Difference between revisions of "Real-time algae monitoring"
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| − | This article | + | This article describes an example of a real-time algae monitoring system for [[harmful algal bloom]]s (HABs) in coastal waters. A specific application using a combination of [[in situ]], [[optical remote sensing]] and computer models for the Dutch coastal zone is presented. The article also pays attention to HAB monitoring in Europe. |
==Introduction== | ==Introduction== | ||
| − | Monitoring and prediction of [[algal | + | Monitoring and prediction of [[algal bloom]]s, including [[harmful algal bloom]]s (HABs), and insight in the impact of human activities on the frequency and intensity of blooms are needed to support water managers deciding on mitigating measures. |
| − | Multiple methods exist for monitoring algae concentrations and signalling the presence of algal blooms. Field data have the highest confidence level but in most coastal areas, spatial and temporal coverage is rather limited. [[Optical remote sensing]] using the new generation ocean colour imaging spectrometers (SeaWiFS, MODIS and MERIS) can provide information on chlorophyll-a concentrations. Because blooms consist of (up to) millions of cells per litre, they generally contain high amounts of chlorophyll-a, and are detectable by this remote sensing method. Remote sensing has the advantage of providing large spatial coverage and resolution however cloud cover can limit the temporal coverage. Computer models can also be used to calculate the temporal and spatial coverage on chlorophyll-a and phytoplankton species but the confidence level is lower than for field data ([[in situ | + | Multiple methods exist for monitoring algae concentrations and signalling the presence of algal blooms. Field data have the highest confidence level but in most coastal areas, spatial and temporal coverage is rather limited. [[Optical remote sensing]] using the new generation ocean colour imaging spectrometers ([[SeaWiFS]], [[MODIS]] and [[MERIS]]) can provide information on chlorophyll-a concentrations. Because blooms consist of (up to) millions of cells per litre, they generally contain high amounts of chlorophyll-a, and are detectable by this remote sensing method. Remote sensing has the advantage of providing large spatial coverage and resolution however cloud cover can limit the temporal coverage. Computer models can also be used to calculate the temporal and spatial coverage on chlorophyll-a and phytoplankton species but the confidence level is lower than for field data ([[in situ]]) and [[remote sensing]] data. |
| − | ==Algae monitoring in the Dutch coastal zone== | + | ==Algae monitoring in the Dutch coastal zone== |
| − | == | + | |
| − | In the Eastern English Channel and the Southern Bight of the North Sea, the spring phytoplankton bloom is dominated by ''Phaeocystis globosa'' (Reid et al. 1990 <ref> Reid | + | ===[[HAB]]s in the Dutch coastal zone=== |
| + | In the Eastern English Channel and the Southern Bight of the North Sea, the spring phytoplankton bloom is dominated by ''Phaeocystis globosa'' (Reid et al. 1990 <ref> Reid, P.C., Lancelot, C, Gieskes, W.W.C, Hagmeier, E., Weichart, G., 1990. Phytoplankton of the North Sea and its dynamics: a review.''Netherlands J. Sea. Res.'' 26 (2-4): 295-331. </ref>; Rousseau et al., 2002 <ref> Rousseau, V., Leynaert, A., Daoud, N., Lancelot, C., 2002. Diatom succession, silicification and silicic acid availability in Belgian coastal waters (Southern North Sea). ''Mar. Ecol. Prog. Ser.'' 236, 61-73.</ref>; Seuront et al., 2006 <ref> Seuront, L. Vincent, D., Mitchell, J.G. 2006. Biologically induced modification of seawater viscosity in the Eastern English Channel during a Phaeocystis globosa spring bloom. ''J. Mar. Syst.'', 61, 118-133. </ref>). The start of the bloom is believed to be determined by the availability of light in the water column (Peperzak et al., 1998 <ref> Peperzak, L., Colijn, F., Vriegling, E.G., Gieskes, W.W.C., Peeters, J.C.H., 1998. Development of the diatom-Phaeocystis spring bloom in the Dutch coastal zone of the North Sea: the silicon depletion versus the daily irradiance threshold hypothesis.''J. Plankton Res.'' 20, 517-537. </ref>) and / or the depletion of ortho-phosphate by diatoms (Lancelot et al., 2005 <ref> Lancelot, C., Spitz, Y., Gypens, N. et.al. (2005) Modelling diatom and Phaeocystis blooms and nutrient cycles in the Southern Bight of the North Sea: the MIRO model. ''Mar. Ecol. Prog. Ser.'', 289, 63–78. </ref>). During the bloom rise, once ''P. globosa'' dominates the ensemble, the formation of colonies with diameters in excess of 200 μm and chlorophyll-a levels above 20 μg/L may occur (Seuront et al., 2006). During bloom decline, the sedimentation of the large colonies may lead to massive mortality of benthic invertebrates via anoxia (Peperzak, 2002 <ref> Peperzak, L. 2002. ''The wax and wane of Phaeocystis globosa blooms''. PhD Thesis. University of Groningen, The Netherlands.</ref>). | ||
