Difference between revisions of "Monitoring the water quality of coastal waters with automatic equipment"

From MarineBiotech Infopages
Jump to: navigation, search
(See also)
Line 85: Line 85:
* [[Using model simulations to support monitoring - Methods&Techniques]]
* [[Using model simulations to support monitoring - Methods&Techniques]]
* [[Using model simulations to support monitoring - Implementation & Results]]
* [[Using model simulations to support monitoring - Implementation & Results]]
* [[Water quality services GMES - MarCoast]]
* [[Water quality services GMES - MarCoast in Germany]]
* [[Real-time algae monitoring]]
* [[Real-time algae monitoring]]
* [[Optical remote sensing]]
* [[Optical remote sensing]]
Line 93: Line 93:
* [http://www.coastlab.org/ GKSS Coastlab]
* [http://www.coastlab.org/ GKSS Coastlab]
* [http://www.emecogroup.org/ EMECO]
* [http://www.emecogroup.org/ EMECO]

Revision as of 18:09, 28 April 2009


Within Europe most maritime countries have monitoring programmes in place to fulfil their regulatory, EU or otherwise conventional commitments. Up till now no European structure has been set up or is in place to harmonize these observations in such a way that a comparison between different regions or a Pan European view is possible. A very limited overview can be obtained from the Annual reports of the EEA (European Environmental Agency), e.g. regarding nutrient concentrations in coastal waters. With the upcoming WFD, directives for transitional waters and the marine strategy requirements for pan European approaches are increasing.

Attempts to set up integrated networks of observations have been made on regional scales e.g. in the Baltic (BOOS), and in the North Sea and adjacent Atlantic (NOOS). However, still these networks do hardly cope with the ecosystem approach as promoted by ICES (International Council for the Exploration of the Sea). Generally most networks intend to observe the primary parameters needed to improve our meteorological models or physical water parameters to improve our transport and current models. What is urgently needed is to extend these lists of parameters to important ecological parameters such as turbidity, phytoplankton and zooplankton biomass, occasional biomass of fishes, macro-benthos, and nutrients in surface and deeper waters. In order to obtain reliable data which can as well be used to drive ecological models intensive quality assurance procedures have to be implemented and integrated into the overall data management schemes.

The present article aims at presenting possible approaches and at showing the possibilities which are currently available. This includes ships-of-opportunity observations, in combination with the CPR (Continuous Plankton Recorder), satellite observations to extend the spatial scale of high frequency ground truth observations, and finally the incorporation through data assimilation of these observations into combined transport and ecological models.

Coastal and shelf sea observatories, the national and international dimension

Over the last decade, increasing national and international effort has been established worldwide in monitoring, forecasting and assessing the state of coastal systems. Around the coasts of the United States, for example, eleven regional Coastal Ocean observing systems have been established as part of the US-Integrated Ocean Observing System (IOOS) (Ocean.US, 2002[1]). In Europe, EuroGOOS (www.eurogoos.org) has fostered the planning and implementation of prototype systems for various European shelf seas, e.g. the Mediterranean Operational Oceanography Network MOON (Pinardi and Flemming 1998[2]), the Baltic Operational Oceanographic System BOOS (Buch and Dahlin 2000[3]), or the North West Shelf Operational Oceanographic System NOOS (Droppert et al. 2000[4]). A prominent example of a national effort is the Previmer Observing System in France, which is set up by a consortium of scientific, public and industrial partners.

The identified needs for these systems comprise aspects of climate change, marine operations, national security, sustainable management, ecosystem conservation and restoration, public health and mitigation of natural and man-made hazards (see e.g. Frosch et al., 1999[5]). Generally, they are based on existing mooring stations of governmental observational systems. Although these stations deliver impressive long-term time series, they capture the time domain at only a single point. Furthermore, reliably measured parameters are often limited to physical state variables. Chemical parameters or even biological are, if any, available in many cases only for limited time periods. Integration of further chemical and biological state variables as well as information from remote sensing images is always put forward as an essential target. As an example of a European prototype system, the GMES Services network MarCoast (Marine & Coastal Environmental Information Services) is developed to deliver satellite-based services in the field of marine and coastal applications. Services integrate detection and monitoring technologies involved in water quality, oil spill and meteorological information into a durable network.

