U.S. Department of the Interior

U.S. Geological Survey

Futurecasting effects of sea

level rise, climate change, and

restoration on individual species

Brad Stith, Zuzanna Zajac, Catherine A.

Langtimm, Eric D. Swain, Don DeAngelis, and

Melinda Lohmann

Hydrodynamic/Climate Models

Abundance of futurecast data from

hydrology/climate models becoming

available.

Huge data volume creates opportunity and

data analysis problem.

Need to incorporate futurecast data into

biological models.

Long temporal scale and large spatial extent

dictate use of simple biological models.

Bisect

TEN THOUSAND

ISLANDS

FLORID

A

MODEL STUDYAREA

24°30́

25°00́

25°30́

26°00́

26°30́

27°00́83°00´ 82°30´ 82°00́ 81°30́ 81°00́ 80°30́ 80°00́

S. Florida Hydrodynamic Models

Before Picayune Strand Restoration Project

After Picayune Strand Restoration Project

Sample output (Figures) Ten-Thousand Islands salinity before and after Picayune Strand Restoration Snapshot: 01 Oct. 2003

Hydrology Model Output • Salinity • Temperature • Stage/depth

Scenarios • CERP restorations • Sea Level Rise

Resolution • 15 minute time step • 500 meter grid cell

Biological Models

Need a simple approach to compare biological implications of different scenarios of restoration, sea level rise, and climate change

Habitat Suitability Index (HSI) and Spatially Explicit Species Index (SESI) models do not require extensive biological datasets

incorporate spatial and temporal variation

allow relative comparisons of different scenarios

model potential habitat suitability, not predicting occurrence

Biological Research Focus

Submerged Aquatic

Vegetation (SAV)

Vallisneria americana (Tape

grass) – freshwater species

Halodule wrightii (Shoal grass)

– salt-tolerant species

Florida Manatee

HSI Model H. wrightii_salinity

Salinity [-]

0 10 20 30 40 50 60 70

HS

I Sa

lin

ity

0.0

0.2

0.4

0.6

0.8

1.0

H. wrightii_light

ADBL [micro mol m-2 s-1]

0 100 200 300 400 500

HS

I AD

BL

0.0

0.2

0.4

0.6

0.8

1.0

H. wrightii_temperature

Temperature [C]

10 15 20 25 30 35 40 45 50

HS

I Te

mp

era

ture

0.0

0.2

0.4

0.6

0.8

1.0

V. americana_salinity

Salinity [-]

0 10 20 30 40 50 60 70

HS

I Sa

lin

ity

0.0

0.2

0.4

0.6

0.8

1.0

V. americana_temperature

Temperature [C]

10 15 20 25 30 35 40 45 50

HS

I Te

mp

era

ture

0.0

0.2

0.4

0.6

0.8

1.0

V. americana_light

0 100 200 300 400 500

HS

I AD

BL

0.0

0.2

0.4

0.6

0.8

1.0

ADBL1

ADBL2

ADBL [micro mol m-2 s-1]

Halodule wrightii Vallisneria americana

Salinity

Temperature

Light/depth

Habitat Suitability Indices (HSIs)

3Total Salinity Temperature ADBLHSI HSI HSI HSI

• Calculated for each grid cell

and every time step

• HSITotal for a cell depends only

on environmental variables in

each cell (i.e. is independent

from neighboring cell values).

Global Uncertainty/Sensitivity Analysis Steps

Uncertainty

Analysis (UA)

factor3

factor2

factor1

RG

LOB

ALL

Y SA

MP

LED

INP

UT

FAC

TOR

S

MO

DEL

OU

TPU

TS

Global Sensitivity

Analysis (GSA)

