Mud transport model for the Scheldt estuary in the framework of LTV
Thijs van Kessel, Joris Vanlede, Marieke Eleveld, Daphne van der Wal
© Deltares, 2008 Prepared for: Waterdienst
Mud transport model for the Scheldt estuary
in the framework of LTV
Thijs van Kessel, Joris Vanlede, Marieke Eleveld, Daphne van der Wal
Report
December 2008
Z4594 Client Waterdienst
Title Mud transport model for the Scheldt estuary
Abstract Vanaf 2006 wordt gewerkt aan de ontwikkeling van een slibtransportmodel van het Schelde-estuarium in het kader van LTV (Lange Termijn Visie), luik Toegankelijkheid. Het doel van dit model is om de beheerders van het Schelde-estuarium te ondersteunen. De kernvraag van dit project is tweeërlei. Enerzijds is er behoefte aan kennis van de huidige slibdynamiek en -balans en de bepaling van de autonome ontwikkeling hiervan. Anderzijds speelt de vraag naar het effect van diverse ingrepen op de slibdynamiek en –balans in verhouding tot de autonome ontwikkeling. Nadat in 2006 en 2007 een literatuurstudie is uitgevoerd en het slibmodel is opgezet, gekalibreerd en toegepast voor een eerste beheersvraag (havenstorting Sloehaven), is hierop in 2008 voortgebouwd door middel van de volgende activiteiten: jaarsimulaties, analyse seizoensdynamiek, validatie aan de hand van langdurige meetreeksen, ondermeer bij Antwerpen. Verder is ook het effect van de tweede verruiming als beheersvraag opgenomen. In 2008 werd er tevens vanuit remote sensing gegevens informatie afgeleid over het zwevend stof gehalte (SPM) in de bovenste laag van de waterkolom, en over het slibgehalte in de bovenste laag van de bodem op platen. Deze gegevens bieden door hun synoptisch karakter een waardevolle extra calibratie- en validatieset voor het slibmodel. Uit de modelvalidatie blijkt dat de slibdynamiek redelijk goed door het model wordt gereproduceerd, maar dat de sterkte van de seizoensdynamiek en van de estuariene circulatie nog onvoldoende door het model worden gereproduceerd. Deze aandachtspunten worden opgenomen in het vervolgtraject van dit project. De voorbeeldtoepassing van het slibmodel voor 2008, namelijk het effect van de tweede verruiming, is berekend door in het model de drempels op de situatie voor 1997 te brengen. Resultaten op basis van deze nieuwe bathymetrie wijzen erop dat de tweede verruiming slechts een klein effect heeft gehad op de slibconcentratie (minder dan 2%). Deze invloed is zeer beperkt in vergelijking tot de natuurlijke variatie in slibconcentratie door getij, wind en seizoenseffecten.
References Raamovereenkomst 31007973, SPA27,1A: LTV Toegankelijkheid
Ver Author Date Remarks Review Approved by Thijs van Kessel 23/12/2008 C. Kuijper Schilperoort Joris VanledeVanlede, Marieke Eleveld Daphne van der Wal
Project number Z4594 mud transport, three-dimensional models, estuary models, Scheldt, remote Keywords sensing Number of pages 94 Classification None Status Final Mud transport model for the Scheldt Z4594 December 2008 estuary
Contents
1 Introduction ...... 1 2 Remote sensing data ...... 2 2.1 Upper part of the water column ...... 2 2.1.1 Introduction...... 2 2.1.2 Method...... 2 2.1.3 Results: Validated MERIS-FR SPM products...... 9 2.1.4 Pilot SPM from Landsat ...... 11 2.2 Bed roughness and bed composition ...... 15 2.2.1 Introduction...... 15 2.2.2 Bed roughness from SAR ...... 15 2.2.3 Mud content of the sediment from SAR...... 18 2.2.4 Mud content from optical remote sensing...... 22 3 Model calibration and validation ...... 23 3.1 Hydrodynamics ...... 23 3.1.1 Numerical grid and resolution ...... 23 3.1.2 Model characteristics ...... 23 3.1.3 Calibration of model east of Cadzand-Westkapelle (simF10) ...... 24 3.1.4 Validation of model with ZUNO boundary (simG06)...... 25 3.1.5 Discussion...... 27 3.2 Mud transport...... 28 3.2.1 Year simulations...... 28 3.2.2 Validation on remote sensing...... 29 3.2.3 Mud balance...... 31 3.2.4 Validation on measurements near Antwerp...... 33 4 Model application...... 38 4.1 2nd fairway enlargement...... 38 4.2 Dredged material release at Zeebrugge...... 39 5 Conclusions and recommendations...... 40 5.1 On data ...... 40 5.2 On model development...... 41 5.3 On model application...... 42 5.4 Synthesis and outlook to LTV- Nature and MONEOS ...... 42
Deltares, Flanders Hydraulics, IVM, NIOO i Mud transport model for the Scheldt Z4594 December 2008 estuary
6 References ...... 43
Appendices A Details on remote sensing data acquisition...... 47 A.1 SIOPs: the optical absorbing and scattering properties of substances in Western Scheldt and North Sea waters...... 47 A.2 Selected suspended particulate matter (SPM) products derived from MERIS remote sensing images for 2006 ...... 50 A.2.1 Western Scheldt sIOP parameterisation (Restwes99Oroma02mean)...... 50 A.2.2 North Sea sIOP parameterisation (Belgica2000)...... 54 A.3 Vertical tidal stage (in m NAP) at image date and time...... 60 B Details on remote-sensing mudflats...... 61 B.1 Maps of bed roughness RMSz: 2006 ...... 61 B.2 Maps of the mud content of the sediment: 2006 ...... 63 C Details on hydrodynamic model...... 65 D Details on mud model...... 76 D.1 Time series ...... 76 D.2 Comparison with OBS data ...... 80 D.3 Seasonal dynamics...... 81 D.4 Comparison between 3-month period, 2000 and 2006...... 85 D.5 Comparison between mud model and remote sensing data...... 86 D.6 Thalweg plots...... 92
Deltares, Flanders Hydraulics, IVM, NIOO ii Mud transport model for the Scheldt Z4594 December 2008 estuary
1 Introduction
In 2006, a work plan was conceived for the development of a mud transport model for the Scheldt estuary in the framework of LTV (Long Term Vision) (Winterwerp and De Kok, 2006). The purpose of this model is to support managers of the Scheldt estuary with the tools to evaluate a number of managerial issues.
Five phases have been defined in the work plan: 1 set-up of mud model 2 elaboration of managerial questions 3 year simulations 4 detail studies 5 sediment mixtures.
In 2006, the first two phases were initiated. The set-up of the hydrodynamic and mud transport model was reported in Van Kessel et al. (2006), whereas the managerial issues were elaborated in Bruens et al. (2006). In 2007, the activities were based on the original work plan (items 1 through 3), but also took into account the findings from the set-up of the mud model and the discussions with Scheldt estuary managers during 2006. In short, most activities for 2007 fell into the following two categories, which are reported in more detail in Van Kessel et al. (2007): 1 further improvement of the mud transport model; 2 providing support for managerial issues: dredging and dumping strategy at Sloe harbour.
In 2008, phases 3 and 4 were initiated, i.e. year simulations with the hydrodynamic and mud model and the first development of a detailed model near Zeebrugge, required to optimize local dredging and dumping strategies. Also the impact of the second deepening of the Scheldt on typical sediment concentration levels, fluxes and harbour siltation was investigated. Also more attention was paid to the use and analysis of remote sensing regarding near-surface suspended sediment concentration in the water and sediment composition of tidal flats.
This report discuses the 2008 activities and results. In Chapter 2, the work on remote sensing data analysis is further elaborated. This work provides valuable information for the further validation of the mud model. In Chapter 3, improvements to the hydrodynamic and mud transport model are discussed, notably with regard to the year simulation 2006. Based on this simulation, conclusions can be drawn regarding how well the model is able to reproduce observed seasonal trends. In Chapter 4, the model is applied to two management issues: 1 the impact of the second deepening of the Western Scheldt on fine sediment dynamics; 2 the impact of release of dredging spoil near Zeebrugge, using a local model.
Regarding the second point, complete results are not yet presented in this 2008 version of the report. Finally, conclusions are drawn and recommendations are made in Chapter 5.
The present report is a collaborate effort with contributions by Deltares (mud model), Waterbouwkundig Laboratorium Borgerhout (hydrodynamic model), IVM (remote sensing water column) and NIOO (remote sensing tidal flats).
Deltares, Flanders Hydraulics, IVM, NIOO 1 Mud transport model for the Scheldt Z4594 December 2008 estuary
2 Remote sensing data
2.1 Upper part of the water column
2.1.1 Introduction
This section reports on collection of remote sensing reflectance data and the application of (semi-)analytical algorithms for retrieval of Suspended Particulate Matter (SPM) concentrations in the upper part of the water column of the Western Scheldt and nearby North Sea. These data are used in Chapter 3 for intercomparison with sediment transport model results.
2.1.2 Method
Selecting and acquiring images
On board the European Space Agency’s ENVISAT spacecraft is an imaging spectrometer, the MEdium Resolution Imaging Spectrometer instrument (MERIS) that was specifically designed to measure ocean colour signals (ESA, 2006). Best Full Resolution (FR) (IPF5.05) data from MERIS, were obtained with the EOLI-SA software (http://eoli.esa.int/) that allowed access to a listing of the raw (Level 0) data and screening of browse products (quick-looks) for unclouded conditions. FR data are not systematically processed up to L1 and atmospherically corrected L2 data but stored only as L0. Whenever there is a request from an EOLI user, the newest processor is used to process the data.
Quick screening in BEAM
Subsequently, the quality of the atmospherically corrected (Level 2) data was screened using the BEAM software (http://www.brockmann-consult.de/beam/).
Despite the data being processed with the newest processor, quick screening in BEAM, showed frequent occurrence of negative reflectances (Figure 2.1) particularly in areas with high sediment load (cf. Ruddick et al., 2000), which are however neatly indicated by the product’s confidence flag (PCD_1_13 which is provided with the data). Negative reflectances are a result of an overcorrection for the aerosol scattering, which can be caused by increased reflectance in the near infrared (Brockmann, 2006).
Land-water flags are correctly set also for tidal flats, which typically have reflectances above 0.075 in NIR (confirmed by Van der Wal, pers. comm.).
Deltares, Flanders Hydraulics, IVM, NIOO 2 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 2.1 Errors in reflectances (negative values) on transition of sea and tidal flats. (Very low or negative reflectances for the green wavelengths (band 5) are indicated here in black).
Background:
PCD_1_13 is a compound flag that is defined as follows: INVALID_F OR [(WATER_CLASS OR LAND_CLASS) AND (ORINP0_F OR OROUT0_F OR RWNEG (b412..b705)] OR [WATER_CLASS AND (UNCGLINT_F OR HIINLD_F OR WHITECAPS_F OR ICE_HIGHAERO_F OR (NOT(CASE2_S OR CASE2Y_F OR CASE2_ANOM) AND DROUT) OR ACFAIL_F OR TAU06 )]
Each of these variables (or flags, F) mean the following (I take the freedom to omit obvious ones):
ORINP0_F Out of range input for atmosphere corrections OROUT0_F Out of range output for atmosphere corrections RWNEG Annotation flag for negative corrected reflectance of the band at 412 ... 709 (b412..b705) UNCGLINT_F uncorrectable sun glint HIINLD_F Flag for low pressure water (=high inland waters; Rayleigh correction not working correctly) WHITECAPS_F Whitecaps flag (threshold at 10 m s-1 windspeed) ICE_HIGHAERO_F Ice or high aerosol loading flag CASE2_S Turbid water flag CASE2Y_F Flag for yellow substance loaded waters CASE2_ANOM Anomalous Scattering flag DROUT The minimum absolute value of ǻȡ510 (difference between measurement at 510 and climatology) is greater than DRO510_threshold ACFAIL_F Flag indicating failure of the bright pixel correction procedure TAU06 The aerosol optical thickness is greater than TAU560_threshold
Deltares, Flanders Hydraulics, IVM, NIOO 3 Mud transport model for the Scheldt Z4594 December 2008 estuary
To overcome these problems with the atmospheric correction a drastic step back would have to be taken. It would require a customised atmospheric correction of L1b products (e.g. using several Beam plug-ins, cf. Santer and Schmechtig, 2000; Doerffer and Schiller, 2007; Schroeder et al., 2007). Subsequently the current processing line would have to be adapted. Both were not foreseen within the current project. However, note that most of the negative values occur in the first band, which subsequently was not used in the processing, and that our algorithm corrects for some errors in atmospheric correction. Nonetheless, the advice is to put most trust in pixels (cells) that are NOT flagged by PCD_1_13 when comparing the remote sensing results with model results.