In Europe, present activity focuses mainly on the harmonisation of existing technology and methods between different countries. Only few countries invest significantly into further development of integrated coastal observation systems (e.g. Previmer in France), although new technologies have been brought to pre-operational maturity, e.g. the development of FerryBox-systems onboard of ships-of-opportunity (Petersen et al., 2007[6]).

In the US and Canada significant effort is raised to increase nowcast/forecast capabilities for coastal waters by a multiplatform sampling approach (Schofield et al. 2002[7], von Alt el al. 1994[8], Simonetti, 1998[9]) combined with a suite of data assimilative models (see below). Temporal and spatial density of in-situ data is enhanced by using autonomously operating underwater vehicles (AUV, Glider) (von Alt et al. 1994[8], Simonetti 1998[9], ) with remote control of actual tracks by automated systems (Fiorelli et al. 2006[10]). The installation of undersea laboratories, connected to the landsite by fiberglass broadband width and electric power cables (Schofield et al. 2002[7], Purdy et al. 2003[11]) offers a complementary, albeit costly approach to permanently observe the environment from the seabed. As power supply and data transfer rates pose no real limitations, the submerged unit can be used as node, which supply basic infrastructure for a vast number of scientific partners to study both episodic events and long-term trends. Despite the individual research flavor of each observatory, these high-resolution regional sites will be unified through a coherent suite of broadband measurements (Schofield et al., 2002[7]). Spectral images from remote sensing are becoming increasingly part of routine coastal observation to derive spatial distributions of dissolved and particulate (abiotic and biotic) material. Their usage for operational purposes, on the other hand, is limited a relatively low temporal resolution and by the presence of clouds. Also validity of reflectance values as proxies for different physical and biological variables is still a matter of ongoing research, so that in-situ measurements are still indispensable. Therefore, we still face the challenge to develop and integrate reliable and, in part, new technologies for long-term observation of water chemical and biological state variables.

In all cases effective data management is prospected or implemented to store, access, distribute and present data in nearly real-time and with documented quality. For operational purposes effective data management must provide tools for data integration from different sources and interfaces to data assimilation schemes and numerical model schemes. Data accessibility and presentation plays also an important role to inform, participate and educate the general public (Schofield et al. 2002[7]).

Global and regional activities such as the Global Ocean Data Assimilation Experiment (GODAE) and Marine Environment and Security for the European Area (MERSEA) give a practical demonstration of gathering up-to-date near real-time observation systems and ocean data assimilation providing regular information about the state of oceans and regional seas, including suitable initial and boundary conditions needed for near coastal models. The activities carried out in the frame of the Global Ocean Observing System (GOOS) have recently been extended towards the coast (Coastal Ocean Observing Panel, COOP/JPICO) with a focus on coastal management, environmental protection, ports and shipping, and monitoring. Documenting and forecasting change in coastal environments will require integration of physical, chemical, biological and geological observations and modelling and could provide scientific products and guidance to a wide range of users.

The European Cluster in Operational Oceanography is developing marine prototype operational systems (in the Arctic Sea and North Atlantic- TOPAZ and in the Mediterranean Sea-MFS) together with capacity building activities (in the Baltic Sea-PAPA, in the Mediterranean Sea- MAMA, in the Black Sea- ARENA). In line with this strategy the European Commission recently launched the European COstal sea Operational observing and forecasting system Project (ECOOP) aiming to build up a sustainable pan-European capacity in providing timely, quality assured marine services (including data, information products, knowledge and scientific advices) in European coastal and shelf seas. It is thus a pivotal part of Europe's contribution to issues affecting the global environment and the safety of our planet now also addressed within the GEOSS initiative. GEOSS and GMES are ambitious concepts, which reconcile the political needs associated with environment and safety issues with the scientific and technological capacities offered by information technologies and Earth observation technologies.

Numerous examples already exist around Europe demonstrating advancements in existing operational observing and forecasting systems. Advanced observing technologies have been developed and demonstrated in projects like EuroROSE, Ferrybox, ADRICOSM and Poseidon. In the North Sea, and in particular in the area of German Bight a huge potential of observing platforms, data transmission and processing, as well as numerical modelling expertise exists. The most advanced level of systems operability has been demonstrated in the activities of BSH and in their product’s delivery. Research institutes and academia usually carry out in parallel, sometimes coordinated, sometimes complementary activities. However, any future development of observational systems and forecasting requires strong cooperation, setting standards, and mutual involvement. Sharing data, technologies and costs would shorten the research time, improve the products and reduce costs. A logical next step is to consolidate and integrate the current observing and model systems with a common information platform and quality standards.

