were also

published in SCHMITT & ALBERS (2014). On Cu Lao Dung Island accretion rates are ranging

from 6.2 to 68.2 m per year over a period of 108 years (see Figure 2-4 A). There, the coastal

processes were mono-directional for the considered time. In contrary to this, the maps of the

coast of Vinh Hai (see Figure 2-4 B) and near Vinh Chau Town (see Figure 2-4 C) show

dynamic processes, with accretion and retreating occurring cyclically. In the northeast of map

B a period of accretion between 1904 and 1965 was followed by erosion between 1965 and

2012. While the shoreline was eroding with up to 15.5 m per year for the period between 1965

and 2012, in the adjacent southwest the land was accreting with up to 35.4 m per year within

the same period.

Figure 2-4: Shoreline changes in Soc Trang Province from 1904 till 2012. The location of the three maps

are shown in Figure 2-2. A: Cu Lao Dung Island, B: Vinh Hai, C: near Vinh Chau Town. Base map

provided by ESRI: DigitalGlobe 2008 & 2011, GeoEye 2000 & 2009 and i-cubed 1999 (own map, also

published in SCHMITT & ALBERS 2014).

Map C depicts the situation that is typical for about 17.5 km of coastline from the border with

Bac Lieu Province in the southwest. In this part of the coast in the time from 1904 through

1965 accretion of up to 23.6 m per year occurred. From then on the coast was eroding up to

2 Methods 2.3 Wind and waves along the coast of Soc Trang Province

18

14.1 m per year. The right side of map C shows the representative coastline for the next 21

km, which is characterized by continuous accretion since 1904, with a rate of up to 12.4 m per

year.

The preceding analysis used only the three given time-steps. Therefore, the figures given per

year are always stretched over a time frame of 47 or 61 years. One more accurate figure is

given in PHAM (2011) with a retreat of coastline by about 300 m within only 10 years. This

expresses how severe the loss of land is in some areas of Soc Trang Province.

2.3. Wind and waves along the coast of Soc Trang Province

Winds in the Mekong Delta are dependent on the two monsoon seasons, the northeast

monsoon in winter and the southwest monsoon in summer (WINDFINDER.COM 2014a). The

dry season lasts from November till March, with the winds coming from northeast. In the

following two months (April and May) the winds turn to southwesterly monsoon winds that

bring the rainy season. This lasts from June to September, until in October, the winds turn

again.

In Figure 2-5 the wind and wave directions at Con Dao Island, approximately 95 km from the

coast of Vietnam (Soc Trang Province), are shown. The described wind patterns can be seen

in the left part of the image, where the wind direction distribution over one year is given in

percent. On the right side the two main wave directions, which are induced by the northeast

and southwest monsoon respectively, are highlighted dependent on their significant wave

heights. During the winter, a larger quantity of higher waves coming from northeast

Figure 2-5: Wind and wave directions at Con Dao Island. Left: Wind direction distribution in percent

over a year (statistics based on observations taken between 10/2009 - 07/2014 daily from 7am to 7pm

local time) (WINDFINDER.COM 2014a). Right: Wave direction distribution of various significant wave

heights at Con Dao Island (ALBERS et al. 2013, data from DAT & SON 1998).


19

dominate the wave climate, in summer the waves approach from the southwest. The presence

of larger waves is reduced, but strong southwestern monsoon winds sometimes create waves

of up to 3 m in height (DAT & SON 1998, qtd. in ALBERS et al. 2013).

According to these general patterns of the wind and wave climate, the wind recordings for the

time of the sensor measurements fit to the general wind field. The wind direction and speed

recordings at Con Dao Island during the rainy season 2013 are presented in Figure 2-6.

Figure 2-6: Wind direction distribution in percent during part of the rainy season 2013 on Con Dao Island

(21.07.2013 - 12.09.2013) (data from WINDFINDER.COM 2014a).

It is possible to relate different wind speeds with their effect they have on sea and land

(WINDFINDER.COM 2014b). While winds with speeds of up to 11 km/h have no big effect,

higher wind speeds cause the following:

- light breeze (6-11 km/h): small wavelets, still short, but more pronounced; crests have

a glassy appearance and do not break

- gentle breeze (12-19 km/h): large wavelets; crests begin to break; foam of glassy

appearance; perhaps scattered white horses

- moderate breeze (20-28 km/h): small waves, becoming larger; fairly frequent white

horses

- fresh breeze (29-38 km/h): moderate waves, taking a more pronounced long form;

many white horses are formed; chance of some spray

N

NNE

NE

ENE

E

ESE

SE

SSE

S

SSW

SW

WSW

W

WNW

NW

NNW

0%

5%

10%

15%

20%

25%

30%

35%wind speed [km/h]

> 25 - 30

> 20 - 25

> 15 - 20

> 10 - 15

> 5 - 10

< 5


20

- strong breeze (39-49 km/h): large waves begin to form; the white foam crests are more

extensive everywhere; probably some spray

Table 2-2 lists the recorded wind speed data according to effect on sea for the rainy season

2013 and the dry season 2013/2014, as well as cropped to the times of successful sensor

measurements (see chapter 2.5.2, p. 45 for further information about the successful

measurement times). The strongest wind recorded was 30 km/h and is the only fresh breeze.

All the other winds, during rainy and dry season, are mainly light air, light breeze or gentle

breeze, especially during measurement times. Because of the lack of strong winds, the

generated wave heights were only moderate.

Table 2-2: Distribution of recorded wind data at Con Dao Island regarding wind speed classes

(WINDFINDER.COM 2014a, 2014b).

rainy season dry season all data* measurement

time** all data*** measurement

time**** km/h label count percent count percent count percent count percent

<1 calm 0 0.0 0 0.0 0 0.0 0 0.0

1-5 light air 43 21.5 23 17.7 37 12.0 9 20.5

6-11 light breeze 129 64.5 85 65.4 162 52.6 25 56.8

12-19 gentle breeze 25 12.5 19 14.6 102 33.1 10 22.7

20-28 moderate breeze 2 1.0 2 1.5 7 2.3 0 0.0

29-38 fresh breeze 1 0.5 1 0.8 0 0.0 0 0.0

∑ 200 100 130 100 308 100 44 100 * 21.07. – 12.09.2013 (all recorded data for the rainy season) ** 22.07. – 21.08.2013 (time frame of successful measurements during rainy season) *** 08.12.2013 – 27.01.2014 (all recorded data for the dry season) **** 10.12. – 13.12.2013 and 31.12.2013 – 03.01.2014 (time frames of successful measurements during dry season)

In the rainy season the winds are blowing from southwest while in the dry season the winds

are blowing from northeast. In open waters, these winds create waves of different heights,

depending on their fetch, that parallel the direction of the wind. However, when the waves

approach the shore, the influence of the decreasing water depth causes refraction, which

changes the wave direction so that the wave front tends to approach parallel to the beach

(ALBERS et al. 2013). This theoretical resulting wave direction for both major wind directions

cross-shore to the beach was also observed on site and resulted in a setup of the study

transects perpendicular to the mangrove forest edge and coastline (see chapter 2.4, p. 21).

In addition, the water levels for the high water are not equally distributed over the year (see

Figure 2-7). The maximum high water level predicted for the hydrological station My Thanh

(in Soc Trang Province) in the year 2013 was 190 cm above mean sea level (MSL), while

most of the high tides range between 85 cm and 140 cm above MSL (ICOE 2012). The

2 Methods 2.4 Study areas

21

distribution of various predicted high water levels over the year in shows the absence of

higher water levels during the summer (see Figure 2-7). Higher water levels that regularly

inundate mangrove forests on higher grounds only occur from October to March.

Figure 2-7: Distribution of the predicted high water levels (left) and absolute frequency for various

predicted high water levels (right) during the year 2013 for the VN hydrological station My Thanh,

Soc Trang Province (data from ICOE 2012).

According to the classification of DAVIS & HAYES (1984) the coast of Soc Trang Province is a

mixed-energy (tide-dominated) environment. It is affected by the discharge regime of the

Mekong River and its sediment load, the tidal regime of the Vietnamese East Sea, and coastal

long-shore currents driven by prevailing monsoon winds and the corresponding wave

conditions (DELTA ALLIANCE 2011). The east coast of the Mekong Delta from north of Ben

Tre Province to the Cape Ca Mau is influenced by the irregular semi-diurnal tide of the East

Sea with a tidal range of 3.0 - 3.5 m (DELTA ALLIANCE 2011).

2.4. Study areas

After the arrival in Vietnam several field trips were undertaken to the coast of Soc Trang

Province. Various mangrove forests spreading along most of the coast were visited to identify

different study sites suitable for this thesis. The aim was to find sites with the same sediment

conditions and the same elevations changes (slope), so that only the vegetation would make

the difference. To find such corresponding areas proved to be impossible due to the diversity

along the coast. Instead, four sites with differing features were identified. They are presented

in more detail in the following chapters concerning their elevation profiles (see chapter 2.4.2,

0

20

40

60

80

100

120

140

160

180

200

pre

dic

ted

hig

h w

ate

r le

vels

[cm

a.

MSL

]

0

5

10

15

20

25

30

35

40

45

Jan

uar

y

Feb

ruar

y

Mar

ch

Ap

ril

May

Jun

e

July

Au

gust

Sep

tem

be

r

Oct

ob

er

No

vem

be

r

De

cem

ber

> 130 cm

> 140 vm

> 150 cm

> 160 cm

> 170 cm

> 180 cm


22

p. 29), their sediment grain size distribution (see chapter 2.4.3, p. 33) and their vegetation

structure for the sites with mangrove forest growing on them (see chapter 2.4.4, p. 34).

Originally, it was planned to compare sites of natural growing mangrove forests with planted

sites to see differences and to obtain estimations about how effective the plantations are in

concern of coastal protection. Such a comparison was not possible because all mangrove

forests along the coast of Soc Trang Province are planted (JOFFRE 2010, PHAM 2011).

Partially the mangrove forests further inland have now a more natural structure than the

monocultures of more recent plantings at the coastal front (HAI et al. 2013). However, in these

older plantings no measurements were conducted as they are too far from the seaward

mangrove forest edge.

As a result of these limitations, the study sites were chosen to be representative for the coastal

conditions (see chapter 2.2, p. 15) as well as the different mangrove species planted along the

coastline (see chapter 2.1, p. 6). Three sites with mangrove forests growing on them were

identified and one without vegetation. The latter can be regarded as reference site because of

the missing vegetation, but stands also for the part of the coast were the mangrove forest belt

is already completely eroded. Differences between the other three sites are the mangrove

species (at two sites Sonneratia caseolaris is growing and at one site Rhizophora apiculata).

The site with Rhizophora represents the part of the coastline with high accretion rates in

recent years, while between the sites with Sonneratia different vegetation patterns occur as

well as a main difference in the bathymetry. Seaward of one of the latter sites exists a

sandbank that stretches into the sea for several kilometres.

2.4.1. Location of the study transects

The two transects with Sonneratia are both located on Cu Lao Dung Island, one in the north

(CLD_n) and one in the south (CLD_s). The transect representing the accretion processes and

also a Rhizophora planting is located close to Vinh Chau Town (VC). For the transect without

vegetation, an erosion site along the coast of Lai Hoa Community was chosen (LH). In Figure

2-2, p. 15, it is shown where the transects can be found.

All transects stretch over a distance of 200 m from the most seaward to the most landward

sensor. They start at the coastline defined as forest border and are oriented perpendicular to

the mangrove forest edge into the forest. This orientation was chosen because of the refraction

processes in shallow water that leads to waves approaching parallel to the forest edge (see

chapter 2.3, p. 18). In previous studies, the setup of the transects was also perpendicular to the


23

coastline (MAZDA et al. 2006, TRAN 2011, HORSTMAN et al. 2014). On the site without

vegetation, the transect was also set up perpendicular to the coastline, in that particular

instance defined by the dyke, with some distance in front of the dyke to prevent influences on

the wave characteristics by diffraction.

The sites were chosen to minimise the time required to carry equipment into the field

(bamboo poles, tools, sensors, levelling instrument, sample frame) and by accessibility. To

reach CLD_s it is necessary to hire a boat either from Tran De (district on the western

riverside of the Hau River) or the local Forest Protection Office in the south of CLD. At

CLD_n a fisherman needs to be hired for transportation along a small channel leading through

the mangrove forest. LH, however, can be accessed by the road through Lai Hoa Community.

In the rainy season the earthen dyke there becomes very slippery and impassable by car so it

is necessary to walk two kilometres to the study site. In VC the access is possible via Vinh

Chau Town from a street ending at Chua Ba Khu Hai Ngư Pagoda. From there a short hike of

one kilometre leads to the site.

Using GPS-devices, the pressure transducers (see chapter 2.5.1, p. 43) were distributed along

the four transects and fixed to bamboo poles that were places as deeply into the sediment as

possible. To prevent further sinking in of the bamboo poles, a small monitoring system using

a spirit level was established at the first transect. A horizontal line (measured with the spirit

level) was stretched from the bamboo pole at each sensor location to the next mangrove tree

and both tree and pole were marked. In addition, several marks indicating the distance to the

sediment surface were recorded on each bamboo pole. After the first measurements, the line

was again stretched to check if the poles had moved and also to confirm changes in the

sediment level.

The results showed no changes of the bamboo poles themselves while there were small

differences in the sediment surface elevation (mainly only at the seaward sensor). Because the

setup of this small monitoring system was still quite time intensive the process was not

repeated for the other sensor locations. However, the bamboo poles at all transects were

marked to monitor sediment surface changes. The sediment levels changed for up to ±3 cm at

various sensor locations on all transects, but no clear trend was observed.

Figure 2-8 presents an overview of the parameters assessed for each transect and in which

distances from the forest edge (seaward sensor location) they are located. In addition, the

location of the sensors along the transects is marked as well.


24

Figure 2-8: Locations of sensors, sediment samples and vegetation assessments along each transect.

Hereafter each transect is presented with an overview. The coordinates for each sensor

location at each transect are given in Appendix III, App. 5. For easier orientation a kml file

for GoogleMaps with the locations of the transects and the plots for vegetation assessments

can also be found on the data drive submitted together with this thesis.

CLD_n: A small channel leads from An Thanh 3 community in the north of Cu Lao Dung

Island through the mangrove forest to the coast and serves as an option to access the transect

CLD_n (see Figure 2-9). During the first investigation of the site it was lowering high tide

water at the time of arrival. That made it possible to go by boat along the coastline. In the

Figure 2-9: Overview of transect CLD_n with sensor locations and spots of vegetation assessments inside

the Sonneratia caseolaris forest.

south of the final location of the transect were many fishing nets installed along the coast. So

the transect was established north of this area, but still in some distance to the channel (≈ 200

0 20 40 60 80 100 120 140 160 180 200

distance along transect - seward sensor (left) to landward sensor (right) [m]

sensor location sediment sample vegetation assessment

CLD_n

CLD_s

VC

LH


25

m). On later visits, with a better understanding of the tidal conditions, the transect was

accessed via the channel and a short hike through the forest.

At the transect CLD_n measurements of the wave characteristics were conducted with up to

four sensors in 0, 70, 140 and 200 m distance from the Sonneratia forest edge. The total

distance from the seaward sensor location to the dyke at this part of the coast is 0.6 km (forest

band width).

CLD_s: The transect inside the Sonneratia

forest in the south of Cu Lao Dung can only

be reached by boat. At low tide a boat can

drive until reaching the sandbank at the

southwestern tip of the island (see Figure

2-10). This sandbank creates a critical

difference compared to the northern transect

CLD_n, where this cannot be found

(SCHMITT et al. 2013). From the sandbank a

trail leads close along the forest edge to the

study site CLD_s.

In Figure 2-11 the sensor locations (0, 67

and 200 m from forest edge) as well as the

spots of vegetation assessments are marked.

Looking at the vegetation shown on the

background satellite image, a separation of

the forest into two parts can be seen.

The first part reaches from the forest edge to the sensor location CLD_s 2 while the second

one stretches from there onwards deeper into the forest, including CLD_s 3. This is an

indication for the different age of the mangrove plantations at this site. The forest between

CLD_s 1 and CLD_s 2 is 7-8 years old, while the inland part is 15-16 years old (SOC TRANG

SUB-FPD 2013). This is also visible in Appendix III, App. 6, where the same map is shown

like in Figure 2-11, but with a background satellite image from 2006. There, the forest

seaward of sensor location CLD_s 2 was not yet planted. The total forest band width at

transect CLD_s is 1.3 km.

Figure 2-10: Sandbank seaward of transect CLD_s.


26

Figure 2-11: Overview of transect CLD_s with sensor locations and spots of vegetation assessments inside

the Sonneratia caseolaris forest.

In the map (Figure 2-11) it looks like the forest in the east and the west of the seaward sensor

location CLD_s 1 is reaching further towards the sea than the position of the most seaward

sensor. This impression is given by the background satellite image which is from seven

months after the measurements. During the measurement times the trees were still small

saplings and could not be regarded as forest edge (see Figure 2-12).

Figure 2-12: View of the area in front of CLD_s 1 with small saplings growing in the western adjacent

area closer to the sea. At the left end of the picture a bamboo pole marks the location of CLD_s 1.


27

At this transect it was possible to observe the wave direction on various occasions when the

tide was rising earlier than expected. The assumption of a wave direction perpendicular to the

coastline was thereby possible to be verified.

VC: The third transect with vegetation cover is accessed through a path leading from the dyke

at Chua Ba Khu Hai Ngư Pagoda to the seaward forest edge and then along the edge of the

mangrove forest plantation. The transect is located inside one of the typical Rhizophora

apiculata plantings of recent years. These plantations are accompanied in most parts by a thin

band of Avicennia marina shrubs, which are growing naturally on the seaward side of the

plantations. In the background satellite images of Figure 2-13 this is visible as a band of

vegetation with lighter green colour, mainly in the west of sensor location VC 1. Beside the

two sensor locations at the seaward and landward end of the transect VC also the three sites

for the vegetation assessments are shown. Again the age of the background image gives a

false impression of an Avicennia belt growing also in front of the transect itself. The plants

were too small during the measurement time to be regarded.

Figure 2-13: Overview of transect VC with sensor locations and spots of vegetation assessments. At VC a

young plantation of Rhizophora apiculata was assessed.


28

The transect is situated in a part of the coast which is still accreting. This leads to less

compacted new sediments on top. The Rhizophora trees at this site were planted in 2009 (SOC

TRANG SUB-FPD 2013) and the total distance from the seaward sensor location to the dyke is

0.7 km. On the seaward side of the dyke the local people have established agricultural fields,

therefore the forest band width is only 560 m.

LH: Almost the whole coastline of Soc Trang Province is protected by a mangrove forest

belt. Just in a few spots in the southwest close to the border with the neighbouring province

Bac Lieu areas completely without forest are found which are characterised by erosion (see

chapter 2.2, p. 15). On the seaward side of sluice gate 2 the GIZ project ‘Management of

Natural Resources in the Coastal Zone of Soc Trang Province, Vietnam’ piloted an area

coastal protection system and therefore installed protective structures, so called T-fences, in

front of the dyke. This site was chosen to be the reference site as well as the representative for

parts of the coast with erosive conditions (see Figure 2-14).

Figure 2-14: Overview of the sensor location at the reference transect LH without mangrove vegetation.

The transect was setup with a distance of 400 m between sluice gate 2 and sensor location LH

2. This was done to prevent influences on the wave characteristics by diffraction processes at

the dyke or the T-fences. The sensor LH 1 was situated 200 m further towards the sea.


29

At this site the top mud layer, where the mangrove trees are usually growing on, has been

eroded completely. Leftovers of this layer with vegetation are only remaining close to the

dyke where they form remaining headlands (see Figure 2-14). The sensor locations are so far

away from the dyke, that no mud layer is left and the ground is made by sandy sediments

instead. Because of the erosion this layer is also in a lower elevation than all the other

transects.

2.4.2. Height profiles of the transects

To measure the elevation profiles an automatic level was used. For each of the transects

CLD_n, CLD_s and LH, three profiles were measured: one along the transect from the

landward to the seaward sensor location and two more parallel on both sides of the transect in

a distance of 20 meters. The results of the three measurements at all sites showed similar

changes in elevation with only minor variations of a few centimetres. Therefore, here only the

profiles of the measurements along the transects are presented. At the transect VC only along

the transect was measured because of the height and density of mangrove trees which

abridged the visual range (see Figure 2-15). To prevent the levelling staff from sinking into

the mud during the measurements an ordinary tub was used as base (right image in Figure

2-15). It alters the measurements slightly, but because of its use for all assessed points along

every transect this can be neglected.

