sea-level rise science and decision making in an uncertain future rob thieler u.s. geological survey...
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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