OCEANOGRAPHY AND MARINE POLLUTION: AN ASEAN-EC ...

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Workshop on Marine Sciences, Manila, Philippines, ... A qualitative image of the possible variabilities of marine environments has been ..... mill ¡aris), d) North.
OCEANOGRAPHY AND MARINE POLLUTION: AN ASEAN-EC PERSPECTIVE Proceedings of the ASEAN-EC Seminar/ Workshop on Marine Sciences, Manila, Philippines, 12-16 April 1987

Edited by

HELENT. YAP Marine Science Institute University of the Philippines

MARTIN BOHLE-CARBONELL Institut fuer Meereskunde Hamburg, Federal Republic of Germany

EDGARDO D. GOMEZ Marine Science Institute University of the Philippines

Marine Science Institute 1990

ON RANDOM SAMPUNG AND ADAPTED DATA INTERPRETATION CASE: INNER GERMAN BIGHT

M. Bohle-Carbonell Institut fuer Meereskunde Troplowitzstr.7 D-2000 Hamburg 54 West Germany

ABSTRACT

Sampling and data analysis techniques are discussed with respect to hydrographic variabilities, especially irregular advection and mixing of different water masses. The Inner German Bight is taken as an example to illustrate more general considerations. It is discussed (i) how aliased, sequential data which resolve insufficiently temporal and spatial scales of this highly variable coastal sea may still be reliable for an analysis taking these data as a random variable, (ii) how these data may be used and (iii) how quasi-synoptic sampling may be replaced by repeated single point observations. In conclusion, it is shown how resources too limited to record non-aliased, sequential data may be used more effectively.

I. Introduction Sampling and data interpretation are some of the most basic tasks in science. Here I like to discuss strategies for sampling and data interpretation in application to. coastal marine environments. The emphasis on coastal waters is due to both the increasing human impact on coastal seas, which demands environmental management activities, and to the variability of coastal seas which renders conclusive investigations difficult. The ecological state of the southeastern part of the North Sea, the inner German Bight (Fig. 1) is an example of a highly variable coastal sea (Banner et al. 1980) and of the need for environmental management (Carlson 1986). Environmental studies combine physical, chemical and biological problems. However, here I will refer to physics. This does not limit the applicability of the discussion below but reduces the number of processes to which one may refer as causes of temporal and spatial variabilities. Variations of physical parameters in a coastal sea are caused by the interaction of the coastal topography with different flow regimes due to tides, wind-driven currents and currents driven by density differences and run-off. Tides, winds and

Fig. 1.

The North Sea and its division in regions of dif­ ferent strength of tidal mixing (from Pingree and Griffiths 1978). The Inner German Bight is indicated in the middle at the right.

density differences exist all over the world’s oceans, but, for example, the tidal currents are enhanced in shallow seas, the wind fields are more irregular along the coasts, and differential heating due to different water depths is typical for coastal waters. Furthermore, these driving forces plus the buoyancy and momentum fluxes 'due to riverine run-off combine differently, depending not only on season and weather, but on the local water depth and the form of the shoreline as well. A qualitative image of the possible variabilities of marine environments has been common knowledge since the deployment of remote sensors on satellites. If we compare, for example, surface temperature obtained by satellites with historical data or data sets obtained by modem ship surveys, then one recognizes that the latter are aliased, smooth patterns with long scales enhanced (Fig. 10). What to do now with the old data from single point monitoring stations, moorings and ship surveys? What to do, if the technical possibilities or economical resources to use satellite data do not exist, or If the phenomenon itself cannot be observed from satellites but is suspected to be highly variable? A possible answer lies in the analysis of the sampling strategy implicit In the existing "classical" observations and in an appropriate data processing technique. That is what this paper is about

2. Sampling in a variable environment Programs in coastal oceanography like 'The Central California Coastal Circulation Study" (Chelton et al. 1987) need substantial economic resources if multiple moorings, drifters, ships and satellites are to be used in a well defined moment and deployment pattern to observe an expected phenomenon. However, the latter is an advanced program compared to the average situation if resources and "a priori" knowledge are compared. Even the state of the art concerning marine research in seas like the Inner German Bight may be well above normal. This is simply due to the fact that this sea has been studied for several decades.

