A Synthesis of Models to Simulate Benthic- Pelagic ... · A Synthesis of Models to Simulate...

A Synthesis of Models to Simulate Benthic-Pelagic Coupling in Florida Bay: Examination of

Ecosystem Restoration and Climate ChangeChristopher J. Madden1, Amanda McDonald1, Marguerite Koch2, Patricia Glibert3,

Cynthia Heil4, Bernard Cosby5

SFWMD Everglades Division, Univ. MD HPL, FL Atl. Univ., FL FWCC, Univ. VAFlorida Bay Science Conference

Naples, FLDecember 2008

Christopher J. Madden1, Amanda McDonald1, Marguerite Koch2, Patricia Glibert3, Cynthia Heil4, Bernard Cosby5

SFWMD Everglades Division, Univ. MD HPL, FL Atl. Univ., FL FWCC, Univ. VAFlorida Bay Science Conference

Naples, FLDecember 2008

22

Florida Bay and the Everglades Watershed:SFWMM, TIME, SICS Model Domains

Florida Bay and the Everglades Watershed:SFWMM, TIME, SICS Model Domains

33

Integration of Watershed Hydrologic Models

Integration of Watershed Hydrologic Models

Langevin et al. (2004)

44

Grid Mesh for Florida Bay Environmental Fluid Dynamics Code (EFDC) & Water Quality ModelGrid Mesh for Florida Bay Environmental Fluid Dynamics Code (EFDC) & Water Quality Model

Hamrick (2006)

55Hunt et al. (2006)

FATHOM (Flux Accounting and Tidal Hydrology Ocean Model)

FATHOM (Flux Accounting and Tidal Hydrology Ocean Model)

Cosby et al. (2005)

66

Florida Bay SAV Numerical Ecological Model Calibration Sites

Nuttle et al. (2005) EvergladesNational

Park TaylorSlough

Florida Bay N

Florida Keys

Madden et al. (2007)

77

Seagrass Community Ecological Model

Madden et al. (2007)

88

Environmental Effects on Plant Growth

(PAR)0 1000 2000

1

15 25 35Temperature (°C)

0

1

Temperature Effect

Salinity Effect

1

0 10 20 30 40 50 60

Phosphorus Concentration (uM)0 1 2

1

Phosphorus Uptake

1

0 5 10 15Nitrogen Concentration (uM)

0 1 2

1

Nitrogen Uptake

Halodule

ThalassiaR

elat

ive

Gro

wth

P vs I

Sulfide Toxicity

Madden et al. (2005)

Halodule

Thalassia

Halodule

Thalassia

Halodule

Thalassia

Halodule

Thalassia

99

Little Madeira Bay (Inner Site)

High Salinity

Salinity output from FATHOM Cosby et al. (2005)

SAV from Madden et al. (2006)

Sal

inity

SA

V

Integrating FATHOM and SAV Models for MFL: Reconstruction of SAV Community: 1970-2002

HaloduleThalassia

1010

Integrating the FATHOM and SAV Models for MFL:Halodule Model Response to Salinity

1111

Eastern Florida Bay Algal BloomChlorophyll a (μg/l)2006-2007 average

Chlorophyll a (μg/l)Jan 8-10, 2008

1212

NewP

Recycled N&P

Variable C:PVariable C:chla

Net Settling 1-15%

Grazing 0-20%

Advection

SAV

Epiphytes

Phytopl

Sediment P

Below ground

Sediment OM

Seagrass Community Phytoplankton Module

Seagrass Community Phytoplankton Module

ResuspensionRemin P

40%~100%

Bound P Available P

Advection (TT=10-120 d)

Geochemical Sequestration

N Substrate

NH4 NO3 urea

1313

Characteristics of the Phytoplankton Module

Characteristics of the Phytoplankton Module

Dependency on RecyclingDependency on Retention TimeDependency on Grazing RateP Limitation/Injection and BloomN Substrate PreferenceMechanistically Incorporates Variables Linked to Climate Change: depth, light, temperature, salinity response

1414

Basin Turnover Time and P Recycling EfficiencyBasin Turnover Time and P Recycling Efficiency

5 YR

Chl

a ug

/L

6

12Turnover= 20d

10 YR

6

6

6

12

6

12

6

12

5 YR 10 YR

Recycling= 0.4

Recycling= 0.6

Recycling= 0.8

Chl

a ug

/L

12

Turnover= 60d

12 Turnover= 40d

1515

Grazing (Sponges)Grazing (Sponges)

Chl

a ug

/L

5

10

10

5

Filtration= 10% d-1

Filtration= 15% d-1

1616

Model SensitivityModel Sensitivity

Least Sensitive to Turnover Time<< Nutrient Competition (epiphytes)<< Recycling Efficiency<< GrazingMost Sensitive to New Nutrient Inputs (in sufficient quantity)

1717

Untitled

Page 1

0.00 912.50 1825.00 2737.50 3650.00

Days

1:

1:

1:

0

10

20

1: chla conc

Untitled

Page 10.00 912.50 1825.00 2737.50 3650.00

Days

1:

1:

1:

0

25000

50000

1: TAG

Thalassia

SAV

mg

C/m

2 25000

5 YR

Chl

a ug

/L 10

20Phytoplankton

Untitled

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Days

1:

1:

1:

0

1000

2000

1: PAR @ SAV leaf

PAR

PAR

5 YR 10 YR

1000

2000

50000

Barnes Sound Algal Bloom Simulation: P injection(Road? C111? Other?)

5 YR

15% (high) settling; 85% recycling

10 YR

10 YR

P injection

1818

SAV Decline in Barnes Sound

Frequency of quadrats with high density and sparse vegetation

dense cover

sparse cover

Oct 05 Jan 06

(DERM data; Rudnick et al. 2008)

Begin increase in freq. of sparse cover Oct. 2005-Jan 2006

00.10.20.30.40.50.60.70.80.9

1

Freq

uenc

y

01/2

001

01/2

003

01/2

005

01/2

007

Y Freq(Bare+Sparse Tot) Freq(Dense Tot)

01/2

006

1919

ConclusionsConclusions

Due to shallow, highly coupled nature, bay is poised

Model can identify inflection or “tipping” points where system has a potential to undergo state changeSometimes small change = Big ChangeWe are somewhere between a Box of Models and a Real Model Synthesis

We can use simple transport and heuristic models to understand ecological responses

Models are now being used in synthetically in developing statutory freshwater requirements and in evaluating restoration designs in the context of climate change

SAV Model Fortran algorithms are incorporated in EFDC and

Model links to FATHOM, therefore SAV and WC/WQ module interacts with 2x2, RSM, SICS, PHAST and the HYCOM boundaryUpwardly linked to GAM Higher Trophic Models and HSMsWill be linked to the mangrove transition zone model