AVHRR reflectance images and in situ observations

ELSEVIER

Journal of Sea Research 39 (1998) 197–215

Distribution of suspended particulate matter in the North Sea as inferred from NOAA=AVHRR reflectance images and in situ observations W. van Raaphorst Ł , C.J.M. Philippart, J.P.C. Smit, F.J. Dijkstra, J.F.P. Malschaert Netherlands Institute for Sea Research (NIOZ), P.O. Box 59, 1790 AB, Den Burg, Netherlands Received 30 June 1997; accepted 6 November 1997

Abstract Distribution patterns of suspended particulate matter (SPM) in the surface water of the North Sea were calculated on the basis of: (1) the 1973–1993 data base of the EC MAST North West European Shelf Programme (NOWESP); and (2) composite reflectance images constructed from data that were collected by the NOAA=AVHRR satellite in 1990–1991. Three models were used for interpolating the in situ data: (1) a distance-weighted interpolation algorithm in which only the in situ data are taken into account; (2) an algorithm in which the ratios between the measured SPM concentrations and reflectances are interpolated, and the distribution of SPM is calculated from the field of interpolated ratios and the synoptic reflectance image; and (3) a distance-weighted algorithm similar to model-1, but with an additional weight factor that is based on local differences in reflectance. The models were tested for periods of 1 and 3 weeks in September 1990 and January 1991, and for the merged set consisting of all in situ data measured in September and January, respectively, between 1973 and 1993. Model-2 and -3 gave largely similar results and had a performance superior to model-1, particularly because they showed more detailed structures in the spatial distributions. Validations and cross-validations showed that the absolute concentrations of SPM predicted by the models were too low at high in situ concentrations and too high at low in situ concentrations. This shortcoming was due to the relatively high degree of smoothing that we applied in the models to account for the large variance of the in situ data. Semivariograms and correlograms indicated that the in situ data had substantial variability and were poorly correlated even at short distances. Only for the 20-year-merged data set did some correlation (< 60%) exist for stations < 50 km from each other. Monthly distributions of SPM were calculated with model-3 and the 20-year data set. The distributions confirm the main patterns previously found by others, such as the turbidity plume crossing the North Sea from southeast England towards the depository in the Skagerrak and the Norwegian Channel. The distributions indicate that materials from this plume may be deposited in the central North Sea in spring and summer and eroded again in autumn and winter. Areas with maximum SPM concentrations were identified off the Belgian coast and north of the Wadden Sea, particularly in winter, from which particles are entrained into the main current in a narrow strip along the continental coast to the German Bight. The results suggest that the two main fluxes of SPM in the North Sea, off England and along the continental coast, remain largely separated until they both end in the Skagerrak.  1998 Elsevier Science B.V. All rights reserved. Keywords: North Sea; suspended matter; remote sensing; in situ data; interpolation

Ł Corresponding

author. Tel.: C31 (222) 369-362; Fax: C31 (222) 319-674; E-mail: [email protected]

1385-1101/98/$19.00  1998 Elsevier Science B.V. All rights reserved. PII S 1 3 8 5 - 1 1 0 1 ( 9 8 ) 0 0 0 0 6 - 9

198

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

1. Introduction In the North Sea, suspended particulate matter (SPM) is transported with the residual current from the shallow Southern Bight to the German Bight and from there to the depository in the relatively deep Skagerrak and the Norwegian Channel (S and NC; Fig. 1; Eisma, 1981; Eisma and Kalf, 1987a,b). The average time it takes a water parcel to cross the distance is several months to about 1 year (Hainbucher et al., 1987; Otto et al., 1990). Particulate matter, however, can be stored temporarily in midshelf sediments (Van Raaphorst et al., 1998) and, consequently, may need much more time (Kempe et

Fig. 1. Map of the North Sea with geographic names which appear in the text. H D Holderness; NF D Norfolk; SF D Suffolk; DS D Dover Strait; FB D Flemish Banks; WS D Wadden Sea; OSP D Outer Silverpit. Arrows indicate the directions of residual currents of the surface water (Otto et al., 1990).

al., 1988). During its travel, SPM loses a substantial portion of the organic carbon associated with it through mineralisation processes, so that the organic material which is finally deposited in the S and NC is aged and largely of a refractory nature (Van Weering et al., 1987; Kempe et al., 1988; Lohse et al., 1995). Similar observations have been made for the material deposited in other continental margin depositories, such as on the slope of the Middle Atlantic Bight (Anderson et al., 1994). Knowledge of the transport, deposition and resuspension of SPM is necessary to estimate the carbon budget of the North Sea, to predict the amount buried in the S and NC and, by extrapolating, to understand the significance of continental shelves in global carbon cycling (Walsh, 1991; Biscaye et al., 1994). The total supply of SPM to the North Sea has previously been estimated at ¾30–35 million tons per year, originating mainly from the Atlantic Ocean, rivers and sea floor erosion (Eisma, 1981; Pohlmann and Puls, 1994). In a recent study, however, McManus and Prandle (1997) calculated the mean annual southern input through Dover Strait (Fig. 1) to be ¾44 million tons, and their results suggest that a similar amount may be supplied across the northern boundary. This seems to confirm the, also recent, estimates based on 210 Pb geochronology pointing to a total accumulation of ¾74 million tons per year in the S and NC (De Haas and Van Weering, 1997). The ongoing debate concerning the SPM budget for the North Sea demonstrates that the processes which determine the cross-shelf transports and final deposition fluxes in the S and NC are not yet well quantified. Processes affecting SPM transport are complex and not easily measured in the field. Thus, some kind of modelling (e.g., Su¨ndermann, 1993; Pohlmann and Puls, 1994; McManus and Prandle, 1997) will be necessary as an additional tool to determine the major fluxes of SPM on the shelf. Models can, however, only provide reliable results when they have been calibrated properly against field data. Such data may be obtained from application of moored sensors at fixed locations (e.g., Jago et al., 1993; Van Raaphorst et al., 1998). This type of instrument is particularly suited to study the short-term variability of SPM concentrations, such as the response to storm events. However, data on the spatial distribution of SPM on the shelf are also necessary to calibrate

