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
Figure 3-6: Reduction of the significant wave height between the seaward and the landward sensors of
the transects CLD_s and LH plotted against water depth for all assessed data ......................... 61
Figure 3-7: Reduction of the significant wave height between the seaward and the landward sensors of
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
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 ................................... 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
2 Methods 2.1 State of existing research
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).
2 Methods 2.1 State of existing research
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
2 Methods 2.1 State of existing research
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.
2 Methods 2.1 State of existing research
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).
2 Methods 2.1 State of existing research
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.
2 Methods 2.1 State of existing research
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)
2 Methods 2.1 State of existing research
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.
2 Methods 2.1 State of existing research
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
2 Methods 2.2 Coastal processes at the shoreline of Soc Trang Province
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
2 Methods 2.2 Coastal processes at the shoreline of Soc Trang Province
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).
2 Methods 2.3 Wind and waves along the coast of Soc Trang Province
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
2 Methods 2.3 Wind and waves along the coast of Soc Trang Province
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
2 Methods 2.4 Study areas
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
2 Methods 2.4 Study areas
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.
2 Methods 2.4 Study areas
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
2 Methods 2.4 Study areas
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.
2 Methods 2.4 Study areas
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.
2 Methods 2.4 Study areas
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.
2 Methods 2.4 Study areas
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.
2 Methods 2.4 Study areas
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,
2 Methods 2.4 Study areas
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
2 Methods 2.4 Study areas
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).
2 Methods 2.4 Study areas
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
2 Methods 2.4 Study areas
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
2 Methods 2.4 Study areas
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
2 Methods 2.4 Study areas
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
2 Methods 2.4 Study areas
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).
2 Methods 2.4 Study areas
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)
2 Methods 2.4 Study areas
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.
2 Methods 2.4 Study areas
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.
2 Methods 2.4 Study areas
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.
2 Methods 2.5 Measurements of wave attenuation
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:
2 Methods 2.5 Measurements of wave attenuation
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
2 Methods 2.5 Measurements of wave attenuation
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.
2 Methods 2.5 Measurements of wave attenuation
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
2 Methods 2.5 Measurements of wave attenuation
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.
2 Methods 2.5 Measurements of wave attenuation
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)
2 Methods 2.5 Measurements of wave attenuation
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
2 Methods 2.5 Measurements of wave attenuation
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
2 Methods 2.5 Measurements of wave attenuation
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
2 Methods 2.5 Measurements of wave attenuation
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).
2 Methods 2.5 Measurements of wave attenuation
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
2 Methods 2.5 Measurements of wave attenuation
53
seaward sensor location. In this figure it is not possible to compare the single transects with
each other.
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 (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)
2 Methods 2.5 Measurements of wave attenuation
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.
3 Results 3.1 Overview
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
3 Results 3.1 Overview
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
3 Results 3.1 Overview
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
3 Results 3.2 Comparison between CLD_n, CLD_s and LH
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)
3 Results 3.2 Comparison between CLD_n, CLD_s and LH
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.
Figure 3-6: Reduction of the significant wave height between the seaward and the landward sensors of the
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
depth at seaward sensor LH 1 [cm]
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
Figure 3-7: Reduction of the significant wave height between the seaward and the landward sensors of the
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
depth at seaward sensor CLD_s 1 [cm]
r 20
0
r [m
-1]
depth at seaward sensor CLD_n 1 [cm]
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)
3 Results 3.3 Cu Lao Dung north (CLD_n)
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]
3 Results 3.3 Cu Lao Dung north (CLD_n)
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]
3 Results 3.3 Cu Lao Dung north (CLD_n)
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]
3 Results 3.3 Cu Lao Dung north (CLD_n)
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
depth at seaward sensor CLD_n 1 [cm]
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
depth at seaward sensor CLD_s 1 [cm]
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.
4 Discussion 4.1 Wave attenuation at the study sites
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
4 Discussion 4.1 Wave attenuation at the study sites
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
4 Discussion 4.1 Wave attenuation at the study sites
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
4 Discussion 4.1 Wave attenuation at the study sites
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
depth at seaward sensor CLD_s 1 [cm]
r [m
-1]
depth at seaward sensor CLD_n 1 [cm]
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.