Current challenges in operational coastal oceanography are that the existing systems are not harmonised, quality assessment is still a problem, real-time data transfer, data exchange between organisations and near-real-time availability to modellers are not solved. Assimilation of various sources of near coastal data is still a complex scientific problem.

Data analysis, numerical modelling and data assimilation

Developing forecasting tools for coastal zones necessitates increasing synergy between traditional and newly available data, modelling with very fine resolution and advanced data assimilation techniques. Extensive data analyses and model validations are a central task. GKSS will take the major responsibility of the information technology and model systems used in COSYNA. It will create hard- and software for collecting, storing and efficiently managing the incoming data flows including first quality checks. However, individual components of the model system or the assimilation module will be developed in strong cooperation with KDM partners including the AWI. Data streams based on observations and model simulations will also be established going from the centre at GKSS to sub-nodes at selected partners within the consortium.

Data analyses as have to rely on linear as well as non-linear methods for detecting common patterns or statistical characteristics (e.g. dimensionality) in multi-variate data sets. We will quantify the spatial as well as temporal cross-correlations of the continuous time-series from sensors at multiple positions. In addition, novel techniques based on Lagrangian transport modelling, which link different reference point can be applied (Callies et al., in prep; Brandt et al, in prep). Statistical results will not only lead to a better scientific understanding of specific mechanisms but also to guideline data assimilation and model validation experiments. One of the major problems here is the short predictability limit of ocean weather and sea state due to short physical temporal scales and atmospheric predictability limits, thus enhancing negative consequences from forcing inaccuracies, uncertainties in initial and lateral boundary conditions, and inappropriate model representation of physical and ecological processes. Some of these limitations also affect the reliability of long-term simulations.

Specific objectives are to: (1) improve the quality of existing physical and biogeochemical models for North Sea, German Bight and Wadden Sea by resolving relevant processes at adequate spatial scales in the entire computational domain, (2) enhance exploitation of existing data streams, (3) develop up-to-date analysis of as much as possible observations from different platforms and providing guidance for data assimilation, (4) provide improved forecast based on increasing the synergy between advanced numerical models, data assimilation methods and traditional and new observational platforms, (5) provide new knowledge and estimates of the state of coastal zone (e.g. ocean turbulence, stratification, transport of particulate organic material and total suspended matter, estuary-coast-offshore exchange, water quality status, source and sinks for major carbon and nitrogen species).

The complex dynamics of coastal regions and exchanges with the atmosphere, near-shore and offshore regions present a major scientific challenge. A drastic shift in ocean physics and in forcing functions is observed in the coastal zone due to (1) rapid change of coastal shape and bathymetry, (2) dominance of another set of physical processes, and (3) multiple forcing. Reliable offshore boundary conditions of coastal models provided by large-scale ocean models have the potential to extend predictability on shelves and enhance the representativeness of forecasting systems. However, small errors in initial and boundary conditions could trigger unphysical processes in the models, therefore substantial care must be given to improving their quality and correcting their outcomes through data assimilation.

Dynamical balances in the coastal zone seem prohibitive to use some relatively simple assimilation schemes (e. g. Cooper and Haines 1996[12]), which have proved useful in open ocean modelling. Therefore a wide range of methods are presently used to assimilate data into coastal models including sequential methods (e.g. simple nudging, optimal interpolation, reduced order optimal interpolation (De Mey and Benkiran 2002[13]), ensemble-based optimal interpolation (Lamouroux et al. 2006[14]), and various forms of Kalman filter (Evensen 2003[15]) including the extended, singular evolutive, and ensemble Kalman filters (Mourre et al. 2004[16], 2006[17]) as well as adjoint-based methods (based on the minimization of cost functions, Taillandier et al. 2007[18]). This variety of approaches reveals, not in the last place, the diversity of data available for the coastal zone. Their complementarities have not yet been well established but promising results exist (Mourre et al 2006[17]). Recent studies demonstrate that some ocean fields can be sufficiently represented by a limited number of multivariate Empirical Orthogonal Functions (EOFs) facilitating the selection of the “optimal set” (Sparnoccia et al. 2003). The existing experience with data assimilation in circulation models of North Sea, German Bight and Wadden Sea is still limited, but the observational (discussed before) and modelling (Dick et al. 2001[19], Stanev et al. 2003[20], Gayer et al. 2006[21], Staneva et al. 2007[22]) practices give an encouraging motivation to propose the below activities. Data assimilation will be used to estimate past (hindcast), present (nowcast) and future (forecast) conditions in the North Sea and in its coastal regions and will provide measures of uncertainty. The systems of interest will include the shelf area, estuaries, tidal flats, river plumes, straits and sills (Fig. 1).