===Monitoring system and communication=== | ===Monitoring system and communication=== | ||
In the Dutch coastal area, harmful algal blooms of ''Phaeocystis'' occasionally cause mass mussel mortality in the aquaculture area Oosterschelde. To enable early warnings about future harmful algal blooms to mussel farmers and other end users, an information system is being developed based on the combination of [[optical remote sensing]] data (MERIS sensor on the ENVISAT satellite), field data and model data from WL | Delft Hydraulics’ ecological model GEM for the Dutch Voordelta area. The information system is being developed as a collaboration between WL | Delft Hydraulics, IVM (Free University, Amsterdam) and the National Institute for Coastal and Marine Management (RIKZ). | In the Dutch coastal area, harmful algal blooms of ''Phaeocystis'' occasionally cause mass mussel mortality in the aquaculture area Oosterschelde. To enable early warnings about future harmful algal blooms to mussel farmers and other end users, an information system is being developed based on the combination of [[optical remote sensing]] data (MERIS sensor on the ENVISAT satellite), field data and model data from WL | Delft Hydraulics’ ecological model GEM for the Dutch Voordelta area. The information system is being developed as a collaboration between WL | Delft Hydraulics, IVM (Free University, Amsterdam) and the National Institute for Coastal and Marine Management (RIKZ). | ||
| − | The field data and remote sensing data give information about the actual status of the spring phytoplankton bloom. The use of the model allows for near real-time forecasting of ''Phaeocystis'' blooms. The complementary use of three data sources compensates for the limitations of each of the data sources | + | The field data and remote sensing [[data]] give information about the actual status of the spring phytoplankton bloom. The use of the model allows for near real-time forecasting of ''Phaeocystis'' blooms. The complementary use of three data sources compensates for the limitations of each of the data sources. |
| − | + | The information on the status of the algal blooms is presented to the local coastal water managers in the form of a weekly HAB Bulletin. This weekly algal bloom information bulletin was started in the spring of 2006. This service bulletin is based on all data that are available for a given week: field data, remote sensing and operational model computations. If no field data or remote sensing data are available the bulletin is based just on the model data. If remote sensing data with only limited cloud cover are available these data are used as the start situation for the model which is used to forecast further transport of the bloom. | |
| − | In shallow near-coastal waters in the Netherlands such as the Oosterschelde (Eastern scheldt), turbidity is rather high and wind-induced waves have a strong impact on turbidity. Since the initiation of spring phytoplankton blooms is light-controlled, accurate simulation of turbidity is assumed to be essential for adequate simulation of spring bloom development. | + | In shallow near-coastal waters in the Netherlands such as the Oosterschelde (Eastern scheldt), [[turbidity]] is rather high and wind-induced waves have a strong impact on turbidity. Since the initiation of spring phytoplankton blooms is light-controlled, accurate simulation of turbidity is assumed to be essential for adequate simulation of spring bloom development. |
To this end the suspended matter forcing of the model is based on monthly composite total suspended matter data of the MERIS satellite, with a superimposed short term variability due to wind-induced waves. This suspended matter forcing corresponds well with field data on suspended matter. | To this end the suspended matter forcing of the model is based on monthly composite total suspended matter data of the MERIS satellite, with a superimposed short term variability due to wind-induced waves. This suspended matter forcing corresponds well with field data on suspended matter. | ||
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==Monitoring of HABs in Europe== | ==Monitoring of HABs in Europe== | ||
| − | == | + | ===Algae bloom service=== |