2.1. Some general remarks The Inner German Bight (IGB, Fig. 2) is the southeastern part of the North Sea. The empirical knowledge of its physics has been derived mainly from data collected by classical methods like single ship surveys, long-term monitoring, moored Instruments and ship-of-opportunlty programs. Air- and satellite-borne remote sensing showed some years ago that the spatial variability of, for example, surface temperature (Katsaros et al. 1983) is higher than thought previously. However, up to now nearly all of the available data have been collected from ships or by moored instruments. Modern ship surveys try to cover the German Bight with grids of 5 or 10 sm resolution (Hickel etai. 1986) and last about one week. This means one observation for about 100 to 300 km2 if towable instruments are not available. Thus the spatial and temporal resolution of the measurements is still coarse and aliasing most probable. Anybody who collects data may have found himself in two distinct situations. First, one discovers "a posteriori" that the resolution has been insufficient, or second, one knows "a priori" that the resolution will be less than sufficient. The second case may be avoided if one may choose freely the object to investigate with respect to the available resources. However, often this will not be possible. Furthermore, from the point of view of the sampling strategy both possibilities are not too distinct. In both cases one has to handle data which are aliased. Small scale variability is projected into longer scales, a problem well known from spectral analysis (Jenkins and Watts 1968). Differences between both cases are simply that in the second, one may adopt directly a sampling strategy which avoids the aliasing problem, and consequently one will use a data analysing technique which makes less use of the sequential order of the observations. One classical example of such a technique is to calculate the monthly mean surface temperatures for large regions from all available observations, as done by Tomczak and Goedecke (1962,1964) and Goedecke et ai. (1967) for the North Sea. One neglects partly the information on time and place of the observation in favor of using ensemble averages. Another example is to use, as done by Radach and Berg (1986) for data from a monitoring station in the IGB, one particular parameter, for example salinity, to define a classification. Then the data from other simultaneous observations may be grouped for predefined salinity ranges. Again ensemble averages are calculated. However, the details

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of any technique should be defined in reference to the phenomenon which one likes to observe. Its characteristics will determine if, for example, a given sequence of observations at a fixed location has to be seen as a time series reflecting the dynamical behavior of the observed object, or if this sequence of observations is qualitatively similar to data from a ship-of- opportunity program. This and related questions will be discussed referring to the IGB and the monitoring station at Helgoland-Roads. Therefore I will review some features of this part of the North Sea. Emphasis will be put to show that advection and mixing of water masses in the IGB are highly variable in space and time and that large scale variations are covered by important small scale variabilities.

2.2. Features of the Inner German Bight The IGB is the shoreward part of the south-eastern quarter of the North Sea named the German Bight. The water depths are rarely greater than 40 m, and the bottom topography as well as the shoreline is irregular. Islands, tidal flats and estuaries form the particularities of the coastal geometry. The main tributary is the river Elbe, with a mean discharge of about 720 m3/s. Central to the IGB are two islands named Helgoland (Fig. 3). These islands

Fig. 3. The topogra­ phy around the is­ lands Helgoland. The arrow indicates the position in the chan­ nels where the stand­ ard monitoring obser­ vations are done. Depths are in m (Re­ drawn from a public map).

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Fig. 4.

Three examples of time series of standard observations done at Helgoland Roads (salinity in^oo.surface.temperature in °C, nitrate in microgram-atom/liter). The numbers are years. Note the different superposition of seasonal and irregular fluc­ tuations. (Courtesy of G. Radach, data source: Biologische Anstalt Helgoland).

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are about 50 km off the coasts, of about I km2 surface area each, separated by shallow channels (6-8 m deep) and are situated at the western end of a shallow ridge (less than 20 m deep). This ridge and the deep (about 40 m) valley of the glacial river Elbe just south-west of Helgoland are the main topographic features of the deeper parts of the IGB. Observations of oceanographic parameters have been done episodically at Helgoland Roads since the end of the last century (Reichard 1910). Regular observations of several physical, chemical and biological parameters have been carried out since the early sixties (Biologische Anstalt Helgoland, Annual Reports 1962-1986). (Time series of similar length, but only on physical parameters, are available from several fire vessels positioned in the German Bight.) Thus one may hope that, due to the particular position of Helgoland, the monitoring data from Helgoland Roads (Fig. 4) may describe properly the physical, chemical and biological state of the dynamical system "IGB".

