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Hypoxia in Narragansett Bay Workshop Oct 2006. Dan Codiga, Jim Kremer, Mark Brush, Chris Kincaid, Deanna Bergondo . “Modeling” In the Narragansett Bay CHRP Project. Does the word “Model” have meaning?. Hydrodynamic Ecological Research vs Applied Prognostic vs Diagnostic

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Hypoxia in narragansett bay workshop oct 2006

Hypoxia in Narragansett BayWorkshop Oct 2006

Dan Codiga, Jim Kremer, Mark Brush, Chris Kincaid, Deanna Bergondo


In the Narragansett Bay CHRP Project

Does the word model have meaning
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
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
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

Heuristic models in research: iterative failure = learning





Runs that fall short

Parameter values

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


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)

An alternative approach 4 state variables 5 processes
An alternative approach? 4 state variables, 5 processes






Processes of the model

(excluding macroalgae...)

Temp, Light,

Boundary Conditions

Chl, N, P, Salinity

O2 coupled




Surface layer

- - - - - - - - -

Deep layer

- - - - - - - - -

Bottom sediment




Photic zone


Flux to















“Strength in numbers”

Shallow test sites

(MA, RI, CT)

Long Island Sound -- Hypoxia

August 20

Deep test sites

(MA, RI, CT, VA, MD)

Narragansett Bay

Chesapeake Bay

Long Island Sound

Hydrodynamic Model


Momentum balance x & y directions:

u + vu – fv = f + Fu + Du

t x

v + vv + fu = f + Fv + Dv


Potential temperature and salinity :

T+ vT = FT + DT


S + vS = FS + DS


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


Hydrodynamic Model

Grid Resolution: 100 m

Grid Size: 1024 x 512

Vertical Layers: 20

River Flow: USGS

Winds: NCDC

Tidal Forcing: ADCIRC

Open Boundary

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)

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)

Model-Data Comparison

Salinity - Phillipsdale


Salinity (ppt)


Time (days)

Hybrid: Driving Ecological model with Hydrodynamic Model:

Lookup Table of Daily Exchanges (k)

dP1/dt = P1(G-R) - k1,2P1V1 + k2,1P2V2 ...

Modeling Exchange Between

Ecological Model Domains












Long term aims hybrid ecological physical model
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
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

  • 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
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)

Visually apparent features
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
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




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)

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)

Advantages disadvantages of neural networks
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

Next steps
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