Variability of suspended particulate matter concentration in ... - LEGOS

Remote Sensing of Environment 150 (2014) 218–230

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Variability of suspended particulate matter concentration in coastal waters under the Mekong's influence from ocean color (MERIS) remote sensing over the last decade Hubert Loisel a,b,c,⁎, Antoine Mangin d, Vincent Vantrepotte c, David Dessailly c, Dat Ngoc Dinh b, Philippe Garnesson d, Sylvain Ouillon a,e, Jean-Pierre Lefebvre a, Xavier Mériaux c, Thu Minh Phan f a

Institut de Recherche pour le Développement (IRD), Université de Toulouse, UPS (OMP), CNRS, UMR 5566 LEGOS, 14 av. Edouard Belin, 31400 Toulouse, France Space Technology Institute (STI), Vietnam Academy of Science & Technology (VAST), 18 Hoang Quoc Viet, Cau Giay, Hanoi, Viet Nam c Laboratoire d'Océanologie et de Géosciences (LOG), Univ. du Littoral Cote d'Opale (ULCO), CNRS UMR 8187, 28 avenue Foch, BP 80, 62930 Wimereux, France d ACRI-ST, 260 Route du Pin Montard, 06904 Sophia-Antipolis, France e University of Science and Technology of Hanoi (USTH), 18 Hoang Quoc Viet, Cau Giay, Hanoi, Viet Nam f Institute of Oceanography, VAST, 1 Cau Da, Nha Trang, Viet Nam b

a r t i c l e

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Article history: Received 10 December 2013 Received in revised form 25 April 2014 Accepted 13 May 2014 Available online xxxx Keywords: Suspended sediment Mekong Delta Ocean color remote sensing Climatology

a b s t r a c t Spatio-temporal patterns of suspended particulate matter, SPM, in coastal waters under the Mekong's influence are examined through remote sensing data collected from January 2003 to April 2012 by the MEdium Resolution Imaging Spectrometer (MERIS) at full spatial resolution (300 × 300 m2). The first SPM climatology over this region is provided and the SPM temporal variation schemes (irregular variability, seasonal variability, and long term trend) are described using the Census-X-11 time series decomposition method. The different spatiotemporal patterns are then analyzed with regard to regional oceanographic and hydrologic conditions. The origin of the processes controlling the seasonality of the Mekong Delta plume is characterized. The increase of turbidity observed from June to December, starts with the Mekong sediment inputs which are maximum during the summer monsoon (the water discharge reaches its maximum in September/October). While the Mekong water discharge decreases, the concentration of suspended sediment keeps increasing in coastal waters during the following two/three months (November to January). This increase is explained by resuspension effects occurring in the shallow coastal areas. Due to higher wave energy and oblique orientation of the waves breaking near the coast, the winter monsoon triggers a high level of agitation and high value of resuspended material concentration which are submitted to a longshore current directed towards the South–West. Deposition (in front of the Delta) and erosion (northern and southern areas of the delta) areas are identified in good agreement with recent results obtained from a prognostic model. While the temporal variability is strongly dominated by the seasonal component, a long term trend of about −5% SPM concentration per year is observed in the pro-delta area and is attributed to the decrease of the Mekong river sediment output during the high flow season. © 2014 Elsevier Inc. All rights reserved.

1. Introduction The Mekong river, which is the largest river in Southeast Asia, runs through five different countries (China, Myanmar, Thailand, Lao PDR, and Cambodia) before entering in Vietnam through its Delta (Fig. 1). The Mekong Delta is one of the largest deltas of the world and is bounded by the gulf of Thailand to the West and the Eastern Sea of Vietnam to the East (Coleman & Roberts, 1989; Nguyen, Ta, & Tateishi, 2000). Its spatial coverage is generally considered to begin at Phnom Penh in Cambodia where the river divides into its two main branches, the Mekong and the Bassac, which respectively divide into six and three ⁎ Corresponding author. Tel.: +33 3 21996420; fax: +33 3 21996401. E-mail address: [email protected] (H. Loisel).

http://dx.doi.org/10.1016/j.rse.2014.05.006 0034-4257/© 2014 Elsevier Inc. All rights reserved.

main channels. Similarly to the other deltaic systems, the Mekong Delta is very sensitive to natural forcing and human activities (Saito, 2000; Syvitski et al., 2009; Xue, Liu, & Ge, 2011). One may cite, among others, the well marked Monsoon regime which strongly conditions the hydrologic and the oceanographic patterns of the coastal region, the intrusion of saline oceanic waters in the delta plain, the land use change, the fast growing population and related environmental disturbances, and the recent construction of many dams as well as the river channel sediment extraction which all directly impact the sediment supply and redistribution. A recent review of the climate and environmental change of the Mekong Delta is provided by Renaud and Kuenzer (2012). The annual sediment flux of the Mekong into the ocean is estimated to about 160 million tons (Milliman & Ren, 1995), making this river to