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

the SPM flux models (Su¨ndermann, 1993; Pohlmann and Puls, 1994; McManus and Prandle, 1997). Although a single measurement of the concentration of SPM is straightforward and relatively easy, it requires tremendous logistical effort to collect an amount of data sufficiently large to assess the North Sea wide spatial distribution. Therefore, only a few such comprehensive data sets from ship cruises are available (Eisma, 1981; Eisma and Kalf, 1987a,b; Ho¨lemann and Wirth, 1988; Wirth and Seifert, 1988; Dyer and Moffat, 1993). All these data sets are quasi-synoptic in the sense that it took a few weeks to cover the entire North Sea. Furthermore, most of these data sets represent only short periods in different years and, consequently, lack the information on the seasonality of the spatial distribution of SPM. A promising tool to overcome the shortcomings of the ship-borne data sets is provided by the synoptic reflectance images that can be obtained from satellites (e.g., Su¨ndermann, 1993; Doerffer et al., 1995). Theoretical studies have shown that remotely sensed reflectance values are related to the inherent optical properties of the sea water (Gordon et al., 1988). Most relevant properties are back-scattering and absorption of light, which, in turn, are related to the total amount of particulate material in the surface layer of the water column (Tassan and Sturm, 1986; Garver et al., 1994). These relations have been confirmed for North Sea waters (Marees and Wernand, 1991; Shimwell, 1996). However, a wide application of this technique is prohibited by calibration problems caused by, e.g., the variability in time and space of the composition and size of the suspended particles and the influence of dissolved organic matter on light absorption. Nevertheless, satellite-borne data may still provide main synoptic patterns of SPM in the North Sea (Doerffer et al., 1995). This paper describes a novel approach in which in situ concentrations of SPM at fixed positions are combined with synoptic patterns of SPM as derived from reflectance images. The method, which is applied to the North Sea, largely circumvents the problem of calibrating the reflectance values by taking into account the spatial trends in (uncalibrated) reflectance in the procedures in which the in situ data are interpolated. The aim of our study was: (1) to determine the seasonal development of the distribution of SPM in the North Sea from existing data sets;

199

and (2) to discuss these distributions in terms of the residual transport of SPM across the shelf. 2. Materials and methods 2.1. In situ observations In situ data of SPM were obtained from the Research Data Base (RDB) of the EC-MAST North West European Shelf Programme (NOWESP), which is stored at the Institut fu¨r Meereskunde (IfM) of the University of Hamburg (Radach et al., 1998). The RDB contains about 140 103 data of SPM concentrations measured at 4000 locations on the northwest European shelf between 1973 and 1993. The largest portion of the SPM data was measured in the North Sea including the English Channel and the Skagerrak, and a much smaller portion in the Irish Sea, the Celtic Sea and the Bay of Biscay. For our analysis, we have selected the SPM concentrations measured in the upper 10 m of the water column in the North Sea, the English Channel and the Skagerrak. The data in the RDB originate from several researchers from different European research institutes who applied different measuring techniques (Table 1). This may have caused structural discrepancies between sub-sets of the RDB. The consistency of the sub-sets has been tested by the NOWESP research group (Radach et al., 1998). It was concluded that the discrepancies between data obtained from the different originators are small when compared to the mostly high in situ variability of SPM in the North Sea, both in time and space. Therefore, we are confident that the consistency of the data sets merged in the NOWESP RDB is sufficient for our purposes. 2.2. NOAA=AVHRR reflectance images The reflectance images used were obtained from the advanced very high resolution radiometer sensor (AVHRR), which is installed on board of the satellites of the TIROS-N series, operated by the National Oceanographic and Atmospheric Administration, USA (NOAA). The satellites produce a daily total of two images of the sunlight reflected from the North Sea surface with a spatial resolution of 1ð1 km (Townsend et al., 1994). However, only a small portion of these images can be used due to the

200

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

Table 1 Overview of the in situ SPM data sets used in this study Origin of data

Main area covered (number of stations)

Period covered

Management Unit of the Mathematical Models of the North Sea, Brussels (MUMM) a Institut fu¨r Biogeochemie und Meereschemie, Hamburg (IfBM) a Institut fu¨r Meereskunde, Hamburg (IfM) a Bundesambt fu¨r Seeschiffart und Hydrographie, Hamburg (BSH) b Netherlands Institute for Sea Research (NIOZ) a British Oceanographic Data Centre (BODC) a British Oceanographic Data Centre (BODC) a National Institute for Coastal and Marine Management, The Hague, NL (RIKZ) a

Belgian coast (75)

weeks 1–52; 1977–1992 Belgian monitoring programme

North Sea (140)

weeks 4–10; 1987

North Sea (122) German Bight (72)

weeks 18–24; 1986 Ho¨lemann and Wirth (1988), ZISCH weeks 1–52; 1986–1992 Ko¨nig et al. (1994), PRISMA

North Sea, Skagerrak, Channel (355) British coast (166)

weeks 1–6, 18–19; 1976, 1977, 1979, 1980 weeks 17–18, 50–52; 1991–1993 weeks 1–52; 1988–1990

North Sea, Channel (2291) Dutch coast (81)

References or programmes

TOSCH

Eisma (1981), Eisma and Kalf (1987a,b) JoNUS programme

Dyer and Moffat (1993), NERC North Sea Project weeks 1–52; 1973–1993 Dutch monitoring programme

All data were obtained from the NOWESP research data base stored at the University of Hamburg. Methods applied: a filtration of water samples over GF=C glass fiber filters, 0.45 µm cellulose acetate membrane filters or 0.4-µm polycarbonate membrane filters. b Optical sensors calibrated against data obtained by method a.

frequently occurring partial or complete coverage of the area with clouds. We used weekly composites of the images to minimise the problem caused by cloud coverage. All images are from 1990 and 1991 which overlap with a large portion of the in situ data. The NOAA=AVHRR records reflected and emitted radiance from both the earth surface and the atmosphere in several spectral channels. For SPM, channel 1, which measures the reflection of the sunlight in the red part of the spectrum (580–680 nm) is best suited (Roozekrans, 1989). Prior to further analysis, the raw reflection data were corrected for transmission through the ozone layer, Rayleigh scattering and aerosol scattering on the basis of the algorithms developed for the coastal zone colour scanner (CZCS) (Roozekrans, 1989). 2.3. Interpolation models Three models were used to estimate the spatial distributions of SPM. The in situ data were interpolated without making use of the reflectance images in model-1. In model-2, local proportional relationships are assumed between the in situ values and the remotely sensed reflectances, and the spatial distri-

bution of SPM is calculated after interpolating these proportionality values. Finally, in model-3, the distribution is based on an interpolation in which the in situ data are locally weighted with the use of the satellite images. 2.3.1. Model-1 The in situ values of SPM were interpolated on a regular 1ð1 km grid which corresponds to the pixel resolution of the NOAA/AVHRR images and covers the North Sea from the English Channel in the south to the Norwegian Channel and the Shetland islands in the north. Model-1 predicts the concentration of SPM in every cell of this grid on the basis of a distance-weighted interpolation algorithm. For a total amount of n data points at the locations j and with the measured concentrations SPM j , the interpolated concentrations Ci in the cells i follow from: n X Ð wi; j ð SPM j Ci D

jD1 n X

(1) wi; j

jD1

in which the weight factors wi; j are defined as:

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

wi; j D exp

di;2 j

!

