Hypoxia in Narragansett Bay Workshop Oct 2006

1. Hypoxia in Narragansett BayWorkshop Oct 2006 Dan Codiga, Jim Kremer, Mark Brush, Chris Kincaid, Deanna Bergondo “Modeling” In the Narragansett Bay CHRP Project

2. Does the word “Model” have meaning? • Hydrodynamic • Ecological • Research vs Applied • Prognostic vs Diagnostic • Heuristic, Theoretical, Conceptual, Empirical, Statistical, Probabilistic, Numerical, Analytic • Idealized/Process-Oriented vs Realistic • Kinematic vs Dynamic • Forecast vs hindcast

4. CHRP Program Goals (selected excerpts from RFP) • Predictive/modeling tools for decision makers • Models that predict susceptibility to hypoxia • Better understanding and parameterizations • Transferability of results across systems • Data to calibrate and verify models  Following two presentations

5. Our approaches • Hybrid Ecological-Hydrodynamic Modeling • Ecological model:simple • Few processes, few parameters • Parameters that can be constrained by measurements • Few spatial domains (~20), as appropriate to measurements available • Net exchanges between spatial domains: from hydrodynamic model • Hydrodynamic model:full physics and forcing of ROMS • realistic configuration; forced by observed winds, rivers, tides, surface fluxes • Applied across entire Bay, and beyond, at high resolution • Passive tracers used to determine net exchanges between larger domains of ecological model • Empirical-Statistical Modeling • Input-output relations, emphasis on empirical fit more than mechanisms • Development of indices for stratification, hypoxia susceptibility • Learn from hindcasts, ultimately apply toward forecasting

6. Heuristic models in research: iterative failure = learning Processes Conceptual Model Formulations Runs that fall short Parameter values

7. But for management models:• Heuristic goal less impt • Accurateeven if not precise• Well constrained coefs• Simple (?) (at least understandable)_____________________________≠ Research models

8. 25-30 state vars 70-110 params A paradox -- “Realism” = many parameters weakly constrained limited data to corroborate i.e. “Over-parameterized” (many ways to get similar results) :.Accuracy is unknown. (often unknowable)

9. An alternative approach? 4 state variables, 5 processes Phytoplankton O D 2 N P Processes of the model (excluding macroalgae...) Temp, Light, Boundary Conditions Chl, N, P, Salinity O2 coupled stoichiometrically Productivity Physics Surface layer - - - - - - - - - Deep layer - - - - - - - - - Bottom sediment mixing flushing Atmospheric Photic zone heterotrophy Flux to bottom deposition . N Sediment Land-use organics Benthic heterotrophy N P Denitri- fication .

10. Corroboration: “Strength in numbers” Shallow test sites (MA, RI, CT)

11. Long Island Sound -- Hypoxia August 20 Deep test sites (MA, RI, CT, VA, MD) Narragansett Bay Chesapeake Bay Long Island Sound

12. Hydrodynamic Model Equations Momentum balance x & y directions: u + vu – fv = f + Fu + Du t x v + vv + fu = f + Fv + Dv ty Potential temperature and salinity : T+ vT = FT + DT t S + vS = FS + DS t The equation of state: r= r (T, S, P) Vertical momentum: f = - r g z ro Continuity equation: u+v+w = 0 x y z Initial Conditions Forcing Conditions ROMS Model Regional Ocean Modeling System Output

13. Hydrodynamic Model Grid Resolution: 100 m Grid Size: 1024 x 512 Vertical Layers: 20 River Flow: USGS Winds: NCDC Tidal Forcing: ADCIRC Open Boundary

14. This project: Mid-Bay focus Narragansett Bay Commission: Providence & Seekonk Rivers Mt. Hope Bay circulation/exchange /mixing study. ADCP, tide gauges (Deleo, 2001) Extent of counter Summer, 07: 4 month deployment (Outflow pathways) Bay-RIS exchange study (98-02)

15. This project: Mid-Bay focus Narragansett Bay Commission: Providence & Seekonk Rivers Mt. Hope Bay circulation/exchange /mixing study. ADCP, tide gauges (Deleo, 2001) Extent of counter Summer, 08: Deep return flow processes Bay-RIS exchange study (98-02)

16. Model-Data Comparison Salinity - Phillipsdale Model Salinity (ppt) Data Time (days)

17. Model-Data Comparison

18. Hybrid: Driving Ecological model with Hydrodynamic Model: Lookup Table of Daily Exchanges (k) dP1/dt = P1(G-R) - k1,2P1V1 + k2,1P2V2 ...