Figure 2-15: Levelling in the mangrove forests. Left: Tripod extension to make measurements above the

trees at transect VC possible. Middle: Dense mangrove trees at VC abridge visual range necessary

for levelling. Right: Tub to stabilise the levelling staff on the muddy ground (CLD_n).

The levelling work was hindered by the difficulty of stabilising the tripod of the levelling

instrument on the soft mud surface. To estimate the influence of this on the results, the offset

of the bubble inside the circular level (pond bubble) was recorded by taking photos. Later,


30

comparative measurements with unchanged as well as manually generated offset of the same

magnitude were carried out on solid ground. They showed no impairment between the results.

The levelling along all transects was done in the week from the 12.-16.08.2013. Inclusion of

the measured heights into the Vietnamese national grid was not possible due to long distances

from reference points and a lack of information about their whereabouts. Because of this, the

0 height value for each transect was set to the elevation of the most seaward sensor location.

Figure 2-16 presents the levelling results as elevation profiles and also gives the slope

gradient of each transect.

Figure 2-16: Elevation profiles and slope gradients of the four study transects. The 0 height value is for

each transect the location of the most seaward sensor.

While the two transects in the southwest of the province have a very gentle slope expressed

by a slope gradient of 0.04° (LH: 0.65/1000, VC: 0.75/1000) the elevation changes on CLD

are steeper, 0.10° (1.9/1000) in the south and 0.18° (3.2/1000) in the north. Over the total

length of 200 m the height increases only by 13 cm at LH and 15 cm at VC. With 36 cm

difference between the seaward and landward sensors the elevation changes at CLD_s. The

biggest change over 200 m is at CLD_n with 64 cm.

Without the knowledge of the transects elevation in reference to the VN national grid,

descriptions of the height relationship between the transects must be based on field

observations and the results of the sensor measurements, not on measurements with the

levelling instrument:

-20

-10

0

10

20

30

40

50

60

70

-50 -25 0 25 50 75 100 125 150 175 200

he

igh

t in

re

lati

on

to

se

awar

d s

en

sor

in c

m

distance from respective seaward sensor in meters

CLD_n

CLD_s

VC

LH

= 3.2/1000

= 1.9/1000

= 0.75/1000

= 0.65/1000


31

LH is the transect which is lowest in elevation. This can be seen in the data of the pressure

measurements because the inundation times of the sensors are longer and the waves are

generally higher than at the other sensor locations. As mentioned in chapter 2.4, p. 21, the

transect had to be set up on sandy sediments in some distance to the dyke where the former

mud layer was eroded in the past, causing the lower elevation.

VC: The transect is located at a part of the coast with young sediments. Today this part of the

coast of Soc Trang is still gaining sediment and slowly increasing in height (SOC TRANG SUB-

FPD 2013). The result is that this area is the highest of all sites. This also became obvious

during first site visits in the rainy season. Relative to the other study sites, the elevation of the

VC site was so high that the usual tides were not able to flood the area high enough to be

measured. Therefore, this site was measured during the dry season only, when the tidal range

is higher (see Figure 2-7, p. 21).

CLD_s and CLD_n: The two transects on Cu Lao Dung Island are almost identical in height.

A more exact estimation for the site was expected to be achieved through parallel

measurements during the dry season, but one of the sensors was stolen during. Figure 2-17

shows the results for the only tide where the coastal sensors on both transects were functional.

It can be seen that the sensor in the north of Cu Lao Dung (CLD_n 1) was measuring wave

activity from 9:00 o’clock onwards while the sensor in the south (CLD_s 1) recorded data

from 10:15 o’clock onwards. While this does not show for sure how much time lies between

the inundation of the two sensors (data had to be excluded and therefore could be 15 minutes

more or less at least), it can be stated that the sensor on CLD_n is in a lower elevation, hence

the earlier and in total longer time of inundation. The comparison of the measurement results

of the transects CLD_n and LH as well as CLD_s and LH also lead to this conclusion (see

chapter 2.5.3.3, p. 52).


32

Figure 2-17: Time gap between measurement results for Hs (significant wave height) of the coastal sensors

of the two transects on Cu Lao Dung Island on the 20.08.2013 indicates CLD_n having a lower

elevation than CLD_s.

However, this only shows us the location of elevation comparison between the two coastal

sensors. The results of the levelling show that the profile of the northern transect is steeper

than of the southern transect, CLD_n 4 is almost 30 cm higher than CLD_s 3 (see Figure

2-16, p.30).

A significant elevation change can also be seen in the presence of channels running through

the soft mud layers perpendicular to the coastline (CLOUGH 2014). In the left of Figure 2-18

such a typical channel at CLD_n is shown. Because of the gentle slope at CLD_s no channels

developed at this part of the coast.

Figure 2-18: Impressions of transect CLD_n. Left: channels running perpendicular to the coastline

indicate faster drainage and therefore a steeper slope. Right: Thick pneumatophores with outer

spongy tissue layers in soft sediment close to CLD_n 1.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Hs

[m]

CLD_s 1

CLD_n 1


33

In addition, the first 20 meters from the seaward mangrove forest edge on CLD_n are

characterised by very thick pneumatophores with outer spongy tissue layers (see right image

of Figure 2-18). Both attributes are typical of poorly drained areas, indicating longer

inundation phases and lower elevation (CLOUGH 2014).

2.4.3. Sediment grain size distributions

Along each transect three sediment samples of the top 15 centimetres were taken. Estimating

the distances using a GPS device, the samples were taken at 0 m, 70 m and 140 m from the

first sensor at the seaward end of each transect. A local laboratory was given a contract to

analyse the samples using sieve analysis and sedimentation techniques. Figure 2-19 shows the

results of soil samples taken along all study transects in a grain size distribution scaled by the

international scale. The results are given in percent by weight (wt%).

Figure 2-19: Grain size distribution by weight of sediment samples taken along each transect (0 m, 70 m

and 140 m inland from the coastal sensor respectively).

HAI et al. (2013) took sediment samples along several transects along the coast of Soc Trang

Province in depth of 10 and 40 cm. Results of their study were that the coastal areas of Soc

Trang Province do not have a clear grain size distribution. In a comparison with other authors

they state that the sediment composition differs in the whole Mekong Delta from place to

place. However, for all collected samples the grain size between fine sand and middle sand

made up to 75-95% of the sample weights. The results of the grain size analysis of the top 15


34

cm sediment made for this study show distributions with larger portions of the finer grain

sizes (see Figure 2-19).

At the reference site in Lai Hoa Community the grain size distribution of the three sediment

samples (LH 1-3) shows almost pure sand (grain size 2 - 0.053 mm). This is due to the

erosion at this part of the coast (see chapter 2.2, p. 15). The mud layers which are usually

overlying the sand are completely eroded so that the sand can be found at the surface.

In contrast to this, at the transect in Vinh Chau the sediment samples contain almost no sand

while the portions of silt (0.053 - 0.002 mm) and clay (<0.002 mm) are dominant. At this part

of the coast a lot of sediment accreted in the last years and still is accreting a lot.

In the south of Cu Lao Dung the sediment samples of the transect show the influence of the

sandbank located in front of the seaward forest edge (see Figure 2-2). The sample at the

coastal side (CLD_s 1) has even higher sand portions than the samples from Lai Hoa (99.23

wt%). From there at the seaward end of the transect, further along the transect into the forest

the grain size distribution shows decreasing amounts of sand. However, the portions of sand

are always bigger than at Vinh Chau or the transect in the north of Cu Lao Dung. At the latter

again the finer grain sizes overweight the sand portions. Especially at the landward sample

CLD_n 3 the clay fraction is dominant with 76.45 wt%.

2.4.4. Vegetation assessments

For the three transects with mangrove forest growing on them (CLD_n, CLD_s and VC),

characteristics of the vegetation were assessed. On Cu Lao Dung Island Sonneratia caseolaris

is predominant. Along the coast of Vinh Chau District, Rhizophora apiculata has been

planted, accompanied by a thin belt (often only a few meters) of Avicennia marina on the

seaward side of the forest (visible in Figure 2-13, p. 27).

Along the transects, several locations for vegetation assessment were chosen on site (see

Figure 2-8, page 24 for the locations along each transect). The locations were chosen

randomly but within patterns of similar structure inside the forests. At each location a big

sample frame of 10 to 10 meters was established to measure the characteristics of the

mangrove tress as well as the seedlings and saplings. In addition, on CLD at least two small

sample frames of 1 to 1 meter were randomly created within these big frames for the

assessment of the pneumatophores. The right image in Figure 2-20 shows the sample frame

used on CLD. A vernier caliper was used to measure the diameters of roots as well as

seedlings and saplings. Additional to the assessments in the big and small sample frames the


35

tree heights were measured randomly inside the forests along the transects with a self-built

clinometer. The left image in Figure 2-20 shows the simple design of the device.

Figure 2-20: Self-built clinometer for tree height measurements on Cu Lao Dung Island and sample frame

to assess pneumatophores in 1 m2.

The vegetation characteristics for the study sites on CLD_s were assessed during the rainy

season while CLD_n and VC were assessed in the dry season. For the site CLD_n the

difference in the season was assumed to be negligible, but at VC it was possible to observe

rapid growth of the young Rhizophora trees of several decimetres within a few months.

Pressure measurements at transect VC were only possible during the dry season because of its

elevation and the fluctuations of the tidal range during the year (see Figure 2-17, p. 32 and

Figure 2-7, p. 21). As such, the vegetation assessments on VC describe the correct state of

vegetation for the time during the sensor measurements.

2.4.4.1. Cu Lao Dung north and south (CLD_n and CLD_s)

The vegetation characteristics chosen to be assessed were determined based on MAZDA et al.

(2006) and PHAM et al. (2011). For the two study sites on CLD these were for the trees the

diameter in breast height (WT), which is measured 130 cm above ground (PHAM et al. 2011),

and the height of the first branch above ground level (HFB). Dead trees, often cut or broken off

higher than 1.5 m above ground, were also assessed because they are quite common and also

function as an obstacle for the waves. For the pneumatophores, the diameter of

pneumatophores at ground (WR) and the height of pneumatophores (HR) were measured.

Because of the large number of seedlings and saplings, in addition to the assessments of

MAZDA et al. (2006) as well as all other known studies, the vegetation characteristics of the

seedlings and saplings were also assessed during this study. According to PHAM et al. (2011)

seedlings are plants <1 m in height and saplings are plants 1 m or more in height with a

diameter in breast height < 2.5 cm. For the seedlings and saplings the same characteristic like


36

for the pneumatophores, the diameter at ground (WS) was measured. In addition, on CLD_s

the height of the first branch above ground was assessed for the seedlings and saplings. In

doubt about its usefulness during later vegetation assessments at CLD_n the height of the

seedlings or saplings (HS) were collected instead of the height of the first branch. Figure 2-21

depicts the measured characteristics for a cross sectional view of a Sonneratia caseolaris tree

and its pneumatophores.

Figure 2-21: Vertical configuration of Sonneratia caseolaris (MAZDA et al. 2006, changed). (a) Cross

sectional view of tree and pneumatophores, (b) Enlarged cross section of a pneumatophore. Seedlings

and Saplings were assessed the same way like the pneumatophores.

The results of the vegetation assessment at the transects CLD_n and CLD_s are presented in

Table 2-3. The results are aggregated and sorted according to their distance from the forest

edge, from the landward at the left side of the table to the seaward on the right. Where

necessary, standard deviations are given in brackets. The locations of the vegetation

assessments can also be seen in Figure 2-9, p. 24 for CLD_n and Figure 2-11, p. 26 for

CLD_s.

The results of the vegetation assessment of the pneumatophores at CLD_s show a higher

density in the seaward aspect of the transect than in the area further inland. While WR is

smaller close to the forest edge than at the landward assessments, HR decreases from seaward

to landward direction. The pneumatophores are thinner and higher at the forest edge. The

seedlings and saplings increase in density from sea to land. The result of Veg_4, which is in

close proximity to Veg_5, is an extreme outlier. A density of 0.38 (Veg_4) seedlings or

saplings in comparison to only 0.04 (Veg_5).


37

T

ab

le 2

-3:

Veg

etati

on

ch

ara

cter

isti

cs o

f S

on

ner

ati

a c

ase

ola

ris

alo

ng

th

e tr

an

sects

CL

D_

s a

nd

CL

D_

n o

n C

u L

ao

Du

ng

Isl

an

d.

Av

era

ge

va

lues

are

giv

en w

ith

sta

nd

ard

dev

iati

on

s in

bra

ck

ets.

C

LD_s

CLD

_n

V

eg_1

V

eg_2

V

eg_3

V

eg_4

V

eg_5

Veg

_1

Veg

_3

Veg

_2

Veg

_4

dis

tan

ce f

rom

fo

rest

ed

ge

170

m

125

m

10

5 m

3

0 m

2

5 m

18

0 m

1

10

m

60

m

40

m

elev

ati

on

ab

ove

sen

sor

1

27

cm

2

0 c

m

19

cm

8

cm

6

.5 c

m

6

0 c

m

40

cm

2

5 c

m

20

cm

Pn

eu

mat

op

ho

res

den

sity

[ro

ots

/m2]

77

84

---

14

4

13

6

1

15

9

6

12

5

99

dia

met

er (

WR)

[mm

] 9

.1 (

4.8

) 8

.0 (

6.0

) --

- 6

.6 (

4.3

) 8

.6 (

5.0

)

10

.4 (

12

.9)

8.0

(7

.5)

7.5

(9

.5)

8.9

(1

1.0

)

hei

gh

t (H

R)

[cm

] 9

.4 (

5.9

) 9

.9 (

7.2

) --

- 1

1.3

(8

.3)

13

.8 (

9.7

)

9.1

(8

.1)

9.3

(7

.2)

10

.5 (

7.2

) 1

3.3

(8

.8)

Seed

lings

or

sap

lings

den

sity

[n

um

ber

/m2 ]

0.1

0

0.0

8

0.0

7

0.3

8

0.0

4

1

.32

0

.40

--

- 0

.01

dia

met

er x

(W

S) [

cm]*

1

.1 (

0.9

) 2

.0 (

1.3

) 2

.6 (

1.8

) 2

.4 (

0.8

) 1

.3 (

0.8

)

1.2

(1

.0)

0.9

(0

.6)

---

0.4

(--

-)

dia

met

er y

(W

S) [

cm]*

1

.3 (

1.1

) 2

.0 (

1.3

) 2

.4 (

1.6

) 2

.5 (

0.9

) 1

.3 (

0.8

)

1.2

(1.0

) --

- --

- 0

.4 (

---)

bra

nch

hei

gh

t (H

FB)

[cm

] 8

.5 (

9.8

) 3

8.3

(16

.0)

26

.4 (

18

.8)

38

.6 (

27

.3)

20

.0 (

10

.0)

--

- --

- --

- --

-

hei

gh

t (H

S) [

cm]

---

---

---

---

---

7

4.3

(4

0.0

) 6

9.1

(4

9.7

) --

- 2

00

.0 (

---)

Tree

s

den

sity

[tr

ees/

m2 ]

0.0

8 (+

0.0

2)*

* 0

.07

0

.10

0

.08

0

.10

0.0

7

0.0

7

---

0.1

1

dia

met

er x

(W

T) [

cm]*

1

7.3

(7

.7)

23

.6 (

5.7

) 1

6.8

(6

.4)

14

.1 (

6.0

) 1

5.0

(6

.0)

2

7.0

(9

.4)

21

.0 (

6.4

) --

- 1

8.4

(7

.1)

dia

met

er y

(W

T) [

cm]*

1

9.6

(7

.8)

23

.1 (

5.6

) 1

6.7

(6

.4)

14

.9 (

6.7

) 1

5.5

(7

.1)

2

4.4

(6

.5)

19

.3 (

6.0

) --

- 1

6.9

(7

.0)

bra

nch

hei

gh

t(H

FB)

[cm

]**

* 3

9.7

(20

.2)

57

.5 (

25.1

) 4

6.7

(4

4.3

) 8

2.5

(3

7.2

) 8

5.8

(4

9.1

)

85

.0 (

48

.6)

98

.0 (

39

.2)

---

93

.6 (

38

.0)

*

dia

met

er

x is

mea

sure

d in

to t

he

assu

med

wav

e d

irec

tio

n w

hile

dia

met

er y

is m

easu

red

per

pen

dic

ula

r to

th

e as

sum

ed

wav

e d

irec

tio

n

**

at t

ran

sect

CLD

_s a

t V

eg_1

tw

o o

f th

e as

sess

ed

tre

es h

ad f

ork

ed t

run

ks

***

HFB

of

the

tree

s m

easu

red

if b

etw

een

0-2

00

cm

hig

h a

nd

dia

met

er >

1 c

m

(see

Fig

ure

2-2

1 f

or

WR, H

R, W

S, H

FB, H

S an

d W

T)


38

HFB of the assessed trees decreases further inland while the stem diameters in both x and y

orientation increase. In the landward part are fewer trees per m2 than at the forest edge.

Because of the older age of the landward forest, two of the eight trees in the sampling plot

there had forked trunks.

At CLD_n, the density of the pneumatophores varies between 96 and 125 per m2. Like at

CLD_s HR is increasing towards the seaward forest edge. A special feature of the mangrove

forest at CLD_n is a band of thick pneumatophores with outer spongy tissue layers in the first

20 meters from the seaward forest edge (see right image of Figure 2-18, p. 32). This is

indicative of poor draining and, in this case, longer inundation phases as described in chapter

2.4.2. It also explains the absence of seedlings and saplings in this part of the transect. While

132 seedlings and saplings per 100 m2 can be found close to the most landward sensor, few

are growing close to the forest edge. Like at CLD_s, more trees are growing at the forest edge

and WT is increasing further away from the forest edge. HFB is decreasing further landward at

CLD_n.

Another characteristic recorded during the vegetation assessments at the transect CLD_n

during the dry season, was the wide spread growth of young branches at lower heights of the

Sonneratia stems, expressed by the assessed HFB values in Table 2-3. Figure 2-22 shows an

image of the green layer formed by these branches. In the rainy season this was not yet

observed in such extend in the forest band so close to the forest edge (first 200 m).

Unfortunately, the vegetation was only assessed during the dry season, so exact figures to

compare this impression cannot be given.

Figure 2-22: Growth of young branches in lower heights of the Sonneratia trees at CLD_n observed

during the dry season and dense pneumatophores which secure the sediments.


39

In the south of CLD, the Sonneratia trees planted in the first 60 m from the seaward forest

edge (between CLD_s 1 and CLD_s 2) are 7-8 years old, while the remaining forest along the

transect is 15-16 years old. Along the transect in the north of CLD, the whole forest is 15-16

years old (SOC TRANG SUB-FPD 2013).

Tree height measurements along the transect CLD_s give an average tree height of 17.4 m for

the forest between Veg_1 and Veg_3 (landward), while the trees between Veg_3 and Veg_5

(seaward) average 18.2 m in height. The trees are taller closer to the seaward forest edge than

further inside the forest. In addition, along the northern transect on CLD the trees closer to the

forest edge are higher. Close to Veg_1 the trees are in average 20.5 m high, while between

Veg_2 and Veg_3 the trees are in average 22.0 m high.

Trees lying on the forest ground also function as obstacles to waves and cause wave

attenuation (see Figure 2-23). At present, there is no method described in the literature of

comparable studies of how to assess their distribution. In this study, no attempt was

undertaken to address this, but it can be noted that there were more dead trees lying on the

ground at CLD_s than at CLD_n.

Figure 2-23: Dead Sonneratia caseolaris trees lying on the forest ground in the south of CLD.

The extensive work on the vegetation assessments on CLD lead to recommendations for

future studies with vegetation assessments in Sonneratia spp. or comparable mangrove

forests. The assessment of data about the height of the seedlings and saplings is more suitable

than the height of the first branch. If necessary, a very detailed description of seedlings and

saplings based on PHAM et al. (2011) would be possible. It would assess the height and the

number of knots (points where branches are emerging from the stem), as typically done

during mangrove monitoring along the coast of Soc Trang Province. However, this process

would potentially be time consuming, depending on the size of the sampling area.

Additionally, measurements of WS (diameter at ground level) for seedlings and saplings in

one direction are enough because the values for x and y differ insignificantly.


40

2.4.4.2. Vinh Chau (VC)

The plantations of Rhizophora apiculata at transect VC are from 2009 and were, at the time of

the measurements, 4.5 years old (SOC TRANG SUB-FPD 2013). Figure 2-24 shows the structure

of one of the young Rhizophora trees growing at Veg_1 in more detail. The young trees are

well developed at this planting site. At the time of measurements, their branches almost

reached the ground and there was no gap between the prop roots and the knots from where

branches emerge off the stem.

