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Integration of field data and ecosystem models for eutrophication management. A.M. Nobre ana@salum.net J.G. Ferreira A. Newton T. Simas J.D. Icely R. Neves. Intitute of MArine Research - IMAR (Portugal). Sagresmarisco (Portugal). www.imar.pt www.ecowin.org. Presentation layout. - PowerPoint PPT Presentation
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Integration of field data and Integration of field data and ecosystem models for ecosystem models for
eutrophication managementeutrophication management
””European Conference on Coastal Zone Research: an ELOISE Approach” European Conference on Coastal Zone Research: an ELOISE Approach” Portoroz, Slovenia, November 14 – 18, 2004Portoroz, Slovenia, November 14 – 18, 2004
Intitute of MArine Research - IMAR (Portugal)Intitute of MArine Research - IMAR (Portugal) Sagresmarisco (Portugal)Sagresmarisco (Portugal)
www.imar.pt
www.ecowin.org
A.M. NobreA.M. Nobre ana@salum.net
J.G. FerreiraJ.G. Ferreira
A. NewtonA. Newton
T. Simas T. Simas
J.D. IcelyJ.D. Icely
R. NevesR. Neves
Presentation layoutPresentation layout
Problem definition
Approach
Application site
Research model
Screening model
Coupling
Conclusion
16 Total
0 1 2 3 4 5 6
no. slides
Eutrophication is difficult to assess in transitional and coastal waters:
The variability of effects are due to the complex processes and interactions occurring in coastal and transitional ecosystems – e.g. flushing times, turbidity
Even more difficult is to assess the system response to predefined scenarios in order to manage eutrophication
– high levels of chlorophyll a – overgrowth of seaweeds and epiphytes – occurrences of anoxia and hypoxia – nuisance and toxic algal blooms – losses of Submerged Aquatic Vegetation
Problem definitionProblem definitionEutrophication management in transitional and coastal watersEutrophication management in transitional and coastal waters
Eutrophication is a natural process in which the addition of nutrients to coastal waters from the watershed and ocean stimulates algal growth
the nutrient loads cause a variety of impacts
nutrient forcing no clear relationship between
eutrophication symptoms
Models for managing eutrophicationModels for managing eutrophication
Screening modelsScreening models
Integrate complex processes into a simplified set of relationships and rates
Assess the state of a system based on a few measured parameters
Link between data collection, interpretation and coastal management
Used by managers to provide overviews and to make comparisons
Research modelsResearch models
Detailed simulation and prediction of the processes
Useful tools to study ecological responses to changes in pressure
Models may be broadly divided into 2 categories:
Hybrid approach for eutrophication Hybrid approach for eutrophication managementmanagement
Screening model
Research model
Screening models driven by field data for the assessment of the Screening models driven by field data for the assessment of the eutrophication stateeutrophication state
Complex models help to fill data gaps and to explore specific Complex models help to fill data gaps and to explore specific scenariosscenarios
Distil the results from research models into these screening modelsDistil the results from research models into these screening models
Coupling of the two model categories:Coupling of the two model categories:
Complex outputs
Distils the results of the complex
model
Simulates the ecosystem under
predefined scenarios
Hybrid approachHybrid approach application application - overview -- overview -
Drivescreening model
Field data
Setupresearch model
Field data
Forceresearch model
Usage scenarios
Responsivenessscreening model
Standard outputs
Sta
nd
ard
sim
ula
tio
n
Scenario outputs
Sce
nar
io s
imu
lati
on
Compare results If validated
Study siteStudy site description description
Ria Fomosa morphology Fast water turnoverExchanged volume / Max volume
Flood Ebb
Max 69 % 49 %
Min 27 % 20 %
Mean 50 % 37 %
• Low pelagic primary production, limited by the fast water turnover
• Presents benthic eutrophication symptoms as a result of nutrient peaks, large intertidal areas and short water residence times
• Most important socio-economic activity is the extensive clam aquaculture
Research model Research model - morphology and hydrodynamics- morphology and hydrodynamics
Water fluxes between boxes and across boundaries
Explicitly simulated with outputs of 3D detailed hydrodynamic model
140 000 cells and a five second timestepUpscaled9 boxes and 30 min timestep
9 boxes4 ocean boundaries
The spring-neap tide period data is cyclically run over a 4 year period
Volume simulation with upscaled water fluxes
Box 1
Box 2
Box 3
Box 4
Box 5
Box 6
Box 7
Box 8
Box 9
Volume
0
20
40
60
80
100
0 6 12 18 24 30 hours
106 m3
0
0.5
1.0
1.5
2.0
2.5
3.0m
Tidal height simulated with harmonic constants
1
Model snapshotModel snapshotoffline outputs
assimilation
Water fluxes per timestep per connection
Data points645corresponds to a spring-neap tide period
Research model Research model - ecological simulation -- ecological simulation -
State variablesState variables and and forcing functionsforcing functions are simulated with the following are simulated with the following objects:objects:
• Dissolved nutrients Dissolved nutrients
• Suspended particulate matterSuspended particulate matter
• Phytoplankton Phytoplankton
• ClamClam
• Man seeding and harvestMan seeding and harvest
• MacroalgaeMacroalgae
• Dissolved oxygen Dissolved oxygen (small scale tide pool model)(small scale tide pool model)
• TideTide
• Light climateLight climate
• Water temperatureWater temperature
The model was implemented in an object oriented ecological The model was implemented in an object oriented ecological modelling platform* modelling platform*
*Ferreira, J. G., 1995. ECOWIN - an object-oriented ecological model for aquatic ecosystems. Ecol. Modelling, 79: 21-34.