Þ2

for di; j  Ž

wi; j D 0 for di; j > Ž

(2a) (2b)

where di; j represents the distances between the cells i and the measuring points j, and Þ and Ž are adjustable parameters (km). 2.3.2. Model-2 In model-2, it is assumed that, within a small area around any grid cell, the concentration of SPM is proportional to the remotely sensed reflectance (REF). Thus, the concentration CiŁ is predicted from the nearest in situ point j according to: SPM j CiŁ D REFi ð (3) REF j The predictive power of this relation may decrease with increasing distance di; j while also the proportionality factor SPM=REF is not necessarily constant for the entire North Sea. Therefore, an interpolation similar to model-1 is performed to calculate the concentrations Ci from the predicted values CiŁ :  n  X SPM j wi; j ð REF j jD1 Ci D REFi ð (4) n X wi; j jD1

where the weight factors wi; j are defined as before. 2.3.3. Model-3 In this model, the local differences in reflectance are taken into account to calculate additional weight factors vi; j in the interpolation formula for the SPM concentrations: n X Ð wi; j ð vi;4 j ð SPM j Ci D

jD1 n X

(5) wi; j ð

vi;4 j

jD1

Similar to model-2, the basic assumption is that, at least locally, SPM is related to the remotely sensed reflectance. This implies that those in situ data points which are near the target grid cell i and have reflectance values REF j close to the reflectance REFi

201

are assumed to be better predictors for the SPM concentration Ci than the data points with reflectance strongly deviating from REFi . The factor vi; j is raised to the power of 4 to ensure its dominating influence and to compensate for the smoothing effect of the distance weight wi; j . For the North Sea, the reflectance measured by the NOAA=AVHRR satellite reaches a saturation plateau at SPM concentrations larger than ¾15–30 g m 3 (Vos, 1995). Consequently, at high concentrations, reflectance may be a poor quantitative indicator of SPM. Therefore, we applied relative reflectance differences to calculate the weight factor vi; j : REFi (6) vi; j D jREF j REFi j C REFi The weight factor has a value of 1 when jREF j REFi j D 0 and decreases for increasing differences. Also, in areas with reflectances close to the saturation level of the satellite, jREF j REFi j − REFi and model-3 is reduced to the simple distanceweighted interpolation described in model-1. 2.4. Statistical analysis To determine the statistical regularity of the in situ data, we calculated the semivariogram (h) of the data (Delhomme, 1978; Cressie, 1993): m X m X Ð2 SPM j SPMk ð g.d j;k /

.h/ D

jD1 kD1

2ð

m X m X

(7) g.d j;k /

jD1 kD1

where the function g.d j;k / has the value of 1 if the distance d j;k between the data points j; k is within predefined lower and upper bounds, and otherwise has the value of 0. The bounds are chosen such that the semivariogram is obtained for a series of nine classes of the distance lags h: 0–1, 1–2, 2–4, 4– 8, 8–16, 16–32, 32–64, 64–128, 128–256 km. For larger lags, we expect no relation between SPM j and SPMk . In case of a regular trend, (h) increases continuously with lag h due to the growing difference between the SPM concentrations at stations spaced at large distances. If such a trend is absent and SPM is determined by random fluctuations in space, (h) shows no relation with distance and

202

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

for all lags is equal to the variance of the data. In practice, the semivariogram may change from the situation of the first case (trend) to the second case (noise) at increasing distance lags. For our purpose, the most important aspect of the semivariogram is the behaviour close to the origin. For a regular spatial distribution (h) approaches zero for very small lags. If, however, there is substantial variation on the microscale (patches) or measurement errors cannot be neglected, a considerable variance can remain, even at short distance lags, which appears in the semivariogram as a positive intercept with the abscissa (nugget effect). Particularly when the nugget effect is large compared to the overall variance of the data and is caused mainly by measurement errors or any other intrinsic variability of the data not related to spatial structure, it makes no sense to apply an interpolation model that behaves as an exact interpolator in the data points, but, instead, some smoothing effect may be preferable (Cressie, 1993). In addition to the semivariogram we also calculated the correlogram of the data, which shows the expected correlation between the concentrations at stations spaced at lags h. To do so the covariogram C(h) is calculated: C.h/ D m X m X ð SPM j

Łð SPM SPMk

jD1 kD1 m X m X

Ł SPM g.d j;k / (8)

g.d j;k /

jD1 kD1

where the over-bar indicates the mean of all in situ data. The covariogram C(h) is converted into the correlogram R(h) through division by the variance, VAR, of the m data points: C.h/ (9) R.h/ D VAR The concentrations predicted by the models were compared with the measurements in validation plots. If the models behave as exact interpolators, all points in these plots lie on the line Y D X. This result can easily be obtained by setting the value of the distance parameter Þ at a sufficiently low value. This, however, does not yet guarantee good predictions outside or between the data points. Thus, we applied the validations merely to check whether the predictions

in the data points were not too much deteriorated at increased values of Þ, i.e., when the models behaved as (slightly) smoothing interpolators. To obtain more insight into the predictive power of the models, we also calculated the cross-validations. To this end, a single observation was deleted from the in situ data set, and the concentration was calculated at this site from the remaining observations. Cross-validation plots were constructed by repeating this procedure for all observations, and points lying close to the line X D Y in these plots now indicate adequate fits outside the observational points also. 3. Results and discussion To validate the models, we selected 2 weeks in September 1990 and January 1991 which had a sufficiently low coverage with clouds and a sufficiently dense coverage with in situ data points (Table 2). The time needed to obtain a synoptic coverage of the North Sea with data collected from a single ship is, however, in the order of about 3 weeks (e.g. Eisma and Kalf, 1987a,b). Consequently, the in situ data measured in 1 week are mostly restricted to small sub-areas, often near the coast. Therefore, we also evaluated the enclosure of data from a wider time frame, viz. 21 days in total. Even in these 3-week periods, the number of data points is limited (Table 2) and mostly restricted to the southern part of the North Sea. Thus, the performance of the models was also analysed in combination with merged data sets from 1973 to 1993. The final step was to determine, after selection of the most appropriate interpolation model, the longterm (1973–1993) mean distribution of SPM in the North Sea for each month of the year. 3.1. In situ observations The first week selected was 23–28 September 1990, in which the 56 in situ data points, as obtained from the NOWESP research data base, were all situated in the southern part of the North Sea. The variance of the data was large compared with the mean (Table 2), which was mainly due to the big difference between the SPM concentrations at stations near the coast and more offshore (Postma, 1981; Visser et al., 1991). The variance was reflected

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

203

Table 2 Characteristics of the NOAA reflection composites and the in situ data sets used Composite

Number of images used in the composite

23–28 13 September 1990

14–19 January 1991 a Part

8

Cloud coverage Period from which the in Number of in Mean of the Variance of the Coefficient of of the situ data were selected situ data a in situ data in situ data variation (CV) (mg l 1 )2 composite (mg l 1 ) (%) 35

5

23–28 September 1990 17 September– 5 October 1990 September 1973–1993

56 161

7.1 7.4

270 259

2.3 2.2

1763

14.0

589

1.7

14–19 January 1991 7–24 January 1991 January 1973–1993

25 77 1467

22.5 24.6 22.5

635 523 930

1.1 0.9 1.4

of the data was obtained at the same station, but at different depths.

