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Innovative Modeling for Simulating Hypoxia and Anoxia in Estuarine Ecosystems

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hanley
30 diapositives · 453 Views · Aug 02, 2014

This study presents an innovative modeling approach to simulate hypoxia and anoxia in estuarine ecosystems. By integrating empirical and mechanistic models, the research addresses challenges in accurately representing both states and rates of ecological processes. The model is designed to aid in nutrient management and evaluate the effects of eutrophication, thereby improving predictability and realism in estuarine systems. The findings also contribute to understanding phytoplankton production and nutrient dynamics, with a focus on the Chesapeake Bay and Greenwich Bay ecosystems.

#innovative modeling for simulating hypoxia and anoxia in estuarine ecosystems #innovative modeling for simulating hypoxia and anoxia in estuarine ecosystems ppt #powerpoint #presentation
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  1. An innovative modeling approach for simulating hypoxia/anoxia in estuarine ecosystems Mark J. Brush James N. Kremer Scott W. Nixon with contributions from: John Brawley Nicole Goebel Jamie Vaudrey

  2. ERSEM I (1995) Also: Reckhow (1994 & others) Håkanson (1995, 2004) Hofmann & Lascara (1998) Pace (2001) Duarte et al. (2003) Fulton et al. (2003) Rigler & Peters (1995) Kremer & Nixon (1978) Baretta & Ruardij (1988) Steele (1974) Riley (1946, 1947) Odum (1983) ERSEM II (1997) Odum (1994) Chesapeake Bay Model USE OF MODELS IN MANAGEMENT NUMBER OF PUBLICATIONS

  3. # of parameters Loss of utility at lowest complexity? predictability Increasing complexity / realism Trade-off between realism & predictability: Generality Precision Realism R. Levins (1966, 1968)

  4. Phytoplankton Primary Production Published Gmax Functions 1971-1998 Brush et al. (2002) elevated Eppley Gmax , d-1 Eppley Curve TEMPERATURE, oC

  5. Duarte et al. (2003) “The Limits to Models in Ecology”

  6. Question: Can a simplified eutrophication model be useful as a heuristic and management tool? Empirical “Stressor-Response” Models Complex, Mechanistic Systems Models Can we find a middle ground? Generality Precision R. Levins (1966, 1968) Realism • Parsimony Principle • Ockam's Razor

  7. Estuarine Eutrophication Model

  8. Phyto Production Pelagic Respiration C flux to sediments Denitrification Estuarine Eutrophication Model * Need to accurately model both states and rates Macro Metabolism

  9. Phytoplankton Primary Production Light x Biomass (“BZI”) Models Pd = *Chl*Zp*PAR +  … capped by available nutrients Brush et al. (2002) MEPS v. 238 Cole & Cloern (1987) MEPS v. 36

  10. Water Column Respiration Rd = *e kT*Chl10

  11. Carbon Flux to Sediments & Benthic Respiration Nixon (1981) Estuaries and Nutrients The Humana Press Csed = 0.25*Pd Rsed = *e kT

  12. Denitrification Nixon et al. (1996) Biogeochemistry 35(1) DENIT = Nload*f(RT)

  13. Empirical Functions • Robust, data-driven, & apply across several systems - ideal when • mechanistic formulations are insufficient or poorly constrained. • Reduce model complexity by integrating multiple processes • (which are often poorly constrained) into simplified, bulk functions. • Produce output we can measure and test. • Excellent tools for model validation. … a hybrid, empirical-mechanistic approach

  14. Greenwich Bay Eutrophication Model Greenwich Bay, RI (Avg Z = 3 m)

  15. Lower West Passage Chl-a Surface Phytoplankton

  16. Surface DIN

  17. Bottom O2

  18. Bottom O2 with Forced Maximum Chlorophyll a original run max chl

  19. MERL fcn of T, Chl, NPP model In the absence of flux measurements  * Need to accurately model both states and rates Rate Processes Annual Primary Production g C m-2 y-1 Observed: 281 – 326 Modeled: 306

  20. System-Level Validation: Nutrient Reduction Scenarios Keller (1988) Nixon et al. (2001) Nixon et al. (1996)

  21. Empirical Models A Simplified, Hybrid Empirical-Mechanistic Systems Model Complex, Mechanistic Systems Models Generality Precision R. Levins (1966, 1968) Realism • Multiple, parallel modeling • approaches, e.g.: • Latour, Brush & Bonzek (2003) • Scavia et al. (2003) • Borsuk et al. (2002, 2004)

  22. Oviatt et al. Models for Hypoxia Applied in Narragansett Bay NOAA Coastal Hypoxia Research Program

  23. Full 3D resolution in ROMS:

  24. Nutrient Reduction Scenarios Bottom O2, mg/L 0% watershed N,P 0% Narr. Bay N,P 0% Narr. Bay N,P & saturating O2

  25. PROVIDENCE RIVER LOWER NARRAGANSETT BAY

  26. Scope for Improvement: Pre-Colonial Inputs Bottom O2 Nixon (1997) Estuaries 20(2)

  27. Effect of Macroalgal Decomposition Bottom O2 Bottom O2

  28. Effect of Macroalgal Decomposition

  29. Stochastic Simulation Kremer (1983) Bottom O2

  30. Dr. Brush’s wardrobe provided by: Bay St. Louis Kmart Acknowledgements James N. Kremer Scott W. Nixon John Brawley Nicole Goebel Jamie Vaudrey