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Fig. 1. Map of the study area. The 10 m isobaths are taken from Becker et al. (2009). The location of the Chau Doc station, where the discharge of the Bassac branch is measured is indicated by a red star. The blue frame represents the area for which the wind and waves/swell data values are extracted (see Fig. 4). The small red frame corresponds to the region where SPM extraction will be performed for comparison with the river discharge measured at the Chau Doc station (see Fig. 9). The three pink lines represent the transects along which satellite data will be extracted (see Fig. 11). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

belong among the 10 largest suppliers of sediment over the world's ocean (Milliman & Meade, 1983). Note that this estimation is based on data recorded before the construction of dams on the Mekong river, which retain about 32 to 41 millions of tons of sediment per year (Kummu, Lu, Wang, & Varis, 2010). While part of this tremendous concentration of sediment is accumulated in the subaqueous delta region, the other part is dispersed towards offshore waters and along the coast. The spatial and temporal patterns of the river-sediment dispersal system within the coastal areas are very complex being modulated by many factors including river outflows, physical oceanographic conditions, bathymetry of the subaqueous delta, and the nature of the sediments themselves. The monitoring of suspended particulate matter, SPM, in coastal areas under the influence of the Mekong river is essential for both ecological and geomorphologic purposes. Riverine suspended sediments reaching the oceanic waters bring a significant amount of new nutrients in coastal waters, and control the availability of light in the water column for photosynthesis and visual predators. These two features tightly control phytoplankton primary production and thus the associated dynamics of higher trophic levels. While these new sediments allow phytoplankton primary production to be maintained, a loss of sediments and their associated nutrients can reduce this productivity, but also alter phytoplankton bloom timing. These two phenomena are known, from both open ocean and coastal waters studies, to affect recruitment and fish catches (IOCCG, 2009). The impact of the Mekong river discharge on the fisheries productivity in coastal waters has been known for a long time (Lagler, 1976). The coastline stability, as well as the maintenance of healthy mangrove largely present in this region (Gebhardt, Dao Nguyen, & Kuenzer, 2012), are also greatly related to sediment input. For all these reasons, the assessment of the spatiotemporal SPM patterns, which reflects and integrates the combined effects of natural and anthropogenic phenomena, is essential for a better understanding of coastal marine areas in the Southern part of Vietnam. Previous works, based on episodic geological and sedimentary field studies, provided spatial details of the Mekong river sediment dispersal

in the subaqueous deltaic system (Xue, Liu, DeMaster, Nguyen, & Ta, 2010 and references therein). These sedimentary field studies mainly focused on the evolution of the Mekong Delta over long geological time periods. A recent study has however documented the modern sediment distribution and accumulation rates on the seafloor in the subaqueous Mekong Delta (Unverricht et al., 2013). Numerical studies were also performed using various models to assess the Mekong's sediment transport and deposition in coastal areas of the Eastern Sea of Vietnam (Hordoir, Nguyen, & Polcher, 2006; Xue, He, Liu, & Warner, 2012). While these studies provided fundamental results on the origin of the spatio-temporal sediment patterns in relation with the hydrologic and oceanographic conditions, they only allow for a description restricted to limited time periods. Besides, the realism of these numerical simulations is tightly linked to the inherent approximations required for modeling the very complex processes involved. As a matter of fact, sediment model solutions are sensitive to the parameterization of sediment properties, as well as to the settling velocity of flocculated sediments (Xue et al., 2012). Some shallow water processes, such as energy dissipation related to wave breaking, are also not systematically taken into account (Hein, Hein, & Pohlmann, 2013). The adopted parameterizations are therefore not entirely representative of the situation encountered especially over the long time period. Ocean color remote sensing data allow the optically significant component in surface waters to be assessed (Gordon & Morel, 1983; IOCCG, 2000; Loisel, Vantrepotte, Jamet, & Dinh, 2013). Satellite data are not as accurate as in situ measurements and are limited to the surface layer. However, the latter limitations are largely compensated by the spatial and temporal coverage offered by satellite observations. The present study aims at describing and analyzing the actual suspended particulate matter, SPM, spatio-temporal patterns of the surface coastal waters under the influence of the Mekong river. Specifically, we propose to assess the seasonal and inter-annual variability, as well as the trend of SPM of the coastal waters under the influence of the Mekong river over the last decade using ocean color remote sensing data collected

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Fig. 2. Location of the in situ data points used for the development of the new algorithm.