Fig. 2. (A,B) Semivariograms (h) and (C,D) correlograms R(h) of the in situ data of September and January. In the semivariograms, [ (h)]0.5 is plotted instead of (h) for easier comparison with the SPM data. Lines are best fits of Gaussian models (Delhomme, 1978), in which (h) and R(h) are related to the distance lag h in a similar way as the weight factor wi; j in Eqs. 2a and 2b. The dashed line in B points at a possible periodicity in the semivariogram of 7–24 January 1990.

204

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

in the sill of the semivariogram which was reached at a distance lag between 25 and 50 km (Fig. 2A). At shorter lags, the semivariogram was highly scattered, pointing to a poor correlation of stations located even at relatively short distances. Enlarging the time window to 3 weeks (17 September–5 October 1990) did not much change the semivariogram and the correlogram (Fig. 2A,C). The merged set of all September data from 1973 to 1993 represented a larger part of the North Sea, although most of the observations were still restricted to the southern part. This enlargement of the area together with year-to-year variations explained the higher mean value and the proportionally higher variance compared to the 1and 3-week periods in September 1990 (Table 2). Both the semivariogram and the correlogram of the merged data set were, however, better structured. The correlation of the SPM concentrations at stations spaced very close to each other was ¾50– 60%, but this correlation declined to zero for stations which were more than ¾50 km apart (Fig. 2C). The average value of the 25 data points measured between 14 and 19 January 1991 was about three times higher than in September 1990 (Table 2). Compared to the mean, the variance of the data was less in January (coefficient of variation (CV) D 1.1) than in September (CV D 2.3), although it was still of considerable magnitude. The average concentrations measured in the 3 weeks between 7 and 24 January 1991 and in all January months from 1973 to 1993 were similar to the single week in January 1991, yet the variance of particularly the 20-year merge was considerably larger (Table 2). As for September, the semivariograms and the correlograms indicated that the influential neighbourhood of the stations was limited to ¾50 km (Fig. 2). In the semivariogram of, in particular, the 3-week period in January 1991, (h) showed a maximum at a distance lag between 4 and 16 km before it decreased to the value determined by the overall variance of the data at lags >¾ 100 km. The maximum of (h) was reflected in the correlogram by a strong negative correlation of about 50% at 4–8 km distance lag, which may have been caused by the strong concentration gradient close to the continental coast during the winter months, in particular (Postma, 1981; Visser et al., 1991). The semivariograms exhibited a nugget effect for both the September and the January data, i.e. the

values of the correlograms at lags approaching zero were substantially < 1. This discontinuity at the origin may have been due to micro-regionalisation on a scale smaller than the spacing of the data points or to measurement errors. For the period 17 September to 5 October 1990, the nuggets were determined by 39 pairs of data points spaced < 1 km from each other. Out of these 39 pairs, 33 were measured at the same station either at different depths in the 10-m surface layer of the water column or with a time lag of < 2 days. From the 3-week period in January, 18 pairs had a distance of < 1 km and of these, 12 pairs represented observations at the same station. We checked these subsets of 33 and 12 pairs, respectively, for a possible trend of the SPM concentrations with sampling depth in the 10-m surface layer, but did not find such a trend. Their differences in SPM concentrations, then, point to either measurement errors or to intrinsic variances due to, for instance, short-time variability, but not to persistent small-scale spatial structures. 3.2. NOAA=AVHRR composite images The composite of 23–28 September had a total cloud coverage of 35% (Table 2), but these clouds mainly occurred in the northeastern part of the North Sea (Fig. 3). Between 14 and 19 January 1991, the sky was almost clear; only 5% of the area was covered with clouds. In the composite of 14–19 January 1991, the highest reflectance appeared in a strip along the Belgian–Dutch coast and, particularly, off the southeastern edge of England from the Isle of Wight to the Norfolk coast. The high reflectance originating from the English coast could be traced as a plume extending into the outer part of the German Bight. The plume was not mixed with the high reflectance waters off the continent which tended to stay close to the coast. Due to the larger cloud coverage, the spatial distribution of reflection was less clear in the composite of 23–28 September 1990 than that of January 1991. The September composite indicated that the main areas with maximum reflectance occurred off the Belgian coast south of the river Scheldt and in the mouth of the River Thames. The relation between the reflectances observed by the NOAA=AVHRR satellite and the suspended mat-

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

205

Fig. 3. Composite reflectance images obtained from the NOAA=AVHRR satellite of (A) 23–28 September 1990 and (B) 14–19 January 1991. Colours are proportional to reflectance, but in arbitrary units. The numbers above the colour bar are to be used only to compare A with B.

ter concentrations measured in the same weeks was non-linear and highly scattered (Fig. 4). Both for 23– 28 September 1990 and 14–19 January 1991, the reflectance increased, together with SPM, until a saturation level of 1.5–2% reflectance was reached at in situ concentrations >¾ 10–20 g m 3 . This result corroborates the findings of Vos (1995), and it appears that the accurate conversion of the NOAA=AVHRR reflectance values directly into SPM concentrations is restricted to areas with low turbidity. 3.3. Performance of the interpolation models The parameters Þ and Ž which determine the weight coefficients wi; j in all three models (Eqs. 2a and 2b were set on the basis of preliminary validation

and cross-validation analysis at 45 and 100 km, respectively. Thus, wi; j reached the value of 0.29 at a distance di; j D 50 km, of 0.06 at di; j D 75 km, and of 0.007 at di; j D 100 km. These values correspond to a larger influential neighbourhood than the 50 km suggested by the semivariograms and the correlograms of the in situ data (Fig. 2). The reason was that we wished to allow some smoothing to account for the variance of the data, as indicated by the nugget effect in the semivariograms. The model results for 14–19 January 1991 and 23–28 September 1990 are shown in Figs. 5 and 6, respectively. All three models roughly predicted similar distribution patterns of SPM, with higher concentrations along the coast than offshore. However, model-1 can predict patterns only at a scale of the