Parameterisation of HYDROPT
HYDROPT (Van der Woerd and Pasterkamp, 2008) was used to retrieve SPM concentrations from reflectances from all water pixels. The workings of this algorithm are illustrated in Figure 2.2.
observed -3 0.02 x 10 5 0.015 0 0.01
0.005 -5
0 -10
400 500 600 700 reflectance differential 400 500 600 700 wavelength (nm)
Kd, iterate compare and TSM, optimize conc CHL, CDOM
modelled -3 0.02 x 10 5 0.015 RT Model 0 sIOPs 0.01 -5 reflectance
0.005 -10
400 500 600 700 reflectance differential 400 500 600 700 wavelength (nm) wavelength (nm)
Figure 2.2 The workings of HYDROPT: Comparing remotely sensed and modelled reflectances to derive concentrations.
Background: HYDROPT comprises of a forward model that generates water-leaving radiance reflectance (ȡw) as a function of, a.o., the Inherent Optical Properties (IOPs) absorption (a) and scattering (b) of North Sea water and its constituents chlorophyll (CHL), SPM and coloured dissolved organic matter (CDOM). It is based on radiative transfer modelling with Hydrolight (Mobley and Sundman, 2001a and b) and parameterised with specific IOPs. The inverse model estimates the concentrations of, a.o., SPM from MERIS water-leaving radiance reflectance ȡw at several optical wavelength intervals based on the Levenberg-Marquard optimization (Van der Woerd and Pasterkamp, 2008). The inversion comprises Ȥ2 fitting of the modelled to the measured water-leaving radiance reflectance, and also renders standard errors (ı) with the retrieved CHL, SPM and CDOM concentrations. In addition, probabilities were derived from the cumulative distribution function for the Ȥ2, and ESA’s Level 2 Product Confidence Data (PCD) flags (ESA, 2006) were passed on.
Deltares, Flanders Hydraulics, IVM, NIOO 4 Mud transport model for the Scheldt Z4594 December 2008 estuary
Information about the specific (mass-normalised) absorption and scattering characteristics (sIOPs) is needed (Eleveld et al., 2008) to retrieve concentrations from reflectances. These sIOPs can differ per area and moment (Babin et al., 2003 a & b; Tilstone et al., resubmitted to JGR). Based on extensive sIOP quality checking: comparison with other sIOP sets in our databases, comparisons with other published sIOPs (Bricaud et al., 1998; Babin et al., 2003a & b; Vantrepotte et al., 2007) and optical modelling, sets of average sIOPs were created. These datasets are Restwes99Oroma02mean (n=4, and 21 respectively) for the Western Scheldt, and Belgica2000 (n=11) for the Voordelta part North Sea, which is close to the Vlaamse Banken (Flemish Banks) (Figure 2.3 and Appendix A-1). Specific backscattering (b*) and absorption (a*) of SPM are higher in the Western Scheldt. This could imply that there are more particles of lighter mass but darker colour in the top of the water column: something to be checked in the field at a later stage.
a*CHL a*SPM 0.04 0.06 Belgica2000 0.035 Belgica2000 Restwes99Oroma02mean 0.05 0.03 Restwes99Oroma02mean
0.025 0.04
0.02 0.03 a*CHL a*SPM 0.015 0.02 0.01
0.005 0.01
0 0 400 450 500 550 600 650 700 750 400 450 500 550 600 650 700 750 wavelength (nm) wavelength (nm)
a*CDOM b*SPM 2 0.6
1.8 Belgica2000 0.5 1.6 Restwes99Oroma02mean 1.4 0.4 1.2
1 0.3 b*SPM a*CDOM 0.8
0.6 0.2
0.4 Belgica2000 0.1 0.2 Restwes99Oroma02
0 0 400 450 500 550 600 650 700 750 400 450 500 550 600 650 700 750 wavelength (nm) wavelength (nm)
Figure 2.3 Synthesized sIOP input datasets used for SlibRS: For wavelengths corresponding to the MERIS bands, the absorption per unit concentration for chlorophyll-a (m2 mg-1), absorption and scattering per unit concentration for suspended particular matter (m2 g-1), -1 and absorption (m normalized at 440 nm) of coloured dissolved organic matter. b*SPM is roughly about a factor 10 higher than a*SPM and even more at higher wavelengths. Belgica2000 is based on measurements near Vlaamse Banken and Voordelta (North Sea); Restwes99Oroma02mean was created for the Western Scheldt.
Deltares, Flanders Hydraulics, IVM, NIOO 5 Mud transport model for the Scheldt Z4594 December 2008 estuary
Processing with HYDROPT
For each image the algorithm was run twice with sIOPs 'Restwes99Oroma02mean' for Western Scheldt and ‘Belgica2000’ for the North Sea. As a first approximation, a topographic boundary Vlissingen-Breskens can be taken to separate the products. In a later stage, a gradient in IOPs could be related to salinity.
Of all available optical MERIS bands (1 ..9, corresponding with centre wavelengths 413, 443, 490, 510, 560, 620, 665, 681, 708), only bands 2 .. 7 and 9 were used. Relectances of Band 1 (413 nm wavelength) validated worst to in situ data (cf. Table 1 Zibordi et al., 2006) and are particularly sensitive to errors in atmospheric correction. Band 8 (681 nm wavelength) was not used because of a possible contribution of fluorescence to the reflectance signal.
All water pixels were processed (in Matlab code msk=bitand(l2_flags,2^21)~=0). Other confidence flag settings (such as PCD_1_13) were delivered with the product and can be used in both plotting and filtering of output.
Finally, the processed area was limited to longitude limits 2 .. 5, and latitude limits 51 .. 52.5.
Quality control: Quick screening of HYDROP output
Results from processing with various sIOP sets were compared confirming theory that higher b*SPM generally gives higher concentrations. In a previous project on retrieval of SPM for the North Sea (TnulTSM) we noticed that a very small number of pixels gave unrealistically high concentrations (extreme values). Therefore, a small programme was written to visualise these concentrations and subsequently see if they were flagged by PCD_1_13 (Figure 2.4).
Validation and selection of best SPM products
In situ MWTL SPM measurements for 2006 were downloaded from Waterbase (http://www.waterbase.nl/). The in situ data (Figure 2.5) confirm the large variability in SPM concentrations. None-the-less, in the Western Scheldt, Hoedekenskerke and Hansweert seem to have SPM concentrations of about 40 mg l-1. In the North Sea an average decrease in concentrations with increase in distance to the coast appears (cf. Eleveld et al., 2008): Walcheren2 has values in the range 10-70 mg l-1, Walcheren20 from 10-25 (occasionally 30), and concentrations at Walcheren70 generally remain below 10 (occasionally 20) mg l-1. These in situ averages compare reasonably to the concentrations derived from the remote sensing for the North Sea; within the Western Scheldt these in situ averages seem higher than concentrations derived from the remote sensing (Appendix A-2).
Deltares, Flanders Hydraulics, IVM, NIOO 6 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 2.4 The top plots show derived SPM concentrations with North Sea sIOPs (left) and Western Scheldt sIOPs (right), the bottom plots show the setting of PCD_1_13 (1 indicates less confidence in remote sensing reflectances). There seem to occur some higher values on the North Sea (left) and in the Western Scheldt (right), but these are mainly in PCD_1_13 flagged regions.
Domburg Badstrand Terneuzen Boei 20 160 160 1246 Walcheren 2 Hoedekenskerke Boei 4 Walcheren20 Hansw eert Geul Walcheren70 120 120 Schaar van Ouden Doel Wielingen
Vlissingen Boei SSVH
80 80 SPM SPM (g m-3) SPM (g m-3) SPM
40 40
0 0 01 Jan 02 Mar 01 May 30 Jun 29 Aug 28 Oct 27 Dec 01 Jan 02 Mar 01 May 30 Jun 29 Aug 28 Oct 27 Dec date date
Figure 2.5 The in situ MWTL data for the year 2006 confirm the large variability in SPM concentrations, but for various stations in both the North Sea and the Western Scheldt average concentrations can be estimated (source: www.waterbase.nl).
Deltares, Flanders Hydraulics, IVM, NIOO 7 Mud transport model for the Scheldt Z4594 December 2008 estuary
The database was also searched for direct match-ups, which are sea-truth measurements at times of clear image data acquisition (Eleveld et al., 2008). Satellite overpasses for the acquired 31 images were between 10:00-11:00 UTC. A selection on Waterbase data for a window of 10:00-13:00 MET (UTC + 1) resulted in 4 co- occurrences with image acquisition (Table 2.1). Large differences are apparent at the two instances when high in situ concentrations were measured. At lower concentrations values seem to match better.
Best SPM products for the North Sea until the line Vlissingen-Breskens are those with Belgica2000 in the filename. Best for the Western Scheldt (until the line Vlissingen- Breskens) are those with Restwes99Oroma02mean in the filename. Most trustworthy results come from best input MERIS reflectances; PCD_1_13 was used for this as diagnostic parameter. However, the PCD_1_13 algorithm could erroneously assign high sediment concentrations to high (sun)glint, and it’s settings could be relaxed if the atmospheric correction algorithm makes a smaller mistake.
Table 2.1 Match-ups
Location SPM remote sensing products In situ data Relevant sIOP set Time SPM Time SPM (MET) (mg l-1) (MET) (mg l-1) Hoedekens- MER_FR…20060109_103009…Restwes99 11:30 10 10:59 39 kerke boei 4 Oroma02 Vlissingen MER_FR…20060710_100956…Belgica2000 * 11:10 8 12:26 12 Boei SSVH Vlissingen MER_FR…20060920_104646…Belgica2000 * 11:47 16 12:16 35 Boei SSVH Terneuzen MER_FR…20061016_102951…Restwes99 11:30 8 12:32 7 boei 20 Oroma02
* Vlissingen is at the boundary of North Sea and Western Scheldt water. Restwes99Oroma02 would have given 4 and 11 mg l-1.
Deltares, Flanders Hydraulics, IVM, NIOO 8 Mud transport model for the Scheldt Z4594 December 2008 estuary
2.1.3 Results: Validated MERIS-FR SPM products
Description delivered SPM products
19 Best products (of 12 images, 7 times covering both North Sea and Western Scheldt) were selected based on coverage of the focus area Western Scheldt and part of the North Sea until Zeebrugge, respecting PCD_1_13. Figure 2.6 gives an example for 16 October 2006. An example showing also other water quality parameters is given in Figure 2.7. All 19 products are given in Appendix A-2.
North Sea Western Scheldt
Figure 2.6 10 log SPM products from good images (PCD-1_13 overlaid). On the left hand side the North Sea, and on the right hand side the Western Scheldt. The patterns seem quite consistent, but values are lower for Western Scheldt sIOPs. Be careful with values given for Oosterschelde, it needs customised processing, because sIOPs might be very different from Western Scheldt. Tide at overpass (cf Appendix A-3) was + 0.95 at Vlissingen and +1.1 m NAP at Hansweert. Potential wind at Vlakte van de Raan was directed 120 deg North and abating from 5.8 to 4.9 m s-1 (www.knmi.nl/samenw/hydra)
Formats delivered SPM products
The resulting matfiles, e.g., MER_FR__2PNEPA20060510_102655_000000982047_00323_21920_3828Restwes99Oroma02Sub .mat contain the following parameters: Kd, P, c, chisq, dc, flags, l2_flags, lat, lon, metaData, msk
The filename contains a date and time indication. In this case 10 May 2006 10:26:55 (UTC)
Delivered processing results are: c = concentrations (1=water, 2=chl, 3=tsm en 4 =cdom). dc = standard errors in the concentrations in same order chisq = ȋ2, a measure of the fit of the simulated the measured MERIS reflectances. P: cumulative probability of ȋ2 (also output as unsigned integer in ‘flags’) l2_flags = all ESA flags msk = mask of all suitable MERIS pixels Kd = KD at 7 MERIS bands
Deltares, Flanders Hydraulics, IVM, NIOO 9 Mud transport model for the Scheldt Z4594 December 2008 estuary
North Sea
Western Scheldt
Figure 2.7 Reprocessing of the 16 Oct image with all 9 MERIS bands gave no large changes in retrieved SPM concentrations, but also not for ecological parameters (CHL and KD). There are minor improvements in all standard error products. Note however that the error on CHL is still frequently higher that its corresponding concentrations. The size of the patch with exceptionally low KD values reduced, which is probably an improvement.
Deltares, Flanders Hydraulics, IVM, NIOO 10 Mud transport model for the Scheldt Z4594 December 2008 estuary
Some notes on MERIS-FR products of other ecological parameters: first estimates of CHL and KD
The algorithm provides also first estimates of CHL and KD. Very low values of these parameters at high SPM might be an artefact. To test if it resulted from processing with only the best 7 bands, the data were reprocessed with all 9 bands in the visible range. The aim was to capture differences in the absorption characteristics between CHL and CDOM (particularly at 681 and 412 nm), eventhough, we know these are not the best of MERIS reflectances (cf. section 2.1.2 Processing with HYDROPT). Note that the influence of fluorescence might be limited in Case-2 waters (Gillerson et al., 2007). The 9-band algorithm seems an improvement but didn’t solve all problems with CDOM. Therefore, checking of atmospheric correction and collection of additional sIOP sets for ecological parameters is recommended. KDPAR can be derived by at a later stage from irradiance weighted integration over all bands. This was not foreseen in the current study.