Figure 2-24: Well developed Rhizophora apiculata tree inside the big sample frame of Veg_1 at the

Transect VC.

Due to the growth pattern of the young trees, different vegetation parameters were assessed at

transect VC than at the ones on CLD. The prop (stilt) roots growing from the trunk of the

Rhizophora apiculata trees were not measured. Instead, only the densities of the trees, their

heights, and their widths (including branches at breast height) were assessed. For older

Rhizophora trees and other species with prop roots, MAZDA et al. (1997b) describes in detail

assessment characteristics.

The vegetation was assessed in three sampling plots along the transect. Their location can be

seen in Figure 2-13, p. 27. Table 2-4 presents the results of the assessment.

110 cm

140 cm

170 cm

20

0 c

m

2 Methods 2.5 Measurements of wave attenuation

41

Table 2-4: Vegetation characteristics of Rhizophora apiculata along the transect VC. Average values are

given with standard deviations in brackets.

VC

Veg_3 Veg_2 Veg_1 distance from forest edge 175 m 105 m 30 m

Trees

density [trees/m2] 0.61 0.59 0.26

height [cm] 211 (17) 201 (28) 188 (19)

width including branches [cm] 135 (25) 133 (20) 153 (22)

The density increases further inside the forest. Close to Veg_2 a density border can be seen

when in the field as well as on satellite images like in Figure 2-13, p. 27. The Heights of the

trees are also increasing further inside the forest, while the width including the branches at

breast height is decreasing. The latter can be explained by the denser growth pattern in the

landward part of the transect. Figure 2-25 shows two impressions of the dense mangrove

plantation at VC. The density for Rhizophora seedlings during planting campaigns usually is

1 per m2. Based on the results of the vegetation assessment more trees survived the first years

further inside the forest than right at the edge. The survival rate at Veg_1 is 26% while at the

other two locations Veg_2 and Veg_3 it is close to 60%.

Figure 2-25: Impressions of the dense vegetation pattern of planted Rhizophora apiculata trees at transect

VC.

2.5. Measurements of wave attenuation

To describe the sea state in nature it can be viewed as a wave field comprising a large number

of single waves. Each of these waves can be characterised by a wave height, wave period,

wave length, and propagation direction (EAK 2002, ALBERS et al. 2013). Figure 2-26 depicts

the wave characteristics for a monochromatic ocean wave.


42

Figure 2-26: Vertical profile of an idealised (monochromatic) ocean wave (ALBERS et al. 2013, changed).

Waves can be categorised by the wave period and the causational physical mechanism

(MCIVOR et al. 2012a). For the coastal morphology and the design of coastal protection, short

waves with periods less than approximately 20 s are one of the most important parameters.

They can be divided into wind waves (relatively steep) caused by local wind fields and swell

waves (relatively long and of moderate height) caused by wind fields far away (MANGOR

2004, qtd. in ALBERS et al. 2013).

Because of the random appearance of natural waves, it is necessary to adequately quantify a

given sea state using statistical parameters (ZEKI & LINWOOD 2002). The most important

parameters are characteristic height (H) and characteristic period (T) as well as parameters

related to the combination of the characteristics H and T. Both wave amplitudes and heights

often follow a Rayleigh distribution. Based on this distribution, statistical wave parameters

can be calculated using spectral analysis. According to ZEKI & LINWOOD (2002) the most

commonly used variables to quantify sea state in coastal engineering are:

significant wave height Hs

The significant wave height (Hs) is defined in EAK (2002) as four times the square root of the

zeroth-order moment of the wave spectrum (Hm0) in a time-series of waves representing a

certain sea state:

𝐻𝑚0 = 4 × √𝑚0 (m0 = the zeroth-order moment, total area under the wave energy density spectrum)

The traditional definition was the mean wave height of the highest third of the waves (H1/3).

Although there are small deviations by a few percent between H1/3 and Hm0 because of the

difference in their recording methods, generally they are assumed to be alike (EAK 2002).

Therefore:


43

𝐻𝑚0 = 𝐻1/3 = 𝐻𝑠

mean wave period Tm

The mean wave period (Tm) is the mean of all wave periods in a time-series representing a

certain sea state determined using the zeroth- and second-order moment (EAK 2002):

𝑇02 = √𝑚0 𝑚2⁄

Other ways to express the mean wave period are:

𝑇02 = 𝑇 = 𝑇𝑚

peak period Tp

The peak period (Tp) is the wave period with the highest energy. The analysis of the

distribution of the wave energy as a function of the frequency for a time-series of individual

waves is referred to as spectral analysis. The peak period is extracted from the spectra

(ALBERS et al. 2013):

𝑇𝑝 = 1 𝑓𝑝⁄ (fp = frequency of the spectral peak [Hz])

To assess the wave attenuation along the study transects, pressure transducers were used.

After several steps of data processing the three parameters required for adequately quantifying

a given sea state are available for analysing the wave reduction. More detailed information

about the sensors can be found in chapter 2.5.1, p. 43, while in chapter 2.5.3, p. 48 the data

processing is explained.

2.5.1. Pressure transducers

To measure the wave parameters Hs (Hm0), Tm (T02) and Tp, pressure transducers were

installed along the study transects. They were developed at the Institute of River and Coastal

Engineering (TUHH) as part of the project “Naturmessprogramm und Modellbildung zur

Analyse morphodynamischer Veränderungen im Neufelder Watt in der Elbmündung” (2006-

2010), commissioned by the Hamburg Port Authority. The sensors were designed to provide

continuous measurements of the sea state, with long intervals between necessary maintenance.

Due to the optimised energy efficiency, the constructed pressure transducer permits long

measuring times. The pressure transducer used is a SenSym 19C030PA from SensorTechnics

which has a measuring range of 0-30 psi (0-206.8 kPa) with a relative accuracy of 0.1% of the


44

range. The accuracy is therefore ± 200 Pa. The frequency used is 10 Hz. The longest

measuring time in the Neufelder Watt was around nine weeks. Together with the hardware, a

MATLAB-script was developed to transform the raw binary code of the pressure sensors into

ASCII datasets. Additionally, a software called PressMea was developed to analyse the data

and to get information about the wave parameters. For more details about the processing of

the gathered data see chapter 2.5.3, p. 48.

Because of the origin of the sensors it is not a commercial product. No user manual for the

sensors is available and the data extraction and processing require several tools and softwares.

Occasionally, maintenance is required that can only be done by the constructor. These factors

make the sensors a highly specialised instrument not available for sale. Thus the results of this

thesis will not be repeatable easily.

In total, six of these sensors were in use for several projects in southern Vietnam over the last

years. Four were available for this study, identified by serial numbers 02, 18, 19 and 20. They

were installed on bamboo poles along the transects with cable ties and tape at a height of 20

cm above ground (CLD_n, CLD_s and LH) and between 9 and 14 cm (VC). For the analysis

of the recorded measurements, all sensors along a transect need to measure waves, which

means that the whole transect needs to be inundated by at least 20 cm of water. The two

pictures in Figure 2-27 show the poles with the sensors at two locations at CLD_s. On top of

each bamboo pole a Vietnamese flag was attached to signal official status and prevent theft of

the sensors. Two of the sensors were stolen weeks before the study while in use for a different

project in Ca Mau Province.

Figure 2-27: Bamboo poles with pressure sensors 20 cm above ground and Vietnamese flag at transect

CLD_s.


45

2.5.2. Schedule and adjustments of measurements

After arrival in Vietnam at the end of May 2013 it was planned to spend the first two weeks in

the field to identify suitable study sites before starting the measurements of the wave

characteristics in the middle of June. Six sensors were expected to be available for the

measurements, allowing two sites to be assessed parallel with three sensors each in the field.

Previous to the Soc Trang study, the sensors were used for measurements along the coast of

Ca Mau Province. Unfortunately, two of the six sensors were stolen while in the field. Two of

the remaining four were broken and had to go into maintenance in Germany before being

returned to Vietnam. As such, the start of the measurements was delayed until 22.07.2013,

almost one and a half months later than anticipated. It was not possible to organize additional

sensors as replacement for the two lost ones. Therefore, the original plan to record one month

of data per site per season was adjusted to the equipment limitations.

The measurements aimed to assess the wave characteristics along each vegetated transect with

as many sensors as possible, in order to get data about the spatial wave height changes.

Parallel measurements between each vegetated site and the reference site (no vegetation,

erosion site) were planned to compare them directly. Additionally, the two study sites on CLD

were to be measured at parallel times.

Table 2-5 shows the scheduled measurement times to acquire the above mentioned data over a

time of eight weeks in the first measurement campaign (during the rainy season). The same

schedule was to be repeated in the dry season in December 2013 and January 2014 resulting

in four weeks each for the two transects on CLD, three weeks for LH and two weeks for VC

per assessed season.

Table 2-5: Planned time schedule for sensor measurements and sensor coding.

However, subsequent problems limited the quantity of the assessed data. One of the two

sensors that remained in Vietnam had a malfunction which was later repaired by a local

electrician. This led to the use of only three sensors in the south of CLD where the first

measurements were carried out and reduced the spatial resolution for this site. A SD card on

which the sensors write the wave data was no longer recognised by the sensors and needed to

be replaced. Furthermore, some of the battery packs were so aged that they caused data loss


46

through power loss during measurements on several occasions and therefore needed to be

replaced. In several cases, the malfunction of one sensor made the whole data set useless (for

example, when one of two sensors installed in the field failed due to battery problems).

Because of these problems, frequent trips to the study sites (every two weeks) were necessary.

However, the frequent visits were not enough to prevent some data getting lost.

Further problems followed for the measurements during the dry season. After the news of the

two stolen sensors in Ca Mau Province, a local guard was hired for each transect to secure the

sensors during low tides. Nonetheless, over Christmas 2013 the sensor at location CLD_s 3

was stolen. The missing sensor was recognised on the 25th

of December, leading to the

recovery of the remaining three sensors on the 26th

. Then, it was discovered that one of the

remaining sensors had malfunctioned (technical problem). When stolen, the two study sites on

CLD had been measured parallel to each other. While in the south a sensor was stolen, in the

north one sensor with a new battery stopped working after only three days.

As previously described, the elevation of the transect VC is too high to be inundated during

the dry season (see chapter 2.4.2, p. 29). So for this transect, measurements were only

possible during the dry season. After the sensor got stolen in CLD_s (despite the presence of a

guard) something else was tried for the measurements at VC. The sensors were disguised

using old plastic bags found in several spots on the surrounding mudflats and entangled into

roots of Rhizophora plants instead of attaching them onto poles with flags (see Figure 2-28).

Figure 2-28: Sensor attached to Rhizophora plant at VC 1 before and after disguising it with plastic bags.

Because of the reasons mentioned above, less data were measured than expected. Table 2-6

presents an overview for each transect of the days were measurements were realised with at

least two sensors working.


47

Table 2-6: Overview of successful measurements for each transect during the wet season (top) and dry

season (bottom).

For the transect LH, three more days of data (10-12.08.2013) were measured, but one of the

sensors was affected which led to dampened pressure recordings. This is further addressed in

the following chapter 2.5.3.

Table 2-7 gives the times as well as the number of days and tides used for the analysis of the

wave reduction along the four transects. A table showing for each sensor location the times of

successful measurements and the time frames for which data analysis was applicable is shown

in Appendix IV, App. 7.

Table 2-7: Successful measurement times and number of tides used for analysis.

from until days tides CLD_n 07.08.2013 20.08.2013 13 20

10.12.2013 13.12.2013 3 5

CLD_s 22.07.2013 06.08.2013 15 20

20.08.2013 21.08.2013 1 1

VC 31.12.2013 03.01.2014 3 3

LH

01.08.2013 09.08.2013 (12.08.2013)*

8 (+3)*

16 (+5)*

* part of the data recorded at transect LH not usable for analysis (see Figure 2-29, p. 48)


48

2.5.3. Data processing and analysis

2.5.3.1. Data processing

Before it is possible to use the data recorded by the pressure transducers for analysing the

wave reduction, several steps of processing are necessary. The pressure transducers record the

data onto SD cards. If the battery failures during the writing process of the data onto the SD

card, the card is no longer readable. A software to recover data, like WinHex, is therefore

necessary. Afterwards, a MATLAB-script is used to process the 10 Hz raw data from binary

code into ASCII. The script produces aggregated datasets of 5 Hz and 5 min for the recorded

pressure values. Additionally, a 5 min barometric dataset is generated with the MATLAB-

script, using the pressure values measured by the pressure transducers during low tide when

the sensors were not inundated and interpolation between the lowest values in this time. The

script often failed to generate correct barometric data due to the time of low tide being too

short and the dropping of water between tides to a minimum that was still above air pressure

(see Figure 2-29). As such, for almost all generated datasets, manual adjustments (and in four

cases even the manual generation of the whole barometric file) were necessary.

Figure 2-29: Example of barometric output file with wrong pressure values. The green triangles mark the

values the MATLAB script chose as points for interpolation.

The minimum values of the 5 min pressure dataset are used to create the barometric files

manually. Between the minima of the 5 min pressure files the interjacent values are


49

interpolated using linear regression. Afterwards, all datasets were checked if there were no

pressure values in the 5 Hz files below the barometric files.

For further data processing, the software PressMea (developed during the same project like

the sensors, see chapter 2.5.1, p. 43) is needed. The software processes the ASCII pressure

data as output of MATLAB into data about the sea state. The resulting output files of

PressMea contain the necessary statistical parameters which quantify the given sea state (see

chapter 2.5, p. 41). PressMea needs the created 5 Hz data together with the barometric data (5

min) as input files. The files are processed in a Fast Fourier Transformation (FFT) already

implemented into the software to create spectral results.

According to EAK (2002), it is common to use time frames of approximately 200 seconds for

the FFT used for wave statistics. The sensors measure data with 10 Hz (10 measurements per

second) which is halved to 5 Hz with MATLAB (value for every 0.2 seconds). To get a

window of 200 seconds for the FFT 1024 bits are necessary (1024 × 0.2 = ~ 200 s).

Beside the set up for the window of the FFT used by PressMea, the time period for which the

wave data is summarised can also be changed for the output files (.txt files). Two different

periods, 5 minutes and 15 minutes were chosen to create output files. In chapter 2.3 Table 2-2,

p. 20 it was already mentioned that because of the lack of strong winds during the

measurement times, no extreme wave events were expected. Still, the output files of 5 and 15

minutes were compared to see if any big differences could be observed. While the aggregated

15 minute data is more descriptive, single higher values which might be of interest are more

likely to be filtered. Table 2-8 shows exemplarily the comparison of the 5 and 15 minute

PressMea output for the maximum values of Hs for each transect. While the maxima of the 5

minutes datasets are all higher than the ones of the 15 minutes datasets, the differences are

only between 1-6 centimetres. Comparisons for further statistical values like minimum and

average showed similar results.

Table 2-8: Results of PressMea software for the maximum value of the wave parameter Hs for the

aggregation periods of 5 and 15 minutes.

transect Hs max. 15 min

Hs max. 5 min

CLD_n RS 0.27 0.32 CLD_n DS 0.36 0.39 CLD_s RS 0.43 0.44 LH RS 0.76 0.82

Based on this short preceding analysis it was decided to use the 15 minutes datasets as the

main source for the further analysis of wave reduction. The final steps of data processing and


50

the analysis of data were done with the software MS Excel. Depending on the algorithm used

in PressMea for the data processing, the calculation of the wave periods of smaller waves

divides through very small values. This causes unrealistically long wave periods in the output

file for relatively small wave heights. To eliminate this noise in the data signal, all values with

Hs ≤ 2 cm were deleted and not regarded for further analysis.

While processing the data, a limitation for the use of transect LH was discovered. Figure 2-30

presents the output data of the MATLAB-script in a graph showing the pressure values for the

two sensors installed at transect LH. At the beginning of the measurements the pressure

values of both sensor locations can be fit together for the times of low tide when the sensors

record the barometric pressure (note that 0 is different for the primary and secondary y-axes).

During high tides, the wave dampening can be seen between the two lines. This changes from

the 10.08.2013 onwards for the last five tides where the pressure values recorded by sensor

LH 2 are not dropping to the same level like the ones recorded by LH 1 during low tides.

Figure 2-30: Recorded pressure values (5 min aggregated) of the seaward (blue) and landward (red)

sensors at transect LH. The pressure curves follow a regular pattern in the beginning of the

measurement time but change at the last five tides (10.08.2013 onwards). The higher pressure values

during low water levels of LH 2 as well as the irregularities in the high water tides lead to the

conclusion that the membrane of the pressure transducer was blocked during this time, probably by

mud.

The higher pressure values during low water levels lead to the conclusion that the membrane

of the pressure sensor was blocked during this time, probably by mud. In addition to the

generally increased pressure recorded by the sensor, the high water tides show irregularities in

comparison to the measurements of the coastal sensor (LH 1). These also influence the

990

1040

1090

1140

1190

1240

1000

1050

1100

1150

1200

1250

reco

rde

d p

ress

ure

LH

2 la

nd

[h

Pa]

reco

rde

d p

ress

ure

LH

1 c

oas

t [h

Pa]

LH 1

LH 2


51

measurements of the wave parameters. Using the relative change rates of the measurements of

LH 1, it would be possible to correct the data of LH 2 for the last five tides. For this thesis,

this extra work was not conducted, but remains an option to gain more data.

2.5.3.2. Data analysis

With the output files of PressMea for each time step, Hs, Tm and Tp are given. To analyse the

data, the values of Hs were used to calculate the wave reduction for both available datasets (5

and 15 minutes).

As mentioned in chapter 2.1, p. 6, the wave reduction can be calculated and expressed using

different formulas and transmission factors. For this study the rate of wave height reduction

(r) per unit distance in the direction of wave propagation is defined as the reduction of the

significant wave height (ΔHs) as a proportion of the initial significant wave height (Hs) over a

distance (Δx) travelled by the wave (based on MAZDA et al. 2006):

𝑟 = ∆𝐻𝑠

𝐻𝑠 ×

1

∆𝑥

The unit of r is /m or m-1

. For example, if the wave height is reduced by 1% over a distance of

1 m, then r = 0.01 m-1

. More common are values of about 0.002 m-1

, which is a reduction of

0.2% per meter or 20% over 100 meters. Using this approach, measurements of wave

attenuation with different initial wave heights and varying traversed distances are standardised

(GEDAN et al. 2011). MCIVOR et al. (2012a) and several other studies also used this equation,

making comparisons easier.

Another parameter to express the rate of wave reduction is used for this study to express the

total wave reduction over a distance (Δx). It is based on MAZDA et al. (1997a):

𝑟𝑥 = ∆𝐻𝑠

𝐻𝑠

The result for rx is dimensionless and lies between 0 and 1, were 1 means a reduction of the

significant wave height of 100% over x meters. Depending on the location of the sensor

which is compared to the most seaward sensor, x represents the distance between the two. For

transect CLD_n x = 70, 140 or 200, for CLD_s x = 67 and 200, while for VC and LH x can

only be 200. For example r200 = 0.4 means an incoming wave of Hs = 1 m at sensor 1 (coast)

is reduced by 40% when it reaches the last sensor 200 m further inland (remaining wave

height is 60 cm).


52

To compare the results of the wave reduction analysis, r and rx are often related to the initial

water depth at the most seaward sensor (MAZDA et al. 1997, MAZDA et al. 2006, QUARTEL et

al. 2007, HORSTMAN et al. 2014). To obtain the water depths, Pascal’s law is used in this

thesis. It says that externally applied pressure on a confined fluid is transmitted equally in all

directions (GOOCH 2007). Based on Pascal’s law it is possible to use the recorded data of the

pressure transducers to calculate the water depth at the time of each measurement:

∆𝑃 = 𝜌 × 𝑔 × (∆ℎ)

where:

∆P is the relative hydrostatic pressure (given in Pa, N/m2), difference between pressure

values and barometric pressure,

ρ is the fluid density, 1020 kg/m3 used in calculations,

g is acceleration due to gravity, 9.80665 m/s2 used in calculations,

∆h is the height of the fluid above the point of measurement (depth of sensors below water

surface)

The reconvert formula from above gives the water depth above the pressure transducers

membrane:

∆(ℎ) = ∆𝑃 𝜌⁄

𝑔

Finally, the height above ground in which each sensor was installed is added to get the total

water depth for each measurement. The calculations are based on the pressure values and the

barometric pressure values extracted with the MATLAB-script in a frequency of 5 minutes.

Therefore the resulting water depth values have the same frequency.

All box-whisker-plots and tables presented in chapter 3 are based on the results of the 15

minutes datasets, while the graphs showing the relationship between water depth and wave

reduction are based on the 5 minutes datasets.