by the MEdium Resolution Imaging Spectrometer (MERIS) from January 2003 to April 2012. In the present study, the consistency of various SPM inversion algorithms for describing SPM spatio-temporal variations in the Mekong Delta has been specifically assessed. In a second step, SPM climatology of the coastal waters under the Mekong's influence has been generated and discussed. Finally, these spatio-temporal patterns have been analyzed with regard to the oceanographic and hydrological variability measured over the same period of time. 2. Data and method 2.1. Ocean color data and algorithms Ocean color data collected by the MEdium Resolution Imaging Spectrometer (MERIS) on board the European Space Agency (ESA)'s Envisat platform are used for the present study (Rast & Bézy, 1995; Rast, Bézy, & Bruzzi, 1999). This data set is entirely composed of full spatial resolution products (300 × 300 m2 at nadir), and covers the

01/01/2003 and 30/04/2012 time period. About 2000 MERIS full resolution (MERIS-FR) images have been recovered from the ESA archive system recovery. Yearly availability of MERIS-FR is fluctuating mainly because of mission programming. For instance, the year 2003 is characterized by a slightly lower number of full resolution images with respect to the other years. The standard masks developed for the processing of the MERIS data over coastal waters have been applied: cloud masking, water detection, no absorbing aerosols, no high and medium sun glint. Unfortunately, whitecap correction is not performed by the MERIS standard algorithm. However, whitecaps generally occur when wind speed is above 8–10 m·s−1 (Monahan & O'Muircheartaigh, 1980), which only covers a small fraction of the conditions encountered in our study area (see Fig. 4b). Additionally, while the impact of whitecaps can be important for clear waters, it is greatly reduced for turbid waters such as those encountered in the present study. The remote sensing reflectance, Rrs, used as an input parameter in SPM algorithms, is derived from the top of atmosphere measurements using the MERIS 3rd reprocessing atmospheric correction algorithm

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for coastal waters (Doerffer, 2011; ESA, 2012). Considering the very limited in situ data available in the Mekong Delta region for algorithm development and validation purposes, three inverse methods, based on different assumptions, were used to derive SPM, and their respective spatio-temporal patterns were compared. The first algorithm is based on a semi-analytical relationship between SPM and the reflectance in the red part of the spectrum (Nechad, Ruddick, & Park, 2010). In this spectral domain the absorption coefficient is fully dominated by pure water, the Rrs variability in the latter spectral domain is driven by the particulate backscattering coefficient, bbp, which is directly proportional to the SPM concentration at first order (Neukermans, Loisel, Mériaux, Astoreca, & McKee, 2012). The SPM relative errors using the MERIS 665 and 681 nm bands have been estimated to be lower than 40% (Nechad et al., 2010). Despite the fact that this algorithm has been developed from an in situ data set collected in the southern North Sea, it has already shown some valuable performances in other coastal regions (Vantrepotte, Loisel, Dessailly and Mériaux, 2012). The second algorithm is based on a Neural Network algorithm (Doerffer & Schiller, 2007). The SPM values are obtained from the inversed particulate scattering coefficient at 442 nm, bp, assuming a specific particulate scattering coefficient, bp/SPM, value of 0.578 g− 1 m2. This formalism is the standard MERIS SPM product distributed by the Kalicôtier web site (http://kalicotier.gis-cooc.org/), which provides MERIS-FR products over five different coastal sites from the 3rd reprocessing. A match-up exercise, based on a limited number of data points (ESA, 2012), has shown that the SPM retrieval values using this algorithm are in a relatively good agreement with in situ data (determination coefficient, r2, of 0.7, and slope, in log scale, of 0.76). The ESA (2012) validation data set is representative of case 2 waters where inherent optical properties are not only driven by phytoplankton and associated material but also by other optically significant components such as suspended sediments. In situ SPM concentration varies between 0.01 and 150 g·m−3 in this validation data set. Finally, the third algorithm is based on the approach developed by Tassan (1994), for which the wavelengths and the values of the coefficients have been adapted for the present study. This algorithm applies the Rrs values at 490, 560, and 665 nm through the following function: SPM ¼ 10 ½a þ b ðRrs ð560Þ þ Rrs ð665ÞÞ−c ðRrs ð490Þ=Rrs ð560ÞÞ

ð1Þ

where a, b, and c are constant coefficients (a = 0.695, b = 27.29, and c = − 0.638). For the present study, this function has been fitted on a