2.1.4 Pilot SPM from Landsat
A first SPM product was also derived from a high-resolution (Landsat-7 ETM+ SLC-off) image (band 1-5 and 7) from NIOO. The image of 10 September 2006 10:30 UTC had been preprocessed (georerefenced and atmospherically corrected for land applications) in Erdas Imagine, and could directly be imported into ENVI. A quick test on NIOO atmospheric correction was favourable: no negative reflectances in the blue and credible water reflectance spectra (Figure 2.8).
The Landat data cannot easily be processed with HYDROPT, due to the difference in band settings: Landsat has a relatively small number of (quite broad) bands in the visible and near-infrared (NIR) (band 1-4, Table 2.2), therefore a new simple algorithm had to be devised. For such an algorithm several approaches are possible: A NIR, (s)IOP-based, or combined ratio-approach.
Figure 2.8 A familiar spectrum for relatively deep and dark waters illustrates a good atmospheric correction.
Deltares, Flanders Hydraulics, IVM, NIOO 11 Mud transport model for the Scheldt Z4594 December 2008 estuary
Table 2.2 Landsat-7 ETM+ and MERIS band settings
Landsat- 7 Wavelength MERIS band Name in file MERIS Opm ETM+ band nr range (nm) nr ȇ Detector averaged centre wavelength 1 413 412.7 2 443 442.6 1 450-520 3 490 489.9 4 510 509.8 2 520-600 5 560 559.7 6 620 619.6 3 630-690 7 665 664.6 8 681 680.8 Fluo 9 708 708.3 10 754 11 760? 12 778 Atm IR 4 760-900 13 865 Atm IR 14 885 15 900? 5 1550-1750 7 2080-2350
Theory of Algorithm development
Several approaches and algorithms have been applied to study SPM in estuaries using broad-banded (land) sensors (such as Landsat). Pasterkamp et al. (1999) performed bio-optical modelling based on one single sIOP set for SPOT bands using the Gordon et al. (1975) approach in a similar fashion as earlier mentioned in this report for sIOP testing. Note that they had to optimize several parameters – scatter to backscatter ratio and the shape (of the light field) parameter – with this bio-optical modelling. Whereas with Hydropt the main assumption concerns the volume scattering function (cf. Volten et al., 1998 and 2006); the shape parameter is modelled by Hydrolight. In their band choice they try to limit sensitivity for optical non- SPM parameters as CDOM and CHL absorption (as in Eleveld et al., 2008). They ended up fitting an exponential function between SPM and reflectance. To further reduce the influences of CHL and CDOM absorption and to avoid saturation of reflectance with high SPM concentrations Ruddick et al. (2006, 2008) strongly suggest to go near-infrared (NIR). However, the NIR range is not covered by sIOP measurements and might be influenced by the adjacency effect. Because of the variability in sIOPS and neat atmospheric correction of the Landsat data this approach was aimed for in the current pilot. As an alternative Doxaran et al. (2002, 2006) have been proposing band ratio algorithms between the NIR and visible wavelengths, which are amongst others relatively independent of illumination conditions and particle size and mineralogy. A comparison of various approaches and cross-validation of products is something to be explored in a next phase.
The following information is available for construction of such an algorithm:
Deltares, Flanders Hydraulics, IVM, NIOO 12 Mud transport model for the Scheldt Z4594 December 2008 estuary
• SPM and reflectances
No in situ match-up data are available for 10 Sept., but there is information about the SPM climatology available: cf. Fig 2.5 for conditions in 2006 and Van Kessel et al. (2006) for long term averages.
In the course of this project a MERIS image for 11 September 10:30:02 has been analysed: both the original data with normalised surface reflectance (ȡ) measurements and derived SPM concentrations (A.2) are available. The Landsat Top Of Atmosphere (TOA) reflectance data had been atmospherically corrected with empirical line calibration using spectral measurements of various targets, giving reflectance measurements that can be compared to this MERIS reflectance (at least for relatively stable land targets). For water, some angular corrections specific for the interactions with light on the air-water interface could be missing. To develop an algorithm for the relationship between Landsat reflectances and SPM, regressions between Landsat and MERIS reflectances, as well as between MERIS reflectances and SPM were explored.
• Other optical and environmental conditions
In addition to the (variable) (s)IOP sets (cf section 2.1.2) information about chlorophyll concentrations in 2006 is available (presented in Figure 2.9, left-hand side). Concentration of CDOM, which is composed of is humic and fulvic acids is usually high for river discharge (cf Section 2.1.2).
Note that both the Landsat and MERIS images have been acquired at ca. 10:30 so that tidal conditions are more-or-less similar. Wind at Vlakte van de Raan was direction is comparable both for direction (not presented) and force (Figure 2.9, right-hand side).
Vlissingen 40 7
Terneuzen 6
30 ) Hansweert -1 5
Schaar van Ouden 4 Doel 20 3
2 wind speed (m s (m speed wind
10 1 10/09/2006 11/09/2006 0
0 0 2 4 6 8 10 12 14 16 18 20 22 24 01-Jan 02-Mar 01-May 30-Jun 29-Aug 28-Oct 27-Dec time (h) UTC
Figure 2.9 Left: the in situ MWTL data suggest mean CHL concentrations of about 3 mg/l for several stations (source: www.waterbase.nl). Right: wind conditions during image acquisition of (a) Landsat image d.d. 10 Sept 2006, and (b) MERIS data d.d. 11 Sept 2006 (source: KNMI).
Deltares, Flanders Hydraulics, IVM, NIOO 13 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deriving SPM from NIR reflectance
First, MERIS reflectances were compared with Landsat reflectances using all corresponding band settings indicated in Table 2.2. (Note that Landsat bands span a much broader wavelength range than MERIS bands). Visual inspection in BEAM and ENVI showed discrepancies in reflectances for both the not-PCD_1_13 flagged waters near Vlissingen and land targets. Nonetheless, a GIS was used to extract all reflectances of the two georeferenced Western Scheldt subsets, n~10.000 (courtesy Van der Wal, NIOO). Coefficients of determination increase with wavelength, but were generally low. MERIS band 3, 5 and 7 all contain negative reflectances, but 2 MERISband 13 = 0.74 Landsatband 4 + 0.02 (r = 0.70) (which might however be largely due to good correlation of reflectances of land targets).
Then, linear regression between MERIS band 13 and SPM from the Restwes99Oroma02mean sIOPs gave 2 SPMRestwes99Oroma02mean = 1903.8 MERIS band 13 + 7.9273 (r = 0.28, n=28) (r2 for 10logSPM = 0.26)
These relations were subsequently used in ENVI to compute SPM from Landsat (Figure 2.10) with band math. The result shows the more and less turbid regions that can be expected within the Western Scheldt. Values are too high though. The MERIS results, SPM climatology, and environmental conditions suggest SPM to be somewhere around 10 near Vlissingen and around 40 near Hansweert. Relating these values directly (linearly) to Landsat reflectances gives Figure 2.11.
Figure 2.10 SPM from Landsat band 4 via MERIS reflectance. Purple = 70, blue = 75, turquoise = 85, green = 95, and red= 100 mg/l. These values are clearly too high.
Figure 2.11 SPM directly from Landsat band 4 using SPM climatology (pragmatic solution). Purple = 13, blue = 20, turquoise = 30 and green = 40, mg/l.
Deltares, Flanders Hydraulics, IVM, NIOO 14 Mud transport model for the Scheldt Z4594 December 2008 estuary
2.2 Bed roughness and bed composition
2.2.1 Introduction
Satellite radar (SAR) has been used for detailed mapping and monitoring of bed roughness and mud content of unvegetated intertidal areas. In the present study, this method is applied to the intertidal areas of the Western Scheldt to gain insight in the spatial distribution and seasonal dynamics of bed roughness and mud content. The use of medium scale resolution optical remote sensing for retrieving mud content of the sediment has also been investigated following procedures described in Van der Wal & Herman (2007). In addition, time-series of chlorophyll have been retrieved from optical remote sensing (Landsat ETM+ and MODIS) to characterize the spatial distribution and dynamics of microphytobenthos. However, this work is mainly carried out for LTV- Nature and falls outside the scope of this report.
2.2.2 Bed roughness from SAR
For this project, C-band SAR images of the ESA satellite ERS-2 are used, which have a nominal resolution of ca 30 m, and a pixel size of 12.5 m. A selection is made of all archived SAR images from 2006 that (1) cover either the entire Western Scheldt or the eastern part of the Western Scheldt, (2) are acquired when water levels in the Western Scheldt were below -1m NAP, (3) have no obvious disturbance from rain showers during overpass, and (4) are acquired in descending mode, i.e. at ca 10:40 UTC. This yielded 9 ERS-2 SAR images of 2006. In addition, a number of historical images were processed for use in algorithm development. The selected SAR data were calibrated following Van der Wal et al. (2005). However, brightness values ȕ0 rather than backscattering coefficients ı0 were calculated in the present study to account for differences in incidence angles across the swath. Average ȕ0 values were calculated for every pixel in each image, using a moving window of 9 by 9 pixels in order to improve radiometric resolution.
The images were rectified and transformed to the Dutch National Grid. A mask was applied to exclude (1) areas below -1 m NAP, (2) vegetation, as detected using a Landsat ETM+ image of 1 July 2006, and (3) construction works and areas outside the Western Scheldt. The extent of the maps obtained from SAR images that only cover the eastern part of the Western Scheldt have been fixed for all these images. Data were exported both as Erdas Imagine (.img) raster files, and as ArcGIS grid files.
The algorithm used for retrieval of bed roughness from SAR is based on an analytical backscattering model, i.e. the Integral Equation Model (IEM) developed by Fung et al. (1992), as described in Van der Wal et al. (2005). Backscatter is assumed to depend both on the configuration of the sensor and on surface characteristics, including moisture conditions and surface roughness. An approximate solution of the model was applied using realistic assumptions on such variables, based on previous field measurements (Figure 2.12). The algorithm was validated using in situ bed roughness data of a number of tidal flats measured in the period 2003-2004 (Van der Wal et al., 2005). RMSz (root-mean-square of the heights, in cm) was taken as a measure for vertical bed roughness. In the intertidal flats of the Western Scheldt, this measure relates very well to both ripple lengthO (RMSz|O and ripple height K (RMSz |K (Van der Wal et al., 2005). The field measurements of RMSz were related to brightness values obtained from matching ERS-2 SAR images taken within the same week/month of that year, in a Geographical Information System. Results show that both
Deltares, Flanders Hydraulics, IVM, NIOO 15 Mud transport model for the Scheldt Z4594 December 2008 estuary
the models and field data are in very good accordance for values of bed roughness under RMSz=0.6 cm (Figure 2.12). For larger values, the model indicates that surface moisture rather than bed roughness becomes dominant in determining the backscatter signal. In this domain, SAR with a larger wavelength should be used to retrieve bed roughness on intertidal flats. Since use of such sensors was outside the scope of this project, an upper limit of RMSz=0.6 cm was set to bed roughness here.
It should be noted that the method yields estimates of the surface roughness. This means that, for example, water staying on the surface, within the surface ripples, is also seen as surface, whereas it is actually masking the sediment bed. This generally leads to an underestimate of bed roughness in areas covered by a water layer. The amount of water staying on the surface will depend on tidal stage, but also on weather conditions, especially on rain falling during emergence of the tidal flats. Daily rainfall (from data obtained from the Royal Netherlands Meteorological Institute) was substantial on 1 May (4.2 mm in 8.6 hours), 17 May 2006 (9.3 mm in 2.7 hours) and especially 14 August 2006 (27.0 mm in 7.5 hours). Thus, especially for these days an underestimation of bed roughness (and hence overestimation of mud content of the sediment, see paragraph 2.2.3) could have occurred, depending on the exact timing of the rainfall.
The retrieval algorithm has been applied to the images to yield maps of bed roughness (Figure 2.13). In general, Rug van Baarland, Plaat van Ossenisse and Plaat van Valkenisse have high values for bed roughness, whereas the eastern part of Hooge Platen and the tidal areas in the upper part of the Western Scheldt (Bath, Appelzak) have low values for bed roughness. A seasonal trend is apparent, with bed roughness being lowest in summer and highest in winter.
1.00
0.80
0.60
0.40 IEM with PCA field conditions RMSz (cm)RMSz 0.20 IEM: H =25, L=4 cm Field data Westerschelde '03-'04 0.00 0.00 0.50 1.00 1.50 2.00 E0
Figure 2.12 Relationship of vertical bed roughness RMSz and brightness ȕ0. The solid blue line indicates model results, assuming wet sediment with a dielectric constant H=25. The model represented by the dashed blue line incorporates correlations between bed roughness and moisture conditions as measured in the field. The black dots represent field measurements of RMSz and associated ȕ0 from matching ESA ERS-2 SAR images, and the black line is a regression line through these measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 16 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 2.13 Maps of bed roughness RMSz for different dates in 2006, derived from ESA ERS-2 SAR images. The dashed lines show the extent of the images. For larger maps see Appendix B-1.
