Integration of field data and ecosystem models for eutrophication management
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Integration of field data and ecosystem models for eutrophication management. A.M. Nobre [email protected] 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.

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Integration of field data and ecosystem models for eutrophication management

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Integration of field data and ecosystem models for eutrophication management

Integration of field data and ecosystem models for eutrophication management

A.M. [email protected]

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

Presentation layout

no. slides

Problem definition

Approach

Application site

Research model

Screening model

Coupling

Conclusion

16 Total


Problem definition eutrophication management in transitional and coastal waters

Problem definitionEutrophication 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

  • 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

  • the nutrient loads cause a variety of impacts

nutrient forcing

no clear relationship between

eutrophication symptoms


Models for managing eutrophication

Models for managing eutrophication

Models may be broadly divided into 2 categories:


Hybrid approach for eutrophication management

Hybrid approach for eutrophication management

  • Screening models driven by field data for the assessment of the eutrophication state

  • Complex models help to fill data gaps and to explore specific scenarios

  • Distil the results from research models into these screening models

  • Coupling of the two model categories:

Simulates the ecosystem under predefined scenarios

Research model

Complex outputs

Distils the results of the complex model

Screening model


Hybrid approach application overview

Hybrid approach application - overview -

Setup

research model

Field data

Standard outputs

Standard simulation

Drive

screening model

Field data

Compare results

If validated

Force

research model

Usage scenarios

Scenario outputs

Scenario simulation

Responsiveness

screening model


Study site description

Study site description

Ria Fomosa morphology

Fast water turnover

  • 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 morphology and hydrodynamics

Box 1

Box 2

Tidal height simulated with harmonic constants

Volume

Box 3

106 m3

m

Box 4

100

3.0

Box 5

2.5

80

2.0

Box 6

60

1.5

Box 7

40

1.0

20

0.5

Box 8

0

0

Box 9

0

6

12

18

24

30

hours

Research model - 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 timestep

Upscaled

9 boxes and 30 min timestep

9 boxes

4 ocean boundaries

Model snapshot

offline outputs assimilation

Volume simulation with upscaled water fluxes

corresponds to a spring-neap tide period

Data points

645

Water fluxes per timestep per connection

The spring-neap tide period data is cyclically run over a 4 year period

1


Research model ecological simulation

Research model - ecological simulation -

The model was implemented in an object oriented ecological modelling platform*

  • State variables and forcing functions are simulated with the following objects:

  • Dissolved nutrients

  • Suspended particulate matter

  • Phytoplankton

  • Clam

  • Man seeding and harvest

  • Macroalgae

  • Dissolved oxygen (small scale tide pool model)

  • Tide

  • Light climate

  • Water temperature

*Ferreira, J. G., 1995. ECOWIN - an object-oriented ecological model for aquatic ecosystems. Ecol. Modelling, 79: 21-34.


Research model boundary conditions and scenarios

PEQ

49 – 1 000

1 001 – 5 000

5 001 – 10 000

10 001 – 20 000

20 001 – 30 000

Research model - boundary conditions and scenarios -

  • Boundary conditions forced with :

  • Land-based nutrient inputs

  • Ocean pelagic component

  • Forced with coastal data series of

  • nutrients and phytoplankton

Population equivalents (PEQ) at the discharge points of the waste water treatment plants


Key aspects of the assets neea screening model

Key aspects of the ASSETS/NEEA screening model

  • The 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)

  • Mixing zone (0.5-25 psu)

  • Seawater zone (>25 psu)

Spatial and temporal quality of datasets (completeness)

Confidence in results (sampling and analytical reliability)

Overall Eutrophic Condition (OEC) index

Overall Human Influence (OHI) index

Determination of Future Outlook (DFO) index

State

Pressure

Response

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 psr

Grade

5

4

3

2

1

Pressure (OHI)

Low

Moderate low

Moderate

Moderate high

High

State (OEC)

Low

Moderate low

Moderate

Moderate high

High

Response

Improve high

Improve low

No change

Worsen low

Worsen high

(DFO)