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large in situ data set, gathering measurements performed in various coastal environments (Fig. 2) and described in previous studies (Babin et al., 2003; Bélanger et al., 2008; Loisel et al., 2009; Lubac et al., 2008). Interestingly, the three constant coefficients are very similar to those calculated by Siswanto et al. (2011) over an independent large data set acquired in a Yellow and East China Seas between 1998 and 2007 (Fig. 3). The similarity in these two sets of coefficient values provides an indication on the robustness of the proposed approaches for the SPM assessment in coastal waters. As atmospheric correction is one of the main challenges for the exploitation of ocean color remote sensing over coastal areas, the impact of potential atmospheric correction errors on the SPM retrieval should be assessed. According to the ESA (2012) database, the relative percentage differences calculated between the estimated and measured reflectance values in coastal waters at 490, 560, and 665 nm are − 8.7%, − 6.3%, and − 3.2%, respectively. The impact of these errors on the SPM retrieval accuracy using Eq. (1) is simply assessed using the in situ data base presented in Fig. 2, by comparing the SPM values calculated using the true Rrs values, SPMtrue, with the SPM values calculated using these Rrs values modified by the relative differences provided above, SPMmod. These atmospheric correction errors only slightly impact the SPM retrieval accuracy, as the mean relative absolute difference and bias values calculated between these two SPM values in linear scale are 4% and 1%, respectively. The median value of the SPMtrue/SPMmod is 1.001 ± 0.059. 2.2. Oceanographic and hydrological data sets over the studied area Since no in situ wave and swell data are available on the Mekong Shelf within the time scale considered in this study, satellite data coupled with large scale dynamical models were considered. The significant wave heights and direction data were obtained from the National Centers for Environments Predictions (NCEP, reanalysis). This data set covers the MERIS time period but with a much lower spatial resolution (grid of 1°). Wind speed and direction are derived at a 0.25° spatial resolution through cross-calibration and assimilation of ocean surface wind data from different sensors SSM/I, TMI, AMSR-E, SeaWinds on QuikSCAT, and SeaWinds on ADEOS-2 (http://podaac.jpl.nasa.gov/ node/31). Daily freshwater discharge time series (01/01/2001–31/12/ 2007) was gathered from the Chau Doc station operated by the Mekong River Commission (see Fig. 1 for the station location). Since this station only gauges the Bassac river, temporal analysis will be performed using MERIS data extracted in the coastal area in front of the Bassac outlet (see Fig. 10). The bathymetry data from the recent study performed by Becker et al. (2009), which provides bathymetry data at 30 arc sec of resolution over the global ocean, were considered. This bathymetry has been recently used over the same region by Xue et al. (2012) for the development of a coupled wave–ocean-sediment transport model on the Mekong shelf. 2.3. Time series analysis Monthly time series of SPM and ancillary data (wave, swell, wind and river discharges) have been temporally decomposed using the Census X-11 method (Pezzulli, Stephenson, & Hannachi, 2005) which have been documented in detail in Vantrepotte and Mélin (2011). Briefly, this iterative analysis based on the successive application of bandpass filters aims at decomposing time series into three additive components representing the seasonal, irregular (sub-annual) and inter-annual modulations in the data. The interest of this approach for precisely describing the temporal variation schemes in the series has been illustrated for various ocean color applications (Beaulieu et al., 2013; Vantrepotte, Gensac et al., 2012; Vantrepotte, Loisel, Mélin, Dessailly, & Duforêt‐Gaurier, 2011). In addition, the presence of significant inter-annual trends in the data has been investigated using non-

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Fig. 5. Inter-comparison of the SPM retrievals between the three different algorithms at the example of September 2010. The black line represents the 1:1 line. Scarce extreme values of up to 1000 g·m−3 observed in the river outlet are not represented by the density plot.

parametric statistics (i.e. seasonal Kendall test and Sen's slope estimator, Gilbert, 1987). A selection of the time series is performed before performing the statistical analysis (X-11 and trend detection). Basically, time series presenting more than 25% of missing data are discarded from the