Deltares, Flanders Hydraulics, IVM, NIOO 17 Mud transport model for the Scheldt Z4594 December 2008 estuary
2.2.3 Mud content of the sediment from SAR
The SAR images described in paragraph 2.2.2 were also used to retrieve surface mud content (percentage of the fraction of sediment < 63 Pm), using a regression method described and discussed in Van der Wal & Herman (2007). This method assumes a non-linear relationship between mud content and bed roughness (with surface ripples being more pronounced in sand than in mud), and hence with the backscatter signal.
In situ mud content data for calibration were obtained from the GeoSea McLaren data set of September 1993 from Rijkswaterstaat. In situ mud content data for validation were collected by NIOO in September 2004 and June 2005 (Van der Wal & Herman, 2007). In order to be able to better compare the data sets, which had been analysed with different particle sizers, the 2004/2005 data were transformed. The in situ data were then related to brightness values of matching ERS-2 SAR images in a Geographical Information System, and used to fit the regression model (calibration) and to validate the model (Figure 2.14). Data from sample sites located below < –1m NAP were discarded, to exclude areas possibly covered by water.
In general, significant predictions of mud content can be made from ERS-2 SAR data. Nevertheless, there is a large amount of scatter in the graphs of predictions from remote sensing versus in situ data (Figure 2.14). Part of this scatter is due the fact that a layer of surface water between the bed ripples (see paragraph 2.2), diatoms and animal activity may affect (apparent) roughness (and hence the backscatter signal). In addition, the relationship between mud content and surface roughness may vary with flow conditions. These problems can be overcome to some extent by using complementary optical remote sensing data (Van der Wal & Herman, 2007). However, part of the scatter in the graphs stems from the mismatch in scale of support of in situ and satellite data (i.e., a sample is taken from a single point, whereas the satellite image takes into account a much larger area). This does not necessarily mean that the error margin of the estimate from remote sensing itself is large (it can also point to variation in mud content at subpixel scale), and the non-linear regression equation between in situ and remote sensing data can still represent a good (‘mean’) relationship valid for this larger spatial scale. However, as can be seen from the figure, an overestimation of the mud content is also apparent from the validation data sets, in particular for the sandy sites. As both the September 2004 and June 2005 data sets suffer from this problem, this may be largely related to the calibration of the laser particle sizers used for the 1993 data and 2004/2005 data, respectively, and pre- treatment of samples (whether or not carbonates and organic matter is removed prior to analysis). 5 5 Sep 1993 Sep 2004 Cal 1:1 4 4 Val Jun 2005 1:1 3 3
2 2 ln(mud+1) observed .
1 ln (mud+1) observed . 1
0 0 0 1 2 3 4 5 0 1 2 3 4 5 ln (mud+1) predicted . ln (mud+1) predicted .
Figure 2.14 Results for calibration (left) and validation (right) of the mud content retrieval scheme using in situ data from Rijkswaterstaat and NIOO.
Deltares, Flanders Hydraulics, IVM, NIOO 18 Mud transport model for the Scheldt Z4594 December 2008 estuary
The mud content retrieval algorithm was applied to the SAR images to yield maps of the mud content (Figure 2.15). In addition, an average, standard deviation, and coefficient of variation have been calculated from the maps that covered the entire Western Scheldt, i.e. the maps of 14 January 2006, 1 May 2006, 14 August and 27 November 2006 (Figure 2.16) to gain further insight in the dynamics of the distribution of mud. The Western Scheldt was also subdivided in separate tidal areas (Figure 2.17). The mean mud content for each area was calculated for each of the images to assess seasonal changes (Figure 2.18).
Figure 2.15 Maps of mud content of the sediment for different dates in 2006 derived from ESA ERS- 2 SAR images. Dashed square indicates remote sensing scene boundaries in case of partial coverage of the Western Scheldt. For larger maps see Appendix B-2.
Deltares, Flanders Hydraulics, IVM, NIOO 19 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 2.16 Spatial distribution of average mud content, and seasonal dynamics of mud content (as expressed by the standard deviation and coefficient of variation) over the year 2006, derived from ESA ERS-2 SAR images.
Deltares, Flanders Hydraulics, IVM, NIOO 20 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 2.17 Overview of the tidal areas used in the analysis.
50 Middelplaat Rug vanBaarland Molenplaat 40 Plaat van Ossenisse Plaat van Walsoorden 30 Plaat van Valkenisse Paulinapolder Slikken van Everingen 20
Mud (%) . (%) Mud Biezelingse Ham Kapellebank Baalhoek 10 Slikken van Saeftinge Slikken van Bath 0 Appelzak 16Jan06 1Feb06 1May06 17May06 26Jul06 14Aug06 30Aug06 8Nov06 27Nov06
30 Mid-estuary flats (platen) Fringing mudflats (slikken) 20 All intertidal areas
Mud (%) . Mud 10
0 16Jan06 1Feb06 1May06 17May06 26Jul06 14Aug06 30Aug06 8Nov06 27Nov06 Figure 2.18 Mud content of the main intertidal areas shown in Figure 2.17.
The spatial distribution of surface mud content is generally persistent over time (Figure 2.15 and Figure 2.16). The general spatial distribution of mud content is represented in the average map in Figure 2.16. Nevertheless, a clear seasonal variation is shown, with low mud contents (on average ca 10 %) in winter, and higher mud content in summer (on average ca 20 %) (Figure 2.). The tidal flats in the estuary ('platen') are found to be sandier than the fringing mud flats ('slikken'), and seasonal change is more pronounced on the tidal flats in the estuary than on the fringing mud flats (Figure 2.18). As can be seen in the map of the standard deviation and especially the map of the coefficient of variation of mud contents over the year (Figure 2.16), there is a large heterogeneity in seasonal dynamics even within the tidal flats and mudflats, with only part of each tidal flat experiencing large seasonal changes, whereas other parts stay sandy or muddy throughout the year.
Deltares, Flanders Hydraulics, IVM, NIOO 21 Mud transport model for the Scheldt Z4594 December 2008 estuary
2.2.4 Mud content from optical remote sensing
One of the Envisat MERIS FR images used for retrieval of water quality parameters (i.e., the image of 11 September 2006 as collected by VU-IVM, see paragraph 2.1) was used to test whether medium resolution data (in this case 300 m spatial resolution) may also be suitable to retrieve mud content. The image was converted and geocorrected to the Dutch National Grid system. In situ data of mud content for calibration were taken from the Plaat van Walsoorden on 31 August 2006 (as collected for a project for Flanders Hydraulics and Maritime Access Division, see Van der Wal et al., 2008). Again, mud contents were transformed as described above. Calibration was performed following Van der Wal & Herman (2007).
The result is presented inFigure 2.19. The general pattern resembles the pattern found with SAR. In addition, spatial resolution of MERIS FR appears suitable for application or validation of surface mud content in the mud transport model. Nevertheless, the mud content is overall very low, except for, especially, the Plaat van Walsoorden Walsoorden and Hooge Platen. A better calibration/validation data set (i.e., one that is representative for the entire Western Scheldt) is recommended. A first improvement could be to calibrate the algorithm on in situ data and a matching MERIS FR image from September 2004, and validate it on in situ data and a matching MERIS FR image from June 2005, respectively, and then apply it to the MERIS-FR imagery of 2006.
Figure 2.19 Mud content derived from a MERIS-FR image of 11 September 2006.
Deltares, Flanders Hydraulics, IVM, NIOO 22 Mud transport model for the Scheldt Z4594 December 2008 estuary
3 Model calibration and validation
3.1 Hydrodynamics
3.1.1 Numerical grid and resolution
The horizontal grid used in the hydrodynamical simulations is shown in Figure 3.1. The grid has its maximal dimensions of (379 x 2948) (written as MMAX x NMAX) and counts 5 sigma layers in the vertical. The sigma layers are distributed in a logarithmic way, with the finest layer closest to the bottom. This vertical discretisation gives the highest resolution in the zone with the highest gradient of velocities and sediment concentrations.
Figure 3.1 Hydrodynamical grid and boundary conditions. Around the port of Zeebrugge, the (horizontal) resolution is between 150 – 200 m. In the far field of the model, the resolution becomes coarser (250m – 400m). The average resolution in the Western Scheldt is around 100m. Further upstream (Upper Sea Scheldt around Gent, upstream reaches of the Nete, Dijle and Zenne tributaries) the grid resolution is up to 30m. The grid counts 224.423 active grid cells per layer. In comparison with the previous version of the grid, as described in (Van Kessel et al., 2006) this version of the grid has a more detailed schematisation of the Rupel basin. Furthermore, special attention was paid to the removing of flow-blockages in the grid and/or the bathymetry in the upstream parts of the model. This process is covered extensively in (Vanlede et al.,2008).
3.1.2 Model characteristics
The 3D hydrodynamics is calculated in TRIWAQ, a module of SIMONA. Approximately one year is calculated, from 05-01-2006 5:30 (HW Vlissingen) to 01-01-2007 00:00. This calculation takes 27 days to complete (speed-up of 13), plus 3 extra days to write the final SDS-files to an external disk. The total amount of data produced equals 2 TB (Terabyte).The calculation was done in Simona release 2007-01, on 11 dual core nodes of the LINUX cluster of Flanders Hydraulics. Some selected numerical settings of the model are included in Table 3.1.
Deltares, Flanders Hydraulics, IVM, NIOO 23 Mud transport model for the Scheldt Z4594 December 2008 estuary
Table 3.1 Selected numerical settings for the hydrodynamic model.
Name Value Unit Timestep 0.125 min Max number of iterations for continuity equation (itercon) 16 - Max number of iterations for momentum equation (itermom) 32 - Convergence criterion for velocities in momentum equation 0.001 m/s Convergence criterion for water levels in continuity equation 0.0005 m Eddy viscosity 1 m²/s Dynamic Viscosity 0.001 kg/m s Diffusion 10 m²/s
3.1.3 Calibration of model east of Cadzand-Westkapelle (simF10)
The 3D model was calibrated using measured waterlevels at Cadzand and Westkapelle as downstream boundary condition. This implies that during calibration, only the roughness in the estuarine part of the model is varied, see Figure 3.2. Downstream the boundary of Cadzand Westkapelle, a uniform roughness of (Manning) 0.024 m-1/3/s is applied. The use of the Manning formula for roughness implies that the computational roughness (which is a Chezy value) is dependent of water depth. Because of project planning, it was impossible to incorporate at this stage the roughness values as estimated from remote sensing (see §2.2.2) in the hydrodynamics simulation. It is expected that a change in roughness on the mudflats will only have a local effect on the simulated velocities. There is however only a limited amount of data available on measured velocities in shallow areas.
Figure 3.2 Manning Coefficient of the calibrated roughness field [m-1/3/s]
Harmonic analysis of water levels
The calibrated model with the measured water levels as downstream boundary condition at Cadzand – Westkapelle gives a good agreement with measurements for the entire modelling domain. This is illustrated in Figure 3.3, which shows the amplitude of the main semidiurnal component of the tidal variation of waterlevel (M2). For a more detailed description of the harmonic analysis of the tidal signal, the reader is referred to (Godin, 1972) or (Foreman, 1977). The tidal analyses presented in this report are
Deltares, Flanders Hydraulics, IVM, NIOO 24 Mud transport model for the Scheldt Z4594 December 2008 estuary
performed in MATLAB using the programme ‘t_tide’, as developed by Pawlowicz et al. (2002).
Figure 3.3 Variation of M2 amplitude over the Scheldt estuary. Model F10 and measurements
Figure 3.4: flow through MOVE transect 7, Pas van Figure 3.5: flow through MOVE transect 7, Terneuzen (model and measurements) Everingen (model and measurements)
Figure 3.3 shows that the propagation of the tidal wave in the estuary is modelled correctly, and that the balancing effect of geometrical convergence of the estuary and friction is well represented.
Figure 3.4 and Figure 3.5 also show that the flow through ebb and flood channels is correctly modelled. The measurements shown in both figures are from 2002, while the simulation results are from 2006. In order to make the comparison, a comparable tide has been selected from the model results.
3.1.4 Validation of model with ZUNO boundary (simG06)
At the seaward boundary, the 3D LTV-slib model is coupled with the larger ZUNO (Zuidelijke Noordzee) model. This is the same model as the one described in §3.3.1, but naturally with a different enclosure and a different set of boundary conditions. The two boundaries perpendicular to the coastline are implemented as velocity boundaries. The boundary parallel to the coastline is implemented as a Riemann boundary (see Figure 3.1 for the location of the different boundaries). This combination of velocity and
Deltares, Flanders Hydraulics, IVM, NIOO 25 Mud transport model for the Scheldt Z4594 December 2008 estuary
Riemann boundaries gave the best results for water levels and velocities in the LTV-slib model. Note that both for the velocity and the Riemann boundary, only the velocity component perpendicular to the boundary is taken into account.