4 Discussion 4.1 Wave attenuation at the study sites
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
depth at seaward sensor CLD_n 1 [cm]
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.
4 Discussion 4.1 Wave attenuation at the study sites
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
4 Discussion 4.1 Wave attenuation at the study sites
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,
4 Discussion 4.1 Wave attenuation at the 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
4 Discussion 4.2 Comparison with previous studies
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
4 Discussion 4.2 Comparison with previous studies
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]
depth at seaward sensor [cm]
LH RS (200 m)
LH RS (300 m)
200 m (3rd order polynomial)
300 m 3rd order polynomial)
4 Discussion 4.4 Recommendations
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
4 Discussion 4.4 Recommendations
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.
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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
Appendix VIII – LH comparison of 2nd
- 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
Appendix I – The three main mangrove species in Soc Trang Province
A-2
Appendix I – The three main mangrove species in Soc Trang Province
App. 1: Overview of the three mangrove species Sonneratia caseolaris, Avicennia marina and Rhizophora
apiculata (SPELCHAN & NICOLL 2011).
Appendix I – The three main mangrove species in Soc Trang Province
A-3
Appendix II – Wave attenuation with concurrent water depth in previous studies
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)).
Appendix II – Wave attenuation with concurrent water depth in previous studies
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.
Appendix IV – Times of successful sensor measurements per sensor location
A-7
Appendix IV – Times of successful sensor measurements per sensor location
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)
Appendix V – Parallel measurements of CLD_n and LH
A-8
Appendix V – Parallel measurements of CLD_n and LH
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
Appendix V – Parallel measurements of CLD_n and LH
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
depth at seaward sensor CLD_n 1 [cm]
r [m
-1]
depth at seaward sensor LH 1 [cm]
LH RS
CLD_n RS
LH RS (3rd order polynomial)
CLD_n RS (3rd order polynomial)
Appendix VI – Parallel measurements of CLD_s and LH
A-10
Appendix VI – Parallel measurements of CLD_s and LH
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
Appendix VI – Parallel measurements of CLD_s and LH
A-11
App. 13: Reduction of the significant wave height per m between the seaward and the landward sensors of
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
depth at seaward sensor CLD_s 1 [cm]
r [m
-1]
depth at seaward sensor LH 1 [cm]
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
Appendix VIII – LH comparison of 2nd
- and 3rd
-order poly. best-fit line
App. 16: Reduction of the significant wave height per m between the seaward and the landward sensors of
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]
depth at seaward sensor [cm]
LH RS
LH RS (2nd order polynomial)
LH RS (3rd order polynomial)
Appendix IX – CLD_n measurement results for Tm and Tp
A-14
Appendix IX – CLD_n measurement results for Tm and Tp
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
CLD_n 1 CLD_n 2 CLD_n 3 CLD_n 4
T m [
s]
0
1
2
3
4
5
6
7
8
9
CLD_n 1 CLD_n 2 CLD_n 3 CLD_n 4
T p [
s]
Appendix X – Reduction of Hs per m (r) against water depth for CLD_n
A-15
Appendix X – Reduction of Hs per m (r) against water depth for CLD_n
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]
depth at seaward sensor CLD_n 1 [cm]
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.
Appendix XI – CLD_s measurement results for Tm and Tp
A-16
Appendix XI – CLD_s measurement results for Tm and Tp
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
CLD_s 1 CLD_s 2 CLD_s 3
T m [
s]
0
1
2
3
4
5
6
7
8
CLD_s 1 CLD_s 2 CLD_s 3
T p [
s]
Appendix XII – Reduction of Hs per m (r) against water depth for CLD_s
A-17
Appendix XII – Reduction of Hs per m (r) against water depth for CLD_s
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]
depth at seaward sensor CLD_s 1 [cm]
CLD_s 2 (67 m) CLD_s 3 (200 m) CLD_n 2 (67 m) log. CLD_n 3 (200 m) log.
Appendix XIII – LH measurement results for Tm and Tp
A-18
Appendix XIII – LH measurement results for Tm and Tp
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