Hypoxia in Narragansett Bay Workshop Oct 2006

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Hypoxia in Narragansett BayWorkshop Oct 2006

• Modeling
• In the Narragansett Bay CHRP Project

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

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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

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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

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Heuristic models in research iterative
failure learning
Processes
Conceptual Model
Formulations
Runs that fall short
Parameter values
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But for management models Heuristic goal
less impt Accurate even if not precise
Well constrained coefs Simple (?) (at least
understandable)_____________________________
? Research models
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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)

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An alternative approach? 4 state variables,
5 processes
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
Flux to bottom
Photic zone heterotrophy
deposition
.
N

Sediment
Land-use
organics
Benthic heterotrophy
N P
Denitri- fication
.
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Corroboration Strength in numbers

Shallow test sites (MA, RI, CT)
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Deep test sites (MA, RI, CT, VA, MD)
Narragansett Bay
Chesapeake Bay
Long Island Sound
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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
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Hydrodynamic Model
Grid Resolution 100 m Grid Size 1024 x
512 Vertical Layers 20 River Flow USGS Winds
NCDC Tidal Forcing ADCIRC
Open Boundary
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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)
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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)
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Model-Data Comparison
Salinity - Phillipsdale
Model
Salinity (ppt)
Data
Time (days)
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Model-Data Comparison
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Hybrid Driving Ecological model with
Hydrodynamic Model
Lookup Table of Daily Exchanges (k)
dP1/dt P1(G-R) - k1,2P1V1 k2,1P2V2 ...
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Modeling Exchange Between Ecological Model
Domains
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Passive Tracer Experiment
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Passive Tracer Experiment
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Passive Tracer Experiment
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Long-term AimsHybrid 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

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Empirical/Statistical ModelingOverall Goals

• Data-orientedcomplements 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

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• 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)

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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)

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2001
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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

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Low-pass and subsample to 12 hrsCompare
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

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Stratification Dst kg m-3
Multiple Linear Regression No lags r20.42
(River alone 0.36)
MLR with lags River 2 days Wind 1 day Tide
3.5 days r20.51 (River alone 0.48)
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Static Neural Net No lags r20.55 (River alone
0.41)
Static Neural Net Lags from MLR r20.59 (River
alone 0.52)
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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

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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