Water levels
At locations Westhinder and Vlakte van de Raan, the model results of ZUNO and LTV- slib were compared. The waterlevel at Westhinder corresponds closely between ZUNO and LTV-slib. This implies that the combination of velocity and Riemann boundary give a well posed hydrodynamical problem. At station Vlakte van de Raan, the correspondance between the two models is less good, specifically around high water. This effect is attributed to the fact that the LTV-slib model is (due to a finer resolution and thus a better representation of the bathymetry) better suited to model water levels close to the shore than the coarser ZUNO model. Water levels at 5 selected stations (model and measured) are included in Appendix C.
Harmonic analysis of water levels
Figure 3.6 shows the variation of M2 amplitude over the Scheldt estuary for the model which is coupled to ZUNO. Compared to the calibrated result (Figure 3.3), this result naturally is less good. This was expected because the measured water level boundary at Cadzand-Westkapelle has been replaced with a modelled velocity and Riemann boundary as taken from the ZUNO model. In the coupled model, the error in M2 amplitude remains smaller than 2% up to Temse. Further upstream, the model predicts an amplitude of M2 which is too high. The error in M2 amplitude at Dendermonde is 20 cm. The most important deviation between model and measurements lies in the phase of the M4 component (see Figure 3.7). Figure 3.7 shows that a phase error of 30 degrees enters the model through the boundary. A sensitivity analysis (see Vanlede, 2008) shows that the phase of M4 can be selectively corrected in the boundary conditions in order to solve this problem. Due to the calculation time needed to simulate one full year, this correction in boundary conditions could not be included in the final hydrodynamic simulation. A subsequent calibration of the model, coupled to the ZUNO model will be done at a later stage in the project.
Figure 3.6 Variation of M2 amplitude over the Scheldt estuary. Model G06 and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 26 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 3.7 Phase of M4 component over the estuary. Model G06 and measurements.
Salinity
The results of simulation G06 for salinity (whole year 2006) are included in Appendix C. The salinity at station Baalhoek is well represented in the model. The salinity in the vicinity of Deurganckdok is however 1 to 2 ppt too high. The results at Vlakte van de Raan suggest that the salinity at the sea-boundary is 2 ppt too low. The measurements however show large vertical gradients (Figure C-6), and frequently inverse stratification is observed where the top sensor measures more saline water then the bottom one. This could indicate a problem with the salinity sensor at this location. At location Hoofdplaat the salinity in the model has a tidal variation which is too low.
3.1.5 Discussion
Two different model configurations have been shown. The model roughness has been calibrated with a water level boundary at Cadzand Westkapelle (simulation F10). The use of measured boundary conditions during the calibration makes sure that the calibration corresponds to some physical reality of friction in the estuary, and that the calibration does not correct for errors in the boundary condition. The transport calculations of suspended sediment however are done with a model of which the boundary conditions are read-in from a previous ZUNO run (simulation G06). The model results are naturally less good when the boundary conditions are read in from a large-scale model, as compared to boundary conditions derived from measurements. Potentially worrisome is the phase error in the M4 tidal component, which will influence the eb/flood tidal assymetry and thus the residual sediment transport in the final model. More specifically, the phase difference between M4 and M2 components will influence § dU · § dU · the ratio ¨ ¸ /¨ ¸ , which in turn will influence the net direction of cohesive © dt ¹ HWS © dt ¹ LWS sediment transport, as is elaborated by Winterwerp (2003).
Deltares, Flanders Hydraulics, IVM, NIOO 27 Mud transport model for the Scheldt Z4594 December 2008 estuary
3.2 Mud transport
The set-up of the mud transport model is discussed in the 2006 and 2007 reports (van Kessel et al., 2006 and 2007).
The discussion of the mud model is split into two parts:
1 Further model improvements and validation 2 Model application
The first part is discussed in the present section, the second part will be discussed in Chapter 4.
In 2008, the model is applied and validated regarding the following aspects:
1 Year simulation, seasonal dynamics (both year 2000 with last year’s hydrodynamic model and 2006 with improved model); 2 Validation on remote sensing data (regarding both TSM and bed composition) 3 Analyses of long-term mud balance 4 Validation on in-situ data near Antwerp
These aspects are discussed below in sequential order.
3.2.1 Year simulations
In 2007, hydrodynamic simulations were made for a period of 3 month. However, a period of at least one full year is required to assess the model performance with regard to seasonal dynamics. Therefore year simulations were made, see also Chapter 3.1. The year 2000 was simulated with the 2007 version of the hydrodynamic model, whereas the year 2006 was simulated with the 2008 version of this model. The rationale behind the year 2006 is that a) remote sensing images and b) high frequency OBS measurements near Antwerp are available for this year, which provides valuable validation material.
The observed seasonal dynamics in the Western Scheldt may have several causes:
• External causes, such as a higher sediment supply from sea and up-estuary in winter; • Internal causes, such as variable wind stress and changed sediment properties (e.g. critical shear stress for erosion on mud flats or settling velocity) because of biological influences; • Anthropogenic causes, such as a higher release rate of dredged material in winter.
The mud model may help to determine which of these causes are more likely to contribute. At present, only the seasonal dynamics in the external mud supply and the wind stress are accounted for in the mud model. Sediment properties are assumed to be constant throughout the year, but seasonal fluctuations can be easily included in the future when justified by empirical evidence. The release of dredged material is accounted for, but this flux is assumed constant throughout the year. Again, no technical limitations exist to apply a more sophisticated dumping scenario, but this requires detailed analysis of dredging works.
Deltares, Flanders Hydraulics, IVM, NIOO 28 Mud transport model for the Scheldt Z4594 December 2008 estuary
Results on the modelled and observed seasonal dynamics at Terneuzen are shown in Appendix D.3. Although the model exhibits distinct seasonal dynamics, its amplitude is too small compared with observations. To reproduce seasonal dynamics better, it is probably required to apply seasonally varying settings for either settling velocity and/or critical shear stress for erosion. It is noted that the modelled seasonal dynamics for 2000 does not differ much from that for 2006 (Appendix D.4). This may be explained by the wind climate, which does not vary much between 2000 and 2006. Wind drives (short) waves that may resuspend sediment. Variable wind forcing is included in the mud transport model, either based on the SWAN wave model or a simpler fetch length approach.
From Appendix 6D.4 it is clear that typical concentrations for the 2000 and 2006 simulations are substantially lower than for the 3 month simulation, although the parameter settings of these simulations are equal. Investigation of the cause for this is ongoing. One of the possible causes is that for the 3 month simulation winter-average boundary concentrations have been used and for the year simulations seasonally varying boundary concentrations, which are lower in year-averaged terms. Another possible cause is the change in vertical layer distribution from uniform for the 3 month simulations to logarithmic for the year simulations. For the time being, higher SPM levels have been attained by reducing the settling velocity of one of the sediment fractions (runID y03, results shown in Appendices D.1 – D.3 and D.5 – D.6). However, also simulations with the original settings have been made (runID y04).
3.2.2 Validation on remote sensing
Appendix 6D.5 shows a comparison between modelled and observed TSM surface concentrations. Only the satellite data considered as best products have been used in the comparison. The original satellite products are shown in Appendix A2. It should be noted that herein, unreliable data is marked with a grey tone. Images have been reprocessed to get an colour scale identical with that of the model simulations. Herein, the error flags of the satellite product have been ignored to show more data. The comparison should therefore be made with caution, keeping in mind the extent of the grey areas shown in Appendix A2. Another complication is that the hydrodynamic model results for 2006 have an upper layer thickness of the vertical grid of 41% of the total water depth. Especially in channels, the satellite data is representative for a much thinner layer (typically less than 2 m, depending on turbidity), which complicates the comparison. For an optimal comparison, a thinner surface layer in the hydrodynamic model is recommended.
At the present stage of research, neither data from remote sensing nor from the model can be considered as ‘reality’, but much is to be learned from their comparison. Notwithstanding the demanding estuarine conditions both for mud model and satellite product, typical spatial patterns and temporal changes agree fairly for most of the selected dates (selection based on the quality of the remote sensing data, not on the match between remotely-sensed and computed data).
Figure 3.8 shows a comparison between modelled and remotely-sensed bed composition of plates. Note that the model also provides output on bed composition in subtidal areas, where the remote sensing technique used can not be applied. At an aggregated level, the model reproduces the observed trends in bed composition satisfactorily, i.e. a lower mud content at exposed plates and a higher mud content at sheltered sandy flats (platen) and mudflats (slikken). For a more detailed spatial
Deltares, Flanders Hydraulics, IVM, NIOO 29 Mud transport model for the Scheldt Z4594 December 2008 estuary
comparison a higher model resolution would be required, as the (aggregated) model grid resolution is about 200 m, whereas the SAR resolution is about 30 m.
The mud model does not yet reproduce the observed seasonal dynamics in bed composition at plates. Although the seasonal dynamics of the suspended sediment level are partly reproduced, this is not reflected in the bed composition. In the model, the mud content in summer is not significantly higher than in winter. In the main channel, even the reverse is true: lower suspended sediment levels in summer result in a lower mud fraction in the channel bed. Apparently, observed seasonal dynamics are determined – at least partly – by other factors than the seasonal physical forcing as applied in the present model (i.e. wind, waves and freshwater discharge). Chemically or biologically induced changes in settling velocity and erodibility of plates are probable factors. If these forcing functions would be known from observations (or can be based on information that can be derived from remote sensing, such as microphytobenthos biomass and surface roughness), they could be applied to the mud model. Technically, this is a minor effort.
Figure 3.8 Typical modelled (top) and observed (bottom) mud percentage in the bed.
Deltares, Flanders Hydraulics, IVM, NIOO 30 Mud transport model for the Scheldt Z4594 December 2008 estuary
3.2.3 Mud balance
Figure 3.9 and Figure 3.10 show the gross and net fine sediment flux between the Western Scheldt and the North Sea through the transect Vlissingen – Breskens. Gross tidal import and export is in the order of 100 kton/day with peaks of 300 kton/day. The net flux fluctuates around zero, with a typical amplitude of 10 kton/day but with peaks up to 50 kton/day. Integrated over a longer period, a net export is modelled, which amounts to about 2 Mton accumulated over the whole year. Note that this number is the difference of a gross export of 37 Mton and a gross import of 35 Mton, i.e. a residual flux of 6%. This number is of the same order as the number reported in previous year’s report on the 3-month simulations. In this respect, the year simulation does not lead to different conclusions.
However, looking at the evolution of the net flux over the year (Figure 3.10), it is observed that the net export in the winter month is slightly less than in the summer month. Storm events at sea result in high TSM concentrations and temporary tide- averaged import towards the Western Scheldt. These events are more frequent in winter than in summer. Export in summer might be reduced when more sediment would be seasonally stored on the tidal flats. This may be achieved by applying seasonally varying settings for the critical shear stress for erosion. However, the following example based on remote sensing data from mudflats suggest that it may only be a partial solution, as the seasonal buffer on mudflats is likely to be much smaller than the 2 MT required to buffer the net yearly export.
Remote sensing only detects the mud content of the surface layer of the intertidal flats and not the thickness of the mud layer, so that a mud balance can not be directly derived from the remote sensing data. However, results from sequential in situ sediment sampling of depth profiles on a site on the Molenplaat in the Westerschelde suggest that this active layer may be 3 to 5 cm deep (Herman et al., 2001). A very crude estimate can thus be derived from the remote sensing results on the importance of the seasonal variation in surface mud content for the mud balance of the Westerschelde. Assuming that the entire intertidal area of the Western Scheldt has an active layer of 3 cm depth that is controlled by dynamics in mud deposition/erosion and not by sand deposition/erosion, the total intertidal area of ca 63 u 106 m2 has an active sediment volume of ca 1.9 u 106 m3. With an average volumetric mud content of 10% in winter, there is ca 190 kton mud present, whereas in summer, with an average mud content of 20%, ca 380 kton mud is stored in this layer, assuming a dry density of mud of ca 1000 kg m-3. Thus, there is a seasonal difference of ca 190 u 103 m3 or 190 kton mud for the entire intertidal area of the Western Scheldt.
As the modelled concentration gradient between the inner and outer estuary appears to be realistic, the computed export is probably caused by an underestimation of the estuarine circulation. An animation of a neap-spring cycle of thalweg plots (see Appendix 6D.6 as an example) demonstrates that estuarine turbidity maximum (ETM) near Antwerp follows the salinity front, which is in agreement with observations. However, the concentration level in the ETM remains too low, notably near the bed. Apart from grid resolution and dispersion, also tidal asymmetry may still be a problem (see Section 3.1). Future focus on this aspect may also resolve the related problem of the inbalance between modelled harbour siltation and sediment release. For further discussion on the latter issue reference is made to last year’s report (Van Kessel et al, 2007).
Deltares, Flanders Hydraulics, IVM, NIOO 31 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 3.9 Gross and net fine sediment flux (kton/day) between Western Scheldt and North Sea through transect Vlissingen – Breskens. Positive is import.
Figure 3.10 Net fine sediment flux(kton/day) between Western Scheldt and North Sea through transect Vlissingen – Breskens. Note that information on net and cumulative fluxes is identical to that in Figure 3.9, but the scale on the vertical axis is different.
