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 (2000) Ria de Vigo T, ChlMERL (Brush, unpublished) MERL mesocosms, RI T, Chl, P, N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
010203040506070
J F M A M J J A S O N DBottom O2, mg/L
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J F M A M J J A S O N D
Kremer (1983)
Acknowledgements
James N. KremerScott W. NixonJohn BrawleyNicole Goebel
Jamie Vaudrey
Dr. Brush’s wardrobe provided by:
Bay St. Louis Kmart