Wave attenuation potential, sediment properties and mangrove growth dynamics data over Guyana’s intertidal mudflats: assessing the potential of mangrove restoration works

10 Coastal mangroves, thriving at the interface between land and sea, provide robust flood risk reduction. Projected increases in the frequency and magnitude of climate impact drivers such as sea level rise, wind and wave climatology reinforce the need to optimize the design and functionality of coastal protection works to increase resilience. Doing so effectively requires a sound understanding of the local coastal system. However, data availability particularly at muddy coasts remains a pronounced problem. As such, this paper captures a unique dataset for the Guyana coastline and focuses 15 on relations between vegetation (mangrove) density, wave attenuation rates and sediment characteristics. These processes were studied along a cross-shore transect with mangroves fringing the coastline of Guyana. The data are publicly available at 4TU Centre for Research Data via https://doi.org/10.4121/c.5715269 (Best et al., 2022) where the Collection: Advancing Resilience Measures for Vegetated Coastline (ARM4VEG), Guyana comprises of six key datasets. a of g/l, that we merely fluid mud conditions across a m depth. of and fluid-mud density variations, recorded interactions in the the the Sediment properties reveal a


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Introduction 35 Mangroves belts are key ecosystems residing in the intertidal area of tropical and sub-tropical coastlines and a key component in the discussion of green-grey infrastructure (Bao, 2011;Borsje et al., 2011;Horstman et al., 2014;Beck, 2016;Blankespoor et al., 2017;Kg et al., 2017). Although, there has been much debate about the quantifiable integration of mangroves into engineered coastal protection works (Feagin et al., 2010;Tusinski and Verhagen, 2014;Van Zelst et al., 2021), the knowledge on the importance of mangroves has been highlighted and discussed 40 by several authors (Zhang et al., 2012;Tusinski and Verhagen, 2014;Sasmito et al., 2016). Mangroves have a high tolerance for harsh conditions in the intertidal area: tidal flooding, exposure to waves and varying degrees of salinity (Mazda et al., 1997;Mazda et al., 2006;Hogarth, 2015;Willemsen et al., 2015;Ratnayake et al., 2018). Forming a buffer between land and sea, in areas with and without robust sea defences, mangroves contribute to the attenuation of wave energy and to the stabilization of the foreshore (Hong Phuoc and Massel, 2006;Horstman, 2014;Pilato, 2019). 45 Waves propagating through submerged and emergent vegetation lose energy due to the turbulent flow separation induced by the stems, roots and branches, resulting in the creation of a drag force. The amount of dissipated wave energy depends upon the mangrove height, surface area, location, density, distribution and root structure (Dalrymple et al., 1984;Mcivor et al., 2012b). Despite these invaluable ecosystem services, mangrove coverage is on a rapid decline (Food and Organization, 2007;Spalding, 2010). Therefore, there is a vital need to explore in depth the physical contribution 50 of mangroves locally to reducing coastal vulnerability to hazards such as sea level rise and extreme waves in order to adequately optimize the project planning and designing phases for green-grey infrastructure (Masson-Delmotte et al., 2021;Van Zelst et al., 2021).
Along the Guyana coastline, a major mangrove coast from a global perspective, the mangrove area is under pressure from the landward side due to the changes in land use (agricultural to residential). Large parts of the mangroves have 55 been removed for fisheries and sea defences and this action has considerably reduced the capacity of the mudbanks to become attached to the coast (Anthony and Gratiot, 2012;Brunier et al., 2019). From the seaward side, the mudbank dynamics offshore influence the sediment delivery towards the mangrove area. The combined impact from landward and seaward reduces the possibilities for mangrove regeneration. In the late 18 th century the mangrove belt covered the entire Guyana coastline, with the exception of the main river outlets. However, a 72% reduction in the mangrove 60 coverage was reported in 2001, with the largest remaining intact mangrove system situated along the Waini-Pomeroon coast in Region 1 (Bovell, 2019). Large areas of the coast have become effective monoculture stands of Black mangrove (Avicennia germinans) (Augustinus, 1978), with the secondary establishment of the Rhizophora mangle (Red mangrove), and Laguncularia racemosa (White mangrove) which vary in density along the coastal plain. However, along the coastal plain the Laguncularia and Avicennia are both pioneering species colonizing parts of the mudflats. 65 Augustinus (1978) proposed that "the sling-mud along the coast of the Guianas is so little consolidated that it can be fluidized by wave action to a certain depth", thus preventing the settlement of Rhizophora. Contrastingly, Avicennia and Laguncularia embryos can establish in this regime.