Metric

Combination matrix

Class

High

P

5

5

5

4

4

4

(5%)

5

5

5

5

5

5

S

5

4

3

5

4

3

R

Good

P

5

5

5

5

5

5

5

4

4

4

4

4

3

3

3

3

3

3

(19%)

5

5

4

4

4

4

4

5

5

4

4

4

5

5

5

4

4

4

S

2

1

5

4

3

2

1

2

1

5

4

3

5

4

3

5

4

3

R

Moderate

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

1

P

(32%)

3

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

3

S

2

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

R

Poor

P

4

4

4

4

4

3

3

3

3

3

3

3

2

2

2

2

2

2

1

1

1

1

1

(24%)

2

2

2

2

2

3

3

2

2

2

2

2

3

3

2

2

2

2

3

3

3

2

2

S

5

4

3

2

1

2

1

5

4

3

2

1

2

1

4

3

2

1

3

2

1

5

4

R

Bad

P

3

3

3

3

3

2

2

2

2

2

1

1

1

1

1

1

1

1

(19%)

1

1

1

1

1

1

1

1

1

1

2

2

2

1

1

1

1

1

S

5

4

3

2

1

5

4

3

2

1

3

2

1

5

4

3

2

1

R

ASSETS scoring system for PSR


Assets a pplication to field data

ASSETS: GOOD

ASSETSapplication to field data

Index

MODERATE LOW

MODERATE

LOW

IMPROVE

LOW

ParametersValueLevel of expression

Chlorophyll a0.25

0.57

Epiphytes0.50

Moderate

Macroalgae0.96

Dissolved Oxygen0

Submerged Aquatic0.250.25

Vegetation Low

Nuisance and Toxic0

Blooms

Indices

Overall Human Influence (OHI)

ASSETS: 4

Overall Eutrophic Condition (OEC)

ASSETS: 4

Determination of Future Outlook (DFO)

ASSETS: 4

Methods

PSM*1

SSM*2

Nutrient inputs based on susceptibility

0.32 Moderate Low

Future nutrient pressures

Future nutrient pressures decrease

*1 – Primary symptoms

method

*2 – Secondary symptoms

method

Symptom level

of expression

value for estuary

n – Total number of zones

Az – Area of zone

At – Total estuary area


Research and screening models coupling

Research and screening models coupling

1 Monthly random sample of the research model outputs to reproduce the way this parameter is applied to field data

2 Same value as OEC application to field data

3 There are no thresholds defined, this symptom is heuristically classified into High, Moderate or No Problem category


Ria formosa assets validation model scenarios

Ria Formosa –ASSETSvalidation & model scenarios

Index

MODERATELOW

MODERATE

LOW

MODERATE

LOW

Index

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

ParametersValueLevel of expression

Chlorophyll a0.25

Epiphytes0.500.57

Macroalgae0.96Moderate

Dissolved Oxygen0

Submerged Aquatic0.250.25

Vegetation Low

Nuisance and Toxic0

Blooms

Chlorophyll a0.25

Epiphytes0.500.57

Macroalgae0.96Moderate

Dissolved Oxygen0

Submerged Aquatic0.250.25

Vegetation Low

Nuisance and Toxic0

Blooms

Chlorophyll a0.25

Epiphytes0.500.42

Macroalgae0.50Moderate

Dissolved Oxygen0

Submerged Aquatic0.250.25

Vegetation Low

Nuisance and Toxic0

Blooms

Field data

Research model

28% lower

Model green scenario

4(5)


Sensitivity analysis i

Sensitivity analysis I

Test different sampling frequencies as input to the screening model

Complex model outputs

Complete dataset

Monthly sub-sampling

Percentile 10 value

Percentile 10 value


Sensitivity analysis ii 2s scenario with different sampling frequencies

Sensitivity analysis II2S scenario with different sampling frequencies

Monthly

outputs

Complete

dataset


Final remarks

Final remarks


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