2.5.3.3. Parallel measurements to relate data

In chapter 2.4.2, p. 29 it was explained that it was not possible to relate the absolute elevation

of the transects to each other because there was no way to integrate the height profiles into the

local benchmark system. Figure 2-31 presents the preliminary results of the data analysis. For

each sensor the significant water depth is plotted against the water depth of the respective


53

seaward sensor location. In this figure it is not possible to compare the single transects with

each other.


sensors of the transects CLD_n, CLD_s and LH plotted against water depth for the rainy (RS) and

dry (DS) season (5-min-period; CLD_n RS: 1,438 values, CLD_n DS: 143 values, CLD_s RS: 780

values, LH RS: 1,894 values).

To relate the results of the transects to each other, the parallel measurements of transect LH

with CLD_n and CLD_s were compared respectively. From 07.08.2013 till 09.08.2013

continuous parallel measurements were gained at the transects LH and CLD_n. A comparison

over a longer time was not possible due to the blocked sensor membrane of LH 1 (see Figure

2-30). For CLD_n, 104 values (15-min-period) when the seaward and landward sensors were

both inundated provided the data, while at LH 143 values were available. The difference in

the available values indicates already that LH is in a lower elevation than CLD_n.

The comparative time for CLD_s with LH was from 04.08.2013 till 06.08.2013. Even though

the measurements were already conducted simultaneously from 01.08.2013 onwards, a low

tide situation from 31.07.2013 till 03.08.2013 prevented recordings of wave parameters at the

transect in the south of CLD. For the comparison, 36 values were available for CLD_s and

R² = 0.5694

R² = 0.7923 R² = 0.5925

R² = 0.2526

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-0.001

0.000

0.001

0.002

0.003

0.004

0.005

0 50 100 150 200 250

r 20

0

r [m

-1]

depth at seaward sensor [cm]

CLD_n RS CLD_n DSCLD_s RS LH RSCLD_n RS (3rd order polynomial) CLD_n DS (3rd order polynomial)CLD_s RS (3rd order polynomial) LH RS (3rd order polynomial)


54

162 for LH, again indicating a much lower elevation of the transect LH. To get a better

estimation of the differences between the respective water depth values, the maximum water

depth values (5-min-periods) during the parallel measured tides were used to adjust the

different graphs which are presented in chapter 3.2, p. 59.

In Appendix V, App. 10 (CLD_n and LH) and Appendix VI, App. 13 (CLD_s and LH), the

adjusted curves for the parallel measurements can be seen. Additionally, information about

the changes of the measured significant wave heights (App. 8 and App. 11) and comparisons

of the wave reduction between the transects during the parallel measurements are provided

(App. 9 and App. 12).

The results from the mentioned adjustments are that the water depth values of CLD_n are 30

cm higher than LH while the values of CLD_s are 95 cm higher than the ones from LH

(maximum water depth value differences vary about ± 5 cm). Indirectly derived from these

results, CLD_s is assumed to be 65 cm higher than CLD_n.

3 Results 3.1 Overview

55

3. Results

3.1. Overview

A summary of the measurement results of Hs and Tm and the calculated wave reduction

expressed by the parameters r and r200 is presented in Table 3-1. The recorded maximum

value for the significant wave heights (Hs) of all transects was 0.76 m at the transect LH (no

vegetation). At the sites with vegetation, the maximum was 0.43 m at the coastal sensor of

CLD_s. The average Hs at the transect CLD_n was 0.15 m and at CLD_s was 0.16 m during

the rainy season (RS), while at LH the average Hs was 0.34 m. In the dry season (DS), at

CLD_n the average Hs was 0.22 m and the maximum Hs at VC was 0.04 m. The maximum

mean wave period at transect CLD_n in the rainy season was measured with Tm = 5.2 s. In

general, the measurements of Tm were between 2.0 s and 5.2 s for all transects. The highest

wave reductions (r) were at the transects on Cu Lao Dung, with 0.0042 m-1

for both transects

in the rainy season and 0.0043 m-1

at CLD_n in the dry season. The minimum wave reduction

of 0.0007 m-1

was calculated for CLD_n and LH in the rainy season, while at CLD_s, the

minimum r was 0.0015 m-1

. The highest minimum wave reduction occurred at transect

CLD_n with r = 0.0020 m-1

.

Table 3-1: Summary of the measured incoming wave characteristics Hs and Tm at the seaward sensors as

well as the rate of wave height reduction r200 and r for all transects during the rainy season (RS) and

dry season (DS) derived from the 15-min-period data.

Hs [m] Tm [s] r200 r [m-1] RS DS RS DS RS DS RS DS CLD_n max. 0.27 0.36 5.2 3.5 0.83 0.85 0.0042 0.0043

avg. 0.15 0.22 3.5 2.5 0.43 0.56 0.0022 0.0028

min. 0.06 0.10 2.2 2.1 0.13 0.39 0.0007 0.0020

CLD_s max. 0.43 3.3 0.84 0.0042

avg. 0.16 2.4 0.56 0.0028

min. 0.07 2.0 0.29 0.0015

LH max. 0.76 3.9 0.77 0.0038

avg. 0.34 2.8 0.46 0.0023

min. 0.08 2.2 0.14 0.0007

VC max. 0.04 --- 1.00* ---

* data analysis of the landward sensor measurements at transect VC did not result in a single wave, but the recorded pressure values indicate correct measurements (see explanations below)

The presented results of transect VC show a wave reduction of 1.00 for the parameter r200

which means complete wave attenuation. However, the dataset for the transect VC has

limitations which need to be discussed.


56

The measurements of the pressure transducers show that the pressure was changing in a

diurnal frequency. Figure 3-1 presents the derived water depths at the sensor locations VC 1

(seaward) and VC 2 (landward) for the time of measurements. The water depth curves show

that both sensors were inundated for several decimetres during the first three days.

Unfortunately, the measurements were corrupted by the early failure of the second sensor in

the landward part of the transect due to battery issues. Therefore, the measurements of VC 2

were cut off on the 03.01.2014.

Figure 3-1: Water depth above sensor membrane at locations VC 1 (seaward) and VC 2 (landward)

derived from the measurements of the pressure transducers. While the high water levels in the

beginning of January were high enough to cause inundation the tidal range from the 6th

of January

onwards was too small.

In total, 825 values were analysed for the time both sensors were operational (5-min-periods).

Out of these values, at sensor VC 1 only 7 measurements were recorded with Hs > 2 cm, the

maximum Hs was measured once with a height of 4 cm. Altogether, Hs ≥ 2 cm 51 times.

While at sensor VC 1 a little wave activity was measured, the analysis of sensor VC 2

concerning wave characteristics produced the output Hs = 0.01 cm continuously for every

time step. Figure 3-1 shows that both sensors worked successfully for the first days in the

field. Even though the incoming waves were very small, they were attenuated completely.

Therefore, the wave reduction after 200 m through the mangrove forest was 100% (r200 =

1.00). Because of the lack of data and higher water levels it cannot be stated that the wave

reduction is always so high. As such, no calculations for r were made. Even without battery

failure, the recorded data at sensor location VC 1 was limited (no recordings after

06.01.2014). The tide table with the predicted high tides for 2014 at My Thanh gives a value

of less than +150 cm above mean sea level for the time when there were no waves recorded.

0

5

10

15

20

25

30

35

40

de

pth

[cm

]

VC 1

VC 2


57

The successful measurements at the other three transects (CLD_n, CLD_s and LH) provided

more data. Only the periods where all sensors (at least 2) were inundated are used for the

results presented on the following pages. Along all three transects, measurements were

successful in the rainy season, but only at CLD_n were measurements successful in the dry

season (see chapter 2.5.2, p. 45).

Figure 3-2 presents the range of measured wave heights (Hs) at the seaward and landward

sensor locations of all three transects (200 m distance) by season. Additionally, the average

wave reduction (r200) is shown. As reminder, a value of r200 = 0.4 means an incoming wave of

1 m at sensor 1 (seaward) was reduced by 40% when it reached the last sensor 200 m further

inland (remaining wave height was 60 cm).

Figure 3-2: Recorded significant wave heights (Hs) at the seaward and landward sensors of the transects

CLD_n, CLD_s and LH (15-min-period; CLD_n DS: 128, CLD_n RS: 482 values, CLD_s RS: 260

values, LH RS: 497 values). The stars represent the average reduction of Hs over the length of the

whole transect (r200).

At transect CLD_n the incoming significant wave height was higher during the dry season

than during the rainy season. In the rainy season, the incoming waves at CLD_n had the

smallest range while the range at CLD_s was wider. The range for the median 50% of

measured Hs was almost identical for both transects. The incoming waves at LH were much

higher in their total range as well as the median 50% of the measurements (for exact values of

maximum, minimum and average see Table 3-1).

Along all transects, the waves were attenuated after 200 m. At CLD_n, the waves were

reduced by averagely 56% in the dry and 43% in the rainy season, while the reduction at

CLD_s was 56%. The wave reduction at the reference site LH was 46% in average. The

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

CLD_n 1DS

CLD_n 1RS

CLD_s 1RS

LH 1RS

CLD_n 4DS

CLD_n 4RS

CLD_s 3RS

LH 2RS

r 20

0

Hs

[m]

200 m


58

remaining wave heights at the landward sensor locations show that the medium 50% of the

measured Hs values at CLD_s were lower, but the total range was still bigger than at CLD_n,.

For LH, the medium 50% were still higher than at the other transects (with little overlapping

with CLD_n), and the measured wave heights during the rainy season were still up to 0.51 m

while Hs max. for CLD_s was 0.22 m and for CLD_n was 0.16 m. In the dry season, Hs max.

was 0.18 m at CLD_n.

Figure 3-3 depicts all results of the reduction of significant wave height for the three transects

(CLD_n, CLD_s and LH) separated by season (dots marking the averages are shown in

Figure 3-2 as crosses). The range of wave reduction at transect CLD_s was generally higher

than at CLD_n and LH in the rainy season. The latter two had almost the same range. The

median 50% of the wave reduction results at LH was slightly higher than the results at CLD_n

in the rainy season. The highest wave reduction was measured in the dry season at transect

CLD_n and in the rainy season at CLD_s.

Figure 3-3: Comparison of the reduction of significant wave heights after crossing through the mangrove

forest along the whole transect (r200) and per m (r) between the transects CLD_n, CLD_s and LH (15-

min-period; CLD_n DS: 128 values, CLD_n RS: 482 values, CLD_s RS: 260 values, LH RS: 497

values.

The correlation between wave height reduction (r200) and significant wave height (Hs) is

displayed in Figure 3-4 for the measurements at transect CLD_n during the rainy and dry

season. For the transects CLD_s and LH, this correlation can be found in Appendix VII, App.

14 and App. 15. The wave attenuation correlated poorly with the incident wave heights at

transect CLD_s and in the rainy season at CLD_n, while it correlated moderately at the

transects LH and in the dry season at CLD_n. The correlation coefficients (r) reached from

-0.18 (CLD_s) over -0.19 (CLD_n RS) and -0.40 (CLD_n DS) to -0.55 (LH) (p < 0.001). The

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

0.0040

0.0045

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

CLD_n DS CLD_n RS CLD_s RS LH RS

r [m

-1]

r 20

0

3 Results 3.2 Comparison between CLD_n, CLD_s and LH

59

correlation is for each transect of negative nature, with increasing significant wave height the

wave reduction is decreasing.

Figure 3-4: Correlation between the initial significant wave height (Hs) at the coastal sensor and the rate

of wave height reduction at the landward sensor 200 m further inland (r200) during the rainy (grey

crosses) and dry (black circles) season at the transect CLD_n (15-min-period).

3.2. Comparison between CLD_n, CLD_s and LH

As described in chapter 2.5.3.3, p. 52, the maximum water depth values of the parallel

measurements of transect LH with CLD_n and CLD_s were used to relate the results to each

other (see also Appendix V, App. 10 and Appendix VI, App. 13). The results presented in the

following graphs show the reduction of significant wave height per m (r) plotted against the

water depth of the respective seaward sensor. Note that the x-axes showing the water depths

in the graphs are shifted to each other to relate the results.

First, Figure 3-5 presents the comparison of the transects CLD_n and LH for all available

datasets (5-min-perriods). The best-fit lines are based on 3rd

-order polynomial equations. An

incoming wave at a water depth of 150 cm at transect LH (black) was reduced by 0.0021 m-1

(0.2% per meter). Because transect CLD_n is about 30 cm higher in elevation, the water depth

value is only 120 cm in comparison (second x-axes). During the rainy season (blue), r was

0.0021 m-1

as well, while during the dry season (red), the wave reduction was higher with r =

0.0030 m-1

. For the reference site LH (total height difference of 13 cm along 200 m), the wave

reduction at shallow water depth was highest, up to 0.0041 m-1

at 45 cm water depth. It

y = -0.7322x + 0.7264 R² = 0.159

y = -0.5798x + 0.5212 R² = 0.0368

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

r 20

0

Hs [m]

CLD_n DS

CLD_n RS


60

decreased with increasing water depth to 0.0020 m-1

at 110 cm water depth. Up to 140 cm, the

wave reduction continued on a low level, then it increased slightly up to 0.0024 m-1

(single

values up to 0.0029 m-1

) at a water depth of 200 cm. Around water depths of 80 to 140 cm,

the wave reduction at transect LH varied over a wide range. Values of 0.0001 up to 0.0032

were calculated.

Figure 3-5: Reduction of the significant wave height between the seaward and the landward sensors of the

transects CLD_n and LH plotted against water depth for all assessed data. Note that the second x-

axis has been shifted (5-min-period; LH RS: 1,894 values, CLD_n RS: 1,438 values, CLD_n DS: 143

values).

In comparison, the water depth at transect CLD_n is higher (total height difference of 64 cm

along 200 m). In the rainy season, the maximum wave reduction of around 0.0038 m-1

occured at a water depth of 85 cm. With increasing water depth, the wave reduction decreased

to 0.0015 m-1

at 170 cm water depth. The 3rd

-order polynomial best-fit line indicates a drop

from there on to a wave reduction less than 0.0010 m-1

(single values of r = -0.0004, which

suggests an increase in wave height instead of wave reduction).

The measurement results of the dry season start with even higher wave reduction for CLD_n

(r = 0.0042 m-1

at 95 cm water depth). They decreased with increasing water depth to 0.0024

m-1

at 160 cm water depth. Further increase in water depth resulted in similar wave reduction.

R² = 0.2526 R² = 0.5694

R² = 0.7923

-30 20 70 120 170 220

-0.001

0.000

0.001

0.002

0.003

0.004

0.005

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150 200 250

depth at seaward sensor CLD_n 1 [cm]

r [m

-1]

r 20

0

depth at seaward sensor LH 1 [cm]

LH RS CLD_n RS CLD_n DS

LH RS (3rd order polynomial) CLD_n RS (3rd order polynomial) CLD_n DS (3rd order polynomial)


61

Figure 3-6 compares the datasets of LH with the one of CLD_s measured in the rainy season

(orange). Along this transect, the total height difference is 36 cm over 200 m. The elevation of

the transect is even higher than CLD_n (+ 95 cm to the elevation of LH). At a water height of

55 cm the wave reduction was around 0.0038 m-1

. Increasing water depth to 125 cm decreased

the wave reduction to 0.0024 m-1

. Values for wave reduction vary between 0.0011 m-1

to

0.0031 m-1

at water depths between 125 and 140 cm.


transects CLD_s and LH plotted against water depth for all assessed data. Note that the second x-

axis has been shifted (5-min-period; LH RS: 1,894 values, CLD_s RS: 780 values).

The measurement results of the sites on Cu Lao Dung Island are presented together in Figure

3-7. Note that both x-axes are shifted and the best-fit lines are based on 2nd

-order polynomial

equations. The biggest difference due to the lower order equation can be seen for the best-fit

line of the rainy season dataset of CLD_n. The drop of wave reduction at water depths higher

than 170 cm is not depicted. The coefficients of determination (R2) for both best-fit lines

show little deviation (0.5694 for 3rd

-order and 0.5477 for 2nd

-order polynomial equations). In

Appendix VIII, App. 16, a comparison of the 2nd

- and 3rd

-order polynomial best-fit lines for

the measured data of transect LH is shown. The coefficients of confidence are 0.2526 for the

3rd

-order and 0.1905 for the 2nd

-order polynomial equation. Based on this the 3rd

-order best-fit

lines were chosen for the comparisons with transect LH since the coefficients of

determination for the other transects do not differ as much.

R² = 0.2526 R² = 0.5925

-95 -45 5 55 105 155

0.000

0.001

0.002

0.003

0.004

0.005

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150 200 250

depth at seaward sensor CLD_s 1 [cm]

r [m

-1]

r 20

0


LH RS CLD_s RS LH RS (3rd order polynomial) CLD_s RS (3rd order polynomial)

3 Results 3.3 Cu Lao Dung north (CLD_n)

62


transects CLD_n and CLD_s plotted against water depth for all assessed data. Note that both x-axis

have been shifted and the best-fit line is 2nd

-order polynomial (5-min-period; CLD_n RS: 1,438

values, CLD_n DS: 143 values, CLD_s RS: 780 values).

In addition to the preceding observations, the direct comparison of CLD_n and CLD_s shows

higher wave reduction at CLD_s for wave heights between 50 and 105 cm, especially in

comparison with the measurements during the rainy season at CLD_n. Around a water depth

of 125 cm (190 cm at CLD_n), the wave reduction of CLD_s RS and CLD_n DS was around

0.0024 m-1

. At the same water depth in the rainy season at CLD_n, the wave reduction was

0.0013 m-1

. For all evaluated datasets, the wave reduction decreased with increasing water

height until the best-fit curves flatten.

3.3. Cu Lao Dung north (CLD_n)

During the rainy season, measurements were successful on more days at transect CLD_n than

in the dry season (see 2.5.2, p. 45). While for the dry season 128 values were processed for

the analysis (15-min-period), in the rainy season up to 482 values were available (sensors 1

and 4). For six days, four sensors were installed simultaneously at transect CLD_n in the

R² = 0.5477

R² = 0.7908

R² = 0.592

5 25 45 65 85 105 125 145

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-0.001

0.000

0.001

0.002

0.003

0.004

0.005

70 90 110 130 150 170 190 210


r 20

0

r [m

-1]


CLD_n RS CLD_n DS CLD_s RS

CLD_n RS (2nd order polynomial) CLD_n DS (2nd order polynomial) CLD_s RS (2nd order polynomial)


63

rainy season (see Appendix IV, App. 7 for the dates of measurements). During this time 218

values were successfully measured and calculated.

The direct comparison of the significant wave heights in the rainy and dry season at transect

CLD_n is shown in Figure 3-8 in more detail, with the rainy season shown in grey. In

comparison to the other transects, the same data is depicted in Figure 3-2, p. 57 and the

corresponding wave reduction calculations in Figure 3-3, p. 58. Most of the recorded

incoming waves were higher in the dry season than in the rainy season, but were attenuated to

almost the same average height at CLD_n 4 in both seasons. Appendix IX, App. 17 gives the

measurements of Tm and Tp for these datasets. While the average mean wave period does not

change between CLD_n 1 and 4 in the rainy season (Tm = 3.5 s), in the dry season it increased

from 2.5 s (CLD_n 1) to 2.8 s (CLD_n 4).

Figure 3-8: Comparison of the sensor measurements of Hs at transect CLD_n during the dry season

(black, 128 values) and rainy season (grey, 482 values) (15-min-period).

In Figure 3-9, the recorded significant wave height during the rainy season at each sensor

location along transect CLD_n is presented. The time when four sensors were installed is

shown in black, while the results of the complete measurement time of the sensors 1 and 4 are

shown in grey. The incoming Hs at the seaward sensor location varied between 0.07 and 0.23

m for the time with four sensors (0.06 and 0.27 m for two sensors). It decreased over CLD_n

2 down to between 0.03 and 0.12 m at CLD_n 3. The medium 50% were with a range

between 0.05 and 0.08 m lowest and closest along the transect. Additionally, it can be seen

that the wave heights at CLD_n 4 (the most landward sensor) were increased again to a range

between 0.03 and 0.15 m (medium 50% between 0.055 and 0.10 m). The measurement results

for the wave period parameters Tm and Tp for the times with two and four sensors for transect

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

CLD_n 1 CLD_n 4

Hs

[m]


64

CLD_n can be seen in Appendix IX, App. 18. Tm was increasing slightly from CLD_n 1 (2.6-

4.8 s) to CLD_n 3 (2.8-4.9 s) before it decreased to 2.6 till 4.5 s at CLD_n 4.