With the country's initiation of the mangrove restoration programme in 2010, the Guyana Mangrove Restoration and Management Department, has taken strides towards advancing the protection and expansion of these greenbelts through 70 a combination of efforts on a national scale and site specific (planting seedlings, incorporating green-grey solutions through the combination with geotextile breakwaters, brushwood dams and, restoring the hydrological functions) (Bovell, 2019). However, Guyana's remaining mangrove forests are threatened by a range of natural and man-made factors. Natural threats to mangroves in Guyana include natural erosive and accretive cycles characteristic of the coastline of the Guianas (Amazon river to the Orinoco river) with the large-scale mud bank movements (Augustinus, 75 1978). The area in between two mudbanks is referred to as the interbank area. Behind a mudbank, the hydrodynamic conditions are mild, and the coastline can accrete, whilst the coastline of the interbank area is exposed directly to ocean waves -coastlines then erode. The man-made factors affecting mangroves include the direct loss of habitat as a result of land development for housing and urban development, agriculture and aquaculture and infrastructure development (e.g. canals, sea defence infrastructure, power lines etc). Therefore, the recognition of the vital ecosystem functions of 80 mangroves, threats and the rising cost of maintenance of the sea defence structures, is a necessity in initiating sustainable mechanisms (infrastructural, community-based, institutional), for Guyana's coastal hinterland.
These sustainable mechanisms encompass data-driven (hydrodynamics, mangrove species adaptation to changing boundary conditions and resilience to climate change, extreme condition analysis, geomorphology and mangrove mudbank monitoring) projects to steer the economical and efficient management of the coastal zone. Therefore, the 85 enhancement of the knowledge on the contributing processes determining wave attenuation in mangroves requires mechanistic studies of the propagation of waves through vegetation. Recent advances in numerical modelling explicitly resolve vegetation induced drag forces by integrating friction forces over a composition of one or several layers of rigid vertical cylinders e.g. (Vo-Luong and Massel, 2008;Best, 2017;Jacobsen and Mcfall, 2019). For a reliable representation of the vegetation, this approach compels detailed, site specific information on vegetation characteristics 90 such as stem and root diameters, vertical vegetation distribution, vegetation densities and (bulk) drag coefficients. Field data comprising accurate measurements of both hydrodynamics (wave heights, water depths and, if possible, flow velocities) and vegetation parameters is indispensable for further development of the abovementioned numerical models (Mcivor et al., 2012a;Mcivor et al., 2012b;Horstman et al., 2014;Smits, 2016). Both hydrodynamics and vegetation parameters are changing from site to site, depending on local geography, wave climate and vegetation composition. 95 Mazda et al. (1997) identified detailed vegetation parameters and quantifying the volume of submerged mangrove biomass. This concept will be deployed in the present paper. This paper addresses measured correlations between vegetation densities, wave attenuation, sediment characteristics and sedimentation rates in mangroves. It forms the first extensive dataset for the Guyana coastline describing the attenuating capacity of the mangroves as well as the morphological development. The dataset proposes some common 100 standards for comparison for mangrove regions globally. We aim to correlate the total wave attenuation through a mangrove system with the volume-percentage of submerged mangrove biomass for variable vegetation compositions and densities, pursuing an explicit relation between the mangrove density and the wave attenuation capacity of mangroves. Along with these bio-physical interactions, we aim to link the attenuated hydrodynamic conditions to sediment properties within mangroves. These correlations will be based on the results of a comprehensive field 105 campaign along a cross-shore transect through the mangrove fringe, combining measurements of the hydrodynamics and sediment dynamics at multiple positions along these transects with the collection of detailed topographic and vegetation data. the raw and processed data as well as relevant metadata and processing scripts (Best et al., 2022).