2.2.1. Tides and tidal currents The M2-tlde is the most prominent feature of the current field In the IGB and has been investigated intensively (see for example: Neumann and Meier 1964, Backhaus 1980, Middelstaedt et al. 1983). The tidal currents have mean amplitudes of some 10 cm/s, maximum amplitudes of about I m/s and are vertically sheared due to the strong bottom friction. The form of the tidal currents, for example the vertical shear, the form of tidal ellipsis, the modulation by the spring-neap cycle (Sager 1966) change with position. The current patterns caused by the tides are slab-like displacements of the water masses (Fig. 5) over areas of some 100 square kilometer surface (Essen et al. 1981). Deviations found on larger scales are due to non-linear interactions of tidal currents and bottom topography (Zimmermann 1978, Nihoul 1980, Siefert et al. 1980) which modulate the spatial pattern of the tidal currents. Further modifications are due to tidal fronts separating well mixed from stratified water masses. Pingree and Griffiths (1978) estimated that one potential region for the formation of a tidal front is within the IGB (Fig. I). However, the actual formation of a tidal front is a particular problem, but the IGB is known for its variable interplay of stratified and non-stratified water masses (see below). Thus the tidal current field is heterogeneous in space for length scales well above the typical radius of the tidal ellipsis of about 5 km. The spatial pattern of the tidal displacements is therefore a regional property. Similar conclusions hold for the tidal mixing. Particular evidence shows that mixing features are different near or far off the islands. (Particularities related to the location of the monitoring station on the island Helgoland will be discussed below).

2.2.2. Wind and wind driven currents Wind speed and wind direction vary slightly with the seasons. Wind speeds below 3 m/s (2 Bft) or above 13 m/s (6 Bft) have probabilities of about 15% each (Richter 1966). The coherence length of the meteorological parameters is about

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Fig. 5. Surface currents over half a tidal cycle between Helgoland and the eastern shore of the Inner German Bight measured by radar back-scattering (from Essen et al. 1981). (a) first image, (b) 2h later, (c) 4h later, (d) 6h later. The star indicates the location of a reference mooring of current meters.

40 to 70 km depending on the distance to the shore line (Fengler I966). Typical velocities of wind driven currents are about Ю cm/s for normal conditions. The observed wind driven currents show both large scale clockwise or counter clockwise flows which flush the IGB and smaller circulation cells of more local extent. The particular form of the flow regimes depends on wind speed and wind direction (Mittelstaedt et al. I983). Similar features have been estimated by numerical modelling for the whole North Sea (Hainbucher et al. I986). Models which resolve the topographic particularities of the IGB are still in development (Pfeiffer and Suendermann I986), but the available models demonstrate that tides, wind and density driven currents are of similar importance (Backhaus I980). Different interplay of tides, winds, run-off and density differences causes different current patterns ranging from local circulation in the IGB to its rapid flushing. The winterly flushing times (Fig. 6) vary from I0 to 60 days (Backhaus and Boehlich I985). Thus the wind driven currents in the IGB may cause events of large scale flushing flows which are interchanged with events of small scale current patterns of large-scale quasi-stagnancy.

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input over the German Bight displayed as an anomaly, (a) 1980/81, (b) 1972/73 (from Backhaus and Boehlich 1985).