| − | + | The pan-European algae bloom service provides a basic daily detection of algae blooms for the whole European sea region and automated email alert to registered users. This Harmful Algae Bloom Monitoring, Evolution and Forecasting Service is also called [https://marine.copernicus.eu/wp-content/uploads/2016/06/r597_9_marcoast2_brochure.pdf GMES-MARCOAST] services. | |
| − | ==Examples of HABs== | + | ===Examples of HABs=== |
Examples of phytoplankton species typically associated with harmful algal blooms in Europe are: | Examples of phytoplankton species typically associated with harmful algal blooms in Europe are: | ||
*''Nodularia spumigena'' | *''Nodularia spumigena'' | ||
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==See also== | ==See also== | ||
| − | ===Internal links== | + | ===Internal links=== |
| − | * [[ | + | * [[Optical remote sensing]] |
| − | * [[ | + | * [[Light fields and optics in coastal waters]] |
| + | * [[Eutrophication in coastal environments]] | ||
| + | * [[Ships of opportunity and ferries as instrument carriers]] | ||
| − | ===External | + | ===External link=== |
*[http://www.waterinsight.nl/ Water Insight] | *[http://www.waterinsight.nl/ Water Insight] | ||
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==References== | ==References== | ||
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|AuthorName=Robertihans}} | |AuthorName=Robertihans}} | ||
| − | + | [[Category:Coastal and marine observation and monitoring]] | |
| − | + | [[Category:Observation of biological parameters]] | |
| − | [[Category:Coastal and marine | ||
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Latest revision as of 21:53, 1 September 2020
This article describes an example of a real-time algae monitoring system for harmful algal blooms (HABs) in coastal waters. A specific application using a combination of in situ, optical remote sensing and computer models for the Dutch coastal zone is presented. The article also pays attention to HAB monitoring in Europe.
Contents
Introduction
Monitoring and prediction of algal blooms, including harmful algal blooms (HABs), and insight in the impact of human activities on the frequency and intensity of blooms are needed to support water managers deciding on mitigating measures.
Multiple methods exist for monitoring algae concentrations and signalling the presence of algal blooms. Field data have the highest confidence level but in most coastal areas, spatial and temporal coverage is rather limited. Optical remote sensing using the new generation ocean colour imaging spectrometers (SeaWiFS, MODIS and MERIS) can provide information on chlorophyll-a concentrations. Because blooms consist of (up to) millions of cells per litre, they generally contain high amounts of chlorophyll-a, and are detectable by this remote sensing method. Remote sensing has the advantage of providing large spatial coverage and resolution however cloud cover can limit the temporal coverage. Computer models can also be used to calculate the temporal and spatial coverage on chlorophyll-a and phytoplankton species but the confidence level is lower than for field data (in situ) and remote sensing data.
Algae monitoring in the Dutch coastal zone
HABs in the Dutch coastal zone
In the Eastern English Channel and the Southern Bight of the North Sea, the spring phytoplankton bloom is dominated by Phaeocystis globosa (Reid et al. 1990 [1]; Rousseau et al., 2002 [2]; Seuront et al., 2006 [3]). The start of the bloom is believed to be determined by the availability of light in the water column (Peperzak et al., 1998 [4]) and / or the depletion of ortho-phosphate by diatoms (Lancelot et al., 2005 [5]). During the bloom rise, once P. globosa dominates the ensemble, the formation of colonies with diameters in excess of 200 μm and chlorophyll-a levels above 20 μg/L may occur (Seuront et al., 2006). During bloom decline, the sedimentation of the large colonies may lead to massive mortality of benthic invertebrates via anoxia (Peperzak, 2002 [6]).
Monitoring system and communication
In the Dutch coastal area, harmful algal blooms of Phaeocystis occasionally cause mass mussel mortality in the aquaculture area Oosterschelde. To enable early warnings about future harmful algal blooms to mussel farmers and other end users, an information system is being developed based on the combination of optical remote sensing data (MERIS sensor on the ENVISAT satellite), field data and model data from WL | Delft Hydraulics’ ecological model GEM for the Dutch Voordelta area. The information system is being developed as a collaboration between WL | Delft Hydraulics, IVM (Free University, Amsterdam) and the National Institute for Coastal and Marine Management (RIKZ).
