8/9/2019 Seagrass Modeling in Banana River 1997

1/23

Prepared By Michael Corsello

8/9/2019 Seagrass Modeling in Banana River 1997

2/23

8/9/2019 Seagrass Modeling in Banana River 1997

3/23

To predict future seagrass growth patterns

To predict the effects of development efforts on

seagrass populations prior to initiation ofconstruction

To assist in devising the most productivemanagement plans for preserving seagrass beds

8/9/2019 Seagrass Modeling in Banana River 1997

4/23

8/9/2019 Seagrass Modeling in Banana River 1997

5/23

8/9/2019 Seagrass Modeling in Banana River 1997

6/23

Collate and analyze existing SAV data to determinemost critical factors

Acquire and analyze data for each factor

Develop a representative model for each factor Integrate independant factor models

Determine the relationship and magnitude of eachfactor with respect to SAV gross primary

productivity

8/9/2019 Seagrass Modeling in Banana River 1997

7/23

8/9/2019 Seagrass Modeling in Banana River 1997

8/23

MTA MMT RN MT MT x x

SD SD

( , )

8/9/2019 Seagrass Modeling in Banana River 1997

9/23

15

17

19

21

23

25

27

29

31

15 17 19 21 23 25 27 29 31

R2 0 9978.

n mos samples mo11 10.@ / .

8/9/2019 Seagrass Modeling in Banana River 1997

10/23

8/9/2019 Seagrass Modeling in Banana River 1997

11/23

8/9/2019 Seagrass Modeling in Banana River 1997

12/23

BVA BV MRA SFAt i( ) ( ) ( * )

MRA MMR RN MVV MVV t t t( ) ( ) ( )

( , )

xi DBVWSDBWWSDBLSFA /)*(2)*(2 )(

8/9/2019 Seagrass Modeling in Banana River 1997

13/23

8/9/2019 Seagrass Modeling in Banana River 1997

14/23

MSA SSQ BVA St t Random( ) ( )( / )1000

SSQ BV Si x( / )( ) 1000

S RN S S x x xRandom SD SD( , )

8/9/2019 Seagrass Modeling in Banana River 1997

15/23

20

22

24

26

28

30

32

34

36

38

40

20 22 24 26 28 30 32 34 36 38 40

R2 5587.

8/9/2019 Seagrass Modeling in Banana River 1997

16/23

20

22

24

26

28

30

32

34

36

38

40

1 2 3 4 5 6 7 8 10 11 12

ACTUAL

SIM

8/9/2019 Seagrass Modeling in Banana River 1997

17/23

20

22

24

26

28

30

32

34

36

38

40

20 22 24 26 28 30 32 34 36 38 40

R2 6859.

MOS Apr Aug Oct Nov. ,

8/9/2019 Seagrass Modeling in Banana River 1997

18/23

Halodule Percent CoverGrowth Dieoff

Monthly Salinity Approximation

Monthly Temperature Approximation

Salinity Calculation

Reset S

Reset TTemperature Calculation

Month Counter

~

Monthly Mean Tempe rature

~

Monthly Mean Rainfall

~

Salinity Dependant Growth SuppressionTemperature Dependant Growth Suppression

Density Dependant Growth Suppression

Monthly Rainfall Approximation

Basin Volume ApproximationBasin Influx Volume Reset BV

Static Salt Quantity

Random Number Generator RF

Sheetflow Area

Random Number Generator S

Seasonal Variation Month Counter~

Density Dependant Death Rate

Grow Suppression Ratio Calculation

~

Random Monthly Devia tion Value Random Number Generator T

~

Monthly Variation Value

~

Variate Salinity Value

8/9/2019 Seagrass Modeling in Banana River 1997

19/23

8/9/2019 Seagrass Modeling in Banana River 1997

20/23

SAA LR DR LR DR HAsin( ) *sin( ) cos( ) *cos( ) *cos( )

AA Arc SAA SAAt( ) tan( / ) * / 1 1802

LR LD* /180

HA HOD(( ) * ) / 12 15 180

DR DOY ( . *sin((( ( )) / . ) / )) / )12 45 360 284 365 25 180 180

8/9/2019 Seagrass Modeling in Banana River 1997

21/23

K KF K r4 125 2 125. ( * . )

KF DOY sin(( ( ) / . ) / )2 365 30 42 24

K RNr ( . ,. )4 4

TL ee T

T(. ( ) )

(. )00000069

28

8/9/2019 Seagrass Modeling in Banana River 1997

22/23

HAL HAL G DDR HALt t t( ) ( ) ( )( ( * ))1 1

G HAL SD LLt( ) *(( * ) ( * )) / 1 4 6 10

LLef

ke e

II

e k

I

I

o

opt

o

opt

'( )

'

k HAL t' . (. * )( )2 09 00144 1

I I C o m ( . )0 71

8/9/2019 Seagrass Modeling in Banana River 1997

23/23

Tune salinity model for better R-squared and slope fit

Integrate all models and validate

Develop herbivory model and integrate

Adapt model to each species of SAV

Incorporate flexibility parameters

Compile model in C++ for added efficiency