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2.2.3. Atmospheric input and water masses Investigations of the climatological mean conditions of the heat fluxes between atmosphere and the North Sea showed that the sea over a year loses heat to the atmosphere (Becker 1981) which has to be replaced by lateral advection. Regional differences are present; the Outer German Bight loses heat to the atmosphere, the IGB gains heat from the atmosphere. The amplitudes of the seasonal variations (monthly climatic means) are about ten times the yearly climatic means. The mean vertical stratification is generally weak (normalised density difference from bottom to surface times gravity acceleration, "D", of 1 cm/s2) but stronger thermohaline stratifications (D = 4 cm/s2) are observable. The seasonal variations of salinities and temperatures are about l°/0o ancl 15°C, respectively. The near surface temperature and salinity distributions show Important small scale variability of some tenth of a degree, half a promille and some ten kilometers length superimposed on structures with larger scales extending from SE to NW (Prahm- Rodewald 1981). Similar horizontal structures are found for the monthly means of the surface to bottom differences of temperature, salinity (about l°C or 0.5°/oo) and density (Fig. 7). In the same paper the authors (Frey and Becker I986) show that the local differences are sufficiently important to detect a displacement of the fire vessel Elbe-I of about 4 km in mean features of both those of the tidal currents and of the salinity and temperature stratification. It should be noted that the time series of daily salinity measurements at different fire vessels show different short periodic variations. The long term variations of periods longer than one year are uniform for the IGB (Becker and Kohnke I978). Water mass analysis shows different distinct water masses due to different influences (Becker et al. I983) of atmospheric input (heat and precipitation), riverine run-off, topographically induced upwelling and water formation processes on the tidal flats (Fig. 8). The different characteristics concern physical as well as chemical-biological parameters (Goedecke I936, Kalle I956, Martens I978, Gienapp I982, Brockmann and Eberlein I986) and vary seasonally. Different water masses are found in the IGB which are distinguishable by their seasonal salinities and temperatures. The SE-NW extending mean pattern of isolines of temperature and salinity found repeatedly is most probably due to the combined effect of the freshwater discharge and topography. It is this pattern that suggested the classical concept of one inclined interface forming one zone of convergence in the IGB (Goedecke I955, I968). This concept is under review since Becker and Prahm-Rodewald (I980) identified, by towed instruments, multiple salinity fronts of some tens of kilometer length. Classical diffusion experiments (Joseph et al. I964) showed already streaky distributions of the injected Rhodamin. Multiple adjacent water masses of different characteristics and relatively small scales thus seem to be typical for the IGB. They are superimposed on a mean pattern and may be sufficiently strong to perturb this pattem to a degree which favors a concept of multiple interfaces between different water masses.

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Fig. 8. Different wa­ ter masses and mix­ ing areas in the Inner German Bight in November 1976 (from Martens 1978). The dots are positions which have been sampled. a)water of the Elbe estu­ ary (low tempera­ ture and low salinity, high nutrient con­ tent), b) water of the Wadden Sea (low temperature, highest concentration of nitrogen, high num­ bers of Noctiluca mill¡aris), d) North Frisian coastal water (intermediate to b and d, similar to c2, but higher numbers of Noctiluca miiiiaris), c2) southern mixing area (similar to d salinity, higher temperature), d) wa­ ter of the North Sea (highest salinity and temperature, lowest amount of nutrients).

2.2.4. Riverine discharge The main tributary to the IGB is the river Elbe, the second is the river Weser. The latter’s discharge is about 30% that of the Elbe. Both rivers enter at the lower southeastern corner of the bight over long, funnel shaped estuaries. The extremes of the accumulated discharges are 400 m3/s and 4000 m3/s. The discharge varies pulse-like due to tidal modulation and seasonally fluctuating run-off. The coastal waters are partly fresh. The mean salinity difference between nearshore water in the IGB and waters of the central North Sea is about 3°/oo. The influence of high riverine discharge on salinity has been detected well north of Helgoland (Hickel I980) and the mean salinities at Helgoland Roads fluctuate anti-parallel to the discharge pattern (Radach and Berg I986). Features of the riverine plume outside the estuaries have been documented by various remote

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sensing methods. Sharp fronts (Gierioff-Emden 1967), smooth gradients (Blume 1982) and heterogeneous patterns (Hoge and Swift 1982) have been observed. Dense ship surveys (Lueneburg 1963) showed intensive exchange processes between the two outer estuaries taking place on and seaward of the tidal flats (Fig. 9). The particularities of the mixing of salty and fresh water in the lower and outer estuaries are unknown. The basic dynamics of a riverine plume, or the relevance of advection, instability and mixing processes, are still a topic of intensive research. Of particular interest are results showing bifurcating outflow (Nof 1987) due to different balances of planetary and local vorticity and baroclinie instabilities (Griffiths and Hopfinger 1984, Whitehead and Chapman 1986). Unstable large scale flow structures seem to reinject energy at length scales near the internal Rossby radius of deformation. The latter value is about I0 km for the IGB and therefore well below its diameter. However, even if the particular form of the mixing mechanisms of the Elbe and IGB waters is not well defined, the front of the river plume will shed off eddies formed by water masses of different composition and will be a place of intensive mixing, especially if the local bottom topography is irregular.