System characteristics
The data collection site is located in the fringing mangroves along the north eastern coastline of Guyana's coastal plain (as defined in Fig. 1 A and Fig. 1 B). This area forms part of the 1600 km long coastal system that is dominated by 115 extensive mudbanks which migrate westward from the Amazon Delta in Brazil to the Orinoco Delta in Venezuela in a wave-form with crests (high bed levels and concentrations) and troughs (low bed levels and concentrations). The wavelength of these mudbanks varies from 25 -50 km, with an approximate average of 40 km (Eisma, 1967;Eisma and Van Der Marel, 1971;Allison et al., 1995). The presence of the wave crest -the 'bank' phase -results in increased deposition due to the reduced turbulence and increased rates of flocculation, while the coastline erodes during periods 120 of the wave trough (the 'interbank' phase). The shoreline and nearshore subtidal areas undergo rapid changes because of the cyclic deposition and erosion patterns with the cycles averaging 30 years.
The mudbanks travel along the coast at a rate of about 1 -3 km per year. The visible intertidal part of the mudbank is characterised by a mudflat and often mangrove colonization. During the bank phase a typical transect would cover a 100-500 m mangrove belt followed by 2000 m of intertidal mudflat adjacent to a 10-12 km mudbank with levels 125 varying between 3.5 m below MSL near the mudflat to 20 m below MSL at the seaward edge.
The Chateau Margot area, as shown in Fig. 1 C, consists of a wide vegetated mudflat with a fringe of 120m -400m in width and length of 1200m. The tide is semi-diurnal with an average tidal range fluctuating between 1.17 m during an average neap tide and 2.5 m during an average spring tide, with reference to the mean sea level. The tidal range is very similar along the coast and tidal filling and emptying of the Guyana coastal system occurs more or less perpendicularly 130 to the coast so the tide hardly generates longshore currents (Van Ledden et al., 2009). The Chateau Margot hinterland is approximately 1m below sea level and the several kilometres of mudflat extending offshore area exposed during the low tide. The Guyana Coast is influenced by stable trade winds; strong winds and associated surges do not often occur and there are no tropical storms in the area. The strongest winds occur in the period December -March/April and vary between 3 -8 m/s from a predominant northeast direction. The currents in the Guyana coastal system are driven by the 135 tide, trade wind and to a lesser extent by waves. The measured currents, at 25 m depths offshore, have a magnitude between 0.1 and 0.5 m/s and a direction varying between 240 °N and 360 °N (Van Ledden et al., 2009).
Using the data obtained from NOAA WWIII Wave Models (Global and WNA) in 2004 by Haskoning-Nederland and Delft-Hydraulics (2005) and later validated with AWAC measurements, along with the ERA5 hindcast data and historical wave data from 1969 -1971 (Table 1), the following can be concluded about the wave environment: 140 • The average offshore significant wave height varies between 1.25 m in July/August to about 2.0 -2.25 m in December/January with a historical maximum of 4m.
• The peak wave period varies between 6 -10 s with an average of 7.5 s, but during the months of September to April the offshore peak period may increase to 16 s. This increase was observed 3 -5 times per year and may be attributed to tropical storms or hurricanes in the Atlantic Ocean or Caribbean Sea, as well as from severe 145 depressions originating from the northern part of the Atlantic Ocean (Van Ledden et al., 2009).
• Waves originate from the North -North-easterly directional sector and vary between 45°N and 75 °N offshore.

Rationale for transect alignment
Chateau Margot is open to the oceanic swells generated by the northeast trade winds, which propagate along the coast refracting around the large expanse of Avincennia germinans along the coastline. The area was restored by the Guyana 150 Mangrove Restoration Project with the planting of 13,000 seedlings in 2011. Over time, the Avicennia germinans has established itself as the dominant species with the secondary establishment of Lunguncularia racemosa, Rhizophora mangle and salt marshes.
Given the limitation of the wave direction to the north eastern directional sector, determined by the ERA 5 hindcast data for the years 1979 -2017, the wave attenuation measurement transect was aligned in the predominant incoming 155 wave direction, i.e. north east as shown in Fig. 2 A. The transect featured a gently sloping foreshore with an average of 1:1500. The zonation patterns can be observed in Fig. 2 B. Along this transect, eight measurement locations were selected to ensure maximum coverage of the area and representation of the variability within the fringe (Fig. 2 A).

Data Collection and Processing
All instruments were deployed five times along the transect MB. Deployments during the period 2019 -2020 spanned: 160 22 -23 November, 24 -29 November, 3 -9 December, 12 -17 December, 20 -28 December and 1 -11 January. Within the mangroves, deployment coincided with the falling tide, while deployment on the mudflat was achieved during the rising tide. A description of all of the measurement stations is shown in Table 2.