Deltares, Flanders Hydraulics, IVM, NIOO 32 Mud transport model for the Scheldt Z4594 December 2008 estuary
3.2.4 Validation on measurements near Antwerp
Observations
The sediment dynamics near the Deurganckdok have been thoroughly monitored and analysed during the HCBS 1, 2, and 3 campaigns, and in the framework of the Slib3D model development. To determine the applicability of the LTV mud model in the Lower Sea Scheldt, both the model and the data is analysed in this section.
Figure 3.11 13-hours measurements DGD on 16-02-2005
Extensive measurements were done on the K-transect downstream of the Deurganckdok (see Figure 3.11). Combined ADCP and siltprofiler measurements were done on February 2005 (IMDC 2005a, b), March 2006 (IMDC 2006a, b), and September 2006 (IMDC 2007a, b). The winter-spring measurements (February and March) were very similar in absolute sediment concentrations (peaking at about 1 g/l near the bed), but also in tidal and cross-sectional variation. The September 2006 measurements revealed lower sediment concentrations (up to 0.3 g/l), but the tidal and cross-sectional variation of sediment concentration was the same. The tidal and cross- sectional variation of the sediment dynamics measured on February 2005 is given in Figure 3.12. These measurements reveal that:
1 The sediment concentrations during the flood reach their maximum 0-1 hrs before high water, 2 The sediment concentrations during the ebb reach their maximum 2-3 hrs before low water, 3 The flood concentrations peak on the south bank of the Scheldt River (i.e. close to the Deurganckdok), whereas the ebb concentrations peak on the north bank, and 4 The ebb- and flood near-bed concentrations are 0.8 to 1 g/l whereas near-surface concentrations are up to 0.2 g/l
Deltares, Flanders Hydraulics, IVM, NIOO 33 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 3.12 Sediment concentration measured on 16-02-2005 at locations Ka, Kb, Kc, and Kd.
Model results
The focus in the LTV mud model has so far mainly been on the sediment concentration patterns in the Western Sea Scheldt. The aim here is therefore not to do a detailed comparison of measured and modeled sediment concentrations. We rather aim to determine to what extent the model reproduces the typical sediment transport processes in the Lower Sea Scheldt. Hence, we also do not compare two identical periods, but compare typical periods. We compare the February 2005 measurements with the April 2006 model results because April is probably reasonably representative for ‘average’ tidal conditions (Figure 3.13). In April, we use the spring tide conditions of 14 April (Figure 3.14), because the 16 February 2005 measurements were also carried out during spring tide conditions.
Two different model runs are analysed here: run y03 (is the ‘fine sediment’ run) and y04 (the ‘coarse sediment’ run). The settling velocity in y04 is 1 mm/s, but y03 is composed of one fraction with a settling velocity of 0.2 mm/s (30%) and another of 1 mm/s (70%).
Both model runs compute:
1 Highest sediment concentrations during low water, and lowest sediment concentrations during ebb. 2 An increase in sediment concentrations from Ka to Kd (see Figure 3.11) at low water. 3 Cross-sectional uniform concentrations during high water. 4 A more pronounced vertical gradient in the sediment concentration at high water than at low water.
Deltares, Flanders Hydraulics, IVM, NIOO 34 Mud transport model for the Scheldt Z4594 December 2008 estuary
The main difference in the model runs is the sediment concentration: in run y03 the sediment concentration varies from 70 to 160 mg/l, and in run y04 the sediment concentration varies from 10 to 100 mg/l. Hence, the cross-sectional and temporal sediment concentration gradients are the same in y03 and y04. This also implies that the associated sediment transport patterns and mechanism are also similar. This contrasts with results obtained with the 3D mud model, where changes in the settling velocity produced pronounced difference in the cross-sectional distribution of sediment concentration peaks (Van Maren, 2007).
The main model agreement with measurements is that the low water sediment concentrations increase from Ka to Kd: the sediment concentration is highest in the inner bend of the river at the end of ebb. The model results disagree with measurements in the following aspects:
1 The cross-sectional sediment concentration variation at low water is underestimated, ranging from 110 to 160 mg/l (y03) or from 60 to 110 mg/l (y04), compared to 100 mg/l to 1000 mg/l in the measurements. 2 The high water near-bed sediment concentration peak observed at Ka-Kb (the outer bend) is not reproduced by the model.
Figure 3.13 Modelled and measured near-bed sediment concentration at Boei84, 2006, run y03.
Deltares, Flanders Hydraulics, IVM, NIOO 35 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 3.14 Modelled near-bed sediment concentration at Boei84, April 2006, run y03.
Discussion
The observation that the model strongly underestimates the cross-sectional variation of the sediment concentration peaks can be explained as follows.
1 The centrifugal acceleration increases with the flow velocity, whereas the lateral total pressure gradient needed to keep the flow in a circular path is constant over depth. Therefore the surface layer (with high flow velocities) is deflected towards the outer bend of a river while the bottom layers are deflected towards the inner bend (e.g. Jansen et al., 1979). A high vertical velocity gradient, caused by stratification, will therefore lead to a stronger secondary circulation. Curvature- induced secondary flows generate a near-bottom flow from the outer bank to the inner bank, and therefore result in concentration peaks near the inner bank. Although the LTV mud model indeed reproduces the higher sediment concentrations at the inner bend (low water), the cross-sectional variation is underestimated. Probably, the model resolution is too low (horizontal as well as vertical) to accurately reproduce the secondary circulation. 2 The horizontal diffusion used in the model is too high.
The observed sediment concentration peaks at the outer bend (Figure 3.12) is not reproduced by the model. However, this peak is also not simulated at the inner bend. This suggests that there is too little sediment in the model domain downstream of the Deurganckdok. However, even if this amount is present downstream of the Deurganckdok, it is questionable whether the computed sediment concentration peak around high water would then be in simulated in the outer bend. Winterwerp et al. (2006a) showed that secondary flow patterns in the lower Sea Scheldt may also be anti-clockwise. With a sufficiently strong longitudinal and transverse salinity gradient, and strengthened by vertical stratification, the secondary currents may be reversed during the accelerating phase of the tide due to reduced vertical mixing. This effect probably plays a role in the cross-sectional location of the sediment concentration peaks. However, to numerically reproduce the effect of vertical mixing on the secondary circulation, requires a high vertical resolution. The five vertical layers modeled with the LTV model are probably insufficient.
The observation that the difference in grain size does not influence the spatial patterns in sediment concentration indicates that the transport processes themselves have not changed.
Deltares, Flanders Hydraulics, IVM, NIOO 36 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure 3.15 Sediment concentration modelled on 16-02-2005 at locations Ka, Kb, Kc, and Kd, run y03. The contour line interval is 20 mg/l.
Figure 3.16 Sediment concentration modelled on 16-02-2005 at locations Ka, Kb, Kc, and Kd, run y04. The contour line interval is 20 mg/l.
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4 Model application
4.1 2nd fairway enlargement
As an example for model application, a scenario analysis is performed on the effect of the second deepening of the Scheldt. The capital dredging east of Vlissingen for this second deepening was performed from July 1997 to July 1998 in the Western Scheldt and the Lower Sea Scheldt, and is estimated to amount to 7.5 Mm³ (RIKZ, 2007). The capital dredging east of Vlissingen is mainly limited to the deepening of the naturally occurring sills in the navigational channel.
Bathymetry
For this scenario, the effects of the second deepening are “un-done” in the bathymetry of 2006, so to speak. This was done as follows. The bathymetry of the Western Scheldt for the year 1996, and the bathymetry of the Sea Scheldt for the year 1990 were made available by RIKZ and “Dienst Hydrografie”, respectively. Both show the bathymetry in the river Scheldt before the second deepening was performed from 1997 to 1998. In order to isolate the effect of the second deepening, the model settings are kept identical to the ones used to calculate the hydrodynamics for the entire year 2006, as reported in (Vanlede et al., 2008). Only the sills in our model are brought to the situation pre-1997. Resulting differences are shown in Figure C-11. Typically, the sill heights for the pre-1997 situation are about 2 m more than for the 2006 situation.
Results on mud concentration
Subsequently, mud simulations have been made based on both hydrodynamic simulations, with equal settings for sediment parameters. Figure 4.1 shows the absolute (in mg/l) and relative (in %) change in surface SPM levels caused by the second deepening. A concentration increase (red colour) implies that present SPM levels are higher than pre-1997 levels, whereas a concentration decrease (blue colour) implies that present SPM levels are lower than pre-1997 levels.
From Figure 4.1 is concluded that the impact of the second deepening is minor. Typically, SPM levels change a few percent only. The modelled changes are small compared to natural variations (tide, wind, seasonal). Typically, concentrations decrease because of the 2nd deepening. This effect increases towards Antwerp, but remains minor. However, near Vlissingen a minor concentration increase is observed. Nearby Breskens a decrease is observed. These changes are probably caused by the location of deepening near Vlissingen: when this channel attracts more current at the expense of the southern channel, the influence of turbidity maximum near Zeebrugge becomes larger near Vlissingen and smaller near Breskens.
The effect of harbour siltation has not been quantified but will be small in view of the minor changes in TSM levels. Sloehaven may have experienced a slight increase in dredging volumes after the 2nd deepening, but the effect is likely to be too small to be noticed by port authorities, given the large natural variations in harbour siltation.
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Figure 4.1 Modelled absolute (mg/l) and relative (%) change in 3-month average surface SPM concentration caused by the 2nd deepening of the Western Scheldt.
4.2 Dredged material release at Zeebrugge
Using a detailed hydrodynamic model of the Zeebrugge area, the effect of the release of dredged sediment from Zeebrugge harbour is investigated. The procedure is similar to the one followed for last year’s assessment of the release of dredged material from Sloehaven: for several release locations the impact on suspended sediment levels and harbour siltation (i.e. return currents) will be investigated. However, at the moment of writing of this report, this is still work in progress that can not yet be reported. It will be included into the 2009 report. We would like to draw your attention however to Appendix D.5 where increased SPM concentrations are occasionally also registered in the remote sensing products.
Deltares, Flanders Hydraulics, IVM, NIOO 39 Mud transport model for the Scheldt Z4594 December 2008 estuary
5 Conclusions and recommendations
5.1 On data
Remote sensing was used to retrieve information on suspended sediment (SPM) near the water surface, and on the bottom sediment composition of intertidal areas.
From the 31 images that seemed good ocean colour measurements under clear conditions covering the Western Scheldt in 2006, 19 best SPM products (of 12 images, 7 times covering both North Sea and Western Scheldt) remained (see Appendix A-2). Best SPM products for the North Sea until the line Vlissingen-Breskens are those with Belgica2000 in the (file)name. Best for the Western Scheldt (until the line Vlissingen- Breskens) are those with Restwes99Oroma02mean in the (file)name. Applying a customised atmospheric correction to the ocean colour measurements might improve this situation and increase the number of SPM products, and cells with valid SPM concentrations. The current best SPM products show distinct variability in space and time, which is a highly valuable contribution to both system understanding and model calibration or validation. Points of further attention are the transitions and areas of validity between optical absorbing and scattering properties of substances in the water (sIOPs) for the Flemish-Dutch coastal zone and the Western Scheldt.
Recommendations with respect to remote sensing of suspended sediment concentration are:
1. Collection of new optical properties (sIOP sets) will improve the parameterisation of the algorithm. The quality of existing sIOP-sets from individual measurement campaigns was reasonable (showing consistent gradients within Western Scheldt sIOPs), but the difference between sets (because of lab measurement accuracy problems) was too large and propagates in the retrieved SPM concentrations. Retrieval of coloured dissolved organic matter (CDOM) seems also prone to errors with the current parameterisation. In addition, a customised atmospheric correction should be performed to obtain more coverage: unveil some of the data that are now still flagged off (by PCD_1_13). 2. Collection of additional validation data in the western part of the Western Scheldt, particularly in situ SPM (and chlorophyll, CHL) concentrations at –1 m (standard MWTL) depth, will allow assessment of the best parameterisation. 3. Besides attention for vertical coupling of concentrations near the water surface with concentrations at the bed through SPM profiles, horizontal gradients in IOPs and SPM over the estuary should be investigated. They should be related to the influence of tide, wind, discharge, and organic/inorganic composition (season), before it is possible to distinguish natural variations from human impact. 4. Outcomes of different SPM algorithms (many narrow bands for MERIS versus simple for Landsat) should be compared. 5. The quality of the demo-CHL and light attenuation (KD) products should be checked; they should be validated and if possible improved.
Deltares, Flanders Hydraulics, IVM, NIOO 40 Mud transport model for the Scheldt Z4594 December 2008 estuary
Regarding remote sensing of bed composition, both information on bed roughness and on the mud content of the sediment of intertidal areas has been obtained, focusing on different seasons of 2006. The results show that there is a clear seasonal variation both in bed roughness and mud content of the sediment, with a maximum bed roughness and minimum mud content in winter (January 2006) and a minimum bed roughness and maximum mud content at the end of the summer (August 2006). The tidal flats fringing the dikes and salt marshes (‘slikken’) were generally more muddy at the surface, but showed less seasonal variation than the shoals (‘platen’).
