Sea-level Rise Science and Decision

Making in an Uncertain Future

Rob Thieler

U.S. Geological Survey

Woods Hole, MA

Thousands of 14C years before present

Rate

of S

LR

(m

m/y

r)

Global delta initiation (Stanley and Warne, 1994)

U.S. Atlantic, U.K.

wetland initiation;

barrier island stability (Shennan and Horton, 2002;

Engelhart et al., in press)

mwp-Ia

mwp-Ib

(SLR rate based on Fairbanks, 1989)

Sea-level rise rates since the Last Glacial

Maximum

Years before present

Rate

of S

LR

(m

m/y

r)

“Geologic past” (Fairbanks, 1989;

Horton et al. 2009)

“Instrumental record” (Church and White, 2006)

“Projections” (Rahmstorf, 2007)

Past, present, and potential future rates of

sea-level rise

(modified after Rahmstorf, 2007; AR4 data from Bindoff, 2007)

Historic and Projected Sea-level Rise

IPCC AR4

A1B envelope

There are now

several

projections

that suggest

~80-150+ cm

rise is possible

over the next

century

Importance of Spatial Scale

Short-term Variance

(hours to decade)

Storm impact/recovery

Annual cycles

El Niño

Long-term Trend

(decades to centuries)

Sediment deficit or surplus

Sea-level rise

Importance of Temporal Scale

U.S. Climate Change

Science Program

Synthesis and Assessment

Product 4.1

Coastal Sensitivity to Sea-Level Rise:

A Focus on the Mid-Atlantic Region

Coastal Elevation Data

• Elevation is a critical factor in

assessing potential impacts

(specifically, inundation)

• Current elevation data do not

provide the degree of confidence

needed for quantitative

assessments for local decision

making

• Collection of high-quality

elevation data (lidar) would be

valuable

(Gesch et al., 2009)

Elevation source: 30-m DEM Elevation source: 3-m lidar data

Dark blue Land ≤ 1 meter elevation

Light blue Area of uncertainty

associated with 1 meter elevation

0 30 60 90 12015Kilometers

Eastern North Carolina

• High quality elevation data

reduce uncertainty of potentially

inundated areas

(Gesch et al., 2009)

Mid-Atlantic Assessment of Potential

Dynamic Coastal Responses to Sea-level Rise

Bluff erosion

Overwash

Island Breaching

Threshold Crossing (Gutierrez et al., 2009)

Monitor Modern

Processes and

Environments

Learn from

the Past

Support Decision Making

Improve Understanding

of Societal Impacts

Improve Predictive

Capability

Science strategy to address the challenge

of climate change and sea-level rise

(Thieler et al., 2009)

The end of “Climate Stationarity” requires that organizations and individuals alter their standard practices and decision routines to take climate change into account. Scientific priorities and practices need to change so that the scientific community can provide better support to decision makers in managing emerging climate risks.

• Decision makers must expect to be surprised because of the nature of climate change and the incompleteness of scientific understanding of its consequences.

• An uncertainty management framework should be used because of the inadequacies of predictive capability.

Informing Decisions in a Changing Climate National Research Council (2009)

A conceptual approach to the multivariate,

uncertainty problem

Explicitly include uncertainties, as well as management application

Step 1. design a network

Driving Forces

Geologic

Constraints

Coastal

Response

Tide

Range

Wave

Height Relative

Sea-level Rise

Coastal

Slope

Geomorphology

Shoreline

Change

(Gutierrez et al., 2011)

Step 2. train a network

Input Data:

Coastal Vulnerability Index (Thieler and Hammar-Klose, 1999)

Utilized existing data for six geological

and physical process variables

Geomorphology

Coastal slope

Relative sea-level rise rate

Mean sig. wave height

Mean tidal range

Historic shoreline change rate

learn

correlations

(Gutierrez et al., 2011)

Geomorphology

Geo 1Geo 2Geo 3Geo 4Geo 5

0 0 0 0

100

5

Shoreline Change (m/yr)

-25 to -2-2 to -1-1 to 11 to 22 to 30

5.166.4777.75.165.54

0.17 ± 5.5

Coastal Slope (%)

0 to 0.0250.025 to 0.040.04 to 0.070.07 to 0.20.2 to 0.411

18.024.737.716.23.32

0.0631 ± 0.062

Tidal Range (m)