Bed level elevation survey
We mapped the topography of the collection site thoroughly using a combination of the Precision Automatic Level, 165 echo sounder and a theodolite. A permanent bench mark (PBM) was first established at the base of the earthen embankment by transferring a nearby benchmark (located at the pump station). A quadrant of 1000 m (parallel to the dyke) by 3500 m (extending offshore from the edge of the dyke) was established and subdivided using 11 transects spaced at 100 m within the mangroves while along the mudflat a 100 m spacing was used. Along each transect, measurements were then taken every 10 m extending seaward. 170 Within the mangroves, a combination of the automatic level and theodolite were used. The limited satellite coverage within the mangroves prevented the use of instruments such as the Differential GPS, and remote sensing techniques were not applied in this study (Proisy et al., 2009). Along the mudflat, the depths were attained using a single-beam echo sounder mounted on a small boat. These measurements were taken over the course of four days to complete ten of the thirteen transects along the mudflat. Due to the shallow water depths (1 -5 m) the single beam echo sounder gives 175 quite accurate results and offers significant cost saving over the multi-beam versions. Due to the shallow depths in the first 1000 m of the mudflat (maximum 0.6 -1 m at HW), this area was inaccessible with the boat and the echosounder which required a 1 m immersion depth. Depths in this section were then derived using the GEBCO bathymetry measurements for 2019 and 2020.

Sediment data collection 180
Sediment samples were collected at all eight measurement stations along the transect (Fig. 2) from the surface layers at depths between 2 -5 cm. For this campaign, two OBSs were used, T9012 and T9011. Both were tested and calibrated prior to use by Nortek. However, during the calibration process, OBS 3+ T9011 malfunctioned. This resulted in the use of the OBS 3+ T9012 to test the field sediment samples (within the mangrove & on the mudflat). As such, its calibration curve was used to convert the instrument's power signal (V) to turbidity (NTU). The turbidity and concentration values 185 were then used to form a relation, which was later used in the post processing. Water samples were also collected to corroborate the field measurements taken by the OBSs at three points near-bottom, mid-depth and near-surface. These samples were taken on the 3 December 2019, 20 December 2019, 8 January 2020 and were analysed at the laboratory of the Ministry of Public Works in Guyana in accordance with the applicable ASTM standards) and 15 July 2021. The analysis was geared towards describing the process of sedimentation of the cohesive samples (settling velocity) which 190 has been shown to be related to the sediment concentration. In saline suspensions with sediment concentrations up to 1 g/l, an increase in the settling velocity coincides with increasing concentrations, while the settling velocity in in-situ samples was observed to decrease with increasing concentrations in excess of 10 g/l.
Along the transect, sediment properties (dry and bulk density, grain size, water content and Atterberg limits, particle size distribution and organic matter content) were determined as well. These values will typically filter into the 195 parameters applied in numerical modelling practices.

Mangrove vegetation survey
High resolution vegetation data obtained during the campaign was transformed into vegetation cover, expressing the relative vegetation coverage of the horizontal surface. The quantification of the vegetation elements was achieved using 10 m x 10 m quadrants along all of the transects. The quadrants were spaced at 30 m, with the exception of the initial 200 20 m between the seawall and the first quadrant. With the vegetation cover at varying depths across the eleven transects, correlation was then determined with the observed wave dissipation.
The vegetation survey method further divided each quadrant into three equally spaced transects, along which all trees were tagged and the characteristics such as the height, diameter and species were recorded. This random sample was then used as a full representation of the characteristics within the quadrant. The diameter of the tree trunks was measured 205 at stipulated heights: 0.1 m, 0.5 m, 1 m, 1.5 m, 2 m and at breast height, 1.3 m (where possible). This procedure applied to both the Avicennia germinans and Laguncularia racemosa species, however for the Rhizophora mangrove (often very sparse and young trees) additional extensive details were recorded for the prop roots. All Rhizophora trees in the plots were counted and diameters of all roots, stems and branches were measured at the same elevations above the bed.
The overall height of the tree was also recorded with the aid of the surveying staff and a fabricated measuring rod. 210 For the seedlings within the quadrant, a 1 m x 1 m quadrant was used at three random locations within the 10 m x 10 m vegetation plot to count the number of seedlings and pneumatophores, and determine their height and diameter. The height and diameter of the pneumatophores were measured for 20 randomly chosen pneumatophores per subplot (1 m x 1 m). In the post processing, the diameters measured were categorized in three groups: 0 -40 mm, 40 -90 mm and 90 -130 m. 215 Salinity and temperature readings were taken at three randomly spaced points within the transect. This process was documented with scaled photographs for qualitative characterization of the geometry and the interaction with the hydrodynamics.
The vegetation data was then transformed into spatially explicit vegetation densities. In achieving this, the data was first transformed into the total horizontal coverage of vegetation elements within each of the 10 m x 10 m quadrants at 220 different levels above the bed. After which, the volume of the vegetation within a water column of arbitrary depth was calculated by integrating the horizontal vegetation coverage over the depth. The density was then expressed as the relative vegetation volume compared to the total submerged volume.