Recommendations with respect to remote sensing of mud content of the bottom sediment are:
1. The method of retrieving mud content of the bottom sediment can be improved. For SAR, a better calibration and validation could lead to more reliable results. In particular, validation should be carried out for different seasons. 2. In addition, improvements are expected when using a combination of optical remote sensing (from Landsat ETM+ or Envisat MERIS FR) and SAR remote sensing (cf. Van der Wal & Herman, 2007). This method is not yet suitable for prediction without groundtruthing, but can be improved for use with limited groundtruthing.
5.2 On model development
Major achievements in 2008 are grid refinements in the upper Sea Scheldt and its tributaries, improved hydrodynamics (notably with respect to tidal propagation and tidal asymmetry), year simulations for the year 2006 (and for the year 2000 with last year’s model) and application hereof in the mud model. As a result, evaluation of the model performance with respect to seasonal dynamics is possible. It is concluded that although the mud model exhibits seasonal dynamics, its amplitude is small compared with observations. This suggests that seasonal trends do not only have external causes such as variable sediment supply, but also internal causes. At present, the only internal forcing in the model with seasonal dynamics is the wind (which generates surface waves), but further inside the estuary its effect becomes relatively small compared to tidal forcing. Therefore also the effect of applying a seasonal trend in settling velocity and/or critical shear stress for erosion should be investigated. This is likely to improve the agreement between mud model and observations, both in the water column and in the bed.
The development of the model has initially focused more on transport through the channels, and less on intertidal flats. However, strong deposition and resuspension of mud may occur on the intertidal flats. Coupling of information from near-surface SPM from remote sensing, bottom sediment from remote sensing and modelling can improve our understanding of vertical and lateral mud exchange. Information on bed roughness and biological variables (such as microphytobenthos biomass is derived from remote sensing and can be used to advance the mud transport model.
Remote sensing proved to be sensitive enough to retrieve the seasonal (and year-to- year) variation in mud content of the intertidal flat surfaces. In addition, time-series of benthic chlorophyll from remote sensing (as carried out for LTV-Nature) also show that there is a clear seasonal and year-to-year variation in microphytobenthos biomass. Such methods are therefore useful for monitoring, for instance in the framework of MONEOS (e.g., for monitoring the effects of channel deepening, and dredging), combined with in situ data (for calibration, validation and for assessing the depth of the mud layer) and modelling.
Deltares, Flanders Hydraulics, IVM, NIOO 41 Mud transport model for the Scheldt Z4594 December 2008 estuary
5.3 On model application
The model is ready for use in the sense that it is operational and that parameters relevant for managerial questions are included. Results can be made available for a period up to one year with full variable forcing (tide, river discharge, wind, waves etc.) at any place at any time.
The model has been demonstrated to include many features of observed estuarine fine sediment dynamics, at least in a qualitative sense. However, in a quantitative sense some aspects need further improvement for unreserved application. Two important aspects are the strength of the estuarine circulation and the amplitude of seasonal dynamics, which both tend to be underestimated. A combination of observations and modelling therefore remains important.
Application of the model to assess the effect of the second fairway enlargement in the Scheldt Estuary shows that its effect is limited to a few percent change in TSM-levels. Near Antwerp the surface TSM levels have slightly decreased owing to the channel deepening, whereas near Vlissingen the surface TSM levels have slightly increased. Modelled changes are very small compared to typical natural fluctuations (intra-tide, inter-tide, seasonal) and will be very difficult to establish with field monitoring, even with a extended and long-term campaign.
5.4 Synthesis and outlook to LTV- Nature and MONEOS
Ocean colour data can also provide information on chlorophyll concentrations (of phytoplankton) and vertical attenuation of the light in the water (KD). These parameters are crucial for primary production modelling, LTV-Nature and MONEOS. If natural variability and human impact can be disentangled, remote sensing may also be used in combination with modelling for the detection of the dumping of Zeebrugge Harbour dredging material dumping.
Remote sensing proved to be sensitive enough to retrieve the seasonal (and year-to- year) variation in mud content of the intertidal flat surfaces. In addition, time-series of benthic chlorophyll from remote sensing (as carried out for LTV-Nature) also show that there is a clear seasonal and year-to-year variation in microphytobenthos biomass. Such methods are therefore useful for monitoring, for instance in the framework of MONEOS (e.g., for monitoring the effects of channel deepening, and dredging), combined with in situ data (for calibration, validation and for assessing the depth of the mud layer) and modelling.
Acknowledgements
ESA provided MERIS-FR data within the Cat-1 project 4453 and ERS SAR data within Cat-1 project 5578. Landsat 7 ETM+ data were acquired from USGS. We acknowledge Machteld Rijkeboer, Steef Peters and Reinold Pasterkamp (in collaboration with Kevin Ruddick (MUMM) and RWS-MD) for databases with historic sIOPs, and Rosa Astoreca (ULB) for discussions (inter-comparison) of Scheldt sIOPs. Finally we thank Reinold Pasterkamp for the Hydropt Software library, Hans van der Woerd for discussions on fluorescence and Annelies Hommersom for sharing advice and literature on atmospheric correction.
Deltares, Flanders Hydraulics, IVM, NIOO 42 Mud transport model for the Scheldt Z4594 December 2008 estuary
6 References
Babin, M., Morel, A., Fournier-Sicre, V., Fell, F. and Stramski, D. (2003a). Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration. Limnology and Oceanography 48(2): 843-859. Babin, M., Stramski, D., Ferrari, G.M., Claustre, H., Bricaud, A., Obolensky, G., Hoepffner, N. (2003b). Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe. J. Geophys. Res. Oceans, 108(C7) doi:10.1029/2001JC000882. Bricaud, A. Morel, A., Babin, M., Allali, K. and H. Claustre (1998) Variations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: analysis and implications for bio-optical models. J. Geophys. Res. 103, 31,033–31,044 Brockmann, C. (2006). Limitations of the application of the MERIS atmospheric correction. Proceedings of the Second Working Meeting on MERIS and AATSR Calibration and Geophysical Validation (MAVT-2006), 20-24 March 2006, ESRIN, Frascati, Italy (ESA SP-615, July 2006). Bruens, A., J. Vanlede, T. van Kessel (2006). Notitie beheersvragen LTV-slibmodel. WL | Delft Hydraulics report Z4210.75 (in Dutch). Doerffer, R. and Schiller, H., (2007). The MERIS Case 2 water algorithm. Int. J. of R.S. 28 (3-4): 517-535. Doxaran, D., Castaing, P., Lavender, S.J. (2006). Monitoring the maximum turbidity zone and detecting fine-scale turbidity features in the Gironde estuary using high spatial resolution satellite sensor (SPOT HRV, Landsat ETM+) data. International Journal of Remote Sensing 27(11): 2302-2321. Doxaran, D., Froidefond, J.-M., Lavender, S., Castaing, P. (2002). Spectral signature of highly turbid waters. Application with SPOT data to quantify suspended particulate matter concentrations. Remote Sensing of Environment 81: 149-161. Eleveld, M.A., Pasterkamp, R. & van der Woerd, H.J. and Pietrzak, J.D. (2008). Remotely sensed seasonality in the spatial distribution of sea-surface suspended particulate matter in the southern North Sea. Est. Coast. Shelf Sci. 80(1), 103-113. ESA (2006) MERIS Product handbook. Issue 2.0 (14 Apr 2006). http://envisat.esa.int/handbooks/meris/ Foreman, M (1977) Manual for tidal heights analysis and prediction. Pacific Marine science report 77-10. Institute of Ocean Services, Victoria. Fung, A.K., Li, Z., & Chen, K.S. (1992). Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing, 30, 356-369. Gilerson, A., Zhou, J., Hlaing, S., Ioannou, I., Schalles, J., Gross, B., Moshary, F. and Ahmed, S. (2007) Fluorescence component in the reflectance spectra from coastal waters. Dependence on water composition. Opt. Express 15, 15702- 15721 Godin, G. (1972) The analysis of tides. University of Toronto Press. Gordon, H.R., Brown, O.B., Jacobs, M.M. (1975). Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean. Applied Optics 14 (2), 417–427. Herman, P.M.J., J.J. Middelburg, C.H.R. Heip (2001). Benthic community structure and sediment processes on an intertidal flat: results from the ECOFLAT project. Cont. Shelf Res. 21: 2055-2071.
Deltares, Flanders Hydraulics, IVM, NIOO 43 Mud transport model for the Scheldt Z4594 December 2008 estuary
IMDC (2005a). Uitbreiding studie densiteitsstromingen in de Beneden Zeeschelde in het kader van LTV Meetcampagne naar hooggeconcentreerde slibsuspensies Deelrapport 2.1: Deurganckdok 17/02/2005, I/RA/11265/05.009/MSA, in opdracht van AWZ. IMDC (2005b). Uitbreiding studie densiteitsstromingen in de Beneden Zeeschelde in het kader van LTV Meetcampagne naar hooggeconcentreerde slibsuspensies Deelrapport 2.5: Deurganckdok 16/02/2005, I/RA/11265/05.013/MSA, in opdracht van AWZ. IMDC (2006a) Uitbreiding studie densiteitsstromingen in de Beneden Zeeschelde in het kader van LTV Meetcampagne naar hooggeconcentreerde slibsuspensies Deelrapport 7.2 22 March 2006 Parel 2 – Deurganckdok (downstream). IMDC (2006b) Uitbreiding studie densiteitsstromingen in de Beneden Zeeschelde in het kader van LTV Meetcampagne naar hooggeconcentreerde slibsuspensies Deelrapport 7.5 23 March 2006 Laure Marie – Deurganckdok (downstream). IMDC (2007a). Uitbreiding studie densiteitsstromingen in de Beneden Zeeschelde in het kader van LTV Meetcampagne naar hooggeconcentreerde slibsuspensies Deelrapport 11.2 Through tide Measurement Sediview 27/9 Veremans - Raai K (I/RA/11291/06.105/MSA), in opdracht van AWZ. IMDC (2007b). Uitbreiding studie densiteitsstromingen in de Beneden Zeeschelde in het kader van LTV Meetcampagne naar hooggeconcentreerde slibsuspensies Deelrapport 11.3 Through tide Measurement Sediview & Siltprofiler 28/9 Stream - Raai K (I/RA/11291/06.106/MSA), in opdracht van AWZ. Jansen, P.P., van Bendegom, L., van den Berg, J., de Vries, M., Zanen, A. (1979). Principles of River Engineering, the non-tidal alluvial river. Pitman, London. Kuijper C., M.C.J.L. Jeuken, H.F.P. van den Boogaard (2008). LTV-O&M Veligheid: deelproject 1. Historische ontwikkeling van de hoogwaterstanden in het Schelde-estuarium. Deltares & WLB (in prep.). Meire, Patrick & Tom Maris (2008). MONEOS, geïntegreerde monitoring van het Schelde-estuarium. Onderzoeksgroep Ecosysteembeheer Universiteit Antwerpen, ECOBE, Rapport 08-R-113. Mobley, C.D. Sundman, L.K. (2001a) Hydrolight 4.2: Technical documentation. (Sec. Print., Oct. 2001). Sequoia Scientific, Redmond (WA), 79 pp. http://www.sequoiasci.com/products/Hydrolight.aspx Mobley, C.D., Sundman, L.K. (2001b) Hydrolight 4.2: Users’ guide. (Sec. printing, Oct. 2001). Sequoia Scientific, Redmond (WA) USA, 88 pp. http://www.sequoiasci.com/products/Hydrolight.aspx Pasterkamp, R., Peters, S.W.M., Rijkeboer, M., Dekker, A.G., 1999. RESTWES:Retrieval of total suspended matter concentrations from SPOT images. IVM report W-99/33. Pawlowicz, R; Beardsley, B; Lentz, S (2002). Classical tidal harmonic analysis including error estimates in MATLAB using t_tide. Computers and geosciences, 28, pp. 929-937. Report LTV ecology RIKZ (2007) Monitoring van de effecten van de verruiming 48’/43’. MOVE eindrapport 2006. Rapport RIKZ/2007.003 Ruddick, K., B. Nechad, Neukermans, G. Park, Y, Doxaran, D., Sirjacobs, D., Beckers, J.M. (2008). Remote sensing of suspended particulate matter in turbid waters: State of the art and future perspectives. Ocean Optics 2008, Barga (Tuscany, I). Ruddick, K.G., De Cauwer, V., Park, Y-J., Moore, G. (2006). Seaborne measurements of near infrared water-leaving reflectance: The similarity spectrum for turbid waters. Limnology and Oceanography 51(2): 1167-1179. Ruddick, K.G., Ovidio, F., Rijkeboer, M. (2000). Atmospheric correction of Sea-WIFS imagery for turbid coastal and inland waters. Applied Optics 39 (6), 897–912.