2.2.5. The islands of Helgoland The influence of an island on the flow field has been the subject of various investigations (for example: Gordon and Hughes I98I, Simpson et al. I982, Wolanski et al. I984, Snyder I985, Pattiarachi et al. I986, Wolanski I986). However, no particular study has been done for the islands of Helgoland. The tidal currents at some locations near the islands are known (Mittelstaedt et al. I983), but the scale and form of, for example, headland eddies are unknown. Thus the flow field near and in the wake of the islands could only be deduced from studies made elsewhere. More questions arise from the particular form of the islands of Helgoland. The two parts of Helgoland are separated by a relatively shallow passage, Helgoland Roads. The latter’s width is similar to the width of the islands. The bottom topography of the passage opens quite more smoothly to the NW than to the SE and is aligned with the direction of the main tidal currents. Relevant questions are: Do the tidal currents, which are not symmetric in time, pass the passage in a similar manner for ebb and flood? What is the form of the island wake or the topographically induced upweliing and mixing? Remote sensing data on surface temperature near Helgoland are available (Becker I978, Becker et al. I979) which show structures evidently linked to the presence of the islands: first a temperature front (Fig. I0) about I km off-shore from Helgoland and second a patchwork of different water temperatures just off-shore of the islands. The temperature differences are about 0.5°C across the front and about l°C between different patches. It may be noted that the standard deviations of the daily temperatures and salinities from the daily climatological means are about l°C or l°/oo if observations from Helgoland Roads are analysed

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Fig. 9. Isolines of surface salinity between Helgoland and the southern shore of the Inner German Bight showing the influence of water from the Elbe estuary (a) and the Weser estuary (b).on 30 October 1962 (from Lueneburg 1963).

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Fig. 10. Isolines of surface temperature as measured shipborne (a) or from airplanes (b) from Helgoland to a position 40 km south of Helgoland. The airborne measurements are accurate to ±0.2° C (from Becker et al. 1979). Note the different forms of the isolines and remember the differences between skin and surface temp­ erature.

(Radach et al. I986). Thus instantaneous differences may be of the same order of magnitude as the presumed interannual variations.

2.2.6. Human impact The description of the IGB may be concluded with some indications on the magnitude of human impact on its ecosystem. During the early eighties several cases of late-summer oxygen deficiency have been observed in the bottom water masses with an extension of several thousand square kilometers

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(Dethlefsen and Westernhagen 1982, Rachor and Albrecht 1983). An increase in the nutrient concentrations in the IGB during the last years has been reported based on analysis of data collected at Helgoland Roads (Lucht and Gillbricht 1978, Radach and Berg 1986). The chemical data from Helgoland Roads cover the last twenty years and are the sole data recorded permanently (each working day). Thus their importance for the understanding of the ecosystem IGB may be tremendous if this monitoring station is suitable for observing the average conditions of the IGB.

2.2.7. Summary The description of the physics of the IGB shows plainly that the hydrographic conditions of this part of the North Sea cause spatial and temporal variability sufficiently high to render generally senseless the notion of "the mean pattern of. On the other hand problems are present which ask urgently for an investigation. Furthermore, some of the investigations have to rely on time series collected at one point, at Helgoland Roads - a point of observation which has quite particular local conditions. Even if these observations could be neglected, a lot of data do not have the spatial and temporal resolution which would be desirable. Thus, how one can proceed to analyse these data consistently with the hydrographic conditions?

2.3. The sampling strategy Two problems have to be discerned. These are, first, the lack of resolution which aliases the sequential data, and second, the local conditions at the point of observation which may cause a distortion of the property which we like to observe. This "distortion" should rather be named "local mapping" because nothing is physically distorted - it is just something like an observation done by a particular device.

2.3.1. The point of observation In order to illustrate what should be understood as "local mapping" let us discuss an example. Surface water temperature is measured daily at Helgoland Roads. The problem is to estimate how well the measurements represent the surface temperature at some distance from the Island assuming similar atmospheric fluxes. The local water depth at Helgoland Roads, depending on the tides, is about 6-8 m. The water depth around the island is substantially greater. The axis of the main tidal currents of I m/s is parallel to the axis of the passage and tidally induced mixing is enhanced around the island. Thus the observed surface temperature may result from nearly instantaneous mixing of two vertically adjacent water masses. The strength of the mixing event will depend on the actual conditions.