Research model Research model - boundary conditions and scenarios -- boundary conditions and scenarios -
Boundary conditions forced with :Boundary conditions forced with :
• Land-based nutrient inputsLand-based nutrient inputs
• Ocean pelagic componentOcean pelagic componentForced with coastal data series of nutrients and phytoplankton
PEQ49 – 1 000
1 001 – 5 0005 001 – 10 000
10 001 – 20 000
20 001 – 30 000
Population equivalents (PEQ) at the discharge points of the waste water treatment plants
ScenarioScenario kg N hakg N ha-1-1 yr yr-1-1
Green (0.5S) 20
Standard (1S) 40
Increase pressure (2S) 80
Key aspects of the ASSETS/NEEA
screening modelThe NEEA approach may be divided
into three parts:
Division of estuaries into
homogeneous areas
Evaluation of data completeness
and reliability
Application of indices
Tidal freshwater (<0.5 psu) Tidal freshwater (<0.5 psu) Mixing zone (0.5-25 psu)Mixing zone (0.5-25 psu) Seawater zone (>25 psu)Seawater zone (>25 psu)
Spatial and temporal Spatial and temporal
quality of datasets quality of datasets
(completeness) (completeness)
Confidence in results Confidence in results
(sampling and analytical (sampling and analytical
reliability)reliability) Overall Eutrophic Condition (OEC) indexOverall Eutrophic Condition (OEC) index
Overall Human Influence (OHI) indexOverall Human Influence (OHI) index
Determination of Future Outlook (DFO) Determination of Future Outlook (DFO)
indexindex
PressurPressur
ee
StateState
ResponseResponse
S.B. Bricker, J.G. Ferreira, T. Simas, 2003. An integrated methodology for assessment of estuarine trophic status. Ecological Modelling, In Press.
ASSETS scoring system for PSRGrade 5 4 3 2 1
Pressure (OHI) Low Moderate low Moderate Moderate high High State (OEC) Low Moderate low Moderate Moderate high High Response (DFO)
Improve high Improve low No change Worsen low Worsen high
Metric Combination matrix Class
P
S
R
5 5 5 4 4 45 5 5 5 5 55 4 3 5 4 3
High
(5%)
P
S
R
5 5 5 5 5 5 5 4 4 4 4 4 3 3 3 3 3 3
5 5 4 4 4 4 4 5 5 4 4 4 5 5 5 4 4 42 1 5 4 3 2 1 2 1 5 4 3 5 4 3 5 4 3
Good
(19%)
P
S R
5 5 5 5 5 4 4 4 4 4 4 4 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 1 13 3 3 3 3 4 4 3 3 3 3 3 5 5 4 4 3 3 3 4 4 4 4 4 3 3 3 2 3 32 1 5 4 3 2 1 5 4 3 2 1 2 1 2 1 5 4 3 5 4 3 2 1 5 4 3 5 5 4
Moderate
(32%)
P
S
R
4 4 4 4 4 3 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 12 2 2 2 2 3 3 2 2 2 2 2 3 3 2 2 2 2 3 3 3 2 2
5 4 3 2 1 2 1 5 4 3 2 1 2 1 4 3 2 1 3 2 1 5 4
Poor
(24%)
P
S
R
3 3 3 3 3 2 2 2 2 2 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 15 4 3 2 1 5 4 3 2 1 3 2 1 5 4 3 2 1
Bad
(19%)
Index
MODERATE LOW
MODERATELOW
IMPROVE
LOW
ASSETS application to field dataIndices
Overall Human Influence (OHI)
ASSETS: 4
Overall Eutrophic Condition (OEC)
ASSETS: 4
Determination of Future Outlook (DFO)
ASSETS: 4
Methods
PSM*1
SSM*2
Parameters Value Level of expression
Chlorophyll a 0.250.57
Epiphytes 0.50 ModerateMacroalgae 0.96
Dissolved Oxygen 0
Submerged Aquatic 0.25 0.25Vegetation Low
Nuisance and Toxic 0Blooms
*1 – Primary symptoms method*2 – Secondary symptoms method
n
i t
z
value
Expression
A
A
1
Symptom levelof expressionvalue for estuary
n – Total number of zonesAz – Area of zoneAt – Total estuary area
ASSETS: GOOD
Nutrient inputs based on susceptibility
Future nutrient pressures Future nutrient pressures decrease
0.32 Moderate Low
Research and screening models couplingResearch and screening models coupling
ASSETS screening model Research model
Index Methods / Parameters
Presure – OHI Nutrient inputs based on susceptibility
Boundary loads
State - OEC
PSM