Figure 3-9: Sensor measurements of the significant wave height Hs at transect CLD_n during the rainy

season. The black bars indicate the measurements during the timeframe of all four sensors (218

values) while the grey bars represent the maximum available data of the sensors 1 and 4 (482 values)

(15-min-period).

The change from decreasing to increasing wave height between sensor locations 3 and 4 can

also be seen in the aggregated results of the wave reduction calculations in Figure 3-10. It

depicts the wave reduction in relation to the most seaward sensor. The grey bars represent the

results of the long measurement time with only two sensors also shown in Figure 3-3, p. 58.

Figure 3-10: Reduction of wave heights at the sensor locations at CLD_n during the rainy season in total

after x meters (left) and per m (right). The grey bars show the wave reduction after 200 m for the

maximum available data of the sensors 1 and 4 (482 values in contrast to 218 values (black bars)) (15-

min-period).

0.00

0.05

0.10

0.15

0.20

0.25

0.30

CLD_n 1 CLD_n 2 CLD_n 3 CLD_n 4

Hs

[m]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

x = 70 x = 140 x = 200

r x

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0 - 70 m 0 - 140 m 0 - 200 m

r [m

-1]


65

In the left part of the figure it can be seen that after 70 m from the forest edge up to 45% (r70 =

0.45) of the incoming significant wave height was attenuated. At the third sensor location

(140 m from forest edge), the wave reduction r140 was up to 0.78. The maximum wave

attenuation increased to 83% at the last sensor (x = 200 m), but the medium 50% were lower

than at 140 m from the forest edge. The right part of the figure shows the wave attenuation per

meter (r). The wide range of wave reduction within the first 70 m of the transect (r = 0.0008-

0.0063 m-1

) was reduced to 0.0027-0.0056 m-1

after 140 m, with increased average from

0.0030 to 0.0039 m-1

. After 200 m at the end of the transect, the wave reduction was 0.0013-

0.0042 m-1

(0.0024 m-1

in average). While the range of wave reduction was still as wide as at

sensor location 3, it was reduced.

The wave reduction between each sensor location per meter is shown in Figure 3-11. It

confirms the previous observations of a higher wave reduction between 70 and 140 m along

the transect than between the seaward forest edge and the second sensor location (0-70 m).

Figure 3-11: Reduction of the significant wave heights per m between the sensor positions of transect

CLD_n during the rainy season (218 values) (15-min-period).

Additionally, the figure shows that three fourths of the calculated wave height reduction

between the third and fourth sensor location (140-200 m) were negative and indicate thereby

an increased, instead of the expected decreased, wave height.

In Figure 3-12, the wave reduction parameter rx is plotted against the water depth of the

seaward sensor. On top, the absolute frequency of the water depth values shows that most

wave recordings occurred during water deaths between 85 and 145 cm. While the logarithmic

best-fit lines of the second (orange) and third (red) sensor location run almost parallel to each

other, the best-fit line of the most seaward sensor (blue) indicates a stronger decrease of wave

-0.0100

-0.0075

-0.0050

-0.0025

0.0000

0.0025

0.0050

0.0075

0.0100

0 - 70 m 70 - 140 m 140 - 200 m

r [m

-1]


66

Figure 3-12: Reduction of the significant wave height between the seaward sensor CLD_n 1 and the three

landward sensors of the transect CLD_n plotted against water depth during rainy season (5-min-

period; 657 values). The absolute frequency of water depth values at CLD_n 1 is given in a grey bar

histogram on top.

reduction with increasing water depth. The coefficients of determination (R2) depict the

scattered form of the measurement results of CLD_n 2 and 3 (R2 = 0.10 and 0.18) in

comparison to CLD_n 4 (R2 = 0.56). The wave reduction after 70 m (CLD_n 2) was around

30% at a water depth of 85 cm and decreased almost linearly to 15% at 170 cm water depth.

After 140 m (CLD_n 3) the wave reduction was around 60% for shallow water depth of 85

cm and decreased to 45% at a water depth of 170 cm. With shallow water depth, the wave

reduction after 200 m (CLD_n 4) was around 63% and decreased to 28% at a depth of 170

cm. At a water depth of 96 cm, the best-fit line of CLD_n 4 crosses downwards over the best-

fit line of CLD_n 3.

In Appendix X, App. 19, the wave reduction per meter (r) for each sensor location plotted

against water depth can be seen. It shows a decreased of wave attenuation with increased

water depth for each chosen distance, with the same results as the right part of Figure 3-10.

R² = 0.1001

R² = 0.1771

R² = 0.5572

80 90 100 110 120 130 140 150 160 170 180

0

15

30

45

60

75

90

105-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

80 90 100 110 120 130 140 150 160 170 180

abso

lute

fre

qu

en

cy o

f w

ate

r d

ep

th a

t C

LD_n

1

r x


CLD_n 2 (x = 70 m) CLD_n 3 (x = 140 m) CLD_n 4 (x = 200 m)

CLD_n 2 (x = 70 m) log. CLD_n 3 (x = 140 m) log. CLD_n 4 (x = 200 m) log.

3 Results 3.4 Cu Lao Dung south (CLD_s)

67

While the wave reduction at the third sensor was highest, the fourth sensor showed the lowest

wave reduction per meter. The best-fit lines for the sensor location CLD_n 2 and CLD_n 4

run parallel to each other, while at CLD_n 3 the wave reduction per meter decreased less with

increasing water depths than at the other two sensors.

For CLD_n 2, single negative values of wave attenuation are displayed in both figures. They

are relics of using the dataset of 5-min-periods for analyses (see chapter 2.5.3.1, p. 48). Such

single extreme negative values also occur at the other sensor locations, but the average and

maximum values are not altered in comparison to the 15-min-period.

3.4. Cu Lao Dung south (CLD_s)

At transect CLD_s, simultaneous measurements at three sensor locations were successful for

eight days. From this time, 202 values of the 15-min-period dataset were used for the

analyses, while for the maximum available sensor recordings of CLD_s 1 and 3 260 values

were used. All results are from the rainy season.

Figure 3-13 presents the aggregated measurement results of the significant wave height along

the transect (grey bars also presented in Figure 3-2, p. 57). In Appendix XI, App. 20 the

results of Tm and Tp are shown. The distribution of the mean wave period stayed almost the

same at all sensor locations (between 2.0 and 3.3 s with 2.4 s in average). At the seaward

sensor CLD_s 1 the measurements of Hs were spread over a wide range from 0.07 to 0.43 m.

Figure 3-13: Sensor measurements of the significant wave height Hs at transect CLD_s during the rainy

season. The black bars indicate the measurements during the timeframe of all three sensors (202

values) while the grey bars represent the maximum available data of the sensors 1 and 3 (260 values)

(15-min-period).

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

CLD_s 1 CLD_s 2 CLD_s 3

Hs

[m]

3 Results 3.4 Cu Lao Dung south (CLD_s)

68

It decreased over CLD_s 2 (0.04-0.33 m) down to a range between 0.03 to 0.22 at the most

landward sensor location. While the total range of measurements was the same for both

analysed datasets, the medium 50% and the averages were each one percentage point lower

for the set with more data.

The significant wave height was reduced up to 50% at the second sensor location 67 m from

the forest edge, with a minimum of 8% (see left part of Figure 3-14). The average reduction of

27% increased to 54% at the landward end of the transect (total range of 29-84%) during the

simultaneous measurements (56% for all data available at CLD_s 3).

Figure 3-14: Reduction of wave heights at the sensor locations at CLD_s during the rainy season in total

after x meters (left) and per m for the distances between the sensor locations as well as the whole

transect (right). The grey bars show the wave reduction after 200 m for the maximum available data

of the sensors 1 and 3 (260 values in contrast to 202 values (black bars)) (15-min-period).

In the right of Figure 3-14 the wave reduction per meter is depicted between each sensor

location (first two bars) and along the whole transect (most right bar). Along the first 67 m,

the waves were reduced by 0.0011 to 0.0075 m-1

(0.0040 m-1

in average). Between the second

and third sensor location (67-200 m), the wave reduction was reduced to 0.0009-0.0055 m-1

with an average of 0.0028 m-1

. The wave reduction along the whole transect (0-200 m) for the

simultaneous measurements at three locations was 0.0027 m-1

in average (0.0015-0.0042 m-1

),

and for all measured data between the seaward and the landward sensor 0.0028 m-1

(shown

also in Figure 3-3, p. 58).

Figure 3-15 presents the wave reduction parameter rx plotted against the water depth of the

seaward sensor. The absolute frequency of water depth values on top shows few

measurements with low water depths up to 65 cm and also high water depths above 130 cm.

The logarithmic best-fit lines of the second (orange) and third (blue) sensor location run

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

x = 67 x = 200

r x

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0 - 67 m 67 - 200 m 0 - 200 m

r [m

-1]

3 Results 3.5 Lai Hoa (LH)

69

almost parallel to each other and have both a slope like the best-fit line of CLD_n 4 in Figure

3-12. The wave reduction after 67 m was around 43% at a water depth of 60 cm and

decreased almost to 16% at 130 cm water depth. After 200 m, the wave reduction was above

70% for shallow water depth of 60 cm and decreased to 43% at a water depth of 130 cm.

Figure 3-15: Reduction of the significant wave height between the seaward sensor CLD_s 1 and the two

landward sensors of the transect CLD_s plotted against water depth during rainy season (5-min-

period; 600 values). The absolute frequency of water depth values at CLD_s 1 is given in a grey bar

histogram on top.

The wave reduction per meter (r) for each sensor location plotted against water depth is

shown in Appendix XII, App. 21. The wave attenuation was decreasing with increasing water

depth for both chosen distances. The results are like in the right part of Figure 3-14 with

higher wave reduction for the second sensor location than for the third. The higher wave

reduction values within the first 67 m of the transect occurred especially at shallow water

depths between 50 and 115 cm.

3.5. Lai Hoa (LH)

At transect LH only two sensors with a distance of 200 m were installed, they provided 497

values during the rainy season (15-min-period). The mean wave period increased slightly

from averagely 2.8 s at sensor LH 1 to 3.0 s at sensor LH 2 (see Appendix XIII, App. 22). The

range varied at both sensors between 2.2 and 3.9 s. In the left of Figure 3-16 the measured

R² = 0.5222

R² = 0.5411

50 60 70 80 90 100 110 120 130 140 150

0

15

30

45

60

750.0

0.2

0.4

0.6

0.8

1.0

50 60 70 80 90 100 110 120 130 140 150

abso

lute

fre

qu

en

cy o

f w

ate

r d

ep

th a

t C

LD_s

1

r x


CLD_s 2 (x = 67 m) CLD_s 3 (x = 200 m) CLD_n 2 (67 m) log. CLD_n 3 (200 m) log.

3 Results 3.5 Lai Hoa (LH)

70

significant wave heights are presented. Hs was decreasing from a range between 0.08 and 0.76

m (average 0.34 m) at LH 1 to between 0.03 and 0.51 m (average 0.20 m) at LH 2.

Figure 3-16: Sensor measurements of the significant wave height Hs (left) and wave reduction (right) after

crossing through the mangrove forest along the whole transect (r200) and per m (r) at transect LH

during the rainy season (497 values) (15-min-period).

On the right of Figure 3-16 the wave reduction is shown which ranged between 14% (0.0007

m-1

) and 77% (0.0038 m-1

) over the whole length of the transect. In average the waves were

reduced by 46% (0.0023 m-1

).

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

LH 1 LH 2

Hs

[m]

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

0.0040

0.0045

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

LH 1 - LH 2 (200 m)

r [m

-1]

r 20

0

4 Discussion 4.1 Wave attenuation at the study sites

71

4. Discussion

4.1. Wave attenuation at the study sites

During the time measurements were being taken, no strong winds occurred (see 2.3, p. 18),

the waves were of moderate height and no extreme high waves were recorded. The results of

the maximum available datasets for the three transects, CLD_n, CLD_s and LH, show

different initial waves concerning significant wave height (Hs) as well as mean wave period

Tm (see Table 3-1, p. 55). In the rainy season, the shortest mean wave period occurred at

transect CLD_s, while at CLD_n it was the longest. The mean wave period at LH lied

between the other two, while the significant wave height of the incoming waves was highest

at transect LH and lowest at CLD_n. The waves at CLD_s were steeper than at CLD_n in the

rainy season (wave steepness = Hs/Tm). However, transect LH had the wave climate with the

steepest waves. In the dry season, the incoming waves at transect CLD_n were steeper than in

the rainy season and even steeper than the incoming waves at CLD_s in the rainy season. The

average wave reduction at all three transects was between 43 and 56% (0.0022-0.0028 m-1

)

along the whole length of the transects.

The correlations between the incoming significant wave heights at the coastal sensors with the

rate of wave height reduction at the respective landward sensor 200 m further inland showed a

moderate correlation at transects LH and in the dry season at CLD_n. At the transects CLD_s

and in the rainy season at CLD_n it correlated poorly. (see Figure 3-4, p. 59 and Appendix

VII, App. 14 and App. 15). Instead of depending on the incoming wave height, the wave

attenuation is dependent mainly on the vegetation characteristics.

4.1.1. Transect VC

The measurement period for transect VC was adjusted to be during the highest tidal phases of

the hydrological year. The pressure transducers at both sensor locations recorded depths of up

to several decimetres, but only for a time of three days (see Figure 3-1, p. 56). Despite the

recorded water depths, only very few waves of small height were recorded (1× Hs max. = 4

cm, 6× Hs = 3 cm). This can be explained by the fact that VC is a site which is currently

accreting and hence has an extensive and high floodplain that stretches further to the seaward

side of the transect. The long distance incoming waves have to travel over this floodplain

attenuates them already before they reach the first sensor installed at the forest edge. Figure

4-1 shows the idealised energy dissipation of waves by a high floodplain.


72

Figure 4-1: Impact of a high floodplain on wave energy dissipation (ALBERS et al. 2013).

As such the recordings were very limited, adding to the early failure of one of the sensors.

The few waves that were recorded were completely attenuated (rx = 1). The reason for this is

the high density of the Rhizophora apiculata plantings at this site (see chapter 2.4.4.2, p. 40).

Even though the measurement results indicate complete wave reduction after 200 m, the lack

of more data makes the results inconclusive.

4.1.2. Transect LH

Due to the low elevation of the reference site without vegetation (LH), the incoming waves

were spread over the widest range (see Figure 3-2, p. 57). Data about the wave characteristics

were assessed for 8 days (16 high tides). The average wave reduction was 46% and is

therefore within the range of the results of the vegetated study sites. Even though the wave

reduction at LH seems able to compete with the other transects, the remaining Hs at LH was

still high in comparison: in average 0.20 m and up to a maximum of 0.51 m. The highest

average of the remaining datasets was with 0.10 m at transect CLD_n in the dry season, while

the maximum remaining Hs was 0.22 m at CLD_s in the rainy season.

The results presented in chapter 3.2, p. 59, show that the waves were attenuated the most

when the water depth was shallow at transect LH. When the water depth reached heights

where the wave reduction at CLD_n and CLD_s was highest, it dropped already to its lowest

value at transect LH, 0.002 m-1

or 40% after 200 m (r200 = 0.4). The high reduction for the low

water depths is due to bottom stress. The slope has no significant influence on the attenuation

of waves because the total height difference along the 200 m of the transect is only 13 cm.

Even though the site has the lowest elevation in comparison to the others, it is just around 30

cm lower than CLD_n (based on the results of the water depth analysis between the transects


73

in chapter 2.5.3.3, p. 52). Like at transect VC, the waves were attenuated through the high

floodplain as long as the water depths were not too high (see Figure 4-1, p. 72). This goes

along with the assumption made by MAZDA et al. (2006). They state that at Hs of 25 cm and a

water depth of 90 cm, the wave height is less influenced by bottom stress. At transect LH

around 100 cm water depth, the line if best-fit of the wave reduction flattens, confirming this

supposition. At LH, the negative correlation between wave height reduction and incoming

significant wave height was also the highest of all transects, showing less wave attenuation of

higher waves.

4.1.3. Transect CLD_n

At transect CLD_n, measurements were successful for 13 days (20 high tides) in the rainy

season and 3 days (5 high tides) in the dry season. The significant wave height (Hs) of the

incoming waves was higher and steeper in the dry season than in the rainy season. ICOE

(2012) predicted a high tide water level of a maximum of 110 cm during the measurement

time in the rainy season and 149 cm during the measurement time in the dry season for the

hydrological station My Thanh. The higher incoming waves in the dry season are a

consequence of this different tidal situation between the seasons (see also chapter 2.3, p. 18).

However, in both seasons, the remaining wave heights at the landward sensor CLD_n 4 were

almost the same (see Figure 3-8, p. 63). As a consequence, the calculated wave reductions

show a remarkable difference between the seasons, with ranges of the medium 50% of

measured wave reductions between 47 and 67% in the dry season and only 35 to 50% in the

rainy season (see Figure 3-3, p. 58). While the average wave attenuation in the rainy season

was the lowest among all studied transects, it was the highest in the dry season, together with

the results of CLD_s. The mean wave period recorded at the landward sensor in the dry

season increased, but the remaining waves were still steeper than in the rainy season (see

Appendix IX, App. 17). Figure 3-7, p. 62, shows that the wave reduction was higher in the

dry season for all water depths. Additionally, the wave reduction demonstrated a strong

negative correlation to the significant wave heights of the incoming waves in the dry season.

Unfortunately, the vegetation at transect CLD_n was only assessed in the dry season, so it is

difficult to explain the significant difference in the results. The visual impression was of less

branches growing off the stems at heights of around 1 m above ground in the rainy season,

which can serve as an explanation (see chapter 2.4.4.1, Figure 2-22, p. 38). In addition, the

growth of the seedlings and saplings could also have caused higher wave reduction in the dry


74

season. The vegetation assessment showed a band of high density seedlings and saplings

(1.32/m2) growing between sensor locations CLD_n 3 and CLD_n 4. Given the four months

between the two measurements, it is not unreasonable to assume their growth caused higher

attenuation in the dry season than in the rainy season.

The wave attenuation also varied within the transect, presented in the results of the

simultaneous measurements with four sensors which were successful for 6 days (see chapter

3.3, p. 62). The significant wave heights were decreasing along the way from sensor 1 to

sensor 3, as expected. The measurements at location 4 contradicted this trend, as the wave

heights were higher than at sensor 3. While Hs increased between CLD_n 3 and CLD_n 4 the

mean wave period was decreasing. This is a sign for shoaling and results in steeper waves at

CLD_n 4 than at CLD_n 3. The results of the wave reduction for both parameters r and rx in

Figure 3-10, p. 64, show the large influence of shoaling. While at the third sensor (0-140 m)

the average wave reduction was 0.0039 m-1

, it is reduced to 0.0024 m-1

at CLD_n 4 (0-200

m).

The results also show that the wave reduction per meter was not spread equally along the

whole transect (see Figure 3-11, p. 65). The drop of r to negative values for the calculations of

the wave reduction between 140 and 200 m indicates an increase of the waves and was

already addressed above (shoaling). However, the wave reduction between 70 and 140 m

(average: 0.0061 m-1

) was also two times the reduction between 0 and 70 m (average 0.0030

m-1

).

To explain these results it is necessary to look at the relationship between wave reduction and

water depth, because this is where the influence of the vegetation patterns can be observed.

Figure 4-2 presents the results for the parameter r combined for the transects CLD_n and

CLD_s from Appendixes X, App. 19 and XII, App. 21.

The height difference along the whole transect is 64 cm (see Figure 2-16, p. 30). When the

first waves can be measured at the landward sensor location, the installation height of 20 cm

above ground results in a water height of at least 84 cm at the seaward sensor. At CLD_n 2

the water depth is then 56 cm, and at CLD_n 3 at least 39 cm. At these depths, the

pneumatophores growing between 0 and 140 m have already little to no impact on the wave

attenuation because they are already inundated (see Table 2-3, p. 37). Besides the stems and

branches of the trees, the seedlings and saplings also dampen the waves. The density of the

trees was higher between 0 and 70 m (0.11/m2) than between 70 and 140 m (0.07/m

2), while

their average diameter was bigger further away from the forest edge. However, a main

difference is the seedlings and saplings growing along the transect. In contrary to the first


75

third of the transect, where only 1 sapling was growing in 100 m2, the vegetation assessment

between CLD_n 2 and CLD_n 3 at 110 m from the forest edge (elevation + 40 cm) showed 40

seedlings and saplings. They had an average height of 69.1 cm with a standard deviation of

49.7 cm and were therefore attenuating the waves up to around 110 cm water depth. This is

visible in Figure 4-2 by a growing gap between the wave reduction results of CLD_n 2

(orange) and CLD_n 3 (red) with increasing water depth (higher wave reduction at CLD_n 3).