Figure 1: Map the German EEZ with existing network and newly proposed observational stations and transects (COSYNA). Transects of FerryBoxes as well as of gliders and AUV. 1: East Frisian offshore platform; 2: FINO 3, 3: North Frisian observational Pile; 4: FINO 1; 5: AWI Helgoland.

Description of a facility

The specific features of the observatory are observational Reference Points (RPs) and the coherence of the data accumulation and retrieval as an essential basis for operational data assimilation into models. COSYNA (Fig. 1) will deliver the spatial representation through

  • a chain of RPs with long-term observations, of, which the geometrical formation is adapted to the typical and characteristic gradients; these also serve as nodes to connect further observational devices (e.g. benthic landers)
  • transects that link the RPs together; here, mobile measuring platforms, such as autonomous underwater vehicles (AUVs, gliders) or ship surveys will collect a subset of parameters. In parallel we will document changes in morphology and habitat of the sea floor along these references transects through optical and/or acoustic devices.
  • Remote sensing information, if available, will be integrated with observational in situ data to provide a better spatial coverage for the southern North Sea. Measures to improve operational ground truth capabilities and atmospheric radiation correction will have been developed within the ICON framework until the end of 2009 and are not part of this investment proposal. Therefore, although remote sensing will be an essential and integral part of the overall observational system, it is not further described here.

Coherence of observations will be achieved through a common sensor package of the RPs and systems implemented on the moving platforms. This common package will comprise standard parameters (oceanography, meteorology) with extensions for more advanced chemical and biological parameters.

The realisation of a critical number of RPs in the framework of this investment proposal including its long-term maintenance through GKSS and its partners calls for a completely new approach. As platforms for the RP’s, all located in the German Bight, we will make use of existing research platforms (FINO 1 and FINO 3) and offshore wind turbines (WEAs). WEAs offer a sustainable offshore construction, are linked through cable routes as well as through telemetry connected to the mainland, and have regular maintenance by ship or helicopter. Therefore an essential characteristic of COSYNA will be to participate in this external infrastructure. To cover larger areas of the North Sea, the installation of measuring devices on ferries (FerryBox), by then already established within the ICON framework, will be continued and extended regarding the number of parameters. Selected, already existing operational or near future operational systems and stations operated by GKSS and KDM partners, will be incorporated, modernised and adapted as part of the COSYNA network. For achieving budget estimation through the entire water column at selected RP’s, we plan to pursue automatic measurements of biogeochemical gradients and fluxes in the sediment–water (benthic landers) and at the water-atmosphere boundary layer.

This set of fixed and moving platforms will base on and incorporate the systems established in the framework of ICON, which is presently constructed by GKSS. In this way, ICON can be regarded as a pilot, first pre-operational phase of COSYNA where the basic features of COSYNA including the remote sensing techniques will have been implemented until the end of 2009. Investments of GKSS within the framework of ICON will already lead to innovative technical solutions with the in-situ technologies of the FerryBoxes and observational piles as well as their integration with remote sensing. Finally, the geometrical arrangement of the COSYNA RP’s and transects will complement the existing observational systems of MARNET, operated by the BSH, and the Ocean Monitoring Network OMS that is presently assembled in the along the North Frisian Coast (Fig. 1).

In detail, the facility is composed of the following elements:

  • The first group of four elements represents the COSYNA backbone infrastructure provided by GKSS for the partners from the KDM and national agencies and water authorities.
1. Basic sensor units for stationary research stations or existing offshore platforms (WEAs)
2. Observational pile North Frisian Wadden Sea
3. Observational platform directly offshore East Frisian Wadden Sea
4. Hard- and software for data management (data processing, storage and visualisation)
  • The second group comprises advanced FerryBox-systems operated on fixed platforms and moving ships of opportunity and autonomously operating underwater vehicles equipped with sensors for standard and extended parameters.
5. FerryBox systems
6. Autonomous underwater vehicles
  • The third group comprises systems for detailed observations of the benthic-water and water-air interfaces.
7. Sensor system for water atmosphere boundary layer
8. Lander systems for sediment-water boundary layer


With this coastal observatory we will be able to measure on line a wide range of parameters of the environmental state in the German Bight and the wider North Sea. This will be an essential German Contribution to the EU strategy for Marine Research towards a sustainable use of the oceans and seas. An extension to other parts of the North Sea is provisioned within the EMECO Project.

