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

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

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

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

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

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

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)

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

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

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

Initial Conditions Forcing Conditions Output Equations Momentum balance x & y directions:  u + v  u – fv =  + F u + D u  t  x  v + v  v + fu =  + F v + D v  t  y Potential temperature and salinity :  T + v  T = F T + D T  t  S + v  S = F S + D S  t The equation of state:  =  (T, S, P) Vertical momentum:  = -  g  z  o Continuity equation:  u +  v +  w = 0  x  y  z Hydrodynamic Model ROMS Model Regional Ocean Modeling System

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

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

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

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

Model-Data Comparison

dP 1 /dt = P 1 (G-R) - k 1,2 P 1 V 1 + k 2,1 P 2 V 2... Hybrid: Driving Ecological model with Hydrodynamic Model: Lookup Table of Daily Exchanges (k)

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

Passive Tracer Experiment

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

Empirical/Statistical Modeling Overall 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

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)

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)

2001

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

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

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

Static Neural Net No lags r 2 =0.55 (River alone: 0.41) Static Neural Net Lags from MLR r 2 =0.59 (River alone: 0.52)

Advantages/Disadvantages of 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

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