Hypoxia in Narragansett BayWorkshop 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

ConceptualModel

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

Atmosphericdeposition

N P

Productivity

Temp, Light,Boundary Conditions

Chl, N, P, Salinity

Phytoplankton

Sedimentorganics

.

.

Physics

Surface layer

- - - - - - - - -

Deep layer

- - - - - - - - -

Bottom sediment

O2

Flux tobottom

Photic zoneheterotrophy

Benthicheterotrophy Denitri-

fication

O2 coupledstoichiometrically

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

EquationsMomentum balance x & y directions:u + vu – fv = + Fu + Du t xv + vv + fu = + Fv + Dv t yPotential temperature and salinity :T + vT = FT + DT

t S + v S = FS + DS

t The equation of state:= (T, S, P) Vertical momentum: = - gz o

Continuity equation:u + v + w = 0x y z

Hydrodynamic Model

ROMS Model

Regional Ocean Modeling System

Grid Resolution: 100 mGrid Size: 1024 x 512Vertical Layers: 20River Flow: USGSWinds: NCDCTidal Forcing: ADCIRC

Open Boundary

Hydrodynamic Model

This project: Mid-Bay focus

Extent of counterMt. 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 counterMt. 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

Sal

inity

(pp

t)

Time (days)

Model

Data

Model-Data ComparisonShallows: North-South Component

-0.25

-0.15

-0.05

0.05

0.15

10 15 20

Time

Model Velocity

(m/s)

-250

-150

-50

50

150

Observed Velocity

(mm/s)

Bottom-model

Bottom

Channel: North-South Component

-0.25

-0.15

-0.05

0.05

0.15

0.25

10 15 20

Time (days)

Model Velocity

(m/s)

-250

-150

-50

50

150

250

Obsevered Velocity (m/s)

Bottom-model

Bottom

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

Hybrid: Driving Ecological model with Hydrodynamic Model:

Lookup Table of Daily Exchanges (k)

DYE_08

DYE_02DYE_03

DYE 05

DYE_01

DYE_09DYE_07

DYE_06 DYE 04

Modeling Exchange Between Ecological Model Domains

Passive Tracer Experiment

Passive Tracer Experiment

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 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

• 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 lagsr2=0.42 (River alone: 0.36)

Observed Model

MLR with lags River 2 days Wind 1 day Tide 3.5 days

r2=0.51 (River alone: 0.48)

Stratificationt [kg m-3]

Static Neural NetNo lags

r2=0.55 (River alone: 0.41)

Static Neural NetLags from MLR

r2=0.59 (River alone: 0.52)

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

• 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