See also

Internal Links

External Links


  1. Ocean.US, 2002. An Integrated and Sustained Ocean Observing System (IOOS) for the United States: Design and Implementation. Ocean.US, Arlington, VA. pp 21
  2. Pinardi, N. and Flemming, N. C. (eds.), 1998. The Mediterranean Forecasting System Science Plan. EuroGOOS Publication No.11, Southampton Oceanography Centre, Southampton. ISBN 0-904175-35-9
  3. Buch, E. and Dahlin, H. (eds.) , 2000. The BOOS Plan: Baltic Operational Oceanographic System, 1999-2003. EuroGOOS Publication No. 14, Southampton
  4. Droppert, L. J., Cattle, H., Stel, J. H., Behrens, H. W. A. (eds.), 2000. The NOOS Plan: North West Shelf Operational Oceanographic System, 2002-2006. EuroGOOS Publication No. 18, Southampton Oceanography Centre, Southampton. ISBN 0-904175-46-4
  5. Frosch, R. et al., 1999. “Toward a U.S. Plan for an Integrated, Sustained Ocean Observing System” U.S. GOOS Publications, 1999
  6. Petersen et al., 2007. Ferry Box: From On-line Oceanographic Observations to Environmental Information. EuroGOOS Publ. No. 25, EuroGOOS Office, SMHI 60176 Norrköping, Sweden. ISBN 978-91-974828-4-4
  7. 7.0 7.1 7.2 7.3 Schofield, M. el al:, 2002. The Long-Term Ecosystem Observatory: An Integrated Coastal Observatory. IEEE J. Ocean. Eng. 27/ 2, 146-154
  8. 8.0 8.1 von Alt, C., B. Allen,B., T. Austin,T., and R. Stokey, R., 1994. Remote environmental measuring units. In: Proc. Symp. Autonomous Underwater Vehicle Technology, 1994, 13–19.
  9. 9.0 9.1 Simonetti, P., 1998. Low-cost, endurance ocean profiler. Sea Tech. 38/ 1, 17–21 Sparnocchia, S., Pinardi, N., and Demirov, E., 2003. Multivariate Empirical Orthogonal Function analysis of the upper thermocline structure of the Mediterranean Sea from observations and model simulations, Ann. Geophysicae, Annales Geophysicae, 21: 167–187.
  10. Fiorelli, E., Leonard, N.E, Bhatta P., Paley, D. A., Bachmayer R., and Fratantoni,D. M., 2006. Multi-AUV Control and Adaptive Sampling in Monterey Bay. IEEE J. Ocean. Eng. 31/4, 935-948
  11. Purdy et al., 2003. RECONN REgional Cabled Observatory Network (of Networks). Report of the Cabled Regional Observatory Workshop October 7-10, 2003, San Francisco, CA, pp 72
  12. Cooper, M. & Haines, K. 1996. Altimetric assimilation with water property conservation. J. Geophys. Res. 101, 1059–1077. (doi:10.1029/95JC02902)
  13. De Mey, P. & Benkiran, M. 2001. A multivariate reduced-order optimal interpolation method and its application to the Mediterranean basin-scale circulation. In: Ocean forecasting, conceptual basis and applications (ed. N. Pinardi & J. D. Woods), p. 472. Berlin: Springer.
  14. Lamouroux, J., P. De Mey, F. Lyard and E. Jeansou, 2006. Control of a barotropic model of the Bay of Biscay in presence of atmospheric forcing errors. J. Geophys. Res., under revision
  15. Evensen, G. 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn. 53, 343–367. (doi:10.1007/s10236-003-0036-9)
  16. Mourre, B., P. De Mey, C. le Provost, and F. Lyard, 2004. An Ensemble-based method for the study of errors due to uncertainties on bathymetry in a free-surface regional model. Outlook for the assimilation of sea-level data over continental shelves. Implications on requirements for future altimeter observing systems. Dyn. Atmos. Oceans, 38, 93-121. doi:10.1016/j.dynatmoce.2004.09.001.
  17. 17.0 17.1 Mourre, B., P. De Mey, Y. Ménard, F. Lyard and C. Le Provost, 2006. Relative performances of future altimeter systems and tide gauges in controlling a model of the North Sea high-frequency barotropic dynamics. Ocean Dynamics, doi: 10.1007/s10236-006-0081-2.
  18. Taillandier V., V. Echevin, L. Mortier, and J.-L. Devenon, 2007. A 4DVAR assimilation approach to estimate the regional forcing from observations of the coastal circulation. J. Mar. Sys., in press.
  19. Dick, S., Kleine, E., Müller-Navarra, S., Klein H., Komo, H. 2001. The Operational Circulation Model of BSH (BSHcmod). Model description and validation. Berichte des Bundesamtes für Seeschifffahrt und Hydrographie. Nr. 29/2001. Hamburg, Germany. 48 pp.
  20. Stanev, E. V., B. Flemming, A. Bartholomae, J. Staneva, and J.-O. Wolff, 2006. Vertical circulation in shallow tidal inlets and back barrier basins. Continental Shelf Research, v. 27, iss. 6, p. 798-831.
  21. Gayer, G., S. Dick, A. Pleskachevsky, W. Rosenthal, 2006. Numerical Modelling of Suspended Matter Transport in the North Sea. Ocean Dynamics, 56, No. 1, 62 –77.
  22. Staneva, J., E. V. et al., 2007. Hydroynamics and Sediment Dynamics in the German Bight. A Focus on Observations and Numerical Modelling in the East Frisian Wadden Sea. Cont. Shelf Res. (in press).