The field data and remote sensing data give information about the actual status of the spring phytoplankton bloom. The use of the model allows for near real-time forecasting of Phaeocystis blooms. The complementary use of three data sources compensates for the limitations of each of the data sources.
The information on the status of the algal blooms is presented to the local coastal water managers in the form of a weekly HAB Bulletin. This weekly algal bloom information bulletin was started in the spring of 2006. This service bulletin is based on all data that are available for a given week: field data, remote sensing and operational model computations. If no field data or remote sensing data are available the bulletin is based just on the model data. If remote sensing data with only limited cloud cover are available these data are used as the start situation for the model which is used to forecast further transport of the bloom.
In shallow near-coastal waters in the Netherlands such as the Oosterschelde (Eastern scheldt), turbidity is rather high and wind-induced waves have a strong impact on turbidity. Since the initiation of spring phytoplankton blooms is light-controlled, accurate simulation of turbidity is assumed to be essential for adequate simulation of spring bloom development.
To this end the suspended matter forcing of the model is based on monthly composite total suspended matter data of the MERIS satellite, with a superimposed short term variability due to wind-induced waves. This suspended matter forcing corresponds well with field data on suspended matter.
The system is planned to be operational in 2007, delivering algal bloom information bulletins to government officials and mussel farmers on the present and expected development of algal blooms in the area. The bulletins will be issues twice weekly during the spring period. Phaeocystis blooms generally occur during spring.
Monitoring of HABs in Europe
Algae bloom service
The pan-European algae bloom service provides a basic daily detection of algae blooms for the whole European sea region and automated email alert to registered users. This Harmful Algae Bloom Monitoring, Evolution and Forecasting Service is also called GMES-MARCOAST services.
Examples of HABs
Examples of phytoplankton species typically associated with harmful algal blooms in Europe are:
- Nodularia spumigena
- Phaeocystis globosa
- Dinophysis acuminata and D. acuta
- Karenia mikimotoi
- Alexandrium minutum
- Alexandrium tamarense
- Pseudo-nitzschia
These species are associated with problems such as nuisance scum layers, nuisance foam, mussel toxicity and fish mortality.
See also
Internal links
- Optical remote sensing
- Light fields and optics in coastal waters
- Eutrophication in coastal environments
- Ships of opportunity and ferries as instrument carriers
External link
References
- ↑ Reid, P.C., Lancelot, C, Gieskes, W.W.C, Hagmeier, E., Weichart, G., 1990. Phytoplankton of the North Sea and its dynamics: a review.Netherlands J. Sea. Res. 26 (2-4): 295-331.
- ↑ Rousseau, V., Leynaert, A., Daoud, N., Lancelot, C., 2002. Diatom succession, silicification and silicic acid availability in Belgian coastal waters (Southern North Sea). Mar. Ecol. Prog. Ser. 236, 61-73.
- ↑ Seuront, L. Vincent, D., Mitchell, J.G. 2006. Biologically induced modification of seawater viscosity in the Eastern English Channel during a Phaeocystis globosa spring bloom. J. Mar. Syst., 61, 118-133.
- ↑ Peperzak, L., Colijn, F., Vriegling, E.G., Gieskes, W.W.C., Peeters, J.C.H., 1998. Development of the diatom-Phaeocystis spring bloom in the Dutch coastal zone of the North Sea: the silicon depletion versus the daily irradiance threshold hypothesis.J. Plankton Res. 20, 517-537.
- ↑ Lancelot, C., Spitz, Y., Gypens, N. et.al. (2005) Modelling diatom and Phaeocystis blooms and nutrient cycles in the Southern Bight of the North Sea: the MIRO model. Mar. Ecol. Prog. Ser., 289, 63–78.
- ↑ Peperzak, L. 2002. The wax and wane of Phaeocystis globosa blooms. PhD Thesis. University of Groningen, The Netherlands.
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