analysis. Further, the presence of recurrent missing values does not represent a limitation for the use of the X-11 decomposition procedure which can be applied on “shortened time series” through a possible adaptation of the period considered for the various bandpass filters (see the detailed procedure in Vantrepotte & Mélin, 2011). The Eigen Vector Filtering approach (Ibanez & Conversi, 2002) is used to fill the potential gaps in the series. In the present study, based on monthly data, missing values represented less than 6% of the time series length. 3. Results and discussion 3.1. The oceanographic and hydrodynamic context of the studied area The lower Mekong area is under the strong influence of Monsoon regime. A wet season, associated with the summer monsoon characterized by South Western winds, generally occurs from May to October (Fig. 4a). The wind speed presents its minimum values during that period (about 1–2 m s−1), with however a marked peak (up to 7 m s−1) generally occurring in August (Fig. 4b). During this period water discharge reaches its maximum value (generally in September or October) with an average maximum flow of about 7000 m3 s−1 measured at the Chau Doc station (Fig. 4c). The same seasonal pattern is measured at Pakse station, located far inland in Cambodia, before the Mekong river separates into its two main branches, with liquid discharges of about 40,000 m3 s−1 (Xue et al., 2012). The remaining part of the year is generally dry and associated with the winter monsoon (November– March) characterized by North Eastern wind of higher intensity (about 8–11 m s−1) compared to those observed during the summer monsoon. During this time period water discharge is greatly reduced, reaching its minimum value in March–May (200–300 m3 s− 1 at the Chau Doc station). This decreasing rate is in agreement with the one observed at the Pakse station. The tidal signal, responsible of sea water intrusion during the dry season, differs between the two sides of the Delta. A diurnal tide is dominant in the Gulf of Thailand, while a semi-diurnal tide is dominant in the Vietnam East Sea. Average daily tidal range varies between 3.5 m and 4.5 m in the East Sea and between 0.5 m and 0.8 m in the Gulf of Thailand (Nguyen, 2012; Wolanski & Nguyen, 2005). The relative shallow waters (b 10 m, Fig. 1) favor deposition processes during water discharge events, as well as resuspension of sediments under the influence of waves and swell forcing following wind seasonal patterns (Fig. 4b, d–e). The significant wave height is maximum in winter (2–2.5 m) and minimum in summer (about 0.5 m) (Fig. 4e) in agreement with Xue et al. (2012). 3.2. Comparison between three SPM algorithms

Fig. 6. Variation coefficient of the suspended particulate matter concentration over the entire study period calculated using (a) new algorithm, (b) Nechad et al. (2010), and (c) standard MERIS products.

The selection of the most relevant algorithm is based on an intercomparison exercise (Fig. 5), and analysis of the variation coefficient spatial patterns (Fig. 6). The variation coefficient is calculated as the

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SPM concentrations higher than 1.2 g·m−3 (Nechad et al., 2010). While SPM variation coefficients for these two algorithms present some large discrepancies offshore, due to their different sensitivities at low SPM values, they are quite consistent in coastal areas (Fig. 6a–b). The seasonal impact of the water discharge is for instance noticeable for the two inverse methods. This is not the case for the standard MERIS product which presents very low variation coefficient in coastal areas, and for which the water discharge impact is not noticeable (Fig. 6c). This is explained by a saturation of this algorithm for SPM concentrations greater than 60 g·m−3 (Fig. 5b–c). The adoption of a constant bp/SPM value in this inversion model may also bias the results, as this ratio presents some natural variability depending on the size and biogeochemical composition of the bulk particulate matter (Neukermans et al., 2012). Strong SPM concentrations above 500 g m−3 are consistent with our present knowledge on SPM concentrations encountered in the different Mekong estuaries. Therefore, based on these different observations, the new, Tassan-like, algorithm (Eq. 1) has been selected for assessing SPM temporal variations in the coastal water masses influenced by the Mekong river. 3.3. Main SPM spatio-temporal variability patterns

Fig. 7. The contribution of the (a) seasonal, (b) irregular, and (c) trend components to the total variance of SPM (new algorithm) as calculated with the Census X-11 method.

ratio of the standard deviation to the climatological mean SPM value for each algorithm. While the new and Nechad et al. (2010) algorithms provide relatively similar SPM retrieval (Fig. 5a), the latter algorithm is not able to retrieve SPM values lower than about 2 g m−3. This is due to the use of the red band which is not sensitive enough at relatively low SPM concentration (Ouillon et al., 2008), and to the formalism of their model. As a matter of fact, this algorithm tends to converge towards offset value appearing in their equation (B = 1.74) for very low Rrs(665) values (see their Eq. 14 with the coefficients provided in Table 1). This result is not surprising since this algorithm was not dedicated for waters with relatively low SPM loads, as it was calibrated using