206

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

Fig. 4. Relation between the SPM concentrations measured in situ and the reflectance observed by the NOAA=AVHRR satellite. The data represent the weeks 23–28 September 1990 and 14–19 January 1991. Lines are best fits of the form Y D .1 ' ð exp. ½X // and are included for clarity only.

same order as the distance parameter Þ, here set at 45 km. Including the synoptic data from the satellite images adds information with a much finer spatial resolution, and this yielded the more detailed interpolations by model-2 and -3 than in model-1. Due to this constraint, model-1 produced erroneous distributions around the two isolated data points in the tidal inlets for January 1991 (Fig. 5). With model-2 and -3, a more plausible distribution of SPM, in agreement with the extensive study of the Dutch coastal zone by Visser et al. (1991), was calculated close to these inlets. The high turbidity area off the Marsdiep tidal inlet appearing in both model results for January 1991 (Fig. 5) is, however, not realistic. Model-1 and -3 are true interpolators of the in situ data. This implies that the concentrations predicted by these models were not beyond the range of concentrations present in the in situ data set. This property represents some limitation as areas with very low or high SPM concentrations were not always covered by the field programmes. In contrast, model-2 interpolates the quantitative relation between reflectance and SPM, and from this it can predict concentrations below or above the range of field observations when this is indicated by the local reflectance. This is best illustrated with the distri-

Fig. 5. Concentrations of SPM calculated with model-1, -2 and -3 for the Dutch coast in the 14–19 January 1991 period. Dots in the upper panel indicate the in situ data points. Additional data points are in the Marsdiep and Friese Gat tidal inlets of the Wadden Sea.

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

207

tively by the application of validation and cross-validation analysis. As exemplified for model-3 in Fig. 7, the smoothing property of the interpolators yielded concentrations which were computed too low at high and too high at low in situ values. To compare the residuals between the calculated and the measured concentrations with the magnitude of the variability of the field data, they were normalized on the nuggets in the semivariograms (Fig. 2). In these normalised plots (Fig. 7C,D), values between 1 and C1 point to adequate performances of the models with residuals less than the variability indicated by the nugget effects, whereas values less than 2 and larger than C2 indicate poor interpolations. The computed rootmean-squares (RMS) of the normalised residuals were relatively large, particularly in the cross-validations, and showed only minor differences between the three models (Table 3). The RMS values were, however, strongly influenced by the large residuals at high in situ concentrations. Most of the normalised residuals were well between 1 and C1 for concentrations <¾ 20 g m 3 in September 1990 and < 40– 50 g m 3 in January 1991, i.e., at concentrations well above the level of saturation of the reflectance sensed by the satellite (Fig. 4). Taking the initial normalised residuals as criterion, together with the RMS of the residuals (Table 3), it is concluded that the performance of model-3 was slightly better than that of model-2, while both these models in which information from remotely sensed reflection images is used, produced better results than the simple distance-weight interpolation by model-1. Fig. 6. Concentrations of SPM calculated with model-2 and -3 for the southern North Sea in the 23–28 September 1990 period. White spots are caused by cloud cover of the reflectance composite image used in the interpolation procedure. Dots in the lower panel indicate in situ data points.

butions calculated with model-2 and -3 for 23–28 September 1990 (Fig. 6). The maximum concentration measured was 23.9 g m 3 in this week, and SPM concentrations between 2 and 12 g m 3 were calculated with model-3. With model-2, however, concentrations as high as 40 g m 3 were predicted close to the Belgian coast. The model predictions were evaluated quantita-

3.4. Long-term mean monthly distributions of SPM Monthly reflection composites were constructed by averaging the weekly composites of 1990 and 1991. The advantage of the monthly composites was that cloud coverage was reduced and that unstable patterns still appearing in the weekly images were removed. Model-3 was used for the interpolations, but with the large amount of data in the 20-year merges, model-2 gave almost identical results. The monthly distributions of SPM (Fig. 8) reflected the residual current system in the North Sea (Fig. 1). The anti-clockwise circulation of water entering the North Sea from the North Atlantic Ocean in

208

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

Fig. 7. (A,B) validations (Ž) and cross-validations () of the SPM concentrations calculated with model-3 for 23–28 September 1990 (A) and 14–19 January 1991 (B). Lines indicate perfect validations and cross-validations (Y D X ). (C,D) Residuals between SPM calculated and SPM measured normalized on the nugget effect obtained from the semivariograms of the in situ data (Fig. 2). Lines are fitted sigmoidal curves drawn for clarity.

the North together with the input through the English Channel and Dover Strait in the south causes the formation of a frontal system where both water masses meet. From here, the ocean water and all river inputs coming from the south are pushed into an easterly direction to the German Bight and from there along the Danish coast to the Skagerrak (Hainbucher et al., 1987; Otto et al., 1990; Pohlmann and Puls, 1994). In the relatively deep and seasonally stratified central and northern North Sea (Otto et al., 1990), SPM remains low during the entire year. In the shallow and permanently vertically mixed southern and eastern part of the North Sea, SPM concentrations

are considerably higher and show seasonal variation. According to Van Alphen (1990), citing data obtained from the Centre National pour l’Exploitation des Oceans France, the water entering Dover Strait from the English Channel has an annual average SPM concentration of ¾3.4 g m 3 offshore and ¾10.6 g m 3 close to the coast. The variability of the concentrations is, however, large and values well above 20 g m 3 have been measured close to the English coast of Dover Strait in winter (Van Alphen, 1990). The interpolated concentrations in our monthly distributions largely confirm these observations, although lack of data in this particular

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215 Table 3 Results of the validation and cross-validation analysis of the interpolation models applied for 23–28 September 1990 and 14– 19 January 1991

September 1990 Validation RMS Cross-validation RMS Initial residuals January 1991 Validation RMS Cross-validation RMS Initial residuals

Model-1

Model-2

Model-3

0.9 1.2 0.8

0.9 1.2 1.1

0.8 1.2 0.8

1.6 2.1 1.0

1.5 1.9 0.7

1.3 2.0 0.7

The residuals were determined from the differences between the calculated and the measured SPM concentrations and were normalized on the nugget effect of the semivariograms (4.3 g m 3 in September 1990 and 12.0 g m 3 in January 1991). RMS denotes the root of the mean of the squared normalised residuals. The initial residuals are determined by the horizontal part of the normalized cross-validation plots (Fig. 6).