Hydrodynamic data collection
Eight (8) high quality sensors, consisting of a mixture of pressure transducers (PTs) and Acoustic Doppler Velocimeters 225 (ADVs) were deployed for collecting wave data, water levels and currents along the transect MB shown in Fig. 2. These sensors are robust and the internal memory and battery housing facilitated autonomous data collections for periods of up to several weeks, depending on the sampling frequency and battery quality.
Six (6) pressure transducers (PTs) were used to collect the water levels and wave heights. These were deployed at stations MB 1, MB 2, MB 4, MB 6, MB 7 and MB 8 over the seven weeks of the field campaign. The setup of the wave 230 loggers, in addition to the ADVs, is intended to capture the wave attenuation over the mudflat as well as within the fringe, and the fringe potential to attenuate both the swell and infragravity waves. As a result, the PTs were configured to record pressure data continuously at a 5 Hz sampling frequency. The PTs were placed at heights above the bed ranging between 5 -15 cm, but most deployments tried to maintain a height of 6 cm above the bed in order to start the data collection at shallow water depths. 235 Two ADVs were used to collect water level data, flow velocity and flow directional data as well as data regarding the suspended sediment concentration. The ADVs collected data at a sampling interval of 30 mins and a frequency of 4 Hz over 20 min. bursts lengths. The ADVs were deployed at two locations, MB 3 and MB 5 over five deployments spanning the period of seven weeks. This allowed for the cleaning of the sensors from blockages due to the high sediment concentrations. The probe was placed at 13 cm above the bed within the mangroves to ensure that it would be immersed 240 during neap/ low tide. Station MB 3 was located within the mangrove fringe while station MB 5 was located on the mudflat. The frames were positioned perpendicular to the prevailing tidal flow direction to ensure that minor disturbances, if any, were created.
Next to these high-frequency pressure transducers, low-frequency pressure (and temperature) loggers (Conductivity, Temperature & Depth loggers (CTD)) were deployed during the field campaign. The CTD logger (Fig. 4 B) was set-245 up at station MB 1 and collected pressure data with a sampling interval of 5 minutes.

Processing hydrodynamic data
Obtained pressure data from the pressure transducers were corrected for the atmospheric pressure prior to the conversion to water depths using linear wave theory. Meteorological data correlated well with the measurements of the CTD and showed that the atmospheric pressure ranged between 10.26 -10.45 m H2O. 250 For both sets of instruments, the resulting data is pre-processed using filtering, averaging and data correction, similar to Horstman (2014). Inaccurate data is removed by only selecting data above a mean correlation threshold for the return signals of the ADV's receiver probes, which is 80% (Chanson et al., 2008;Colosimo et al., 2020). Due to the filtering procedure major disturbances (e.g. fishing boats) and minor disturbances (e.g. air bubbles) were removed. The filtered data showed continuous data without gaps throughout the duration of the deployment. 255 Spectral analysis of the obtained wave signal was executed according to the Hegge and Masselink (1996) Fourier analysis scheme, resulting in wave energy density spectra for each burst. From these energy density spectra, the total significant wave height Hm0 (m), mean wave period Tm01 (s) and total wave energy Etot (J/m 2 ) were derived for each burst of the wave data. Subsequently, data bursts were selected for time spans during which the entire transect was flooded. This selection allowed the assimilation of coherent datasets of the wave characteristics at the measurement 260 stations. The swell (>0.04 Hz) and infragravity (< 0.04 Hz) wave bands were then selected using a bandpass filter to ascertain their presence and magnitude within the mangrove fringe.