There will therefore be days when the measured surface temperature represents well the water temperature of the near surface waters around Helgoland, whether the water is vertically well mixed or the stratification is sufficiently stable to prevent substantial tidal mixing. However, if the stratification is weak, then the temperature measured will be too low because the measured water mass Is a mixture of surface water and near bottom water. Thus, the local physical conditions map one value of a state variable of the system; here temperature, with another value creating a local image of a given regional situation. However, it is obviously better to avoid "a priori" points of observations which cause a mapping to be too particular. On the other hand, situations exist where the local mapping is desirable. The location of a tidal gauge will be chosen depending on the local tidal range. Finally, therefore, discussing the "local mapping" is nothing more than investigating in a broad sense the characteristics of the used sensor. However, it seems to be necessary to emphasize this, because quite a lot of papers summarize these problems under the heading "discussion" and not under its obvious heading "methods and instruments".

2.3.2. Aliased sequential data What to do now with aliased sequential data? One has to accept that these data are a more or less random variable describing an ensemble which may change its average characteristics in time or space. In order to establish this concept one may imagine a sea, like the IGB, which is characterized by fluctuating main currents and several regions where water masses of different properties (physical, chemical, biological) are formed. These water masses will be subject to advection by the current field and by mixing. As the main currents are unsteady, advection and mixing will be variable in space and time, even if some general trend may be present. Thus to predict the path of a particular water mass and the development of its properties will be difficult. However what the details are, essential to the concept is that different water masses are present and that they are moving. Generally it will be unknown, in the moment of observation, what the actual drift of the sampled water masses is and what the details of its formation processes are. Thus any sample of observations will be random to some degree. In practice one tries generally, and often in vain, to carry out observations in a manner to minimize the randomness (noise). This is difficult to do because it im­ plies predicting (or knowing) fairly well the paths and the developments of some particular water masses. Thus,if this is too difficult, one may consider as hypothesis of the average conditions: Any kind of water mass will be present sometime in any part of the sea; or at any moment each kind of water mass will be present somewhere in the sea. The realibilty of this hypothesis will increase with increasing variability of the advection and mixing. If realiable, then the hypothesis implies that quasi-sypnotic observations done to determine the different water masses present may be replaced by repeated observations at one place. The number of repeated observations, their frequency and the extent of the region for which temporal

sampling at one place may replace spatial sampling have to be determined independently like the "local mapping" characteristis of the point of observation. If done mostly in an iterative process, then single point monitoring data will be assimilable into spatial sampled data, for example, data from a ship-of-opportunity program. The frequency of observations (in space or time) will be considered next. If one wishes to observe the development of the sequence of a single event, then one has to resolve the smallest relevant scale. Observations done too sparsely and nevertheless analysed as a sequence of observations will provide, due to the aliasing, the impression of gradual transitions like the classical concept of one zone of convergence in the IGB. If one wishes to determine just the different water masses present, then one may relax the sampling frequency down to the point where each observation is independent. Thus the observations seem to be a random sample taken from a noise process. If, as in most cases, the observations are not completely independent because the sampling frequency has been higher than necessary but too low to resolve all relevant scales, then the data set has to be reduced to independent observations. Now, one new feature may be considered. The configuration of the sea, as postulated above, does not include any seasonal (or other) changes of the conditions which determine the general pattern of advection, mixing or formation of water masses. Adding this implies that the ensemble described by the random variable changes. Thus the observations will have drifting average characteristics which determine the ensemble properties and which may be subject to a sequential analysis. Several conclusions may be drawn at this point: Either sample to resolve all relevant scales or use independent observations to get certain data. Determine how synoptic sampling in space may be replaced by repeated observations in time to minimize the sampling effort. Construct sequential data as average properties of independent observations.

