Sea-level Rise Science and Decision Making in an Uncertain Future

Rob ThielerU.S. Geological Survey

Woods Hole, MA

Thousands of 14C years before present

Rat

e of

SLR

(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

Rat

e of

SLR

(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 AR4A1B envelope

There are now severalprojections that suggest ~80-150+ cm rise is possible over the next century

Importance of Spatial Scale

Short-term Variance(hours to decade)

Storm impact/recoveryAnnual cycles

El Niño

Long-term Trend(decades to centuries)

Sediment deficit or surplusSea-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 PotentialDynamic 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 ClimateNational 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

GeologicConstraints

Coastal Response

TideRange

WaveHeight

RelativeSea-level Rise

CoastalSlope

Geomorphology

ShorelineChange

(Gutierrez et al., 2011)

Step 2. train a networkInput Data:

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

Utilized existing data for six geological and physical process variables

GeomorphologyCoastal slopeRelative sea-level rise rateMean sig. wave heightMean tidal rangeHistoric 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 likelyProb. (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 NetworksProbability 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 UncertaintyHigher probability = higher certainty of outcome

(Gutierrez et al., 2011)

-75.4 -75.35 -75.3 -75.25 -75.2 -75.1537.85

37.9

37.95

38

38.05

38.1

38.15

38.2

38.25

38.3

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

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

babi

lity

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

Foreduneheight

(in x-section)

Elevation

Barrier cross-sectional area

orwidth

GeomorphicSetting

Overwash fanDuneMarsh

Ephemeral pools

Vegetation DensityAbsent

LighModerate

High

Shoreline ChangeRate forx-section

Plover Presence(outcome)

Sediment type

Horiz. Distto MHW Horiz. Dist

to MOSH

Prey abundance

Horiz. Distto 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