SPM variation coefficient calculated over the entire MERIS archive using the new algorithm exhibits a very specific spatial pattern (Fig. 6a). The main temporal variability, with a variation coefficient higher than 80%, is observed offshore and decreases towards the coast. The main reason of the relatively lower temporal variability in coastal areas compared to offshore waters is the presence of the river plume and of shallow waters promoting a nearly permanent sediment resuspension process under tide and wave forcing. The impact of the strong seasonality in the river water discharge is however clearly noticeable in front of the different rivers outlets and is characterized by a strong variation coefficient of about 100%. In contrast to the Eastern area, a thin turbidity belt with high coefficient of variation (100%) is observed along the coast in the Western part of the study area (Gulf of Thailand). The Census-X11 method is used to assess the relative contribution of the seasonal, trend, and irregular components to SPM temporal variability patterns over the whole studied area (Fig. 7). Between 50% and 90% of the total variance is dominated by the seasonal component (Fig. 7a). The coastal area where the seasonality in the data is maximal is located in front of the rivers outlets and in the Rach Gia Bay. The irregular component can explain up to 40% of the temporal variability (Fig. 7b). The irregular component extracted from the X-11 procedure represents the variability of the signal remaining when the long term (interannual) and seasonal oscillations have been subtracted. It therefore corresponds to the variation at sub annual scale. In the case of monthly data this can be related to temporal variations in terms of resuspension, and advection processes, for instance. The coastal area in front of the Delta is characterized by relatively low irregular changes (between 10 and 20%), except in a narrow band at the limit of the prodelta, along the 10 m depth isobath. The trend-cycle component explains about 10% of the temporal variability, but maximum values can reach 20–25% especially along the west coast (Fig. 7c). The seasonality in the SPM distribution over the whole studied area is analyzed in further detail through the climatological maps established from the entire MERIS data set (Fig. 8). The high turbidity belt, characterized by SPM concentrations greater than about 5 g m− 3, presents its maximum extension to the shore in winter (November to February), the maximum being reached in January. This extension goes far offshore in the South Western direction at the Camau peninsula (up to about 120 km from the coast). This period of time is characterized by the Northern Monsoon with relatively high winds blowing from the North-Eastern direction (Fig. 4a–b). This South-Western SPM extension pattern is related to surface currents mainly following the wind trend over the studied area at this time scale (Xue et al., 2012): northeastern direction during summer season and southwestern direction in winter.


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Fig. 8. Monthly SPM climatology based on the entire MERIS archive.

The mean significant wave heights are maximum during this period, so does the wave-driven bottom stress. The latter forcing induces strong resuspension of sediments which were deposited in the shallow nearshore area during the previous summer monsoon. During summer, the river discharge is maximum, and new continental sediments are brought to the coastal area. This is clearly seen in the August–October period when high SPM concentrations appear just in front of the different river outlets. The relatively low significant wave height occurring during this period (see Fig. 4d) induces a rapid settlement of suspended sediments down to the sea bottom. The tight relationship between river outflow measured at the Chau Doc station and SPM concentration extracted in front of the Bassac estuary is exhibited in Fig. 9. The main SPM peak in October and minimum SPM loads in April/May correspond to the maximum and minimum water discharge values, respectively (Fig. 9a–b). In contrast, the second SPM peak observed for some years in December does not correspond to an increase in water discharge. This supports the hypothesis that resuspension processes generated by waves are responsible for this second peak in winter. Further, the water discharge–SPM relationship is in good agreement with Kummu and Varis (2007) for the lower Mekong (Fig. 9c). The second South-Western wind speed maximum occurring around July/August (Fig. 4b), of lower intensity compared to the main one in winter, could explain the slight off-shore extension of the turbidity zone during that period. The minimum offshore extension is observed in April/May, when both river outflow and oceanographic forcing are low. During this inter-

monsoon period, sediment transport is dominated by tidal currents in the subaqueous delta and adjacent shelf regions (Unverricht et al., 2014). These patterns, established from ocean color remote sensing data, are in good agreement with the Xue et al. (2012) results established from sediment transport simulations. The impact of oceanographic conditions (waves, swell, and wind) on SPM variability over the entire study area is summarized through correlation maps between physical parameters and remotely sensed SPM values (Fig. 10). Note that waves, swell, and wind data fields present heterogeneous spatial resolution. The validity of this exercise is therefore tightly linked to the assumption that the variations of wind, swell, and wave fields follow shorter length scales compared to SPM. Strong positive correlations, meaning that low/high wave heights induce low/ high SPM values, are observed between SPM and significant wave heights along the turbidity belt, as well as in the northern and southern parts of the Delta (Fig. 10a). The same observation stands for the swell alone, with however lower regression coefficient values (Fig. 10b). This confirms that SPM variability in this region is mainly driven by waves and swell actions over a seasonal scale. Very low, positive, regression coefficients are generally found in the outlet of the different river mouths, reflecting the regulation of SPM loads by river discharges in these peculiar areas. These different patterns are in very good agreement with the identification of deposition (low r values in front of the rivers outlets) and erosion (high r values northern and southern areas of the delta) areas identified from a prognostic model recently used to

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Water Discharge (m3/s) Fig. 9. Temporal evolution of (a) monthly SPM concentrations extracted from the MERIS data at the outlet of the Bassac river (see Fig. 1) and (b) river discharge measured at the Chau Doc station. (c) Scatter plot of SPM at the Bassac outlet versus river discharge at Chau Doc. The relationship (y = 0.00085 x1.3) established by Kummu and Varis (2007) is over-plotted.