area and smoothing may have yielded too high concentrations in the interpolations of some months (e.g. March and April in Fig. 8). Eisma (1981) attributes the high SPM concentrations in the coastal area of the English Channel to the supply of particulate matter by local river discharges, by erosion of the coasts and the sea floor, and by tidal dispersion and advection from the North Sea during northerly winds (Eisma, 1981). Once Dover Strait is passed, the streamlines of the residual current widen and most of the particles present in the mouth of the river Thames and off Belgium stay close the coast on further transport (Hainbucher et al., 1987). From October to March this is seen in the monthly distributions of SPM as a diamond of relatively clear water bordered by turbid waters on either side. As previously described by others (e.g. Eisma, 1990; Su¨ndermann, 1993; Dyer and Moffat, 1993) a plume of high turbidity off the southern English coast which crosses the North Sea into the German Bight is well developed in winter. The plume slowly diminishes during the following months until it has disappeared from the surface waters in May, when the stratification in the central North Sea is firmly established. In October and November, when the stratification is broken up, a beginning of the plume can be recognised from the Oyster Ground in the central North Sea to the German Bight, while the

209

entire plume is re-established in December. From data collected in 1988 and 1989, Dyer and Moffat (1993) estimated the annual particle transport in the plume at ¾ 6:6 ð 106 tons in 1988–1989, of which 70% occurred in 2 months with strong southwesterly winds (December 1988, March 1989). This flux is larger than the total supply from the rivers Thames and Humber and the erosion from the southeastern English coast, which, together, amounts to ¾4–5 ð 106 tons per year (Eisma, 1981, 1990; McManus and Prandle, 1997). Taking into account that a part of this supply is deposited close to the coast (McCave, 1987), this suggests that the English sources are not sufficiently large to explain the high concentrations in the main plume. The distributions in Fig. 8 show that SPM brought in from Dover Strait may be an equally important source. The input of SPM from the Humber and from erosion of the Holderness cliffs just north of it is transported with the southward residual current to the Wash where it is trapped due to inshore recirculation, and where large portions are deposited (McCave, 1987). This pattern is best recognised in the winter distribution with high concentrations in the mouth of the Humber and around the south point of Holderness. In December and to a lesser extent in January also, a plume distinct from the main plume off the Norfolk coast south of it leaves the Humber–Wash area into the North Sea and disappears upon approaching the Dogger Bank. In addition, in the reflectance composite of 23–28 September 1990, two plumes off the Wash and Norfolk–Suffolk, respectively, could be distinguished (Fig. 3). It is concluded that particles from north of the Wash may be transported offshore along a more northern route than particles originating south of it. These results suggest that a portion of the Humber and Holderness materials may be deposited in the relatively deep trench of the Outer Silver Pit south of the Dogger Bank (Eisma, 1990). Su¨ndermann (1993) used the model developed by Puls and Su¨ndermann (1993) and showed that the major part of the fine particles (‘mud’) transported with the plume off Suffolk–Norfolk are deposited in the Skagerrak and Norwegian Channel (S and NC). A minor part, however, settles in the Oyster Ground, where the tidal current velocities are just low enough to allow net deposition (Creutzberg and Postma, 1979). This deposition of mainly fine

210 W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215 Fig. 8. Monthly distributions of SPM in the North Sea calculated with model-3 and based on the 1973–1993 merged data set from the NOWESP data base and monthly composite reflection images 1990–1991. White areas in the distribution plots are caused by the lack of data in that particular area, black spots in the distributions of November and December are due to cloud cover in the reflectance composites. For each month between 1000 and 2000 in situ data points were used to construct the interpolations. The reflectance images applied were monthly composites based on weekly composites of 1990–1991.

211

Fig. 8 (continued).

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

212

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

particles was confirmed with in situ experiments under calm weather conditions (Van Raaphorst et al., 1998), but more energetic conditions imposed by autumn and winter storms are assumed to erode a large portion of what has been accumulated in the Oyster Ground in the previous months (Eisma and Kalf, 1987b; Van Raaphorst et al., 1998). Indeed, our distributions indicated that SPM from the plume may be deposited in the Oyster Ground in spring and summer. The SPM distribution in October suggested, however, that resuspension from the Oyster Ground occurs, probably under influence of winds which also break up the stratification, before the main turbidity plume off the English coast is established in December. Hence, the distributions confirm the conclusion from previous experimental work (Van Raaphorst et al., 1998) that the Oyster Ground may function as a temporary or, more accurately, seasonal depo-centre for fine particulate matter. On the continental side, areas with high turbidity were found off the Belgian and the southeastern English coasts, the narrow strip along the Dutch coast and off the Wadden Sea tidal inlets, and the inner German Bight off the Elbe estuary. The highturbidity zone off the Belgian coast, discussed in Section 3.3, was recognised in almost all months of the year, but most evidently from September to March. The average concentrations were > 80 g m 3 in January–March, but some individual data points indicated concentrations as high as 200 g m 3 (the highest values allowed in our data base). The Belgian coast thus represents the most turbid area in the North Sea, not taking into account estuaries and other inshore areas. Northwards from the Belgian coast along the Dutch coast, concentrations decrease when the Rhine delta is passed, abruptly in winter and more gradually in summer (see also Figs. 5 and 6). Here, concentrations of SPM were < 5 g m 3 as close as a few kilometres to the Dutch coast from May to September. The high level of SPM off Belgium may be due to erosion of the nearby Flemish Banks (Gosse´, 1977; Eisma, 1981) or to the dumping of harbour sludge (Van Alphen, 1990). More likely, however, the high concentrations observed on either side of the southern North Sea, i.e. off the Belgian coast and also off the mouth of the river Thames, are caused by offshore vortexes and shore-bound bottom currents (Otto et al., 1990) that drive the suspended