3
Mangrove characteristics and environmental conditions

Mangrove density
The measurements showed on average 12 seedlings per quadrant along every transect. There were six to eight quadrants 265 per transect allowing for the properties of a total of 1056 trees, both black and white mangroves, to be captured. The Avicennia germinans was seen to be the dominant species with the Languncularia racemosa and the Rhizophora mangle being secondary species (Fig. 5). However, further west of the central transect (transects 10 and 11), the Languncularia racemosa, was equally balanced with the Avicennia germinans in the outer quadrants as seen in Fig. 5. Noticeably, the Languncularia species had very thin stem diameters as compared to the Avicennia but the heights were comparable. 270 The data revealed the relationships between the mangrove height and stem diameter for the two-dominant species ten years after the restoration. The maximum stem diameter and height for the Avicennia were 12 cm and 1300 cm respectively, while the Languncularia was characterized by a maximum height and diameter of 1200 cm and 7 cm respectively.
The high-resolution data were transformed into the vegetation parameter AM/V (where AM is the area of the obstacles 275 and V is the total submerged volume), which according to Mazda et al. (1997), is key in representing the hydrodynamics in mangrove fringes. In addition, Petryk and Bosmajian (1975) defined the vegetation density by AM/V, but noted slight variations with increasing water depths. The vegetation parameter was calculated from measurements of the number trunks, pneumatophores, and their elements in all quadrants (Fig. 6). For the calculation of the area, the shapes of the trunks and pneumatophores were simplified to a cylinder. 280 At the mean height of the pneumatophores, approximately 5 cm, the vegetation coverage for the Avincennia germinans was quite high ranging from 16 -50 % (Fig. 6) due to the dense cover of the roots varying from 135 -400 pneumatophores/100 m 2 (Fig. 5 C and Fig. 6). Above this layer, at 10 cm, the vegetation coverage reduces gradually due to the large diameters for the base of the trees and multiple shoots for one plant. A sharp decrease, to less than 5%, is then observed in the vegetation coverage with increasing heights since the trunks taper off. 285

Sediment properties & bathymetry
Along the main transect, three sets of disturbed samples were collected at each of the eight (8) measurement stations indicated in Fig. 2 (3 sets in vegetation and 5 sets along the bare mudflat). Within the mangrove fringe, samples were taken from the upper layers of the forest floor (consolidated mud). While on the mudflat, due to the fluid mud layer, samples were retrieved from the interface of the fluid mud and consolidated mud layers. 290 Sediment characteristics were averaged over two measurements for each test. In the laboratory, sediment samples were dried to determine bulk density (g/cm 3 ). Within the fringe, the bulk density ranged from 1.39-1.66 g/cm 3 , while the density along the mudflat ranged from 1.53-1.79 g/cm 3 . These measurements correlated well with the range of values produced by field measurements along the Suriname coastline (Wells, 1977) . Grain size was determined using a combination of the American Society for Testing and Materials standards: ASTM D6913 Wet sieve method and the 295 ASTM D7928 Hydrometer Analysis, enabling the determination of silt/clay particle diameter size between 0.0003 mm and 0.05 mm.
All stations consistently showed that sediment was of a silty clay nature with varying degrees of silt. The grain size distribution (Fig. 7) shows a high amount of clay and silt particles in the mangrove belt (MB 1, MB 2, MB 3) and along the mudflat, where flow velocities are higher than at the back of the mangrove. The organic matter content (Table 3) is 300 highest in the centre of the mangrove, similar to other studies e.g.  and lower values can be found at the back of the mangrove and in the creeks. Additionally, there is some variability in the silt/ clay content in the mangroves, increasing towards the edge, but this stabilizes and is consistent along the mudflat.
The particular state of consistency of any particular sediment sample depends primarily upon the amount of water present in the soil-water system thereby making the behaviour of sediment directly related to the amount 305 of water present. The Atterberg limit (ASTM 4318-10) represents a water content at which the sediment changes from one state to another. The values of the Atterberg limits of the sediment samples are shown in Table 3. The liquid limit ranges from 35% to 99% with an average liquid limit of 61%. The plasticity index, which indicates the degree of plasticity of a soil, ranges from 15% to 66% with an average plasticity index of 36%. From the results obtained, MB 1, MB 2 and MB 3 can be classified as soils with high plasticity because their plasticity indices 310 ranged between 47 to 66; while stations MB 4, 5, 6, 7 and 8 range from medium to high plasticity clays.
Shallow water waves propagating over the muddy bottom maintains a large volume of sediment in suspension. Wells (1977), showed that during mudbank periods, the suspended concentrations are orders of magnitude higher than during the interbank periods. High concentrations were recorded by the optical backscatter instruments (OBS 3+) and frequently exceeded the measuring capacity of the instruments (< 5 g/l) (Downing, 2006). The presence of a fluid-mud 315 bottom, with higher suspended concentrations, changes the form of incoming waves from sinusoidal to a solitary-like appearance (flatter troughs). Sediment suspension by waves is more likely to occur than by tidal currents in this region due to the solitary wave characteristics over the fluid mud, where high concentrations are observed (Wells et al., 1978;Wells and Coleman, 1981a). Wave shear stresses generally exceed the stress by tidal currents, and concentrations decay seaward. 320 The OBS 5+ (and similar capacity turbidity sensors with a capacity of 50 g/l or higher) is therefore the recommended choice for data collection campaigns along the north eastern South American coast. Field samples taken confirmed the observations from the instruments as concentrations varied between 40 g/l to 60 g/l along the intertidal mudflat, which is characteristic for fluid mud (Fig. 8). Fluid mud refers to the sediment-water mixture in which the sediment concentration is greater than 10 g/l (Krone, 1962;Wells and Coleman, 1981b). The mean settling velocity was 325 determined graphically and ranged between 0.02 mm/s to 0.2 mm/s. There was limited variability in the concentration across the depth, and as such the 2D-depth averaged representation is apt at capturing the nearshore processes for similar mangrove-mudflat systems. For the entire coastal plain, the movement of the mudbanks offshore dictates that spatial and temporal variations in the concentrations are expected.
The measured mud concentrations (similar to Anthony (2016)), generally range from very high-suspended sediment 330 concentrations (1-10 g/l), and through the fluid mud, to settled mud, ranging from under-consolidated (< 650 g/l) to over-consolidated beds (>750 g/l). Therefore, this shows the extensive depth of the fluid mud layer along the Guyana coast which often exceeds 1 m and potentially covers the entire water depth.