assess the fine sediment dynamics in the region under Mekong's water influence (Hein et al., 2013). Logically, offshore waters present the lowest regression coefficient due to the presence of deeper waters. Wind intensity presents much lower co-variation with SPM than waves and swell (Fig. 10c). 3.4. Spatial extension of the turbidity belt in relation with environmental parameters The spatial extension of the turbidity belt is examined through nearshore to off-shore SPM evolution along transects computed for three contrasted months: January, May, and October (Fig. 11). Three transects, perpendicular to the coastline, have been selected according to their

ability to catch the main spatial patterns previously described from SPM climatology (see Fig. 1). The offshore delimitation has arbitrarily been fixed by visually determining the distance from which the SPM value does not present strong spatial variability. Two transects are positioned in front of the main river outlets in the North part of the area, and one is located in the South. The bathymetry of two northern transects is characterized by a plateau of about 3 meter depth from the coast to about 13 km offshore (corresponding to the limit of the prodelta, which is the part of the delta lying beyond the delta front, and sloping gradually down to the basin floor of the delta), followed by a sharp decreasing to reach a bottom depth of about − 20 m 24 km offshore. Conversely, the bottom depth progressively decreases towards offshore waters for the southern transect.


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coast in the North (transects 1 and 2), and 40 km offshore in the South (transect 3). While SPM progressively and consistently decreases from near-shore to off-shore waters in the South (transect 3), two different spatial patterns can be noticed in the North. SPM values are indeed relatively constant up to 12–15 km offshore, and then sharply decrease due to the settling of particles (see below). Winter period is characterized by low water discharge and strong waves. The maximum value of the significant wave height is about 2.5–3 m which roughly implies a closure depth value of 5–6 m (Hanson, 1989). The formulation provided by Hanson (1989) was for sandy sea floor, which corresponds to the dominant sediment type (with silty sand) in front of the Mekong Delta area (Unverricht et al., 2014). Particles previously deposited on the sea floor can thus be resuspended in the water column for a water depth less than five meters (definition of the closure depth), which corresponds to the coastal region spreading up to 15 km offshore. This potential resuspension area is in good agreement with the near-shore SPM plateau observed in the northern region. SPM progressively decreases all along the southern transect, following the bathymetry. During the inter-monsoon season (April–May), when the water discharge is still low and the significant wave heights present their minimum values, the turbidity belt reaches its minimum offshore extension (about 12, 16, and 6 km for transects 1, 2, and 3, respectively). The combination of these hydrological and oceanographic conditions explains the sharp SPM decrease noticed along the transects during this period of time. In October, when the significant wave height values are still low, the strong water discharge is responsible for the offshore extension of the turbidity belt, especially, in the Northern part (about 25, 25, and 12 km for transects 1, 2, and 3, respectively). 3.5. Long term trends

Fig. 10. Correlation maps between SPM and (a) waves intensity, (b) swell, and (c) wind speed. Due to the unavailability of wind speed data in near shore areas, the correlation coefficient between wind speed and SPM concentration cannot be calculated (gray areas in panel c).

In January, the turbidity belt is wider only for the Southern transect considering a SPM threshold value of 5 g m−3. This relatively constant value of SPM of about 5 g m−3, used to arbitrarily characterize the offshore limit of the turbidity belt, is found at about 25 km from the

The analysis of MERIS time series (01/2003–04/2012) reveals the presence of significant inter-annual modulations in SPM loads (Fig. 12a). Strong decreasing trends (up to 6%/year) are observed in front of the Mekong Delta along the 10 m isobath. The average X-11 trend-cycle signal extracted for the corresponding region reveals that the observed changes correspond to a rather continuous decrease along the time series considered (not shown). Interestingly, no significant concomitant trend in wind, waves, and swell data can be identified. This result suggests that SPM trend pattern in front of the Mekong Delta is most likely related to modification in the Mekong river SPM discharge. The detailed description of the inter-annual changes in SPM loads considering the two different water outflow regimes confirms the latter assumption (Fig. 12b). As a matter of fact, the SPM decreasing pattern is much more pronounced during high flow time period (July–November) than during low flow period (January–May) (Fig. 12b). Different natural factors (climate and hydrological cycle variations) but also anthropogenic ones, such as dams and sand extractions, can be at the origin of the observed decreasing trends (Xue et al., 2011). For instance, Xue et al. (2011) have shown an abrupt drop of the maximum and minimum water levels on the delta plain between the pre-dam (1950–1993) and post-dam (1993–2005) periods, which inevitably should reduce the load of sediment discharge to the coast (which is observed in Fig. 12). Meteorological regional factors may also play an more important role, as they also showed a much better correlation between runoff and regional precipitation and ENSO during the post-dam period than during the pre-dam period. 4. Summary and concluding remarks Different approaches have been tested to assess SPM from remote sensing reflectance data delivered by the MERIS sensor between January 2003 and April 2012 at a relatively high spatial resolution (300 × 300 m2). This study stressed the importance of the analysis on the distribution of variation coefficient patterns in the selection of the appropriate inverse bio-optical algorithm. The temporal variability of


