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Ecosystem Monitoring in the
Black Sea
Nicolas Hoepffner
Global Environment Monitoring Unit
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Monitoring and Assessment of
Coastal & Marine Environments (ECOMAR)
ECOMAR provides a pan-European scientific and technical
support in the definition, implementation and monitoring of EU
policies and Directives related to the coastal and marine
environment.
EU Policy Context:
 European Marine Strategy
Global Monitoring for Environment and Security (GMES)
 Water Framework Directive (2000/60/EC)
 Maritime Policy
Regional Marine Conventions (HELCOM, Black Sea Comm.,
OSPARCOM)
European Environmental Agency (EEA)
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ECOMAR scientific and technical studies
Development, processing and validation
of Earth Observation data (optical and
thermal sensors)
 Regional Seas numerical modeling,
ecosystem assessment
 Data integration and development of
environmental indicators
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Visible Spectral Radiometry from Satellite
(Ocean Colour)
MODIS Terra May 10, 2002
MODIS Aqua Sept. 15, 2004
Phytoplankton blooms and
Coccolithophores in the Black
Sea
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Ocean Colour Data Analysis
Emerging spectral light –
412-670nm
Correction for the
Atmosphere effect
412nm
443nm
490nm
510nm
555nm
670nm
Apply in-water
algorithm
R
R
R 
R  max  443 , 490 , 510 
 R555 R555 R555 
Restitution of
geophysical products
Chl  100.343.07 R 1.93R
2
 0.65 R 3 1.53 R 4
  0.041
03/2001
Data Collection and
merging orbital scenes
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JRC-IES Ocean Colour Data Archive
• Coverage: global and European
Seas
• Period: Oct. 1997 to March 2006
• Sensor used: SeaWiFS (Oct.97
to Dec. 2004) and MODIS-Aqua
(Jun 2002 to present)
• Spatial resolution: 2 km
Archived products
- Water leaving radiances at various
wavelengths
- light attenuation coefficient
(transparency, turbidity)
- water particle loads (chlorophyll
concentration, Total suspended matter)
http://marine.jrc.cec.eu.int/
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Black Sea Chlorophyll Distribution
Daily Scene
8-days composites
SeaWiFS
MODIS
monthly composites
http://marine.jrc.cec.eu.int/
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Black Sea : Light attenuation coefficient
Jan
Mar
Nov
Satellite retrieval
 Lwn 490 
K d 490  0.016  0.205

 Lwn 555 
1.754
Water transparency Index
SD  0.55Kd 490  0.04
1
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Ocean Colour Cal/Val activities
Measurement campaigns in the North
Adriatic (1995-present)
 Advanced methods for the absolute
calibration of marine instruments
 Autonomous systems for continuous
validation of primary remote sensing
optical products
 Assessment of ocean color primary
products from most relevant space
sensors (official calibration site for NASA
and ESA)
SeaWiFS
MODIS
MERIS
http://www.esa.int/esaLP/LPcampaigns.html
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Ocean Colour Cal/Val activities
Cruise campaigns over Europe
 North Adriatic , July 2000 - JRC -
 Baltic Sea (Baltic proper) , May 2004 - JRC/ IOPAS
 English Channel , June 2004, - JRC/ Univ. Littoral
 Baltic Sea (Baltic proper) , Sept. 2004 - JRC/IOPAS
 Baltic Sea (Baltic proper) , April 2005 - JRC/IOPAS
 Black Sea (western), June 2006 - JRC/IOBAS
 Baltic Sea (Gulf of Finland), Aug. 2006 - JRC/FIMR
 Eastern Mediterranean, Sept. 2006 - JRC/CNR-ISAC
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From Ocean Colour to Marine
Productivity
Spectral irradiance
model
surface lightl
Optical model
underwater lightl,z
Water-column
biomass distribution (z)
PPz, l, t   f Bz , l, z , EPAR l, t 
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International Productivity Algorithms Comparative
Experiment. PPARR 3 (part 3)
17 models , and ~ 900 quality-controlled C14
measurements of primary production, spanning more than
a decade (1983-1996; ClimPP) in the Tropical Pacific.
JRC model showed the lowest total rms
error (0.231) on PP log-difference,
as well as the lowest centered pattern
rms (0.227) which is an indication of the
model performance to detect the
variability in the dataset.
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Black Sea Marine Productivity
Seasonal Variations
Feb
PP ~ 1
gC.m-2.d-1
Apr
Jul
Nov
Inter-annual variability
open sea
Aug. 98
coastal
Aug. 01
Aug. 03
+
-
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Modelling and Indicator development
Combining Satellite data (and/or in situ data) with modelling to derive
Ecological Indicators applicable to all European Seas (large scale) and
comparable for dife ferent region
Conceptual Model for Eutrophication Risk Assessment
 Physical Sensitive
Area Index (PSA)
 Oxygen Depletion
Risk Index (Oxyrisk)
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Eutrophication Risk Assessment:
Black Sea (Sept. 2002)
mixed layer depth
PSA Index
Sea surface temp.
chlorophyll
Oxyrisk
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Eutrophication Risk Assessment:
Black Sea (Oct. 2002)
mixed layer depth
PSA Index
Sea surface temp.
chlorophyll
Oxyrisk
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Eutrophication Risk Assessment:
European Coastal Zone
Feb. 2003
Aug. 2003
Oxygen depletion risk index to support the evaluation of the
impact of high nutrient inputs (e.g. resulting from agriculture
and urbanization) into coastal waters.
Index represents high (high value=red) / low (low values = blue)
risk to hypoxia and anoxia
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Concluding Notes
• through satellite data analysis and modelling, ECOMAR can provide reliable
and comparable information on the marine ecosystem status from regional to
European scale
• These information constitute a basis for an environmental tool for policy
managers, e.g. in the context of the EU Marine Strategy and up-coming
Maritime Policy
– Investigate ecosystem response to anthropogenic pressures
– Identify critical / vulnerable European marine areas
– Assess climate change impacts
• Activity is optimized through direct collaboration and networking with
– European Environment Agency (EEA)
– Regional Marine Conventions and Programmes
– Referenced research bodies in EU Member and Accession states through e.g. Partnerships in
EU-funded projects (e.g. SeaDataNet, SPICOSA)
• FP7, toward an operational monitoring of European regional Seas following a
coherent and harmonized methodology (Policy Theme 2 “Solidarity and the responsible
management of resources “ ; Agenda 2.2 ‘ Natural Resources’)
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EMIS- European Marine Information
System
 Web based marine GIS
 Physical and Biological Marine variables (SST, Chl-a, PP, tmx, smx, etc.)
 Eutrophication risk indicators (Oxyrisk & PSA)
 Navigate and browse European wide and predefined regions maps (pan, zoom)
 Query maps - extract numerical values and compute statistics - by point or by area
(mean, max, min, std, time-series, climatologies)
 Save maps and statistics (graphs) in png, pdf and ascii
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Numerical Model
General Estuarine Transport Model / GETM
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