John Haines

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USGS Coastal (Climate) Change Activities - A
Foundation in Observations 86th Coastal
Engineering Research Board Meeting San Diego,
CA 3 June 2009
John Haines
USGS, Coastal and Marine Program Coordinator
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Issue Coastal Change - from Storms to Sea-Level
Rise
(modified after Bindoff, 2007 Rahmstorf, 2007)
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
Focus How Risk and Vulnerability evolve in
response to natural and human factors.
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Complex Systems Complex Responses ?
Comprehensive, Integrated Research

• Multiple human and natural drivers spanning
  multiple time scales
• Diversity of systems glaciated coasts to
  tropical atolls, wetlands, and barriers
  responding dynamically
• Observations Research Modeling
• Needs span policy/management scales National
  and Regional
• Modeling/Assessment needs from simple to complex

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A Schematic of the Process
Required Input
LIDAR OBS
Evaluate output
Area for collaboration prioritize national
observation resources to provide accurate and
up-to-date elevation data
Area for collaboration prioritize national
observation resources to minimize uncertainty
LIDAR OBS
Bathy/Topo
low
Overwash and Erosion models

high
Weather
medium
Risk Analysis
Response
Probability
Observations
Processes
Areas for collaboration 1. Nested modeling using
national observation resources and large scale
models to support high resolution modelswe need
accurate Boundary Condition inputs 2. Scenario
exploration for likely climate changes and
extreme storms
wave/water OBS
wave/waterMODELS
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Partnering USACE and USGS
Observations National (Lidar) shoreline
characterization, Storm response, field test beds
(FRF)Regional characterization and modeling
(Fire Island, SW Washington, Gulf Coast,
Carolinas)Model Development Commercial,
Research, Applied
Distribution and Volume of Holocene Sediment on
Inner Shelf Relates to Migration Rate of
Barrier-Island System
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National Observations, Research Products
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National Observations, Research Products
Storm Vulnerability Assessment
Coastal Change Assessment
Surge Monitoring
Sea-level Rise Vulnerability Index
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Regional Observations, Research Products -
Focus on Geologic Setting Processes, Sediment
Inventory and Budget
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Regional Observations, Research Products -
Modeling Sediment Transport and Coastal Evolution
net flux to the NE
Cold Front
Tropical Storm
net flux to the SW
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Regional Observations, Research
ProductsChandeleur Islands
XBEACH simulations
Sediment Volumes
Subaerial Change
STWAVE/ADCIRC
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Moving Forward Science for Decision-making in
response to Sea-Level Rise

• Explicitly including uncertainty
• Explicitly including management application
• Extracting information from data/information
  resources

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

• Utilized existing data for six geological and
  physical process variables ( 8km grid)
• Geomorphology
• Historic shoreline change
• Coastal slope
• Relative sea-level rise rate
• Mean sig. wave height
• Mean tidal range

Solve this differential equation? d(state)/dt
funct.(geomorphology, wave-climate, sea level,
etc.) OR, solve this probabilistic
versionP(state inputs) Bayes Rule
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Bayesian method Predict SLR impact on coastal
erosion

• Data (and uncertainty) are input
• Prediction of probability of all possible
  outcomes is output
• Straightforward to evaluate likelihood of outcome
  to exceed a user-specified tolerance

prob. erosiongt2m/yr 28.95
inputs
SLR
tide
wave
geormorph
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Evaluate probability of erosion given different
SLR ranges
1. Select SLR category
Increasing Sea Level
2. Examine P (SLC lt -1 m/yr)
For Very High SLR P(SLC lt -1 m/yr) 56.9
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Coastal data sets can be evaluated with Bayesian
network.Map probability of critical scenarios.
Probability of Erosion gt 2 m/yr
Mapping Erosion Probabilities
Boston
New York
D.C.
Atlantic Ocean
Charleston
Miami
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Prediction Uncertainty
Certainty of most likely outcome (probability)
high confidence

• We can also map uncertainty which can be used to
  identify where we need better information.
• Areas of low confidence require
• better input data
• better understanding of processes
• Use this map to focus research resources

low confidence
high confidence
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Observational Requirements

• Data/Observations integrate for us capturing
  processes we dont fully understand and including
  information on uncertainty
• Data collection should be designed to lead to
  understanding of processes of interest/importance,
  reduce uncertainty in prediction, and inform
  improved data collection
• This means more data but it also means more
  thoughtful parameterization of how we describe
  the system, the forcing, and the response and
• Observations must be consistent span scales
  appropriate for a variety of decision-making
  needs.