0%

20%

40%

60%

80%

100%

0

50

100

150

200

250

300

350

0. 00 0. 01 0. 01 0. 02 0. 02 0. 03 0. 03 0. 04 0. 04 0. 05 0. 05 0. 06 0. 06 0. 07 0. 07 0. 08 0. 08 0. 09 0. 09 0. 10 0. 10 0. 11 0. 11 0. 12 0. 13 0. 13 0. 14 0. 14 0. 15 0. 15 0. 16 0. 16 0. 17 0. 17 0. 18 0. 18 0. 19 0. 19 0. 20 0. 20 0. 21 0. 21 0. 22 0. 22 0. 23 0. 23 0. 24 0. 25 0. 25 0. 26 0. 26 0. 27 0. 27 0. 28 0. 28 0. 29 0. 29 0. 30 0. 30 0. 31 0. 31 0. 32 0. 32 0. 33 0. 33 0. 34 0. 34 0. 35 0. 36 0. 36 0. 37 0. 37 0. 38 0. 38 0. 39 0. 39 0. 40 Mor eBin

HSI

MODEL

3

6

5

2 1

4

1. Identify uncertain spatially distributed inputs and define uncertainty models

(PDFs).

2. Generate input values pseudo randomly from assigned PDFs using Sobol

method.

3. Run the model for multiple alternative input sample (Monte Carlo).

4. Construct PDF for the model output (from N output values).

5. Perform SA using SIMLAB.

Specification of uncertainty for look-up tables, using

HSIsalinity vs. salinity lookup table as an example variable

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70

HS

I_S

ali

nit

y

Salinity [-] Uniform ± 20% around base value

Uniform ± 20% around base value,

truncated to 1 for the upper limit

Mean HSI (left) and Uncertainty/SD (right)

for Vallisneria americana.

Benchmark Cells for Sensitivity Analysis

1

2

3

4

5 6

7 8 9

10

1. Area 1 (Turner River) 2. Area 2 (Sunday Bay Upper) 3. Area 5 (Alligator Bay) 4. Area 6 (Lostmans Second Bay) 5. Area 10 (Broad River Bay) 6. Area 11 (Upper Broad River) 7. Area 12 (Harney River) 8. Area 13 (Tarpon Bay) 9. Area 14 (Upper Shark River) 10. Area 16 (Lower Shark River)

Shark River

Dry Season Salinity Trends Wet Season Salinity Trends

Mean Salinity (5-year mean)

Salinity Variance

PDFs of HSI values – 4 ENP sites

H_2_6

HSITotal

0.0 0.2 0.4 0.6 0.8 1.0

Pro

bability

0.0

0.1

0.30.50.81.0

cell2 cell5 cell8

V_2_6

HSITotal

0.0 0.2 0.4 0.6 0.8 1.0

Pro

bability

0.0

0.1

0.30.50.81.0

cell 9

Halodule wrightii Vallisneria americana

Habitat suitability PDFs reflect uncertainty,

but show high and low suitabilities for the 2

SAV species that differ among sites

Sensitivity Indices – 4 ENP sites

Vallisneria americana Halodule wrightii

Halodule model shows more sensitivity to light,

Vallisneria model shows more sensitivity to salinity

Picayune Strand Restoration (lower left map) shows reduced bay salinities, but differences absent with sea level rise (July 1998-2008 mean)

TTI Salinity Difference Maps

Fakahatchee Bay salinity differences for 6 scenarios

Picayune Strand Restoration shows reduced salinities compared to sea level rise and existing condition scenarios. Variance peaks during beginning and end of wet season.

Salinity (mean) Variance

Fakahatchee Bay Halodule HSI differences for 6 scenarios

Habitat suitability for Halodule is lower for sea level rise scenarios at this site. Variance is high, especially at dry-wet season transition.

HSI (mean) HSI Uncertainty

Summary HSI/SESI approach provides a simple modeling

framework to analyze and compare biological implications of large futurecast datasets and alternative restoration scenarios

Uncertainty and Sensitivity Analysis shows which model parameters produce the greatest variation and provide estimates of model uncertainty in space and time can help direct monitoring resources to measure

parameters and sites with greatest uncertainty and sensitivity

uncertainty maps can help managers evaluate model results

Difference maps and graphs of changes in habitat suitability can reveal trends and relative differences associated with restoration and sea level rise.

Acknowledgements

USGS FISCHS (Future Impacts of Sea

Level Rise on Coastal Habitats and

Species)

Funding from USGS Greater Everglades Priority Ecosystems Science

USGS Ecosystems Mapping

USGS Global Change Research & Development

Program