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Integrated Benthic Ecosystem Modeling: Findings and Recommendations

This study explores assumptions and conclusions related to benthic ecosystem modeling in aquatic environments. Sensitivity runs and optimizations are discussed alongside improvements in dissolved oxygen computations and the integration of optical models. Recommendations for refining existing parameters and calibrating ecosystem components are provided.

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Integrated Benthic Ecosystem Modeling: Findings and Recommendations

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  1. Putting It All Together

  2. Three Assumptions • Vertically integrated primary production is constant across any cross section. • Volumetric rates of planktonic community respiration are constant across any cross section. • Benthic respiration rates in littoral areas are half those in adjacent deep sediments.

  3. MB NB SB

  4. As a sensitivity run, route all mortality and predation to water column as DOC Normally, all mortality and predation goes to sediments as POC

  5. Previous Result Sensitivity Run

  6. KDOC = 0.011/d KDOC = 0.017/d

  7. Run 48 is sensitivity to benthic algal DOC. The rest are sensitivity to KDOC Model High Model Low

  8. Conclusions • Improvement of dissolved oxygen computations in the deep trench requires production of more dissolved organic carbon. • Dissolved oxygen computations are largely insensitive to first-order DOC respiration rate • Investigate DOC production through benthic algae, SAV, deposit feeders, filter feeders

  9. Advanced Optical Model • The model is finished, field work, report completed. • Implemented in water quality model • Parameter set has been updated since initial implementation

  10. Partial Attenuation Model Revised Parameter Set

  11. Partial Attenuation Model Revised Parameter Set

  12. Conclusions • The optical model is implemented and running well • Shortcomings are perhaps more related to the solids calculation than to the optical model • Still, additional parameter refinements are likely

  13. Vallisneria Ruppia Zostera

  14. Zostera Stand-Alone Calibration Leaf Root

  15. Vallisneria Stand-Alone Calibration Ruppia Stand-Alone Calibration

  16. Compensation Irradiance Pm(T) = maximum production at temperature T (g C g-1 DW d-1) Fam = Fraction of production devoted to active metabolism (0 < Fam < 1) Acdw = plant carbon-to-dry-weight ratio (g C g-1 DW) Ic = compensation irradiance (E m-2 d-1) BM = basal metabolism (d-1)

  17. Zostera and Ruppia Marsh et al. (1986). Ic is a function of Temperature

  18. Vallisneria Chesapeake Bay

  19. Zostera Chesapeake Bay

  20. Ruppia Choptank River

  21. Vallisneria Potomac River

  22. Conclusions • The SAV model is reasonably well-calibrated in terms of SAV response to light attenuation • Some tuning is always possible • Final calibration depends on calculation of light attenuation

  23. Bankloads • The bankloads are in. About 11,600 tonnes/day. • Half coarse, half fine. Less than 2% organic system-wide. • Previous loads employed in model were 12,800 kg/day. • Not that different. Why?

  24. Bankloads • Previous shoreline was based on model cell length. 3290 km. • New shoreline is based on map. 7000 km. • Previous load was 3.9 kg/m/d. Now 1.7 kg/m/d. • For now, they are input as daily-average loads.

  25. No Bank Loads With Bank Loads

  26. No Bank Loads With Bank Loads

  27. Suspension-Feeding Benthos • In terms of abundance and distribution, the dominant species are rangia, mya, and corbicula • We received from the Bay Program a data base of more than 10,000 benthos records, 1985 – 2005 • Roughly 1,800 rangia, 800 mya, 250 corbicula, 1985 - 2005

  28. How Do We Place Them? • Consider only CBPS with median AFDW > 10 g/sq m • We know from the oyster model that 2 g AFDW/sq m has no effect on anything • Effect of density < 10 g AFDW/sq m is absorbed into generalized predation term

  29. Where do We Place Them? • Rangia > 10 are found in 22 of 98 CBPS • Mya > 10 are found in 11 of 98 CBPS • Corbicula > 10 are found in 6 of 98 CBPS

  30. What Cells? As per your request for habitat characterization for corbicula,mya and rangia, Based on the book Chesapeake Bay: Nature of the Estuary : A Field Guide by Christopher P. White, Organism Salinity Range Bottom Type Rangia 0.5-10 psu Mud and Sand, like high turbidity areas Mya 5-30 PSU Prefers Sand, but will tolerate Mud Corbicula 0-10 PSU Prefers Sand, but will tolerate clay Sand habitat is defined as an area having < 40 percent silt-clay content.

  31. What Cells? • Bay Program (Kate Hopkins) provided a table of cell versus sand content • Effect of salinity on filtration rate is coded in the model

  32. Is the problem the living resource criterion or the assignment of sand to model cells?

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