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Fig. 11. Spatial evolution of the surface suspended particulate matter concentration along the three near-shore off-shore transects depicted in Fig. 1. Dotted blue: bathymetry, solid red: SPM concentration. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

suspended particulate matter, SPM, at the seasonal and inter-annual scales, has been estimated for the very first time over the Mekong coastal areas using the Census X-11 time series decomposition procedure. SPM variability is tightly related to the river discharge especially in summer, and to the marked resuspension processes triggered by high waves appearing in winter. The spatial extension of the turbidity zone is maximum in winter and minimum in April/May. This extension is visible far offshore, up to 120 km of the Camau peninsula in January. The strong impact of waves, and to a lesser extent of swell, is particularly apparent in the northern and southern parts of the delta. The impact of the river sediment outflow in the coastal area is particularly well noticed from August to November when high SPM values are confined to a restricted area in front of the different river outlets. The present patterns are in agreement with the observations of Walsh and Nittrouer (2009) who classified the Mekong Delta as a Subaqueous-delta-Clinoform system, based on tidal range (greater than 2 m), mean significant wave height (lower than 2 m), and annual sediment flux (greater than 100 million tons). This family of riversediment dispersal system on continental margin, is characterized by two different patterns. First, the coastline is characterized by a “tidedominated delta” presenting a triangular shape where tidal flows represent the major forcing parameter acting on fine-sediment load transport towards offshore waters. Second, the sediment accumulation zone is generally aligned with bathymetric isobaths, and varies in depth and distance depending on the impact of waves and tides. According to

Walsh and Nittrouer, “This pattern reflects the impact of waves and tides on the depth and distance of sediment accumulation”. While the main temporal variability is dominated by seasonal oscillation, a significant SPM decreasing trend has been noticed over the examined period of time. This decreasing is mainly observed during the high flow season. The reported spatio-temporal patterns of suspended particulate matter, SPM, of the coastal waters under the Mekong's influence reflect and integrate the combined effects of natural and anthropogenic forcings affecting the Mekong Delta. While further examinations, based on longer data records including specifically extended in situ data, are needed for understanding the observed decreasing trends in suspended fine sediment, the impact of different natural and anthropogenic forcings such as hydropower dam impact, river bed aggregate extraction, and meteorological extreme events should be specifically examined. The remote sensing approach applied in this study confirms the main results obtained from numerical simulation of the sediment transport and deposition in the Mekong Delta coastal area. Differences are however noticeable. This for instance includes the spatial extension of the plume in front of the Delta which is less pronounced than predicted by numerical models (Hein et al., 2013). The remotely sensed SPM data represents therefore a valuable source of information for improving hydro-sedimentary models for which the availability of SPM data over long period of time and at relatively high spatial resolution is crucial (see, e.g., Ouillon, Douillet, & Andréfouet, 2004; Sipelgas, Raudsepp, &


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funded by the CNES (MSAC/116516/127951), using ESA ENVISAT MERIS FR data.

References

Fig. 12. (a) Significant monotonic trend in % per year (seasonal Kendal test, p b 0.05) of SPM. Non-significant areas are represented in white. (b) Time series of averaged SPM values as a function of year during low (red dots) and high (black dots) river flow conditions. These values are extracted over the region in front of the delta (the prodelta region) where significant trends are observed to examine the origin of the trend observed in panel a (in blue in panel a). The linear regression equation are indicated for each sub-data set. Dashed lines represent the 95% confidence interval. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Kouts, 2006; Chen et al., 2010). For that purpose, the present SPM climatology is made available at the following link: http://mren3.univlittoral.fr/~david/SPM/climato_SPM_LOG_2013.tar.gz. The methodology applied here can be transferred to other areas where deep understanding of suspended particulate matter dynamics is crucial. Acknowledgments This research has been funded by the WWF (Project Number: 9S084001 - FFEM Task 18) through the project “Decision support for generating sustainable hydropower in the Mekong Basin: Analysis and variability of transfer of nutrients in the Mekong Delta”, and by the GlobCoast project (www.foresea.fr/globcoast) which is funded by the French Agence Nationale de la Recherche (ANR 2011 BS56 018 01). The GlobCoast project is affiliated to the LOICZ and AQUIMER. Products were processed by ACRI-ST and distributed on the GIS COOC data portal in the frame of the Kalicôtier project,

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