matter supplied by the English Channel and Dover Strait to both coasts and, at the same time, keep the locally eroded particles close to their sources (Eisma, 1990; McManus and Prandle, 1997). The interpolations suggest that the high concentrations observed in the inner German Bight were associated with a narrow turbidity zone just outside the Wadden Sea extending from The Netherlands to Denmark. The maximum levels of SPM in this zone, almost as high as in front of the Belgian coast, were found in the Friese Gat (for location see Fig. 5) where the Wadden Sea is narrowest and its sediments are muddy (Postma, 1981). This suggests that the Wadden Sea is a source of fine SPM which is in contradiction with the common view that it is a net depository for particles delivered by the North Sea (Postma, 1954, 1981; Eisma, 1981; Eisma and Kalf, 1987a,b). Our distributions of SPM refer to the upper 10 m of the water column. The transport of particulate matter from the North Sea into the Wadden Sea is, however, believed to occur largely with the bottom current (Postma, 1954) which has a landward component along the Dutch coast (Visser, 1993), and in the German Bight is directed into the Elbe mouth (Otto et al., 1990). We believe that the high surface concentrations just outside the Wadden Sea are due to the re-suspension of particles which are first deposited in the Wadden Sea, subsequently re-dispersed into the North Sea, and from there transported eastwards. The distributions reflect the cross-shore salinity gradient along the Dutch and German coast. Visser et al. (1991) have shown that this gradient drives a cross-shore circulation which depletes suspended sediment from the offshore waters (downward vertical velocity) and concentrates SPM in a narrow coastal strip. The fact that the turbid coastal strip is narrowed northward of the Rhine outflow is related to this estuarine circulation pattern, but also to other phenomena (Dronkers et al., 1990). The transport flux of SPM along the northern Dutch coast is, on average, strongly increased as the near-surface waters of the coastal current of the Rhine are accelerated by geostrophic effects and by wind stress. This appears also from numerical simulations by De Kok (1994). As a result, the residence time of SPM along the northern Dutch coast is small and concentrations are relatively low compared with the coastal area south of the Rhine.

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

In the inner German Bight, concentrations of SPM were as high as 50 g m 3 in February and a second, but lower, maximum was reached in June. This summer maximum is not due to the blooming of phytoplankton that shows a temporary minimum in this part of the North Sea in June and has its maximum concentration in terms of chlorophyll in July–August (Radach et al., 1990; Joint and Pomroy, 1993). Although we have no definite explanation for the summer maximum of SPM in the German Bight, we may speculate that it is associated with the annual variability of the local current system and the vertical structure of the water column which are both influenced by wind conditions and the freshwater discharge of the river Elbe. The turbidity plume originating from the English coast and the zone of high SPM concentrations along the continental coast seem to have merged north of the Wadden Sea islands in some months (January, October), but remained separated in other months (November, December). In addition, for October and particularly January, the merging was not complete, however. Thus, the fluxes of SPM along the continental coast and crossing the North Sea off England tend to follow their own routes towards the final depository in the S and NC. Nevertheless, some of the English materials may be entrained by the southeastern flux and reach the inner German Bight, particularly at favourable wind conditions. And also, from the surface SPM distributions, it cannot be ruled out that transverse circulations along the bottom may be capable of capturing sediment from the turbidity plume and transport it southwards to the Wadden Sea area. Furthermore, the monthly distributions presented here are based on 20-year-merged in situ data and as such represent decadal averages only. These average distributions indicate that most of the particles originating from the western coast of the English Channel to which particles from the English coast are added, do not enter the inner German Bight, but, instead, are transported via the Oyster Ground (where they may be deposited temporarily) directly to the Skagerrak and the Norwegian Channel. Acknowledgements The stimulating discussions with our colleagues from the NOWESP project, particularly Peter Bot

213

and Marijke Visser from the RIKZ in The Hague, the Netherlands, are greatly appreciated. Jens Gekeler and Gu¨nther Radach from the IfM in Hamburg, Germany, are responsible for the NOWESP research data base and organized the data in such a way that we could use them. All institutes and colleagues who made their data available to the NOWESP data base are greatly acknowledged. Hans Roozekrans from the KNMI in de Bilt, the Netherlands, put corrected the reflectance images at our proposal. Two anonymous reviewers are acknowledged for their helpful suggestions, especially concerning the SPM transport processes along the Dutch coast. This project would have been impossible without financial support from the EC within the Marine Science and Technology Programme (MAST II), Contract MAS2-CT93-0067, and from the Dutch Organisation for Space Research (SRON), Contract SRON EO-94=009 which were both granted to W.v.R. This is NIOZ publication no. 3178. References Anderson, R.F., Rowe, G.T., Kemp, P.F., Trumbore, S., Biscaye, P.E., 1994. Carbon budget for the mid-slope depocenter of the Middle Atlantic Bight. Deep-Sea Res. 41, 669–703. Biscaye, P.E., Flagg, C.N., Falkowski, P.G., 1994. The Shelf Edge Exchange Processes experiment, SEEP-II: an introduction to hypotheses, results and conclusions. Deep-Sea Res. 41, 231–252. Cressie, N.A.C., 1993. Statistics for Spatial Data, Revised Edition. Wiley, New York, pp. 1–900. Creutzberg, F., Postma, H., 1979. An experimental approach to the distribution of mud in the southern North Sea. Neth. J. Sea Res. 13, 99–116. De Haas, H., Van Weering, T.C.E., 1997. Recent sediment accumulation, organic carbon burial and transport in the northeastern North Sea. Mar. Geol. 136, 173–187. De Kok, J., 1994. Numerical modelling of transport processes in coastal waters. Ph.D. thesis, University of Utrecht, Utrecht, pp. 1–155. Delhomme, J.P., 1978. Kriging in the hydrosciences. Adv. Water Resour. 1, 251–266. Doerffer, R., Puls, W., Pan, W., Essen, H.H., Gurgel, K.W., Hessner, K., Pohlmann, T., Schirmer, F., Schlick, T., 1995. Evaluation of the North Sea, joining in situ and remotely sensed data with model results. In: Su¨ndermann, J. (Ed.), Circulation and Contamination Fluxes in the North Sea. Springer, Berlin, pp. 434–457. Dronkers, J., Van Alphen, J., Borst, K., 1990. Suspended sediment transport processes in the southern North Sea. In: Cheng, R. (Ed.), Proc. Symp. Coastal and Estuarine Studies. Springer, New York, pp. 302–320.

