an innovative modeling approach for simulating hypoxia/anoxia in estuarine ecosystems

An innovative modeling approach for simulating hypoxia/anoxia in estuarine ecosystems

Mark J. BrushJames N. Kremer

Scott W. Nixon

with contributions from:John BrawleyNicole Goebel

Jamie Vaudrey

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ASFA SEARCH FOR "ECOSYSTEM MODEL"

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Chesapeake Bay Model

Baretta & Ruardij(1988)

ERSEM I(1995)

ERSEM II(1997)

Odum(1983)

Odum(1994)

Riley(1946, 1947)

Steele(1974)

Kremer& Nixon(1978)

Rigler & Peters(1995)Also:

Reckhow (1994 & others)Håkanson (1995, 2004) Hofmann & Lascara (1998)Pace (2001) Duarte et al. (2003)Fulton et al. (2003)

USE OF MODELS IN MANAGEMENT

Generality

Realism

Precision

R. Levins (1966, 1968)

Trade-off between realism & predictability:

Increasing complexity / realism

# of parameters

predictability

Loss ofutility atlowest

complexity?

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TEMPERATURE, oC

Gm

ax, d

-1

Published Gmax Functions1971-1998

Gm

ax ,

d-1

EppleyCurve

Brush et al. (2002)

elevatedEppley

TEMPERATURE, oC

PhytoplanktonPrimary

Production

Duarte et al. (2003)

“The Limits to Modelsin Ecology”

Generality

Realism

Precision

R. Levins(1966, 1968)

Complex, Mechanistic

SystemsModels

Empirical “Stressor-Response”

Models

Can we find a middle ground?

Question:Can a simplified eutrophication model be useful as a heuristic and management tool?

• Parsimony Principle• Ockam's Razor

Estuarine Eutrophication Model

MacroMetabolism

C flux to sediments

* Need to accurately model both states and rates

Estuarine Eutrophication Model

PhytoProduction

PelagicRespiration

Denitrification

Light x Biomass (“BZI”) Models

Pd = *Chl*Zp*PAR +

… capped by available nutrients

PhytoplanktonPrimary

Production

Cole & Cloern (1987) MEPS v. 36 Brush et al. (2002) MEPS v. 238

Water Column Respiration

Source Location PCR = f of:Nixon & Oviatt (1973) Bissel Cove, RI TTurner (1978) Georgia creek TNowicki (1983) Potter Pond, RI THolligan et al. (1984) English Channel ChlJensen et al. (1990) Roskilde Fjord ChlIriarte et al. (1991) North Sea ChlSampou & Kemp (1994) Chesapeake Bay TSmith & Kemp (1995) Chesapeake Bay TFourqurean et al. (1997) Tomales Bay, CA T, ChlCaffrey et al. (1998) San Francisco Bay, CA ChlMoncoiffe et al. (2000) Ria de Vigo T, ChlMERL (Brush, unpublished) MERL mesocosms, RI T, Chl, P, N

Rd = *e kT*Chl10

Nixon (1981)Estuaries and Nutrients

The Humana Press

Carbon Flux to Sediments &

Benthic Respiration

Csed = 0.25*Pd

Rsed = *e kT

Nixon et al.(1996)

Biogeochemistry 35(1)

Denitrification

DENIT = Nload*f(RT)

• Robust, data-driven, & apply across several systems - ideal when

mechanistic formulations are insufficient or poorly constrained.

Empirical Functions

• Reduce model complexity by integrating multiple processes

(which are often poorly constrained) into simplified, bulk functions.

• Produce output we can measure and test.

• Excellent tools for model validation.

… a hybrid, empirical-mechanistic approach

Greenwich Bay Eutrophication Model

Greenwich Bay, RI(Avg Z = 3 m)

Surface Phytoplankton

Lower West Passage Chl-a

Surface DIN

Bottom O2

Bottom O2 with Forced Maximum Chlorophyll a

max chl

original run

Rate Processes

Mid-Bay: Sediment Carbon

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g C

m-2

Mid-Bay: Daily P & R

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g C

m-2 d

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Annual Primary Productiong C m-2 y-1

Observed: 281 – 326 Modeled: 306

Lower Bay: Water Column Respiration

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g O 2

m-2

d-1

In the absence of flux measurements

model

MERL fcn of T, Chl, NPP

* Need to accurately model both states and rates

System-Level Validation:Nutrient Reduction Scenarios

Keller (1988)Nixon et al. (2001) Nixon et al. (1996)

Generality

Realism

Precision

R. Levins(1966, 1968)

Empirical Models

Complex, Mechanistic

SystemsModels

A Simplified, HybridEmpirical-Mechanistic

Systems Model

Multiple, parallel modeling approaches, e.g.:

• Latour, Brush & Bonzek (2003)• Scavia et al. (2003)• Borsuk et al. (2002, 2004)

Oviatt et al.

Models for Hypoxia Applied in Narragansett Bay

NOAA Coastal Hypoxia Research Program

Parameter

Values

Chl-a

DIN

DIP

O2

System Py

C:Chl 30, 60 37 16 12 18 mBZI0 ± 20% 14 12 16 Chl tavg ± 20% 10 12 ƒNPPSED 0.15, 0.35 12 17 10 wtrclm Rƒ0 ± 20% 11 12 wtrclm RƒQ10 ± 20% 12 11 R tavg ± 20% 11

Full 3D resolution in ROMS:

Nutrient Reduction Scenarios

02468

101214

J F M A M J J A S O N D

02468

101214

J F M A M J J A S O N D

02468

101214

J F M A M J J A S O N D

Bottom O2, mg/L

0% watershed N,P

0% Narr. Bay N,P

0% Narr. Bay N,P& saturating O2

LOWERNARRAGANSETT

BAY

PROVIDENCERIVER

Scope for Improvement: Pre-Colonial Inputs

Bottom O2

Nixon (1997) Estuaries 20(2)

Effect of Macroalgal Decomposition

Bottom O2

Bottom O2

Effect of Macroalgal Decomposition

Resultant O2, mg/L

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0 1E+08 2E+08 3E+08 4E+08Area, m2

Stochastic Simulation

Bottom O2

Parameter

Values

Chl-a

DIN

DIP

O2

System Py

C:Chl 30, 60 37 16 12 18 mBZI0 ± 20% 14 12 16 Chl tavg ± 20% 10 12 ƒNPPSED 0.15, 0.35 12 17 10 wtrclm Rƒ0 ± 20% 11 12 wtrclm RƒQ10 ± 20% 12 11 R tavg ± 20% 11

Surface Chl- a, mg/m3

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J F M A M J J A S O N DBottom O2, mg/L

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Kremer (1983)

Acknowledgements

James N. KremerScott W. NixonJohn BrawleyNicole Goebel

Jamie Vaudrey

Dr. Brush’s wardrobe provided by:

Bay St. Louis Kmart