Chlorophyll Chlorophyll aa Percentile 90 value 1
EpiphytesEpiphytes Not simulated 2
MacroalgaeMacroalgae Biomass % increase3
SSM
DODO Percentile 10 value 1
SAVSAV Not simulated 2
Nuisance and toxic blooms Nuisance and toxic blooms Not simulated 2
Response - DFO Future nutrient pressure Scenario definition1 Monthly random sample of the research model outputs to reproduce the way this parameter is applied to field data2 Same value as OEC application to field data3 There are no thresholds defined, this symptom is heuristically classified into High, Moderate or No Problem category
Model
green
scenario
Ria Formosa –ASSETS validation & model scenariosIndex
Overall Eutrophic Condition (OEC)
ASSETS OEC: 4
Overall Eutrophic Condition (OEC)
ASSETS OEC: 4
Overall Eutrophic Condition (OEC)
ASSETS OEC:
Methods
PSM
SSM
PSM
SSM
PSM
SSM
Parameters Value Level of expression
Chlorophyll a 0.25Epiphytes 0.50 0.57Macroalgae 0.96 Moderate
Dissolved Oxygen 0Submerged Aquatic 0.25 0.25Vegetation LowNuisance and Toxic 0Blooms
Chlorophyll a 0.25Epiphytes 0.50 0.57Macroalgae 0.96 Moderate
Dissolved Oxygen 0Submerged Aquatic 0.25 0.25Vegetation LowNuisance and Toxic 0Blooms
Chlorophyll a 0.25Epiphytes 0.50 0.42Macroalgae 0.50 Moderate
Dissolved Oxygen 0Submerged Aquatic 0.25 0.25Vegetation LowNuisance and Toxic 0Blooms
Field data
Research
model
Index
MODERATELOW
MODERATE
LOW
MODERATE
LOW
28% lower
4(5)
Sensitivity analysis ISensitivity analysis I
Julian Day
02468
101214
0 x load
02468
101214
0 60 120 180 240 300 360
2 x load
Dis
solv
ed o
xyge
n (m
g L
-1)
(4. 1 mg L-1)
2 x loads
0 60 120 180 240 300 360
(5.1 mg L-1)
(4. 6 mg L-1)
0 x loads
(5.8 mg L-1)
Julian DayJulian Day
02468
101214
0 x load
02468
101214
0 60 120 180 240 300 360
2 x load
Dis
solv
ed o
xyge
n (m
g L
-1)
(4. 1 mg L-1)
2 x loads
0 60 120 180 240 300 360
(5.1 mg L-1)
(4. 6 mg L-1)
0 x loads
(5.8 mg L-1)
Julian Day
Test different sampling frequencies as input to the screening model
Complete dataset Monthly sub-sampling
Complex model outputs
Percentile 10 value
Percentile 10 value
Sensitivity analysis IISensitivity analysis II 2S scenario with different sampling frequencies Index Method Parameter Value
Level ofexpression
Indexresult
ASSETSresult
OHINutrient inputs based on
susceptibility 0.49 Moderate Moderate
Moderate
OEC
PSM
Chlorophyll a 0.250.57Moderate
Moderatelow
Epiphytes 0.50
Macroalgae 0.96
SSM
Dissolved oxygen 00.25LowSAV loss 0.25
Nuisance and toxic blooms 0
DFO Future nutrient pressure Future nutrient pressuresincrease
Worsenlow
OHINutrient inputs based on
susceptibility 0.49 Moderate Moderate
Poor
OEC
PSM
Chlorophyll a 0.250.57Moderate
Moderate
Epiphytes 0.50
Macroalgae 0.96
SSM
Dissolved oxygen 0.460.46ModerateSAV loss 0.25
Nuisance and toxic blooms 0
DFO Future nutrient pressure Future nutrient pressuresincrease
Worsenlow
C
ompl
ete
Com
plet
e
data
set
data
set
M
onth
ly
Mon
thly
outp
uts
outp
uts
Final remarksFinal remarksThe integration of field data, research and screening models is a useful approach for managing eutrophication:
Assess the eutrophication state using screening models
Synthesis the complex outputs into management information with the screening model
Use research models for simulating management scenarios and use outputs for assessing the resulting system state
Definition of appropriate sampling frequencies for symptoms evaluation
Which means that allows to find the best management options to improve water quality status
The authors thank the OAERRE project (EVK3-CT-1999-00002) for sponsoring this work
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