0 to 11 to 22 to 44 to 66 to 10

0 100 0 0 0

1.5 ± 0.29

Mean Wave Height (m)

0 to 0.550.55 to 0.850.85 to 1.051.05 to 1.251.25 to 1.6

0 0 0

100 0

1.15 ± 0.058

Relative Sea-Level Rise Rate (mm/yr)

0 to 1.81.8 to 2.52.5 to 2.952.95 to 3.163.16 to 4.1

0 100 0 0 0

2.15 ± 0.2

Step 3. Make predictions

For average long-term SLR (2 mm/yr):

No-change is most likely

Prob. (Erosion < -1 m/yr) = 12%

Step 4. Make projections

Geomorphology

Geo 1Geo 2Geo 3Geo 4Geo 5

0 0 0 0

100

5

Shoreline Change (m/yr)

-25 to -2-2 to -1-1 to 11 to 22 to 30

17.634.641.41.554.80

-2.1 ± 7.2

Coastal Slope (%)

0 to 0.0250.025 to 0.040.04 to 0.070.07 to 0.20.2 to 0.411

4.0424.039.029.23.72

0.0806 ± 0.066

Tidal Range (m)

0 to 11 to 22 to 44 to 66 to 10

0 100 0 0 0

1.5 ± 0.29

Mean Wave Height (m)

0 to 0.550.55 to 0.850.85 to 1.051.05 to 1.251.25 to 1.6

0 0 0

100 0

1.15 ± 0.058

Relative Sea-Level Rise Rate (mm/yr)

0 to 1.81.8 to 2.52.5 to 2.952.95 to 3.163.16 to 4.1

0 0 0

100 0

3.055 ± 0.061

For higher long-term SLR (3 mm/year):

Prob. (Erosion < -1 m/yr) = 52%

Mapping Erosion Risk Using Bayesian Networks Probability of shoreline erosion >2 m/yr

Miami

D.C.

New York

Boston

Charleston

(Gutierrez et al., 2011)

• Uncertainty map can be

used to identify where

better information is

needed

• Areas of low confidence

require

• better input data

• better understanding of

processes

• Can use this map to

focus research

resources

Mapping Prediction Uncertainty Higher probability = higher certainty of outcome

(Gutierrez et al., 2011)

Low

Current conditions SLR +1 mm/yr, Wave ht. +10%

Erosion rate Erosion rate

Higher likelihood of

erosion

Broader distributions

Increased uncertainty

Narrow probability

distributions

Relatively low uncertainty

High

Pro

ba

bili

ty

Application of a Bayesian network to an uncertain future:

Probability of shoreline erosion >1 m/yr at Assateague

Island National Seashore

0.2

0

0.4

0.6

0.8

1

0.2

Bill Byrne, MA F&W

• Listed species

• DOI management responsibility

• Lifecycle includes substantial time on

NPS lands for breeding, migrating,

wintering

• Have interesting and specific habitat

requirements that we can predict

• Rangewide habitat availability

• Attributes and distribution of breeding,

foraging areas

• Wave run-up and inundation sensitivity

(morphologic and hydrodynamic detail)

• Can feed predictions back into

population dynamics models

Decision Support for DOI Agencies

Piping plover, C. melodus

Plovers in a Bayesian Network

Foredune

height (in x-section)

Elevation

Barrier cross-

sectional area

or

width

Geomorphic

Setting Overwash fan

Dune

Marsh

Ephemeral pools

Vegetation Density Absent

Ligh

Moderate

High

Shoreline Change

Rate for

x-section

Plover Presence

(outcome)

Sediment type

Horiz. Dist

to MHW Horiz. Dist

to MOSH

Prey

abundance

Horiz. Dist

to ephem.

pools

Summary

• Future sea-level rise is problematic • It is a certain impact (we have already made a commitment to

several centuries of SLR)

• It is an uncertain impact (rates and magnitudes poorly constrained; human response unknown)

• Effective climate change decision support will require changes in how we do science and how decision makers assimilate and use scientific information

• Probabilistic approaches have many applications • Convey what we know and what we know we don’t know

• Synthesize data and models

• Provide basis to focus research resources

• Furnish information to support decision-making