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

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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