Figure 4-2: Reduction of the significant wave height per m between the seaward sensors of CLD_n and

CLD_s and the landward sensors of the respective transect plotted against water depth during rainy

season (5-min-period; CLD_n: 657 values, CLD_s: 600 values). The two x-axes are shifted to each

other.

Additionally, the branches also act as obstacles to the waves causing bigger wave attenuation.

Above, it was mentioned that the branches at 1 m above ground were not as prominent during

the rainy season. Still, their small presence caused wave reduction between the second and

third sensor location, especially at higher water depths. The vegetation assessment Veg_3,

between CLD_n 2 and CLD_n 3, shows a height of the first branch (HFB) of 98.0 cm (39.2

R² = 0.10

R² = 0.18

R² = 0.56

R² = 0.52

R² = 0.54

0 20 40 60 80 100 120 140

-0.004

-0.002

0.000

0.002

0.004

0.006

0.008

0.010

65 85 105 125 145 165 185 205


r [m

-1]


CLD_n 2 (70 m) CLD_n 3 (140 m) CLD_n 4 (200 m) CLD_s 2 (67 m) CLD_s 3 (200 m)

CLD_n 2 (70 m) log. CLD_n 3 (140 m) log. CLD_n 4 (200 m) log. CLD_s 2 (67 m ) log. CLD_s 3 (200 m) log.


76

cm) plus 40 cm for the elevation of the plot. The branches wave attenuation can be seen by

the growing gap between the results of CLD_n 2 and CLD_n 3 with increasing water depth in

Figure 4-2. Aggregated, this results in the observed higher wave reduction between 70 and

140 m than between 0 and 70 m, as mentioned above.

The effect the vegetation has between CLD_n 3 and CLD_n 4 cannot be observed in the

figure above, because the wave reduction per meter divides through 200. Instead, it is

depicted in Figure 4-3, showing the combined results from Figure 3-12 and Figure 3-15 for

the wave reduction parameter rx for the measured water depth values.

Figure 4-3: Reduction of the significant wave height between the seaward sensors of CLD_n and CLD_s

and the landward sensors of the respective transect plotted against water depth during rainy season

(5-min-period; CLD_n: 657 values, CLD_s: 600 values). The two x-axes are shifted to each other.

The longer the distance an incoming wave travelled along the transect, the higher the resulting

rx should be. While this is true for the results of CLD_n 2 and CLD_n 3 in Figure 4-3, the

results of CLD_n 4 are lower due to shoaling (see above). Additionally, it can be observed

R² = 0.10

R² = 0.18

R² = 0.56

R² = 0.52

R² = 0.54

0 20 40 60 80 100 120 140

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

65 85 105 125 145 165 185 205

depth at seward sensor CLD_s 1 [cm]

r x


CLD_n 2 (x = 70 m) CLD_n 3 (x = 140 m) CLD_n 4 (x = 200 m) CLD_s 2 (x = 67 m) CLD_s 3 (x = 200 m)

CLD_n 2 (x = 70 m) log. CLD_n 3 (x = 140 m) log. CLD_n 4 (x = 200 m) log. CLD_s 2 (x = 67 m) log. CLD_s 3 (x = 200 m) log.


77

that the gradient of the decreasing wave reduction with increasing water depth is much steeper

at CLD_n 4 than at the other two sensor locations. This indicates a change in the vegetation

pattern which dampens the waves. The very dense band of seedlings and saplings (1.32/m2)

growing between CLD_n 3 and CLD_n 4 was already discussed above. They had an average

height of 74.3 cm with a standard deviation of 40.0 cm in the dry season. With the additional

60 cm (height difference at 180 m from forest edge), the seedlings and saplings are

dampening the waves up to water depth values around 135 cm. Above these depths their

dampening effect is reduced, as are the results for rx in Figure 4-3.

4.1.4. Transect CLD_s

Measurements of wave characteristics were successful for in total 15 days (20 tides) at

transect CLD_s. The sandbank on the seaward side of the transect causes shoaling and

therefore steepening of the incoming waves. As a consequence, the sea state was different

from the one at CLD_n. The significant wave height was higher and the mean wave period

was shorter of the incoming waves at the first sensor location than at CLD_n (see Table 3-1,

p. 55).

The wave attenuation results for the measurement period with three concurrent sensors

(successful for 8 days) are presented in Figure 3-14, p. 68. They show higher reduction in the

first 67 m between CLD_s 1 and CLD_s 2 than between 2 and 3 (67-200 m). The height

difference along the whole transect is 36 cm (see Figure 2-16, p. 30). When the first waves

can be measured at the landward sensor location, the installation height of 20 cm above

ground results in a water height of at least 56 cm at the seaward sensor, while at CLD_s 2 the

water depth is then at least 41 cm. As at transect CLD_n, at these depths the pneumatophores

growing along the whole transect have already little to no impact on the wave attenuation

because they are already inundated (see Table 2-3, p. 37). The density of the trees was a little

bit higher in the seaward part than in the landward part of the transect, while their average

diameter was bigger further away from the forest edge and the branch height decreased (see

Table 2-3, p. 37).

Unfortunately, the heights of the seedlings and saplings were not assessed along transect

CLD_s, only the heights of the first branches were recorded. This makes it difficult to

estimate the water depth at which the seedlings and saplings influenced wave reduction.

Assuming that the first branch is at least at the middle height of the young mangroves, their

total height would be around 77 cm between sensor location 1 and 2 (at Veg_4: HFB = 38.6


78

(27.3) × 2 = 77.2 cm). Adding 8 cm for the elevation results in a water depth of around 85 cm

at which the seedlings and saplings attenuate waves. This estimation goes along with the

wave attenuation results (r) displayed in Figure 4-2, p. 75. There, the best-fit curve of CLD_s

2 shows the influence of the seedlings and saplings for water depth up to around 100 cm.

With further increasing water depth, the influence of the seedlings and saplings decreases,

which is visible in the reduced wave attenuation. The vegetation assessment shows less

seedlings and saplings in the forest between sensor 2 and 3 than in the first 67 m. As a

consequence, the results for CLD_s 3 in Figure 4-2, p. 75, show less wave reduction at lower

water depths and the two best-fit curves align.

Branches grow from water depths of 90 cm between CLD_s 1 and CLD_s 2 and from around

70 cm between sensor locations 2 and 3. They influence the waves at higher water depths and

are responsible for the flattening of the reduction curves.

As at transect CLD_n, the wave reduction per meter (r) is lower at the landward sensor

location than after the first third of the transect in Figure 4-2, p. 75. , However, in Figure 4-3,

p. 76, showing the reduction parameter rx, it is higher. In Figure 4-3, the gradient of the best-

fit curve of CLD_s 2 is like the one of CLD_n 4. It shows the wave reduction caused by the

seedlings and saplings between CLD_s 1 and CLD_s 2 at lower water depths. The best-fit line

of CLD_s 3 is almost parallel to the one of CLD_s 2 because it still includes the wave

dampening already caused in the first third of the transect. However, the gap between CLD_s

2 and CLD_s 3 is closing with increasing water depth. This shows that between CLD_s 2 and

CLD_s 3 seedlings and saplings are also attenuating the waves (difference in reduction is

higher at lower water depths), but not as much as in the first 67 meter of the transect. Most of

the wave attenuation happened already in the forest band between the seaward forest edge and

67 m.

4.1.5. Comparison between CLD_n and CLD_s

In Figure 3-7, p. 62, the wave reduction depending on the water depth at CLD_s and CLD_n

are compared. The figure demonstrates that the wave reduction was higher at the southern

transect than in both seasons at CLD_n for the related water depths. As described in chapter

2.4.4.1, p. 35, more dead trees cover the forest floor at CLD_s than at CLD_n. This could be

part of the explanation for the differences in wave reduction. Additionally, the results of the

measurement times with more than two sensors showed the effect of more interaction with

vegetation (mainly of seedlings and saplings) at CLD_s than at CLD_n. At both study sites,


79

the seedlings and saplings have a big influence on the wave attenuation along the transects.

Between CLD_s 1 and 2, the density of seedlings and saplings is higher than between CLD_n

1 and 2. The comparison of the curves of rx show a higher wave attenuation at the southern

transect than at the northern for the first part of the transects (70 and 67 m). This can also be

observed in Figure 4-4, showing a comparison of the wave reduction per meter along different

parts of the transects CLD_n and CLD_s. While the wave reduction in the first third of the

two transects differs a lot, the attenuation along the second two thirds is almost alike. In total,

after 200 m the wave attenuation during the measurement time with more than two sensors

during the rainy season was higher at CLD_s than at CLD_n.

Figure 4-4: Comparison of wave reduction per meter at transects CLD_n (black) and CLD_s (grey) for

distances between sensor locations (two left bars) as well as the whole transect (right bar) (15-min-

period; CLD_n: 218 values, CLD_s: 202 values).

The results of the measurements with four sensors at transect CLD_n indicate that shoaling

influences the wave attenuation by mangrove forests when the slope of the floodplain gets

steeper (CLD_n is steeper than CLD_s where no shoaling occurred).

0 - 67 m 67 - 200 m 0 - 200 m

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0 - 70 m 70 - 200 m 0 - 200 m

part of transect CLD_s (grey)

r [m

-1]

part of transect CLD_n (black)

4 Discussion 4.2 Comparison with previous studies

80

4.2. Comparison with previous studies

While at transect VC all waves were attenuated completely, the average wave reduction at the

remaining three transects assessed in this study was between 43 and 56% (0.0022-0.0028 m-1

)

along the whole length of the transects. Within this range also the results for the reference site

LH. Other studies, like VO-LUONG & MASSEL (2006, 2008), obtained wave attenuation rates

of 0.0125-0.035 m-1

(see Table 2-1, p. 11). BRINKMAN (2006) obtained at the transect

Oonoonba 75% or 0.015-0.024 m-1

(mean: 0.019 m-1

) after 40 m and at the transect on

Iriomote Island 54% or 0.008-0.022 m-1

, also over a length of 40 m. Meanwhile, TRAN (2011)

assessed wave reduction of 82% (r = 0.0082 m-1

) over 100 m during measurements in the Can

Gio Mangrove Biosphere Reserve. These results are exceeding the results of present thesis but

also of other previous studies.

The obtained wave attenuation rates in the presented thesis range between 0.0007 and 0.0043

m-1

for the vegetated study sites. They compare well with observations by MAZDA (1997a) for

a 2-3 year-old Kandelia candel forest (r = 0.0008-0.0015 m-1

) and a 5-6 year-old forest (r =

0.0015-0.0022 m-1

) in the Tong King Delta. In addition, the observations of BRINKMAN

(2006) and MASSEL et al. (1999) at Cacoa Creek show comparable reduction of waves with

0.0003-0.003 m-1

. The measurements of HORSTMAN et al. (2014) obtained in sparse, mixed

Avicennia sp. and Sonneratia sp. forests at Palian (r = 0.0032 m-1

) and Kantang (r= 0.0024 m-

1) lie also within the range of the present results in this thesis.

In contrast, their results of the dense Rhizophora sp. forests exceed the here presented

reduction values (Palian: 0.012 m-1

, Kantang: 0.0061 m-1

). The highest wave reductions

observed by MAZDA et al. (2006) (r = 0.002-0.006 m-1

) over 100 m also outdo the measured

attenuation along the coast of Soc Trang Province, as do the results of TRAN (2011) along a

200 m long transect in the Red River Delta (r = 0.0041-0.0065 m-1

). The highest wave

attenuation rates in the presented study were obtained along the first thirds of the transects on

Cu Lao Dung Island reaching up to 0.0063 m-1

at CLD_n (70 m) and 0.0075 m-1

at CLD_s

(67 m). They compare well with the obtained reduction rates of the last mentioned previous

studies.

NGO et al. (2005) also measured wave reduction in a Sonneratia caseolaris forest (8-9 year-

old) with incoming wave heights of 0.55-0.72 m, which exceeds the maximum significant

wave heights (Hs) recorded on the study sites on Cu Lao Dung Island in this study (CLD_n:

0.36 m; CLD_s: 0.43 m). The forest between CLD_s 1 and CLD_s 2 is 7-8 years old, while

the inland part is 15-16 years old (SOC TRANG SUB-FPD 2013). Even though based on single,

manual measurements, NGO et al. (2005) recorded a wave reduction of 0.0033 m-1

over the


81

length of 120 m which fits well to the average wave reduction of 0.0039 m-1

(ranging from

0.0027-0.0056 m-1

) at sensor location CLD_n 3 after 140 m from the forest edge.

Observations made by QUARTEL et al. (2007) in a Kandelia candel forest and MAZDA et al.

(2006) in a Sonneratia sp. forest describe wave attenuation by branches and leaves when the

water depth is high enough for the waves to reach them. In the present study, the same was

observed on the two study sites on Cu Lao Dung for different parts of the two transects.

In contrary to previous studies, the high wave reduction values at shallow water depths could

not be linked to the aerial root system of the Sonneratia caseolaris trees on CLD, since the

roots are not high enough to influence the observed wave motion. The sensors were installed

too high above the substrate surface to measure it. In contrast, the partially very dense

seedlings and saplings were identified to be the cause for the higher reduction rates. No

previous study reviewed assessments of seedlings and saplings.

The results of the wave reduction at transect LH were higher than expected. With wave

reduction between 14 and 77% (average 46%) or 0.0007 and 0.0038 m-1

(average 0.0023 m-1

),

the attenuation was as high as at the vegetated study transects (see Table 3-1, p. 55). In

previous studies, when assessments on non-vegetated areas were conducted, the reference

sites were chosen to be a part of the assessed transects, usually the mudflat in front of the

mangrove forest (MAZDA et al. 2006, QUARTEL et al. 2007, HORSTMAN et al. 2014).

Therefore, they were better able to directly compare the areas without vegetation to the

vegetated parts of their transects. HORSTMAN et al. (2014) assessed a wave reduction on

mudflats of 0.002 m-1

on their Kantang transect and 0.0019 m-1

on their Palian transect. In

QUARTEL et al. (2007), the wave attenuation on a sandy mudflat was between 0.0005 and

0.0020 m-1

, while MAZDA et al. (2006) measured wave reduction of 0.001 to 0.002 m-1

. Their

results all show a range up to around 0.002 m-1

, which is not even close to the average of the

results presented in this study. The highest incoming wave height was measured by QUARTEL

et al. (2007) with 0.25 m (see Table 2-1, p. 11). In contrast, the measured Hs in the presented

study was up to 0.76 m at transect LH. Most of the observed wave reduction at transect LH

happened at shallow water depths, but this cannot explain the difference to previous studies.

This will be addressed in the following chapter.

HORSTMAN et al. (2014) measured an increase of significant wave height within both of their

observed transects similar to that of CLD_n, but only for parts of their measurements. They

observed this between two sensor locations, of which the second ones were at the border of a

much denser part of the mangrove forests. They concluded that possibly wave reflection at the

edge of the dense vegetation caused the increased significant wave heights. In this study, a


82

denser part of the forest was not observed at transect CLD_n behind sensor location 4.

Instead, it was found that the slope of the study transect must have caused shoaling and

thereby increased the wave heights in comparison to the previous sensor location CLD_n 3.

While MAZDA et al. (2006) and HORSTMAN et al. (2014) were able to observe the effect of

wave attenuation by mangroves during storm waves, this was not measured in this study.

HORSTMAN et al. (2014) criticised the description of vegetation characteristic in previous

studies as often not sufficient enough to explain the observed measurement results of wave

attenuation. TRAN (2011) and QUARTEL et al. (2007) only assessed the number of trees, the

tree height and leaf cover. Therefore, HORSTMAN et al. (2014) chose to assess the vegetation

in a more detailed way by quantifying the volume of submerged mangrove biomass, like

introduced by MAZDA et al. (1997b). Even though not assessed in such detail, the presented

vegetation assessment in this study enabled to draw relations for the wave attenuation in

mangroves under different hydrodynamic conditions.

The mean observed significant wave heights decrease (on average) observed by HORSTMAN et

al. (2014) along their Palian transect was 30-43%, which was 98 m long. However, during

their last measurement period (three months later than their previous measurements), a lower

reduction of only 22% was obtained. A similar difference in wave reduction results was

observed between the rainy and dry season measurements at transect CLD_n and emphasises

the importance of future comparative assessments in different seasons, already suggested by

ANDERSON et al. (2011).

All studies agreed on the positive contribution of mangroves to the dampening of wind and

swell waves of limited height and period and thus their importance for coastal protection.

Additionally, MCIVOR et al. (2013) provided a comprehensive overview of the current

knowledge on how mangrove soil surface elevations respond to SLR. They conclude that

mangroves have kept pace with SLR over thousands of years, with rates of rise depending

largely on external sediment inputs and the growth of subsurface roots. The rates are between

1 and 10 mm per year and show that mangroves, if maintained and protected, will be able to

continue to protect coasts. However, this is only true if conventional coastal engineering does

not reduce the natural accumulation of sediment and does not exacerbate land subsidence, in

which case the shorelines will be able to keep up with relative SLR through their natural

adaptive capacity (TEMMERMAN et al. 2013).

4 Discussion 4.3 Limitations

83

4.3. Limitations

At the transects CLD_n and CLD_s, the sensors were installed at 20 cm above ground to

avoid blocked membranes of the pressure transducers by accreting sediments. Even though

the concern for big amounts of incoming sediments was proven wrong, the chosen installation

height makes the assessment of the influence of pneumatophores on wave attenuation

impossible, because they are at most parts of the transects too low (see Table 2-3, p. 37).

In the discussion of the results for transect CLD_n, it became obvious that the vegetation

assessment during only one season made the interpretation difficult, particularly in trying to

determine the difference of the wave reduction results between rainy and dry season (see

chapter 4.1.3, p. 73). At the southern transect on Cu Lao Dung, the measurements of the

height of the first branch instead of the complete height of the seedlings and saplings limit the

author’s ability to draw conclusions for their wave dampening effect. In comparison to the

observed changes for wave reduction per water depth at transect CLD_n, it was still possible

to assess their influence on wave attenuation (see chapter 4.1.4, p. 77).

Additionally, the results of the wave reduction at transect LH were higher than expected with

regard to previous studies (see chapter 4.2, p. 80). With wave reduction between 14 and 77%

(average 46%) or 0.0007 and 0.0038 m-1

(average 0.0023 m-1

), the attenuation was as high as

at the vegetated study transects. The high and extensive mudflat at transect LH causes a

significant amount of wave attenuation, especially at lower water depths (see chapter 4.1.2, p.

72). MAZDA et al. (2006) stated that at 90 cm water depth and Hs of 25 cm the bottom stress

has less of an influence on the wave reduction. This leaves the question of why the wave

reduction at LH, though reduced, is still quite high at higher water depths. For all

measurements, there were no recordings of the actual wave directions, which can also alter

the results. At the vegetated study sites the assumed cross-shore direction of the waves caused

by refraction most likely represents the true state. At the reference site LH the water depths

and waves were higher than at the other transects. The lower elevation of the reference site

could have resulted in less refraction of the incoming waves. During measurement period in

the rainy season, the southwest monsoon caused prevailing winds from the west-southwest

(see chapter 2.3, p. 18). Whereas the setup of the transect perpendicular to the coast at LH was

in south-southeast direction (see Figure 2-14, p. 28). If the refraction did not cause change in

wave direction of around 90 degrees, it is possible that the length waves were travelling was

longer than 200 m. As an example, the wave reduction per m is shown in Figure 4-5 for the

assumed case that the waves had to cover a distance of 300 m instead of 200 m. This would

be a change in wave direction to approximately south-southwest. All reduction values would

4 Discussion 4.4 Recommendations

84

be reduced by one third in the case of 300 m traversed distance. If the described scenario was

the actual case it could explain the higher than expected attenuation rates on the reference site

and would also agree better with results presented in previous studies of wave attenuation of

0.002 m-1

on non-vegetated sites (see chapter see chapter 4.2, p. 80).

Figure 4-5: Reduction of the significant wave height per m between the seaward and the landward sensors

of transect LH plotted against water depth for all assessed data (5-min-period; LH: 1,894 values).

Black (circles): assumed transect length 200 m if wave direction is cross-shore; Grey (triangles):

comparative transect length of 300 m if wave direction more influenced by southwest monsoon.

4.4. Recommendations

The analysis of the obtained data from measurements in the 4.5 year-old Rhizophora

apiculata plantation at transect VC resulted in the complete attenuation of incoming waves

(see chapter 4.1.1, p. 71). However, the results are non-conclusive because of the short time of

successful measurements and the few and small waves recorded. Because this mangrove

species is popular for mangrove reforestation programmes in the Mekong Delta (PHAM et al.