19. Modeling Exchange Between Ecological Model Domains DYE_01 DYE_02 DYE_03 DYE_06 DYE 04 DYE 05 DYE_07 DYE_09 DYE_08

20. Passive Tracer Experiment

21. Passive Tracer Experiment

22. Passive Tracer Experiment

23. Long-term Aims:Hybrid Ecological-Physical Model • Increased spatial resolution of ecology: approach TMDL applicability • Scenario evaluation • Nutrient load changes • Climatic changes • Alternative to mechanistic coupled hydrodynamic/ecological modeling

24. Empirical/Statistical ModelingOverall Goals • Data-oriented—complements Hybrid– less mechanistic • Synthesize DO variability • Spatial (Large-scale CTD; towed body) • Temporal (Fixed-site buoys) • Develop indices • Stratification • Hypoxia vulnerability • First: Hindcasts to understand relationship between forcing (physical and biological) and DO responses • Long-term: Predictive capability for forecasting and scenario evaluation

25. Candidate predictors for DO • Biological • Chlorophyll • Temperature & solar input • Nutrient inputs (Rivers, WWTF, Estuarine exchange) • Others • Physical • River runoff, WWTF water transports • Tidal range cubed (energy available for mixing) • Windspeed cubed (energy available for mixing) • Others (Wind direction; Precip; Surface heat flux)

27. Strategy: start simple & develop method • Start with Bullock Reach timeseries • 5 yrs at fixed single point (no spatial information) • Investigate stratification (not DO-- yet) • Target variable: strat = [sigt(deep) – sigt(shallow)] • Include 3 candidate predictor variables: • River runoff (sum over 5 rivers) • Tidal range cubed (energy available for mixing) • Windspeed cubed (energy available for mixing)

28. 2001

29. Visually apparent features • Stratification reacts to ‘events’ in each of: • River inputs • Winds • Tidal stage • Stratification ‘events’ appear to be • Triggered irregularly by each process • Lagged by varying amounts from each process

30. Low-pass and subsample to 12 hrs…Compare techniques • Multiple Linear Regression (MLR) • No lags • Optimal lags – determined individually • Static Neural Network • No lags • Lags from MLR analysis • [coming soon] Dynamic Neural Network • Varying lags • Multiple interacting inputs

31. Observed Model Stratification Dst [kg m-3] Multiple Linear Regression No lags r2=0.42 (River alone: 0.36) MLR with lags River 2 days Wind 1 day Tide 3.5 days r2=0.51 (River alone: 0.48)

32. Static Neural Net No lags r2=0.55 (River alone: 0.41) Static Neural Net Lags from MLR r2=0.59 (River alone: 0.52)

33. Advantages/Disadvantagesof Neural Networks • Advantages • Nonlinear, can achieve better accuracy • Excels with multiple interacting predictors; • Dynamic NN: input delays capture lags • Varying lags from multiple interacting inputs • Transferable; conveniently applied to other/new data • Easy to use (surprise!!) • Main disadvantage • opaque “black-box” can be difficult to interpret; ameliorated by: complementary linear analysis, sensitivity studies, isolating/combining predictors

34. Next steps • Stratification • Consider additional predictors: • Surface heat flux; precipitation; WWTF volume flux • Different sites (North Prudence, etc) • Treat spatially-averaged regions • Apply similar approach to DO • Finish gathering forcing function data • Chl; solar inputs; WWTF nutrients • Corroborate Hybrid Ecological-Hydrodynamic Model