2.3.3. Helgoland: The implicit sampling strategy Summarizing the general considerations on the sampling strategy and the dynamic conditions of the example, IGB and data from Helgoland Roads, it seems to be most likely that these data reflect the dynamics of the IGB besides some distortions due to "local mapping". The main arguments are: Small scale variability is strong and may be understood by considering intensive mixing processes caused mainly by tides and instabilities of frontal structures. Advection is present but does not result in a preferred current pattern due to the different interplay of tides, winds and density driven currents. Permanent sources of different water masses exist, mainly riverine run-off, processes on the tidal flats and advection of water masses from the central North Sea. Thus different water masses will be created permanently. Their

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particular features will vary with the seasons and main weather conditions. These water masses will pass the island. The observed features will be Influenced by the enhanced mixing around the island. Work to prove that time series from Helgoland Roads reflect fairly well the particularities of the IGB is under way. It Is at the stage of comparing variances of different parameters observed at Helgoland Roads and elsewhere in the IGB. Summarizing the general considerations on the sampling strategy and the general question, what to do if one cannot resolve the variability of the observed dynamics, one should opt for a measuring program which provides a sufficient number of independent observations. This choice, still less common in oceanography but habitual in other sciences tackling stochastic problems, may be made easier if one notes: The recent progress in oceanography shows that marine systems are a lot more random than assumed previously.

3. Data analysing strategies If, as stated above, the data are a random sample taken from an ensemble (environment) which changes its characteristics with time and space, then the data analysing techniques should be correspondingly adapted to this. Procedures like drawing lines between two successive observations or simple Fourier analysis would be in fundamental violation of the sampling strategy. Data which represent a random variable need statistical data analysing procedures. However, ail data analysing techniques have to be applied on the background of theoretical knowledge of the observed dynamics.

3.1. Some "a priori" knowledge The theoretical background, or "a priori" knowledge of the observed dynamics, which would help to analyse the data presumed to be random variables, should describe If possible: - the mechanisms causing the random character, - the expected influence on the particular conditions at the point of observation, the "local mapping" of the general conditions. Information which is lacking, such as for example the time interval between independent observations, should be derived during the measurements using methods which predict the value of the observed variable to be measured next. Deviations between observed and predicted values may then be analysed to derive some more information on, for example: - the possible dynamic states of the observed system, - the probabilities of different values for the state variables.

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3.2.

—

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Some techniques The first step during data analysis is most probably to use ensemble averages to determine mean conditions and mean deviations. Techniques relying strictly on the sequential order of the data (El-Shaarawi and Esterby 1982) may be used to analyse sequences of the ensemble averages. Various modern techniques to determine ensemble averaged features, often originating from analysis techniques for sequential data, are available (Krishnaiah 1980, Krishnaiah and Kanal 1983, Brillinger and Krishnaiah 1983, Chave et al. 1987). No precise advice for 'the best method" can be given because the particular choice of a certain technique will be determined by the particular structure of the data, especially the degree of randomness of the observations and the scales over which the ensemble properties change. However, success will depend often on the ability to order the observations in distinct groups, each being representative of a certain state of the observed dynamics. Thus classical, non-robust (Kendall and Stuart 1976) and modern, robust techniques (Huber 1972, Hampel 1973, Koutitonsky and El-Sabh 1985) to determine significant differences between different sub-ensembles of the data will be essential.

Conclusions Finally, several points should be emphasized. Sequential data which are Insufficient to resolve the temporal or spatial variability of the observed dynamics may still be sufficiently reliable for forms of analysis which rely on the fact that the data may be reduced to a random variable. Thus the answer to limitations which would cause sampling of aliased sequential data should be to design a sampling strategy which is more optimal to provide a good random sample. This would be a better use of limited resources. The price to pay for this is that certain urgent problems linked to special sequences of single events may not be solved. However, I feel strongly that a distorted solution and spoiled resources are worse than no solution.

REFERENCES Backhaus, J.O. 1980. Simulation von Bewegungsvorgangen in der Deutschen Bucht Dt. hydrogr. Z. Erg.-H. B15, 56 p. Backhaus, J.O. and M.J. Boehlich. I985. Estimates of the circulation and turn-over times of the German Bight during winter with respect to eutrophication. Umweltforschungsplan des Bundesministers des Innern, Wasser, Forschungsbericht 85-I02 04 2I5, 4I p. Banner, F.T., M.B. Collins and K.S. Massie. 1980. The north- west European sheif seas: the sea bed and the sea in motion II. Physical and chemical oceanography, and physical resources. Elsevier Oceanography Series 24B:467-493. Becker, G.A. 1978. Infrarot-Scanner-Aufnahme der Wassertemperatur um Helgoland. Dt. hydrogr. Z. 3I(3):I05-106.

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