Hypoxia in narragansett bay workshop oct 2006
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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

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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

“Modeling”

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

Processes

Conceptual

Model

Formulations

Runs that fall short

Parameter values


Hypoxia in narragansett bay workshop oct 2006

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


Hypoxia in narragansett bay workshop oct 2006

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)


An alternative approach 4 state variables 5 processes

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

.


Hypoxia in narragansett bay workshop oct 2006

Corroboration:

“Strength in numbers”

Shallow test sites

(MA, RI, CT)


Hypoxia in narragansett bay workshop oct 2006

Long Island Sound -- Hypoxia

August 20

Deep test sites

(MA, RI, CT, VA, MD)

Narragansett Bay

Chesapeake Bay

Long Island Sound


Hypoxia in narragansett bay workshop oct 2006

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


Hypoxia in narragansett bay workshop oct 2006

Hydrodynamic Model

Grid Resolution: 100 m

Grid Size: 1024 x 512

Vertical Layers: 20

River Flow: USGS

Winds: NCDC

Tidal Forcing: ADCIRC

Open Boundary


Hypoxia in narragansett bay workshop oct 2006

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)


Hypoxia in narragansett bay workshop oct 2006

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)


Hypoxia in narragansett bay workshop oct 2006

Model-Data Comparison

Salinity - Phillipsdale

Model

Salinity (ppt)

Data

Time (days)


Hypoxia in narragansett bay workshop oct 2006

Model-Data Comparison


Hypoxia in narragansett bay workshop oct 2006

Hybrid: Driving Ecological model with Hydrodynamic Model:

Lookup Table of Daily Exchanges (k)

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


Hypoxia in narragansett bay workshop oct 2006

Modeling Exchange Between

Ecological Model Domains

DYE_01

DYE_02

DYE_03

DYE_06

DYE

04

DYE

05

DYE_07

DYE_09

DYE_08


Hypoxia in narragansett bay workshop oct 2006

Passive Tracer Experiment


Hypoxia in narragansett bay workshop oct 2006

Passive Tracer Experiment


Hypoxia in narragansett bay workshop oct 2006

Passive Tracer Experiment


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


Hypoxia in narragansett bay workshop oct 2006

  • 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)


Hypoxia in narragansett bay workshop oct 2006

2001


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


Hypoxia in narragansett bay workshop oct 2006

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)


Hypoxia in narragansett bay workshop oct 2006

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


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