214

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215

Dyer, K.R., Moffat, T.J., 1993. Suspended sediment distributions in the North Sea. Report Institute of Marine Studies, University of Plymouth, Plymouth, pp. 1–9. Eisma, D., 1981. Supply and deposition of suspended matter in the North Sea. Spec. Publ. Int. Ass. Sediment. 5, 415–428. Eisma, D., 1990. Transport and deposition of suspended matter in the North Sea and the relation to coastal siltation, pollution, and bottom fauna distribution. Rev. Aq. Sci. 3, 181–216. Eisma, D., Kalf, J., 1987a. Distribution, organic content and particle size of suspended matter in the North Sea. Neth. J. Sea Res. 21, 265–285. Eisma, D., Kalf, J., 1987b. Dispersal, concentration and deposition of suspended matter in the North Sea. J. Geol. Soc. Lond. 144, 161–178. Garver, S.A., Siegel, D.A., Mitchell, B.G., 1994. Variability in near-surface particulate absorption spectra: what can a satellite ocean color imager see? Limnol. Oceanogr. 39, 1349–1367. Gordon, H.R., Brown, O.B., Evans, R.H., Brown, J.W., Smith, R.C., Baker, K.S., Clark, D.K., 1988. A semianalytic radiance model of ocean color. J. Geophys. Res. 93, 10909–10924. Gosse´, J.G., 1977. A preliminary investigation into the possibility of erosion in the area of the Flemish Banks. Rijkswaterstaat, report FA-7702, The Hague, pp. 1–20. Hainbucher, D., Pohlmann, T., Backhaus, J., 1987. Transport of conservative passive tracers in the North Sea: first results of a circulation and transport model. Cont. Shelf Res. 7, 1161– 1179. Ho¨lemann, J., Wirth, H., 1988. Concentration, major element ratios and scanning electron microscopy of suspended particulate matter from the North Sea, spring 1986. Mitt. Geol. Pala¨ont. Inst. Univ. Hamburg 65, 183–206. Jago, C.F., Bale, A.J., Green, M.O., Howarth, M.J., Jones, S.E., McCave, I.N., Millward, G.E., Morris, A.W., Rowden, A.A., Williams, J.J., 1993. Resuspension processes and seston dynamics, southern North Sea. Phil. Trans. R. Soc. Lond. A343, 475–491. Joint, I., Pomroy, A., 1993. Phytoplankton biomass and production in the southern North Sea. Mar. Ecol. Prog. Ser. 99, 169– 182. Kempe, S., Liebezeit, G., Dethlefsen, V., Harms, U., 1988. Biogeochemistry and distribution of suspended matter in the North Sea and implications to fisheries biology. Synopsis. Mitt. Geol. Pala¨ont. Inst. Univ. Hamburg 65, XI–XXIV. Ko¨nig, P., Frohse, A., Klein, H., 1994. Measurements of suspended matter dynamics in the German Bight. In: Su¨ndermann, J. (Ed.), Circulation and Contamination Fluxes in the North Sea. Springer, Berlin, pp. 250–270. Lohse, L., Malschaert, J.F.P., Slomp, C.P., Helder, W., Van Raaphorst, W., 1995. Sediment–water fluxes of inorganic nitrogen compounds along the transport route of organic matter in the North Sea. Ophelia 41, 173–197. Marees, G., Wernand, M.R., 1991. Interpretation of optical remote sensing data over coastal waters. Netherlands Remote Sensing Board (BCRS), report No. 91-27, pp. 1–105. McCave, I.N., 1987. Fine sediment sources and sinks around the East Anglian coast (UK). J. Geol. Soc. Lond. 144, 149–152. McManus, J.P., Prandle, D., 1997. Development of a model to

reproduce observed suspended sediment distributions in the southern North Sea using Principal Component Analysis and Multiple linear Regression. Cont. Shelf. Res. 17, 761–778. Otto, L., Zimmerman, J.T.F., Furness, G.K., Mork, M., Saetre, R., Becker, G., 1990. Review of the physical oceanography of the North Sea. Neth. J. Sea Res. 26, 161–238. Pohlmann, T., Puls, W., 1994. Currents and transport in water. In: Su¨ndermann, J. (Ed.), Circulation and Contamination Fluxes in the North Sea. Springer, Berlin, pp. 345–402. Postma, H., 1954. Hydrography of the Dutch Wadden Sea. Arch. Ne´erl. Zool. 10, 405–511. Postma, H., 1981. Exchange of materials between the North Sea and the Wadden Sea. Mar. Geol. 40, 199–213. Puls, W., Su¨ndermann, J., 1993. Modelling the distribution of suspended matter and lead in the North Sea. In: Mehta, A.J. (Ed.), Nearshore and Estuarine Cohesive Sediment Transport. Coastal and Estuarine Studies 38. Springer, New York, pp. 520–540. Radach, G., Berg, J., Hagmeier, E., 1990. Long-term changes of annual cycles of meteorological, hydrographic, nutrient, and phytoplankton time series at Helgoland and at FV Elbe in the German Bight. Cont. Shelf Res. 10, 305–328. Radach, G., Gekeler, J., Pegler, K., Herbig, K., Laane, R.W.P.M., Van Raaphorst, W., 1998. The NOWESP research data base. Germ. J. Hydrogr., in press. Roozekrans, J.N., 1989. Rapportage ten aanzien van verwerking NOAA–AVHRR beelden 1989. Internal report Royal Netherlands Meteorological Institute (KNMI). Shimwell, S.J., 1996. An investigation into the optical properties of North Sea coastal waters in relation to ocean colour remote sensing. Ph.D. thesis, University of Southampton, Southampton, pp. 1–237. Su¨ndermann, J., 1993. Suspended particulate matter in the North Sea: field observations and modelling. Phil. Trans. R. Soc. Lond. A343, 45–52. Tassan, S., Sturm, B., 1986. An algorithm for the retrieval of sediment content in turbid coastal waters from CZCS data. Int. J. Remote Sensing 7, 643–655. Townsend, J.R.G., Justice, C.O., Skole, D., Malingreau, J.P., Cihlar, J., Teillet, P.M., Sadowski, F., Ruttenberg, S., 1994. The 1-km AVHRR global data set: needs of the International Geosphere Biosphere Program. Int. J. Remote Sensing 15, 3319–3332. Van Alphen, J.S.L.J., 1990. A mud balance for Belgian–Dutch coastal waters between 1969 and 1986. Neth. J. Sea Res. 25, 19–30. Van Raaphorst, W., Malschaert, J.F.P., Van Haren, J.J.M., 1998. Tidal resuspension and deposition of particulate matter in the Oyster Grounds, North Sea. J. Mar. Res. 56, 257–291. Van Weering, T.C.E., Berger, G.W., Kalf, J., 1987. Recent sediment accumulation in the Skagerrak, north eastern North Sea. Neth. J. Sea Res. 21, 177–189. Visser, M., 1993. On the transport of fine marine sediment in the Netherlands coastal zone. Ph.D. thesis, Utrecht University, Utrecht, pp. 176. Visser, M., De Ruijter, W.P.M., Postma, L., 1991. The distribu-

W. van Raaphorst et al. / Journal of Sea Research 39 (1998) 197–215 tion of suspended matter in the Dutch coastal zone. Neth. J. Sea Res. 27, 127–143. Vos, R.J., 1995. Restwaq: Applications of remote sensing to water quality modelling. Report T1083=T1479 Delft Hydraulics, Delft, pp. 1–70. Walsh, J.J., 1991. Importance of continental margins in the

215

biogeochemical cycling of carbon and nitrogen. Nature 350, 53–55. Wirth, H., Seifert, R., 1988. Mineralogy, geochemistry and SEMobservations of suspended particulate matter from the North Sea, spring 1984. Mitt. Geol. Pala¨ont. Inst. Univ. Hamburg 65, 163–181.