Integration of field data and ecosystem models for eutrophication management

1. Integration of field data and ecosystem models for eutrophication management A.M. Nobreana@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

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

3. 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

4. Models for managing eutrophication Models may be broadly divided into 2 categories:

5. 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

6. 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

7. 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

8. 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

9. 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.

10. 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

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 • 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.

12. 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

13. ASSETS: GOOD ASSETSapplication to field data Index MODERATE LOW MODERATE LOW IMPROVE LOW Parameters Value Level of expression Chlorophyll a0.25 0.57 Epiphytes 0.50 Moderate Macroalgae 0.96 Dissolved Oxygen 0 Submerged Aquatic 0.25 0.25 Vegetation Low Nuisance and Toxic 0 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

14. 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

15. 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 Parameters Value Level of expression Chlorophyll a 0.25 Epiphytes 0.50 0.57 Macroalgae 0.96 Moderate Dissolved Oxygen 0 Submerged Aquatic 0.25 0.25 Vegetation Low Nuisance and Toxic 0 Blooms Chlorophyll a 0.25 Epiphytes 0.50 0.57 Macroalgae 0.96 Moderate Dissolved Oxygen 0 Submerged Aquatic 0.25 0.25 Vegetation Low Nuisance and Toxic 0 Blooms Chlorophyll a 0.25 Epiphytes 0.50 0.42 Macroalgae 0.50 Moderate Dissolved Oxygen 0 Submerged Aquatic 0.25 0.25 Vegetation Low Nuisance and Toxic 0 Blooms Field data Research model 28% lower Model green scenario 4(5)

16. 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

17. Sensitivity analysis II2S scenario with different sampling frequencies Monthly outputs Complete dataset

18. Final remarks