2011), it is recommended to measure on such a site again. A similar site, preferably lower in

elevation should be assessed. Additionally, the thin Avicennia marina belt, typically growing

in front of most Rhizophora plantations, should also be included in the observed transect (see

Figure 2-13, p. 27 for the Avicennia belt), so that the influence on wave attenuation through

R² = 0.25

R² = 0.25

0.000

0.001

0.002

0.003

0.004

0.005

0 50 100 150 200 250

r [m

-1]


LH RS (200 m)

LH RS (300 m)

200 m (3rd order polynomial)

300 m 3rd order polynomial)


85

this belt, which is important for the protection of the sediments against erosion, could also be

assessed. At the chosen transect in VC, such a belt was not yet established.

The young plantation had a very good wave attenuation rate due to the density of the

vegetation. In comparison, measurements in an older Rhizophora plantation are recommended

to observe the long-time usefulness for coastal protection of this species. These older forests

are typically monocultures in Soc Trang Province, and are planted in rows without bigger

biodiversity, shown in Figure 4-6. This is especially of interest because such forests at the

southwest coast of Soc Trang Province and in the neighbouring province Bac Lieu are facing

severe erosion (see chapter 2.2, p. 15). HORSTMAN et al. (2014) measured in older Rhizophora

sp. forests, but these were natural and not planted forests.

Figure 4-6: View into an older monocultural Rhizophora apiculata plantation at the southwest of the coast

of Soc Trang Province.

Due to the theft of a sensor during measurements in the dry season, it was chosen to disguise

the remaining sensors during the last measuring campaign at transect VC (see chapter 2.5.2, p.

45). During future long-term assessments in areas with frequent viewings of resource

collectors, sensors should also be disguised. A possibility could be to build the sensors into

bamboo poles which would be a good disguise because they are often found within the coastal

forests and are easy to make.

In addition, the discussions of the results obtained in the rainy and dry season for transect

CLD_n showed that the vegetation assessment during only one season made the interpretation

of the results difficult. Therefore it is recommended to assess vegetation whenever a

measurement campaign is conducted, especially within young forests.

At both transects on Cu Lao Dung Island the installation height of the sensors could have

been lower, even though the sites face accretion (in some parts along the coast of Soc Trang


86

Province fast and sudden accretion of sediments occur within short times, that is why 20 cm

installation height was chosen). The influence of the pneumatophores on the wave attenuation

was therefore not assessed. Future assessments should install the sensors in lower heights and

if necessary check more frequently if the accreting sediments are endangering accurate

measurements. For example, VO-LUONG and MASSEL (2006) checked their sensors daily

during their measurement time of 16 days.

In contrary to previous studies, where the reference site was part of the extended transects, in

the present work the reference site was not part of the transect (MAZDA et al. 2006, QUARTEL

et al. 2007, HORSTMAN et al. 2014). This proved to cause some difficulties during data

interpretation and comparison with the vegetated transects (see chapter 4.3, p. 83). In case of

the characteristics along the coast of Soc Trang Province, the set up and maintenance of

sensors in the mudflat at the seaward side of the transects would be accompanied by difficult

working conditions like very soft sediments. Still, this should be preferred instead of

searching for a different site without vegetation.

The now available data, especially from the transects on CLD, should be used for various

further analysis. In this work, the most landward sensors determine via the water depth

(landward sensor locations are higher) how many measurements are analysed for parts of the

transects (e.g. first 70 m). Possibilities for more analysis are further comparisons of parts of

the transects on CLD without excluding values for which not all four sensors were inundated.

This would allow more information about the influence of the vegetation growing between the

sensor locations in the seaward part of the transects to be gained. Also, single tides can be

analysed in more detail or the limits for the analysis changed (e.g. only data > 5 cm, like it

was done by QUARTEL et al. 2007). Furthermore, various correlations of wave reduction,

water depth, the significant wave height, and the mean and peak wave period can help to

further understand wave attenuation in mangroves under variable hydrodynamic conditions.

Mangroves often grow on very gently sloping shores, and no studies have been found that

have specifically looked at the effect of slope on wave energy dissipation in mangroves

(MCIVOR et al. 2012a). In the presented work, it was observed that the slope caused shoaling

between CLD_n 3 and CLD_n 4 which lead to higher remaining wave heights after 200 m

than after 140 m of the transect. More studies are necessary to take this effect properly into

account. Also, the seasonal impact of dry and rainy season observed in the presented study as

well as in HORSTMAN et al. (2014) should be further investigated.

5 Conclusion

87

5. Conclusion

This study presented field observations of wave attenuation along four cross-shore transects

along the coast of Soc Trang Province in the Mekong Delta. To measure the dampening

effect, sensors with pressure transducers were used. Due to the number of sensors available,

four study sites were identified to give information about various mangrove forests including

one reference site. The 200 m long study sites were further described concerning their

elevation changes along their profile, their sediment grain size distribution and their

vegetation characteristics (if vegetated). On Cu Lao Dung Island (CLD) in the northeast of

Soc Trang Province, two transects were established with different vegetation characteristics of

the observed Sonneratia caseolaris forests. Additionally, an extensive sandbank stretched

seaward in front of the southern study site on CLD. The third assessed transect was located in

a young Rhizophora apiculata plantation close to Vinh Chau Town, while the reference site

was set up in front of an erosion site in the southwest of the province (LH).

The aim to measure over a longer time frame to get better information about wave attenuation

was successful. However, it was not possible to measure as long as anticipated because of

various reasons. Only along the northern transect on CLD it was possible to measure in both

the southwest and the northeast monsoon season. Only the recently published observations by

HORSTMAN et al. (2014) measured the wave attenuation by mangroves over a longer time than

it was possible in the presented study.

The mangrove forests along the coast of Soc Trang Province have a valuable effect on costal

protection. Especially at the young and dense Rhizophora apiculata planting site (VC),

observed waves were attenuated completely, but further assessments on a comparable site as

well as at an older planting site are recommended to gain more data of a young plantation and

to see if this sort of monocultural reforestation is also effective in a more mature stage.

When the water depth increased, the vegetated study sites were more effective for coastal

protection than the reference site (LH). At the latter, especially at lower water depths, the

waves were attenuated more than when the water level was higher. Even though the average

wave reduction was comparable to the vegetated transects on Cu Lao Dung Island, the

remaining wave heights at the landward sensor at LH were still higher in comparison. Beside

the planted Rhizophora site, the average wave reduction was between 0.0022 and 0.0028 m-1

along the transects. At transect LH the wave attenuation rate was between 0.0007 and 0.0038

m-1

and at transect CLD_s between 0.0015 to 0.0042 m-1

(both assessed in the rainy season).

In the rainy season, waves were attenuated by 0.0007 to 0.0042 m-1

at CLD_n, while the

attenuation rate ranged from 0.0020 to 0.0043 m-1

in the dry season.

5 Conclusion

88

At the two study transects on Cu Lao Dung Island, the influence of the seaward sandbank at

the southern transect caused steeper incoming waves at the forest edge than at the northern

transect. Such a shoaling process also occurred along the transect CLD_n within the last 60 m

of the in total 200 m long transect. Along both transects, particularly within different parts of

them, the waves were attenuated by seedlings and saplings of Sonneratia caseolaris. The

influence of these young trees was never discussed in previous studies, but showed a major

influence at the forests on CLD.

Besides the use for numerical modelling, the information about the wave dampening by

mangroves is also needed in environmental protective arguments and when decisions have to

be made whether it is more reasonable to build a massive dyke or to focus on a more holistic

area coastal protection strategy that includes mudflats and mangrove forest in front of a

simple earthen dyke. The latter option includes valuable co-benefits like improved

biodiversity with options for use by local people and in monetary concerns (TEMMERMAN

2013). A holistic area coastal protection strategy like it was implemented in Soc Trang

Province by the GIZ project ‘Management of Natural Resources in the Coastal Zone of Soc

Trang Province’ offers a cheaper solution than building big (concrete) dykes alone (SCHMITT

& ALBERS 2014).

At the moment, the coastline of the Mekong Delta is in most parts naturally protected by a

band of mangrove forest. It protects social as well as economic values in the hinterland behind

the dyke. The results presented in this thesis give a better understanding about the mangroves’

protective functions and their importance to cope with the projected sea level rise due to

continuing anthropogenic climate change. However, further studies are necessary. As

observed for the measurements at transect CLD_n, HORSTMAN et al. (2014) also observed

changes in the wave reduction rate between seasons. To understand and estimate these

changes, more studies are recommended. Additionally, it is still possible to conduct further

analysis with the now available datasets for Soc Trang Province.

References

89

References

Albers, T.; Dinh, C. S. & K. Schmitt (2013): Shoreline Management Guidelines. Coastal

Protection in the Lower Mekong Delta. Management of Natural Resources in the

Coastal Zone of Soc Trang Province. Deutsche Gesellschaft für Internationale

Zusammenarbeit (GIZ) GmbH. – <http://czm-soctrang.org.vn/Publications/EN/Docs/

Shoreline%20Management%20Guidelines%20EN.pdf> (accessed: 13.10.2014).

Anderson, M. E.; Smith, J. M. & S. K. McKay (2011): Wave dissipation by vegetation.

Coastal and Hydraulics Engineering Technical Note ERDC/CHL CHTN-I-82. US Army

Engineer Research and Development Center. – <http://chl.erdc.usace.army.mil/library/

publications/chetn/pdf/chetn-i-82.pdf> (accessed: 05.11.2014).

Arcement Jr., G. J. & F. R. Schneider (1989): Guide for Selecting Manning's Roughness

Coefficients for Natural Channels and Flood Plains. United States Geological Survey

Water-Supply Paper 2339. Prepared in cooperation with the United States Department

of Transportation, Federal Highway Administration. – <http://pubs.usgs.gov/wsp/2339/

report.pdf> (accessed: 10.09.2014).

Boateng, I. (2012): GIS assessment of coastal vulnerability to climate change and coastal

adaption planning in Vietnam. – In: Journal of Coastal Conservation 16/1:25-36.

Brinkman, R. M. (2006): Wave attenuation in mangrove forests: an investigation through

field and theoretical studies. – PhD thesis James Cook University, Townsville,

Australia.

Carew-Reid, J. (2007): Rapid assessment of the extent and impact of sea level rise in Viet

Nam. Climate change discussion paper 1. – ICEM International Centre for

Environmental Management, Brisbane.

Chen, C.; McCarl, B. & C. Chang (2011): Climate change, sea level rise and rice: global

market implications. – In: Climate Change 110/3-4:543-560.

Clough, B. (2013). Continuing the Journey Amongst Mangroves. ISME Educational Book

Series No.1. International Society for Mangrove Ecosystems, Okinawa, Japan, and

International Tropical Timber Organization, Yokohama, Japan. –

<http://www.mangrove.or.jp/isme/english/books/educational-series.book1.pdf>

(accessed: 27.10.2014).

Clough, B. (2014): Site Assessment Guidelines for Mangrove Rehabilitation in Bac Lieu

Province, Vietnam. Adaptation to Climate Change through the Promotion of

Biodiversity in Bac Lieu Province, Vietnam. Deutsche Gesellschaft für Internationale

Zusammenarbeit (GIZ) GmbH.

Dasgupta, S.; Laplante, B.; Meisner, C.; Wheeler, D. & J. Yan (2009): The impact of sea level

rise on developing countries: a comparative analysis. – In: Climate Change 93/3-4:379-

388.

Davis Jr., R. A. & M. O. Hayes (1984): What is a wave-dominated coast? – In: Marine

Geology 60/1-4:313-329.

http://czm-soctrang.org.vn/Publications/EN/Docs/Shoreline%20Management%20Guidelines%20EN.pdf

http://czm-soctrang.org.vn/Publications/EN/Docs/Shoreline%20Management%20Guidelines%20EN.pdf

http://chl.erdc.usace.army.mil/library/publications/chetn/pdf/chetn-i-82.pdf

http://chl.erdc.usace.army.mil/library/publications/chetn/pdf/chetn-i-82.pdf

http://pubs.usgs.gov/wsp/2339/report.pdf

http://pubs.usgs.gov/wsp/2339/report.pdf

http://www.mangrove.or.jp/isme/english/books/educational-series.book1.pdf

References

90

Delta Alliance (2011): Mekong Delta Water Resources Assessment Studies. Vietnam-

Netherlands Mekong Delta Masterplan Project. – <http://wptest.partnersvoorwater.nl/

wp-content/uploads/2011/08/WATERRESOURCESfinaldraft.pdf> (accessed:

06.11.2014).

DPI - Department of Planning and Investment of Soc Trang Province (2014): Booklet: Soc

Trang - Potential and Investment Opportunities.

Duke, N. C.; Ball, M. C. & J. C. Ellison (1998): Factors Influencing Biodiversity and

Distributional Gradients in Mangroves. – In: Global Ecology and Biogeography Letters

7: 27-47.

EAK (2002): Empfehlungen für die Ausführung von Küstenschutzwerken durch den

Ausschuss für Küstenschutzwerke. – In: Kuratorium für Forschung im

Küsteningenieurwesen (Hrsg.): Die Küste, Heft 65. Korrigierte Ausgabe 2007. Heide in

Holstein.

Gedan, K. B.; Kirwan, M. L.; Wolanski, E.; Barbier, E. B. & B. R. Silliman (2011): The

present and future role of coastal wetland vegetation in protecting shorelines: answering

recent challenges to the paradigm. – In: Climate Change 106/1:7-29.

Gooch, J. W. [ed.] (2007): Encyclopedic Dictionary of Polymers. Pascal's law. New York. -

<http://link.springer.com/referenceworkentry/10.1007/978-0-387-30160-0_8309>

(accessed: 31.10.2014).

Hai, Y. T. N.; Duy, M. C. & K. Schmitt (2013): Soil particle-size composition and coastal

erosion and accretion study in Soc Trang mangrove forests. – In: Journal of Coastal

Conservation 17/1:93-104.

Horstman, E. M.; Dohmen-Janssen, C. M.; Narra, P. M. F.; van den Berg, N. J. F.; Siemerink,

M. & S. J. M. H. Hulscher (2014): Wave attenuation in mangroves: A quantitative

approach to field observations. – In: Coastal Engineering 94:47-62.

ICOE - Institute of Coastal and Offshore Engineering (2012): Tide-table forecasts for the VN

national hydrological stations 2013. – <http://www.icoe.org.vn/index.php?pid=551>

(accessed: 18.07.2013).

IPCC - Intergovernmental Panel on Climate Change (2012): Summary for Policymakers. – In:

Field, C. B.; Barros, V.; Stocker, T. F.; Qin, D.; Dokken, D. J.; Ebi, K. L.; Mastrandrea,

M. D.; Mach, K. J.; Plattner, G.-K.; Allen, S. K.; Tignor, M. & P. M. Midgley [eds.]

(2012): Managing the Risks of Extreme Events and Disasters to Advance Climate

Change Adaptation. A Special Report of Working Groups I and II of the

Intergovernmental Panel on Climate Change. – Cambridge University Press,

Cambridge, UK, and New York, NY, USA.

IPCC - Intergovernmental Panel on Climate Change (2013): Sea Level Change. – In: Stocker,

T. F.; Qin, D.; Plattner, G.-K.; Tignor, M.; Allen, S. K.; Boschung, J.; Nauels, A.; Xia,

Y.; Bex, V. & P. M. Midgley [eds.] (2012): Climate Change 2013: The Physical

Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the

Intergovernmental Panel on Climate Change. – Cambridge University Press,

Cambridge, UK and New York, NY, USA.

http://wptest.partnersvoorwater.nl/wp-content/uploads/2011/08/WATERRESOURCESfinaldraft.pdf

http://wptest.partnersvoorwater.nl/wp-content/uploads/2011/08/WATERRESOURCESfinaldraft.pdf

http://link.springer.com/referenceworkentry/10.1007/978-0-387-30160-0_8309

http://www.icoe.org.vn/index.php?pid=551

References

91

ISPONRE - Institute of Strategy and Policy on Natural Resources and Environment (2009):

Vietnam Assessment Report on Climate Change (VARCC). – <http://www.unep.org/

pdf/dtie/VTN_ASS_REP_CC.pdf> (assessed: 08.10.2014).

Joffre, O. (2010): Mangrove Dynamics in Soc Trang Province 1889-1965. Management of

Natural Resources in the Coastal Zone of Soc Trang Province. Deutsche Gesellschaft

für Internationale Zusammenarbeit (GIZ) GmbH. – <http://czm-soctrang.org.vn/

Publications/EN/Docs/Mangrove%20history%201889-1965%20EN.pdf> (accessed:

27.10.2014).

Kotera, A.; Sakamoto T.; Nguyen, D. K.; & M. Yokozawa (2008): Regional Consequences of

Seawater Intrusion on Rice Productivity and Land Use in Coastal Area of the Mekong

River Delta. – In: Japan Agricultural Research Quarterly 42/4:267–274.

Massel, S. R.; Furukawa, K. & R. M. Brinkman (1999): Surface wave propagation in

mangrove forests. – In: Fluid Dynamics Research 24/4:219-249.

Mazda, Y.; Magi, M.; Ikeda, Y.; Kurokawa, T. & T. Asano (2006): Wave reduction in a

mangrove forest dominated by Sonneratia sp. – In: Wetlands Ecology and Management

14:365-378.

Mazda, Y.; Magi, M.; Kogo, M. & N. H. Phan (1997a): Mangroves as a coastal protection

from waves in the Tong King delta, Vietnam. – In: Mangroves and Salt Marshes 1:127-

135.

Mazda, Y.; Wolanski, E.; King, B.; Sase, A.; Ohtsuka, D. & M. Magi (1997b): Drag force due

to vegetation in mangrove swamps. – In: Mangroves and Salt Marshes 1:193-199.

McIvor, A. L.; Spencer, T.; Möller, I. & M. Spalding (2013): The Response of Mangrove Soil

Surface Elevation to Sea Level Rise. Natural Coastal Protection Series: Report 3.

Cambridge Coastal Research Unit Working Paper 42. – <http://www.wetlands.org/

Portals/0/McIvor%20et%20al%202013%20Response%20of%20mangrove%20soil%20

surface%20elevation%20to%20sea%20level%20rise.pdf> (accessed: 25.10.2014).

McIvor, A. L.; Möller, I.; Spencer, T & M. Spalding (2012a): Reduction of wind and swell

waves by mangroves. Natural Coastal Protection Series: Report 1. Cambridge Coastal

Research Unit Working Paper 40. – <http://www.wetlands.org/Portals/0/publications/

Report/reduction-of-wind-and-swell-waves-by-mangroves.pdf> (accessed: 25.10.2014).

McIvor, A. L.; Spencer, T.; Möller, I. & M. Spalding (2012b): Storm Surge Reduction by

Mangroves. Natural Coastal Protection Series: Report 2. Cambridge Coastal Research

Unit Working Paper 41. – <http://www.wetlands.org/Portals/0/publications/Report/

reduction-of-wind-and-swell-waves-by-mangroves.pdf> (accessed: 25.10.2014).

MONRE - Ministry of Natural Resources and Environment (2012): Climate change, sea level

rise scenarios for Viet Nam. – Ministry of Natural Resources and Environment, Hanoi.

Ngo, N. C.; Pham, H.T.; Do, D. S. & N. B. Nguyen (2005): Status of coastal erosion of

Vietnam and proposed measures for protection. Overview of mangrove forests of

Vietnam. – Agricultural Publishing House, Hanoi. – <http://www.fao.org/forestry/

11286-1-0.pdf> (accessed: 06.11.2014).

http://www.unep.org/pdf/dtie/VTN_ASS_REP_CC.pdf

http://www.unep.org/pdf/dtie/VTN_ASS_REP_CC.pdf

http://czm-soctrang.org.vn/Publications/EN/Docs/Mangrove%20history%201889-1965%20EN.pdf

http://czm-soctrang.org.vn/Publications/EN/Docs/Mangrove%20history%201889-1965%20EN.pdf

http://www.wetlands.org/Portals/0/McIvor%20et%20al%202013%20Response%20of%20mangrove%20soil%20surface%20elevation%20to%20sea%20level%20rise.pdf



http://www.wetlands.org/Portals/0/publications/Report/reduction-of-wind-and-swell-waves-by-mangroves.pdf




http://www.fao.org/forestry/11286-1-0.pdf

http://www.fao.org/forestry/11286-1-0.pdf

References

92

Nguyen, A. L.; Dang. V. H.; Bosma, R. H.; Verreth, J. A. J.; Leemans, R. & S. S. De Silva

(2014): Simulated Impacts of Climate Change on Current Farming Locations of Striped

Catfish (Pangasianodon hypophthalmus; Sauvage) in the Mekong Delta, Vietnam. – In:

AMBIO, online version. – <http://link.springer.com/article/10.1007%2Fs13280-014-

0519-6> (accessed: 01.11.2014).