Wave climate
Time-averaged wave energy density spectra at all cross-shore monitoring positions are plotted for each deployment 335 (periods with spectra for less than eight positions are due to malfunctioning sensors). Obtained wave data cover a wide range of tidal conditions, although neap tides are poorly represented in the data as water levels remained too low to flood the entire transect.
Despite the high-energy period observed over the last deployment ( Fig. 9 and Fig. 11) there were overall periods with moderate wave conditions (Fig. 10). The wave energy density spectra at the front most monitoring positions at both 340 transects were found to be independent of the local water depth. In general, the observed wave energy was low during the first two periods of data collection (Fig. 9), but heightened from mid December when wind directions turned onshore.
Wave energy increased during the subsequent deployments with the most energetic conditions being observed in the December/January deployment (Fig. 9, Fig. 10). This may be attributed to tropical storms or hurricanes in the Atlantic Ocean or Caribbean Sea, as well as from severe depressions originating from the northern part of the Atlantic Ocean 345 ((Van Ledden et al., 2009).
The wave spectra for the Chateau transect were not characteristically uni-modal but the mean wave periods varied mainly between either 2 -7 s for all deployments. Offshore, the wave climate is characterised by average peak periods between 6 and 10 s with highs of 16 s during the period of September to April. The transect was typically exposed to swell waves (10 -20 s) and infragravity waves (25 -250 s) during the deployments. The infragravity waves ranged 350 between 5 -10 cm in height while the swell waves were 20 -80 cm.
Local increases of the wave energy density in the swell wave regime was observed between consecutive sensors during the measurements ( Fig. 9 and Fig. 10). Such an increase of the energy density of the low-frequency component of the energy density spectra can be induced by (i) enhanced shoaling of shorter waves and (ii) energy transfer to lower frequencies by nonlinear wave-wave interactions (Elgar and Raubenheimer, 2008). 355

Cross-shore changes in wave properties
Changes of the wave characteristics along the transects were obtained from the wave energy density spectra for each data collection period (Fig. 10). Typical incident waves did not exceed 70 cm in height. While the significant wave heights decreased along the transect, mean wave periods were slightly increasing towards the back of the forest. Mean wave periods changed from 4.0-4.5 s to 4.0-5.0 s (Fig. 11). 360 This is corroborated by the wave energy density spectra presented in Fig. 9, showing that the shorter sea waves (i.e. frequencies > 0.1 Hz) lost more energy when propagating into the forest than the longer swell waves (< 0.1 Hz). Hence, shorter waves got attenuated more effectively when propagating into the forest, as opposed to the longer-period swell waves that hardly got attenuated, giving rise to an increase of the mean wave periods.
Observed wave heights to measured water depths ratios ranged from 20% along the mudflat to 70% within the 365 mangroves (Fig. 11). Wave breaking occurs for wave heights exceeding 60 -83% of the water depth (Battjes and Stive, 1985). Hence, wave breaking could not have contributed significantly to the wave energy losses along the mudflat portion of the transect, but was contributed to some extent within the mangroves. Therefore, we conclude that the observed attenuation of wave energy must have been caused by drag and friction forces induced by the mangrove vegetation and interactions with the forest floor along with minimal wave breaking. 370