Aikman, F., et al. 1996. Towards an operational nowcast/forecast system for the US East Coast. In: Modern approaches to data assimilation in ocean modelling (ed. P. Malanotte-Rizzoli) Elsevier oceanography series 61, 347–376. Amsterdam: Elsevier.

Andreu-Burillo, I., J.T. Holt, R. Proctor, J.D. Annan, I.D James, and D. Prandle, 2006. Assimilation of Sea Surface Temperature in the POL Coastal Ocean Modelling System. J. Mar. Sys., in press.

Barth A, A. Alvera-Azcarate, M. Rixen and J.-M. Beckers, 2005. Two-way nested model of mesoscale circulation features in the Ligurian Sea, Progress In: Oceanography, 66, 171—189.

Dankert H., A. Herrmann, H. Günther and H.D. Niemeyer, Regional Shallow-Water Wave Modeling using k-Model and SWAN in Coastal Areas of the German Bight, J. Geophys. Res., in review 2007.

Dobrynin M., G. Gayer and H. Günther, Modeling the Dynamics of Suspended Particulate Matter in the North Sea using a Fully Coupled Circulation and Transport Model, Ocean Dynamics, under review 2007

Friedrichs, M., et al. (2007): Assessment of skill and portability in regional marine biogeochemical models: role of multiple plankton groups. J. Geophys. Res. 112, C8/2006JC003852.

Herman, A., 2007. Nonlinear principal component analysis of the tidal dynamics in a shallow sea. Geophys. Res. Lett, Vol 34. http://dx.doi.org/10.1029/2006GL027769

Herman, A., Kaiser, R., Niemeyer, H. D. 2007. Modelling of a medium-term dynamics in a shallow tidal sea, based on combined physical and neural network methods. Ocean Modelling, Volume 17, Issue 4, 277-299. http://dx.doi.org/10.1016/j.ocemod.2007.02.004

Lermusiaux P.F.J., 2006. Adaptive Modelling, Adaptive Data Assimilation and Adaptive Sampling, Physica D, 230, 172-196.

Schartau, M. and A. Oschlies, 2003. Simultaneous data-based optimization of a 1D-ecosystem model at three locations in the North Atlantic: Part I-Methods and parameter estimates. J. of Mar. Res. 61, 765-793.

Wiltshire, K.H., et al. Top down control of North Sea phytoplankton blooms in spring: a reaction to warmer winters. Limnol. Ocean, acc.

The main author of this article is Colijn, Franciscus
Please note that others may also have edited the contents of this article.

Citation: Colijn, Franciscus (2009): Monitoring the water quality of coastal waters with automatic equipment. Available from http://www.coastalwiki.org/wiki/Monitoring_the_water_quality_of_coastal_waters_with_automatic_equipment [accessed on 7-07-2020]