1. Integration of field data and ecosystem models for eutrophication management ”European Conference on Coastal Zone Research: an ELOISE Approach” Portoroz, Slovenia, November 14 – 18, 2004 Intitute of MArine Research - IMAR (Portugal) Sagresmarisco (Portugal) www.imar.pt www.ecowin.org A.M. Nobre ana@salum.net ana@salum.net J.G. Ferreira A. Newton A. Newton T. Simas T. Simas J.D. Icely J.D. Icely R. Neves R. Neves

2. Presentation layout Problem definition Approach Application site Research model Screening model Coupling Conclusion 16 Total no. slides

3.  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 definition Eutrophication 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

4. Models for managing eutrophication Screening 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 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:

5. Hybrid approach for eutrophication management Screening model Research model  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: Complex outputs Distils the results of the complex model Simulates the ecosystem under predefined scenarios

6. Hybrid approach application - overview - Drive screening model Field data Setup research model Field data Force research model Usage scenarios Responsiveness screening model Standard outputs Standard simulation Scenario outputs Scenario simulation Compare results If validated

7. Study site description Ria Fomosa morphology Fast water turnover Exchanged volume / Max volume FloodEbb Max69 %49 % Min27 %20 % Mean50 %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

8. 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 The spring-neap tide period data is cyclically run over a 4 year period Volume simulation with upscaled water fluxes 1 Model snapshot offline outputs assimilation Water fluxes per timestep per connection Data points 645 corresponds to a spring-neap tide period

9. Research model - ecological simulation - State variables and forcing functions are simulated with the following objects: Dissolved nutrients Dissolved nutrients Suspended particulate matter Suspended particulate matter Phytoplankton Phytoplankton Clam Clam Man seeding and harvest Man seeding and harvest Macroalgae Macroalgae Dissolved oxygen (small scale tide pool model) Dissolved oxygen (small scale tide pool model) Tide Tide Light climate Light climate Water temperature Water temperature The model was implemented in an object oriented ecological modelling platform* *Ferreira, J. G., 1995. ECOWIN - an object-oriented ecological model for aquatic ecosystems. Ecol. Modelling, 79: 21-34.

10. Research model - boundary conditions and scenarios - Boundary conditions forced with : Land-based nutrient inputs Land-based nutrient inputs Ocean pelagic component Ocean pelagic component Forced with coastal data series of nutrients and phytoplankton PEQ 49 – 1 000 1 001 – 5 000 5 001 – 10 000 10 001 – 20 000 20 001 – 30 000 Population equivalents (PEQ) at the discharge points of the waste water treatment plantsScenario kg N ha -1 yr -1 Green (0.5S)20 Standard (1S)40 Increase pressure (2S)80

11. 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 l Tidal freshwater (<0.5 psu) l Mixing zone (0.5-25 psu) l Seawater zone (>25 psu) Spatial and temporal quality of datasets (completeness) Spatial and temporal quality of datasets (completeness) Confidence in results (sampling and analytical reliability) Confidence in results (sampling and analytical reliability) Overall Eutrophic Condition (OEC) index Overall Eutrophic Condition (OEC) index Overall Human Influence (OHI) index Overall Human Influence (OHI) index Determination of Future Outlook (DFO) index Determination of Future Outlook (DFO) index Pressure State State Response Response S.B. Bricker, J.G. Ferreira, T. Simas, 2003. An integrated methodology for assessment of estuarine trophic status. Ecological Modelling, In Press.

12. ASSETS scoring system for PSR

13. Index MODERATE LOW MODERATE LOW IMPROVE LOW ASSETS application to field data 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 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 *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 ASSETS: GOOD Nutrient inputs based on susceptibility Future nutrient pressuresFuture nutrient pressures decrease 0.32 Moderate Low

14. Research and screening models coupling ASSETS screening modelResearch model Index Methods / Parameters Presure – OHINutrient inputs based on susceptibility Boundary loads State - OEC PSM Chlorophyll a Percentile 90 value 1 EpiphytesNot simulated 2 MacroalgaeBiomass % increase 3 SSM DO Percentile 10 value 1 SAVNot simulated 2 Nuisance and toxic blooms Not simulated 2 Response - DFOFuture nutrient pressureScenario definition 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

15. Model green scenario Ria Formosa –ASSETS validation & model scenarios 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 Index MODERATE LOW MODERATE LOW MODERATE LOW 28% lower 4(5)

16. Sensitivity analysis I Test different sampling frequencies as input to the screening model Complete datasetMonthly sub-sampling Complex model outputs Percentile 10 value

17. Sensitivity analysis II Sensitivity analysis II 2S scenario with different sampling frequencies IndexMethodParameterValue Level of expression Index result ASSETS result OHI Nutrient inputs based on susceptibility 0.49 Moderate Moderate Moderate OEC PSM Chlorophyll a 0.25 0.57 Moderate low Epiphytes 0.50 Macroalgae 0.96 SSM Dissolved oxygen 0 0.25 Low SAV loss 0.25 Nuisance and toxic blooms 0 DFO Future nutrient pressure Future nutrient pressures increase Worsen low OHI Nutrient inputs based on susceptibility 0.49 Moderate Moderate Poor OEC PSM Chlorophyll a 0.25 0.57 Moderate Epiphytes 0.50 Macroalgae 0.96 SSM Dissolved oxygen 0.46 Moderate SAV loss 0.25 Nuisance and toxic blooms 0 DFO Future nutrient pressure Future nutrient pressures increase Worsen low Complete Completedataset Monthly Monthlyoutputs

18. Final remarks The 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