Pham, T. T. (2011): Mangroves of Soc Trang 1965 - 2007. Management of Natural Resources

in the Coastal Zone of Soc Trang Province. Deutsche Gesellschaft für Internationale

Zusammenarbeit (GIZ) GmbH. – <http://czm-soctrang.org.vn/Publications/EN/Docs/

Mangroves%20of%20Soc%20Trang%201965-2007%20EN.pdf> (accessed:

27.10.2014).

Pham, T. T.; Meinardi, D. & K. Schmitt (2011): Monitoring of Mangrove Forests.

Management of Natural Resources in the Coastal Zone of Soc Trang Province.

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH. – <http://czm-

soctrang.org.vn/Publications/EN/Docs/Mangrove%20monitoring%20manual%202011

%20EN.pdf> (accessed: 25.10.2014).

Quartel, S.; Kroon, A.; Augustinus, P. G. E. F.; Van Santen, P. & N. H. Tri (2007): Wave

attenuation in coastal mangroves in the red river delta, Vietnam. – In: Journal of Asian

Earth Sciences 29:576-584.

Renaud, F. G.; Thi, T. H. L.; Lindener, C.; Vo, T. G. & Z. Sebesvari (2014): Resilience and

shifts in agro-ecosystems facing increasing sea-level rise and salinity intrusion in Ben

Tre Province, Mekong Delta. – In: Climate Change, online version. – <http://link.

springer.com/article/10.1007%2Fs10584-014-1113-4> (accessed: 01.11.2014).

Schmitt, K.; Albers, T.; Trinh, H. & C. S. Dinh (2013): Site-specific and integrated adaptation

to climate change in the mangrove zone of Soc Trang Province, Viet Nam. – Journal of

Coastal Conservation 17/3:545-558.

Schmitt, K. & T. Albers (2014): Area Coastal Protection and the Use of Bamboo Breakwaters

in the Mekong Delta. – In: Nguyen, D. T.; Takagi, H. & M. Esteban [eds.] (2014):

Coastal Disasters and Climate Change in Vietnam. Engineering and Planning

Perspectives. 107-132. Amsterdam, Boston, Heidelberg.

Soc Trang sub-FPD - Sub-Department of Forest Protection (2013): Interviews during several

field trips with Nguyên Đưc Hoang, the responsible officer of the Soc Trang Sub-

Department of Forest Protection.

Spelchan, D. G. & I. A. Nicoll (2011): Mangroves Manual for Junior High and High School

Teachers. Management of Natural Resources in the Coastal Zone of Soc Trang

Province. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH. –

<http://czm-soctrang.org.vn/Publications/EN/Docs/Teachers%20Manual%20-%20Man

groves%20EN.pdf> (accessed: 05.11.2014).

Tanaka, N.; Sasaki, Y.; Mowjood, M. I. M.; Jinadasa, K. B. S. N. & S. Homchuen (2007):

Coastal vegetation structures and their functions in tsunami protection: experience of

the recent Indian Ocean tsunami. – In: Landscape Ecology and Engineering 3/1:33-45.

http://link.springer.com/article/10.1007%2Fs13280-014-0519-6


http://czm-soctrang.org.vn/Publications/EN/Docs/Mangroves%20of%20Soc%20Trang%201965-2007%20EN.pdf

http://czm-soctrang.org.vn/Publications/EN/Docs/Mangroves%20of%20Soc%20Trang%201965-2007%20EN.pdf

http://czm-soctrang.org.vn/Publications/EN/Docs/Mangrove%20monitoring%20manual%202011%20EN.pdf





http://czm-soctrang.org.vn/Publications/EN/Docs/Teachers%20Manual%20-%20Mangroves%20EN.pdf

http://czm-soctrang.org.vn/Publications/EN/Docs/Teachers%20Manual%20-%20Mangroves%20EN.pdf

References

93

Temmerman, S.; Meire, P.; Bouma, T. J.; Herman, P. M.; Ysebaert, T. & H. J. De Vriend

(2013): Ecosystem-based coastal defence in the face of global change. – In: Nature

504/7478:79-83.

Tran, Q. B. (2011): Effect of mangrove forest structures on wave attenuation in coastal

Vietnam. – In: Oceanologia 53/3:807–818.

Tran, T. N. T. & T. P. Nguyen (2014): The impact of climate change on salinity intrusion and

Pangasius (Pangasianodon Hypophthalmus) farming in the Mekong Delta, Vietnam. -

In: Aquaculture International, online version. – <http://link.springer.com/article/

10.1007%2Fs10499-014-9833-z> (accessed: 01.11.2014).

UN – United Nations (2012): Climate Change Fact Sheet: The effects of climate change in

Viet Nam and the UN’s responses. Version of 10 August 2012. –

<http://www.un.org.vn/en/publications/government-agency-publications/doc_details/

111-climate-change-fact-sheet-updated-august-2012.html> (accessed: 04.11.2014).

Vo-Luong, H. P. & S. R. Massel (2006): Experiments on wave motion and suspended

sediment concentration at Nang Hai, Can Gio mangrove forest, southern Vietnam. – In:

Oceanologia 48/1:23–40.

Vo-Luong, H. P. & S. R. Massel (2008): Energy dissipation in non-uniform mangrove forests

of arbitrary depth. – In: Journal of Marine Systems 74:603–622.

WindFinder.com GmbH & Co. KG (2014a): Wind direction distribution in percent over a

year at the station Côn Sơn Island/Côn Đao. – <http://www.windfinder.com/

windstatistics/con_son_island_con_dao> (accessed: 25.08.2014).

WindFinder.com GmbH & Co. KG (2014b): Wind speed units and wind directions

concerning their effects on sea and land. – <http://www.windfinder.com/wind/

windspeed.htm> (accessed: 19.09.2014).

Wolanski, E. (2006): Synthesis of the protective functions of coastal forests and trees against

natural hazards. – In: Braatz, S.; Fortuna, S.; Broadhead, J. & R. Leslie (eds.): Coastal

protection in the aftermath of the Indian ocean tsunami: What role for forests and trees?

Proceedings of the Regional Technical Workshop. 161-183. FAO, Khao Lak.

Zeki, D. & V. Linwood (2002): Water Wave Mechanics. – In: US Army Corps of Engineers,

Coastal & Hydraulics Laboratory [ed.] (2002): Coastal Engineering Manual Part II -

Coastal Hydrodynamics. Updated 2006. – <http://chl.erdc.usace.army.mil/chl.aspx?

p=s&a=ARTICLES;104> (accessed: 05.08.2014).

http://link.springer.com/article/10.1007%2Fs10499-014-9833-z

http://link.springer.com/article/10.1007%2Fs10499-014-9833-z

http://www.un.org.vn/en/publications/government-agency-publications/doc_details/111-climate-change-fact-sheet-updated-august-2012.html

http://www.un.org.vn/en/publications/government-agency-publications/doc_details/111-climate-change-fact-sheet-updated-august-2012.html

http://www.windfinder.com/windstatistics/con_son_island_con_dao

http://www.windfinder.com/windstatistics/con_son_island_con_dao

http://www.windfinder.com/wind/windspeed.htm

http://www.windfinder.com/wind/windspeed.htm

http://chl.erdc.usace.army.mil/chl.aspx?p=s&a=ARTICLES;104

http://chl.erdc.usace.army.mil/chl.aspx?p=s&a=ARTICLES;104

Appendices

Appendices

Appendix I – The three main mangrove species in Soc Trang Province ......................... A-2

Appendix II – Wave attenuation with concurrent water depth in previous studies ...... A-4

Appendix III – Coordinates of sensor locations and overview of transect CLD_s 2006 A-6

Appendix IV – Times of successful sensor measurements per sensor location ............... A-7

Appendix V – Parallel measurements of CLD_n and LH ................................................ A-8

Appendix VI – Parallel measurements of CLD_s and LH ............................................. A-10

Appendix VII – Correlations between Hs and r200 at transects CLD_s and LH ........... A-12


- and 3rd

-order poly. best-fit line ..................... A-13

Appendix IX – CLD_n measurement results for Tm and Tp .......................................... A-14

Appendix X – Reduction of Hs per m (r) against water depth for CLD_n ................... A-15

Appendix XI – CLD_s measurement results for Tm and Tp ........................................... A-16

Appendix XII – Reduction of Hs per m (r) against water depth for CLD_s ................. A-17

Appendix XIII – LH measurement results for Tm and Tp .............................................. A-18


A-2


App. 1: Overview of the three mangrove species Sonneratia caseolaris, Avicennia marina and Rhizophora

apiculata (SPELCHAN & NICOLL 2011).


A-3


A-4

Appendix II – Wave attenuation with concurrent water depth in previous

studies

App. 2: Wave attenuation rates r [m−1

] plotted against depths [m] at (A) the Kantang transect and (B) the

Palian transect in Thailand with Avicennia sp. and Rhizophora sp. (error bars indicate mean ± the

standard deviation) (HORSTMAN et al. 2014).

App. 3: Left: Wave height reduction in an area recently planted with Kandelia candel, showing reduction

through 6-month-old saplings (▲, area A), 3-4 year-old trees (+, area B) and 5-6 year-old trees (■,

area C) (data from MAZDA et al. 1997a, qtd. in MCIVOR et al. 2012a). Right: Wave height reduction

plotted against depth in a mangrove forest dominated by Sonneratia sp. (mangrove forest (■) and

area without mangroves (□) (data from MAZDA et al. 2006, qtd. in MCIVOR et al. 2012a)).


A-5

App. 4: Wave height reduction in a forest dominated by Kandelia candel (mangrove forest (■) and area

without mangroves (□) (data from QUARTEL et al. 2007, qtd. in MCIVOR et al. 2012a).

Appendix III – Coordinates of sensor locations and overview of transect CLD_s 2006

A-6

Appendix III – Coordinates of sensor locations and overview of transect

CLD_s 2006

App. 5: UTM coordinates of the sensor locations at all four study transects.

sensor location zone easting northing CLD_n 1 (seaward) 48P 641508 1056349 CLD_n 2 48P 641440 1056365 CLD_n 3 48P 641375 1056388 CLD_n 4 (landward) 48P 641315 1056410

CLD_s 1 (seaward) 48P 636127 1049411 CLD_s 2 48P 636120 1049477 CLD_s 3 (landward) 48P 636075 1049604

VC 1 (seaward) 48P 608832 1027963 VC 2 (landward) 48P 608756 1028151

LH 1 (seaward) 48P 593047 1022329 LH 2 (landward) 48P 592985 1022524

App. 6: Overview of transect CLD_s with sensor locations and spots of vegetation assessments. Satellite

Image from 2006 shows younger plantings in the coastal part of the transect.


A-7


App. 7: Overview of the times with successful sensor measurements for each sensor location and time

frames applicable for data analysis.

sensor location

from until plus days usable

CLD_n CLD_n 1 (coast) 10.12.2013 26.12.2013

(dry season) CLD_n 4 (land) 10.12.2013 13.12.2013 usable over 200 m 10.12.2013 13.12.2013 3

CLD_n) CLD_n 1 (coast) 07.08.2013 05.09.2013

(rainy season) CLD_n 2 13.08.2013 19.08.2013

CLD_n 3 13.08.2013 19.08.2013

CLD_n 4 (land) 07.08.2013 20.08.2013

usable all 4 sensors 13.08.2013 19.08.2013 6

usable over 200 m 07.08.2013 20.08.2013 13

CLD_s CLD_s 1 (coast) 22.07.2013 06.08.2013 20.08.2013

(rainy season) CLD_s 2 22.07.2013 30.07.2013

CLD_s 3 (land) 22.07.2013 06.08.2013 20.08.2013

usable all 3 sensors 22.07.2013 30.07.2013 8

usable over 200 m 22.07.2013 06.08.2013 15*

usable over 200 m 20.08.2013 1 (one tide)

LH LH 1 (coast) 01.08.2013 12.08.2013

(rainy season) LH 2 (land) 01.08.2013 09.08.2013

usable over 200 m 01.08.2013 09.08.2013 8

VC VC 1 (coast) 31.12.2013 11.08.2014

(dry season) VC 2 (land) 31.12.2013 03.01.2014

usable over 200 m 31.12.2013 03.01.2014 3

* after the 29.07.2013 the tidal range was not so big resulting in the necessary inundation of two sensors (1 and 3) for only

eight tides (out of 16)


A-8


App. 8: Comparison between the recorded significant wave heights (Hs) at the seaward and landward

sensors of the transects CLD_n and LH in the time of 07.08.-09.08.2013 (15-min-period; CLD_n: 104

values, LH: 143 values). The stars represent the average reduction of Hs over the length of the whole

transect (r200).

App. 9: Comparison of the reduction of significant wave heights after crossing through the mangrove

forest along the whole transect (r200) and per m (r) between the transects CLD_n and LH in the time

of 07.08.-09.08.2013 (15-min-period; CLD_n: 104 values, LH: 143 values).

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

CLD_n 1 LH 1 CLD_n 4 LH 2

r 20

0

Hs

[m]

200 m

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

0.0040

0.0045

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

CLD_n LH

r [m

-1]

r 20

0


A-9

App. 10: Reduction of the significant wave height per m between the seaward and the landward sensors of

the transects CLD_n and LH plotted against water depth in the time of 07.08.-09.08.2013 (5-min-

period; CLD_n RS: 310 values, LH RS: 429 values).

R² = 0.7208

R² = 0.4778

-30 20 70 120 170 220

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

0.0040

0.0045

0 50 100 150 200 250


r [m

-1]


LH RS

CLD_n RS

LH RS (3rd order polynomial)

CLD_n RS (3rd order polynomial)


A-10


App. 11: Comparison between the recorded significant wave heights (Hs) at the seaward and landward

sensors of the transects CLD_s and LH in the time of 04.08.-06.08.2013 (15-min-period; CLD_s: 36

values, LH: 162 values). The stars represent the average reduction of Hs over the length of the whole

transect (r200).

App. 12: Comparison of the reduction of significant wave heights after crossing through the mangrove

forest along the whole transect (r200) and per m (r) between the transects CLD_s and LH in the time

of 04.08.-06.08.2013 (15-min-period; CLD_s: 36 values, LH: 162 values).

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

CLD_s 1 LH 1 CLD_s 3 LH 2

r 20

0

Hs

[m]

200 m

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

0.0040

0.0045

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

CLD_s LH

r [m

-1]

r 20

0


A-11


the transects CLD_s and LH plotted against water depth for the time of 04.08.-06.08.2013. Note that

the second x-axis has been shifted (5-min-period; CLD_s RS: 115 values, LH RS: 485 values).

R² = 0.5675 R² = 0.3381

-95 -75 -55 -35 -15 5 25 45 65 85 105

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

0.0030

0.0035

0.0040

0.0045

0 20 40 60 80 100 120 140 160 180 200


r [m

-1]


LH RS CLD_s RS

LH RS (3rd order polynomial) CLD_s RS (3rd order polynomial)

Appendix VII – Correlations between Hs and r200 at transects CLD_s and LH

A-12

Appendix VII – Correlations between Hs and r200 at transects CLD_s and

LH

App. 14: Correlation between the initial significant wave height (Hs) at the coastal sensor and the rate of

wave height reduction at the landward sensor 200 m further inland (r200) at the transect CLD_s.

App. 15: Correlation between the initial significant wave height (Hs) at the coastal sensor and the rate of

wave height reduction at the landward sensor 200 m further inland (r200) at the transect LH.

y = -0.3425x + 0.6154 R² = 0.0328

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

r 20

0

Hs [m]

CLD_s RS

y = -0.4955x + 0.6301 R² = 0.3059

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

r 20

0

Hs [m]

LH (rainy season)

Appendix VIII – LH comparison of 2nd- and 3rd-order poly. best-fit line

A-13


- and 3rd

-order poly. best-fit line


the transect LH plotted against water depth for all assessed data (LH RS: 1,894 values). The 2nd-

and 3rd-order polynomial best-fit lines are depicted.

R² = 0.1905

R² = 0.2526

0.0

0.2

0.4

0.6

0.8

1.0

0.000

0.001

0.002

0.003

0.004

0.005

0 50 100 150 200 250

r 20

0

r [m

-1]


LH RS

LH RS (2nd order polynomial)

LH RS (3rd order polynomial)


A-14


App. 17: Comparison of the sensor measurements of Tm and Tp at transect CLD_n during the dry season

(black, 128 values) and rainy season (grey, 482 values).

App. 18: Sensor measurements of mean wave period Tm and the peak wave period Tp at transect CLD_n

during the rainy season. The black bars indicate the measurements during the timeframe of all four

sensors (218 values) while the grey bars represent the maximum available data of the sensors 1 and 4

(482 values).

0

1

2

3

4

5

6

CLD_n 1 CLD_n 4

T m [

s]

0

1

2

3

4

5

6

7

8

9

10

CLD_n 1 CLD_n 4

T p [

s]

0

1

2

3

4

5

6


T m [

s]

0

1

2

3

4

5

6

7

8

9


T p [

s]


A-15


App. 19: Reduction of the significant wave height per m between the seaward sensor CLD_n 1 and the

three landward sensors of the transect CLD_n plotted against water depth during rainy season (5-

min-period; 657 values). The absolute frequency of water depth values at CLD_n 1 is given in a grey

bar histogram on top.

R² = 0.1001

R² = 0.1771

R² = 0.5572

80 90 100 110 120 130 140 150 160 170 180

0

20

40

60

80

100

120-0.004

-0.002

0.000

0.002

0.004

0.006

0.008

80 90 100 110 120 130 140 150 160 170 180

abso

lute

fre

qu

en

cy o

f w

ate

r d

ep

th a

t C

LD_n

1

r [m

-1]


CLD_n 2 (70 m) CLD_n 3 (140 m) CLD_n 4 (200 m)

CLD_n 2 (70 m) log. CLD_n 3 (140 m) log. CLD_n 4 (200 m) log.


A-16


App. 20: Sensor measurements of mean wave period Tm and the peak wave period Tp at transect CLD_s

during the rainy season. The black bars indicate the measurements during the timeframe of all three

sensors (202 values) while the grey bars represent the maximum available data of the sensors 1 and 3

(260 values).

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5


T m [

s]

0

1

2

3

4

5

6

7

8


T p [

s]


A-17


App. 21: Reduction of the significant wave height per m between the seaward sensor CLD_s 1 and the two

landward sensors of the transect CLD_s plotted against water depth during rainy season (5-min-

period; 600 values). The absolute frequency of water depth values at CLD_s 1 is given in a grey bar

histogram on top.

R² = 0.5222

R² = 0.5411

50 60 70 80 90 100 110 120 130 140 150

0

10

20

30

40

50

60

70

80

900.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

50 60 70 80 90 100 110 120 130 140 150

abso

lute

fre

qu

en

cy o

f w

ate

r d

ep

th a

t C

LD_s

1

r [m

-1]


CLD_s 2 (67 m) CLD_s 3 (200 m) CLD_n 2 (67 m) log. CLD_n 3 (200 m) log.


A-18


App. 22: Sensor measurements of mean wave period Tm and the peak wave period Tp at transect LH

during the rainy season (497 values).

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

LH 1 LH 2

T m [

s]

0

1

2

3

4

5

6

7

8

9

10

LH 1 LH 2

T p [

s]

Erklärung

„Hiermit erkläre ich, dass ich die vorliegende Arbeit selbständig und ohne fremde Hilfe

angefertigt und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe.

Weiterhin versichere ich, dass diese Arbeit noch nicht als Abschlussarbeit an anderer Stelle

vorgelegen hat.

Die eingereichte schriftliche Fassung der Arbeit entspricht der auf dem elektronischen

Speichermedium (1005160-Sorgenfrei-Masterarbeit.pdf).

Ich stimme zu, dass meine Abschlussarbeit durch das Geographische Institut der CAU in der

Bibliothek bzw. im Wissenschaftsnetz veröffentlicht wird. Meine Urheberrechte als Autor

bleiben von dieser Einwilligung unberührt. Für in meiner Arbeit enthaltene künstlerische,

photographische u. ä. Abbildungen, die ein gesondertes Copyright besitzen, liegt mir die

Genehmigung des Rechteinhabers zur Veröffentlichung vor. Einen Sperrvermerk aus

triftigem Grund kann ich beim Prüfungsausschuss beantragen.“

Datum, Unterschrift

Assessing the role of mangrove forests in coastal...

Documents

Transcript of Assessing the role of mangrove forests in coastal...