Deltares, Flanders Hydraulics, IVM, NIOO 44 Mud transport model for the Scheldt Z4594 December 2008 estuary
Santer, R. and Schmechtig, C. (2000). Adjacency effects on water surfaces: primary scattering approximation and sensitivity study. Applied Optics 39(3): 361-375. Schroeder, Th., Schaale, M., Fischer, J. (2007). Retrieval of atmospheric and oceanographic properties from MERIS measurements: A new Case-2 water processor for BEAM. Int. J. of R.S. 28 (24): 5627-5632. Tilstone, G., van der Woerd, H., Krasemann, H., Martinez-Vicente, V., Peters, S., Eleveld., M.A., Schoenfeld, W., Blondeau-Patissier, D., Høkedal, J., Jorgensen, P., Pasterkamp, R., Röttgers, R., Sørensen, K. (2008). Novel ways of parameterising ocean colour algorithms for European coastal waters using trends in absorption properties. Resubmitted to J. Geophys. Res –Oceans. Van der Wal, D., Herman, P.M.J. (2007). Regression-based synergy of optical, shortwave infrared and microwave remote sensing for monitoring the grain-size of intertidal sediments. Remote Sensing of Environment 111 (1): 89-106. Van der Wal, D., Herman, P.M.J., Wielemaker-van den Dool, A. (2005). Characterisation of surface roughness and sediment texture of intertidal flats using ERS SAR imagery. Remote Sensing of Environment 98 (1): 96-109. Van der Wal, D., P.M.J. Herman, R.M. Forster, T. Ysebaert, F. Rossi, E. Knaeps, Y.M.G. Plancke, S.J. Ides (2008). Distribution and dynamics of intertidal macrobenthos predicted from remote sensing: response to microphytobenthos and environment. Marine Ecology Progress Series 367: 57-72. Van der Woerd, H.J., Pasterkamp, R. (2008). HYDROPT: A fast and flexible method to retrieve chlorophyll-a from multispectral satellite observations of optically complex coastal waters. R.S.Env. 112, 1795-1807. Van Kessel, T., J. Vanlede, A. Bruens (2006). Development of a mud transport model for the Scheldt estuary in the framework of LTV. WL | Delft Hydraulics & WL Borgerhout report Z4210. Van Kessel, T., J. Vanlede, J. de Kok (2007). Development of a mud transport model for the Scheldt estuary in the framework of LTV. Phase 1. WL | Delft Hydraulics & WL Borgerhout report Z4375.. Van Maren, D. S., J. C. Winterwerp, and R. E. Uittenboogaard (2007). New developments in the mud transport module of Delft3D, report II: Implementation, sensitivity analysis, calibration and validation. Delft Hydraulics report Z3824.55, Delft. Vanlede, J.; Boudewijn Decrop; Bob De Clercq; Stefaan Ides; Tom Demulder; Frank Mostaert (2008). Permanente verbetering modelinstrumentarium. Verbetering Randvoorwaardenmodel. Deelrapport 2: afregelen 2D-Scheldemodel. WL Rapporten, 753. Waterbouwkundig Laboratorium en IMDC: Borgerhout, België. Vantrepotte, V., Brunet, C., Mériaux, X., Lécuyer, E., Vellucci, V., Santer, R. (2007). Bio-optical properties of coastal waters in the Eastern English Channel. Est. Coast. Shelf Sci. 72, 201-212. Volten H, de Haan JF, Hovenier JW, Scheurs R, Vassen W, Dekker AG, Hoogenboom, J.J., Charlton, F., Wouts, R. (1998). Laboratory measurements of angular distributions of light scattered by phytoplankton and silt. Limnol. Oceanogr. 43:1180–97. Volten, H., Munoz, O., Hovenier, J.W., Waters, L.B.F.M. (2006). An update of the Amsterdam Light Scattering Database. Journal of Quantitative Spectroscopy & Radiative Transfer 100: 437–443. Winterwerp, J.C. (2003). The transport of fine sediment in the Humber estuary. ESTPROC report 4 (Z3040), 47pp. Winterwerp, J. C., Wang, Z. B., van der Kaaij, T., Verelst, K., Bijlsma, A., Meersschaut, Y. and Sas, M. (2006a). Flow Velocity profiles in the Lower Sea Scheldt. Ocean Dynamics 56, p 284 - 294.
Deltares, Flanders Hydraulics, IVM, NIOO 45 Mud transport model for the Scheldt Z4594 December 2008 estuary
Winterwerp, J.C. and De Kok, J. (2006). Plan van aanpak LTV-slib: modelinstrumentarium t.b.v. beheersproblematiek slib. Intern document no. Z4210.95 – M756/01. Zibordi, G., F. Mélin, and J.-F. Berthon (2006). Comparison of SeaWiFS, MODIS and MERIS radiometric products at a coastal site, Geophys. Res. Lett., 33, L06617, doi:10.1029/2006GL025778.
Deltares, Flanders Hydraulics, IVM, NIOO 46 Mud transport model for the Scheldt Z4594 December 2008 estuary
A Details on remote sensing data acquisition
A.1 SIOPs: the optical absorbing and scattering properties of substances in Western Scheldt and North Sea waters
Figure A.1 SIOPs for the Western Scheldt from the RestWes 1999 dataset.
Deltares, Flanders Hydraulics, IVM, NIOO 47 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure A.2 SIOPs for the Voordelta and Vlaamse Banken from the Belgica 2000 dataset.
Deltares, Flanders Hydraulics, IVM, NIOO 48 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure A.3 SIOPs for the Western Scheldt from the OROMA 2002 set.
Deltares, Flanders Hydraulics, IVM, NIOO 49 Mud transport model for the Scheldt Z4594 December 2008 estuary
A.2 Selected suspended particulate matter (SPM) products derived from MERIS remote sensing images for 2006
A.2.1 Western Scheldt sIOP parameterisation (Restwes99Oroma02mean)
Deltares, Flanders Hydraulics, IVM, NIOO 50 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 51 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 52 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 53 Mud transport model for the Scheldt Z4594 December 2008 estuary
A.2.2 North Sea sIOP parameterisation (Belgica2000)
Deltares, Flanders Hydraulics, IVM, NIOO 54 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 55 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 56 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 57 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 58 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 59 Mud transport model for the Scheldt Z4594 December 2008 estuary
A.3 Vertical tidal stage (in m NAP) at image date and time
Image filenames (incl. Date and time in UTC) Tide at overpass Vlissingen Hansweert MER_FR__2PNUPA20060109_103009_ 0.75 1.44 000000982044_00094_20188_5070.N1 MER_FR__2PNUPA20060129_100129_ 0.07 -0.39 000000982044_00380_20474_5109.N1 MER_FR__2PNUPA20060323_103539_ -1.1 -0.67 000000982046_00137_21233_5111.N1 MER_FR__2PNUPA20060401_105238_ -1.66 -2.36 000000982046_00266_21362_5113.N1 MER_FR__2PNUPA20060402_102124_ -1.9 -1.44 000000982046_00280_21376_5107.N1 MER_FR__2PNUPA20060503_104702_ -1.52 -1.14 000000982047_00223_21820_5115.N1 MER_FR__2PNUPA20060504_101552_ -1.14 -0.74 000000982047_00237_21834_5117.N1 MER_FR__2PNEPA20060510_102655_ 1.46 0.76 000000982047_00323_21920_3828.N1 MER_FR__2PNUPA20060608_101550_ 1.66 1.48 000000982048_00237_22335_9560.N1 MER_FR__2PNUPA20060610_105240_ 1.15 0.3 000000982048_00266_22364_9562.N1 MER_FR__2PNUPA20060611_102132_ -0.36 -0.81 000000982048_00280_22378_5119.N1 MER_FR__2PNUPA20060617_103257_ -1.44 -1.18 000000982048_00366_22464_5121.N1 MER_FR__2PNUPA20060623_104418_ 1.99 1.6 000000982048_00452_22550_5095.N1 MER_FR__2PNUPA20060629_105538_ -1.29 -1.84 000000982049_00037_22636_5105.N1 MER_FR__2PNUPA20060630_102426_ -1.77 -1.87 000000982049_00051_22650_5089.N1 MER_FR__2PNUPA20060703_103008_ -0.2 0.45 000000982049_00094_22693_5091.N1 MER_FR__2PNUPA20060704_095850_ -0.06 0.56 000000982049_00108_22707_5093.N1 MER_FR__2PNEPA20060710_100956_ 0.13 -0.21 000000982049_00194_22793_3830.N1 MER_FR__2PNUPA20060712_104708_ -0.77 -1.07 000000982049_00223_22822_5097.N1 MER_FR__2PNUPA20060713_101601_ -1.62 -2.12 000000982049_00237_22836_5099.N1 MER_FR__2PNUPA20060715_105249_ -1.86 -2.28 000000982049_00266_22865_5101.N1 MER_FR__2PNUPA20060716_102137_ -1.76 -1.28 000000982049_00280_22879_5103.N1 MER_FR__2PNUPA20060718_105831_ -0.93 -0.26 000000982049_00309_22908_5083.N1 MER_FR__2PNUPA20060719_102713 0.77 1.04 _000000982049_00323_22922_9564.N1 MER_FR__2PNUPA20060725_103843_ -0.45 -0.73 000000982049_00409_23008_5073.N1 MER_FR__2PNUPA20060819_105246_ 1.16 1.75 000000982050_00266_23366_5076.N1 MER_FR__2PNUPA20060911_103002_ -1.46 -2.23 000000982051_00094_23695_5078.N1 MER_FR__2PNEPA20060920_104646_ 1.08 0.5 000000982051_00223_23824_3832.N1 MER_FR__2PNUPA20060921_101548_ -0.15 0.53 000000982051_00237_23838_9566.N1 MER_FR__2PNUPA20061016_102951_ 0.59 1.1 000000982052_00094_24196_5080.N1 MER_FR__2PNEPA20061117_ 1.36 1.57 102407_000000982053_00051_24654_3834.N1
Deltares, Flanders Hydraulics, IVM, NIOO 60 Mud transport model for the Scheldt Z4594 December 2008 estuary
B Details on remote-sensing mudflats
B.1 Maps of bed roughness RMSz: 2006
Deltares, Flanders Hydraulics, IVM, NIOO 61 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure B.1 Maps of bed roughness RMSz: 2006. Dashed square indicates remote sensing scene boundaries in case of partial coverage of the Western Scheldt.
Deltares, Flanders Hydraulics, IVM, NIOO 62 Mud transport model for the Scheldt Z4594 December 2008 estuary
B.2 Maps of the mud content of the sediment: 2006
Deltares, Flanders Hydraulics, IVM, NIOO 63 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure B.2 Maps of the mud content of the sediment: 2006.
Deltares, Flanders Hydraulics, IVM, NIOO 64 Mud transport model for the Scheldt Z4594 December 2008 estuary
C Details on hydrodynamic model
Figure C.1 Water level at Vlissingen: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 65 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.2 Water level at Walsoorden: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 66 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.3 Water level at Zandvliet: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 67 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.4 Water level at Antwerpen: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 68 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.5 Water level at Dendermonde: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 69 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.6 Salinity Vlakte van de Raan: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 70 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.7 Salinity Hoofdplaat: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 71 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.8 Salinity Baalhoek: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 72 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.9 Salinity Boei 84: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 73 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.10 Salinity Boei 97: model and measurements.
Deltares, Flanders Hydraulics, IVM, NIOO 74 Mud transport model for the Scheldt Z4594 December 2008 estuary
Figure C.11 A – B bathymetry
A = situation around 2006; B = sills at pre-1997 level
Deltares, Flanders Hydraulics, IVM, NIOO 75 Mud transport model for the Scheldt Z4594 December 2008 estuary
D Details on mud model
D.1 Time series
Deltares, Flanders Hydraulics, IVM, NIOO 76 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 77 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 78 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 79 Mud transport model for the Scheldt Z4594 December 2008 estuary
D.2 Comparison with OBS data
Deltares, Flanders Hydraulics, IVM, NIOO 80 Mud transport model for the Scheldt Z4594 December 2008 estuary
D.3 Seasonal dynamics
Deltares, Flanders Hydraulics, IVM, NIOO 81 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 82 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 83 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 84 Mud transport model for the Scheldt Z4594 December 2008 estuary
D.4 Comparison between 3-month period, 2000 and 2006
Deltares, Flanders Hydraulics, IVM, NIOO 85 Mud transport model for the Scheldt Z4594 December 2008 estuary
D.5 Comparison between mud model and remote sensing data
Note: tim = tsm; model time = CET = satellite time + 1 = GMT + 1, i.e. satellite 10:30 = model 11:30
SIOP Belgica: no reliable solution
Deltares, Flanders Hydraulics, IVM, NIOO 86 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 87 Mud transport model for the Scheldt Z4594 December 2008 estuary
SIOP Western Scheldt: no reliable solution
Deltares, Flanders Hydraulics, IVM, NIOO 88 Mud transport model for the Scheldt Z4594 December 2008 estuary
SIOP Western Scheldt: no reliable solution
SIOP Western Scheldt: no reliable solution
Deltares, Flanders Hydraulics, IVM, NIOO 89 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 90 Mud transport model for the Scheldt Z4594 December 2008 estuary
Deltares, Flanders Hydraulics, IVM, NIOO 91 Mud transport model for the Scheldt Z4594 December 2008 estuary
D.6 Thalweg plots
neap tide
spring tide
Deltares, Flanders Hydraulics, IVM, NIOO 92