Wave attenuation potential for mangrove belt
Our field observations represent relatively mild conditions with significant wave heights ranging from 0.5 -1.0 m. Figure 12 provides a clear depiction of the attenuating capacity of both the mudflat and the mangrove vegetation. Due to bed friction, the wave height attenuated significantly by some 85 -90 % over the mudflat from offshore to the edge of the mangrove fringe. The mangroves are able to reduce the height further by 50% within the first one-third (120 m) 375 of the fringe width. This corresponds well with other field measurements collected in forests in Vietnam (Mazda et al., 2006;Vo-Luong and Massel, 2008;Cuc et al., 2015). At the most landward point (~ 340 -360 m), this section is predominantly dry except for spring tides. However, even during spring tides, the waves heights did not exceed 0.1 -0.24 m within the landward sections of the fringe during the field campaign. This corresponds to a further 30 -40 % decline in the wave height. A contrasting difference between the Guyana coast and those of Vietnam lies in the extensive 380 layers of fluid mud which certainly contribute towards the attenuating capacity of these mudflats (Kit, 2016;Gratiot et al., 2017).
The rate of wave height reduction (α) per unit distance in the direction of wave propagation is defined as the reduction in wave height (Δ Hs,0) as a proportion of the initial wave height (Hs,0) over a distance (∆x) travelled by the wave (see Eq. (1)). The unit of 'α' is m -1 . For example, when wave height is reduced by 1% over a distance of 1 m, then r = 0.01 385 m -1 as calculated using Eq. (1).
Using Eq. (1), the attenuation rate within the Chateau Margot mangrove fringe corresponds to a range of 0.002 -0.0032 m -1 . On the mudflats the attenuation rate varies between 0.0003 -0.0004 m -1 .

Data availability 390
The data presented in this paper have been published at 4TU Centre for Research Data (4TU.ResearchData) following the FAIR principles (Wilkinson et al., 2016), and can be accessed via https://doi.org/10.4121/c.5715269 (Best et al., 2022) for the public download of the entire collection: Advancing Resilience Measures for Vegetated Coastline (ARM4VEG) , Guyana. The collection contains five key datasets including the in-situ concentration data, wave data, bathymetry, sediment properties and mangrove characteristics (Best et al., 2022). The datasets are published in a 395 combination of .mat, .xlsx and .xyz formats with the file naming convention which specifies the measurement location along transect MB. A map is available, indicating the measurement locations of each dataset. The underlying raw data as produced by the instruments together with the scripts with metadata are maintained under version control. Processing scripts are written in MATLAB code and the metadata in the mat. files specify the date and version number of underlying raw source data and in order to provide replicable information. Mean observed significant wave heights decreased (on average) by 50 -76% by bed friction and mangrove drag along the Chateau Margot transect, which was 1620 m long. Wave attenuation was most efficient for short sea waves (< 10 s), while swell waves (10 -20 s) and infragravity waves ( 25 -250 s) tended to maintain their energy. 415 The generalized total wave attenuation rates in the mangrove belt, obtained by the gradient of the relation between wave height reduction and incident wave height, ranged between 0.002 and 0.0032 m − 1 while it varied between 0.0003 and 0.0004 m -1 on the mudflat. These rates showed a significant positive correlation with the volumetric vegetation density.
Further, studies show that a minimum attenuation capacity of 50 % is needed to prevent wave reflection at the inner wall of the dike which would lead to increased scour volumes Van Wesenbeeck et al., 2021). 420 These features can substantially reduce costs for retrofitting of levees under changing future wave climates.
These findings emphasize the coastal defence function of mangroves and provide a starting point for modelling studies to investigate the processes contributing to the attenuating and sediment trapping capacity of mangroves.

Author contributions
This study forms a section of the doctoral research of USNB. Therefore, USNB coordinated the set-up of the campaign 425 and the frames, coordination and analysis under the supervision of MvdW, JD, JR, BCvP and DR. USNB analysed the data from the pressure transducers, acoustic doppler velocimeters (ADVs), optical backscatter sensors and Conductivity, Temperature & Depth logger (CTD) with the guidance from JR and DR. JR also assisted in carrying out quality checks for the processed data. The collection and processing of the mangrove characteristics and sediment properties was organized and achieved by USNB. While the data analysis was done jointly between USNB, JD, JR and 430 MvdW. The data storage on the repository and visualizations were done by USNB. All authors participated in the writing and the proofing of the paper. Funding was acquired by DR and MvdW with the support Deltares.     • Mangrove fringe should be homogeneous around the measurement locations, i.e. there should not be any rivers, tidal creeks, sudden elevation changes or other disturbances.
• The transect should consist a mudflat in front of the forest, a forest fringe and an inner mangrove part , which should all be convincingly flooded (more than 0.20m, because of the equipment) during spring tide.