John Haines USGS, Coastal and Marine Program Coordinator USGS Coastal (Climate) Change Activities -...
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Transcript of John Haines USGS, Coastal and Marine Program Coordinator USGS Coastal (Climate) Change Activities -...
John HainesUSGS, Coastal and Marine Program Coordinator
USGS Coastal (Climate) Change Activities
- A Foundation in Observations
86th Coastal Engineering Research Board Meeting
San Diego, CA
3 June 2009
(modified after Bindoff, 2007; Rahmstorf, 2007)
Issue: Coastal Change - from Storms to Issue: Coastal Change - from Storms to Sea-Level RiseSea-Level Rise
Short-term Variance
(hours to decade)
Storm impact/recoveryAnnual cycles
El Niño
Long-term Trend
(decades to centuries)
Sediment deficit or surplus
Sea-level rise
Focus –Focus –
How Risk and Vulnerability How Risk and Vulnerability evolve in response to natural evolve in response to natural and human factors.and human factors.
Complex Systems + Complex Responses Complex Systems + Complex Responses Comprehensive, Integrated ResearchComprehensive, Integrated Research
• Multiple human and natural drivers spanning multiple Multiple human and natural drivers spanning multiple time scalestime scales• Diversity of systems – glaciated coasts to tropical Diversity of systems – glaciated coasts to tropical atolls, wetlands, and barriers responding dynamicallyatolls, wetlands, and barriers responding dynamically• Observations – Research – ModelingObservations – Research – Modeling• Needs span policy/management scales – National and Needs span policy/management scales – National and RegionalRegional• Modeling/Assessment needs from simple to complexModeling/Assessment needs from simple to complex
A Schematic of the ProcessA Schematic of the Process
Bathy/Topo
Response Probability
low
high+
medium
Weather
Overwash andErosion models
Observations Processes
Infrastructure Risk
Habitat Risk
Low HighMed.
Low HighMed.
Risk Analysis
LIDAR OBS
LIDAR OBS
Required Input
Evaluate output
Area for collaboration: prioritize national observation resources to minimize uncertainty
wave/water OBS
wave/waterMODELS
Areas for collaboration1. Nested modeling using national observation resources and large scale models to support high resolution models—we need accurate Boundary Condition inputs2. Scenario exploration for likely climate changes and extreme storms
Area for collaboration: prioritize national observation resources to provide accurate and up-to-date elevation data
Partnering: USACE and USGSPartnering: USACE and USGSObservations – National (Lidar) shoreline Observations – National (Lidar) shoreline characterization, Storm response, field test beds characterization, Storm response, field test beds (FRF)(FRF)Regional characterization and modeling (Fire Island, Regional characterization and modeling (Fire Island, SW Washington, Gulf Coast, Carolinas)SW Washington, Gulf Coast, Carolinas)Model Development – Commercial, Research, AppliedModel Development – Commercial, Research, Applied
Distribution and Volume of Holocene Sediment on Inner Shelf Relates Distribution and Volume of Holocene Sediment on Inner Shelf Relates to Migration Rate of Barrier-Island Systemto Migration Rate of Barrier-Island System
National Observations, Research & ProductsNational Observations, Research & Products
National Observations, Research & ProductsNational Observations, Research & Products
Sea-level Rise Vulnerability IndexSurge Monitoring
Coastal Change Assessment
Storm Vulnerability Assessment
Regional Observations, Research & ProductsRegional Observations, Research & Products - Focus on Geologic Setting & Processes, Sediment Inventory and - Focus on Geologic Setting & Processes, Sediment Inventory and BudgetBudget
A A’
A A’
Regional Observations, Regional Observations,
Research & ProductsResearch & Products - Modeling Sediment - Modeling Sediment Transport and Coastal Transport and Coastal EvolutionEvolution
net fluxto the
NE
TropicalStorm
net fluxto the SW
Cape Fear
Cape
Romain
ColdFront
Regional Observations, Research & ProductsRegional Observations, Research & ProductsChandeleur IslandsChandeleur Islands
Subaerial Change
Sediment Volumes
STWAVE/ADCIRC
XBEACH simulations
Moving Forward: Science for Decision-making in Moving Forward: Science for Decision-making in response to Sea-Level Rise response to Sea-Level Rise
• Explicitly including uncertaintyExplicitly including uncertainty
• Explicitly including management applicationExplicitly including management application
• Extracting information from data/information resourcesExtracting information from data/information resources
Input Data:Coastal Vulnerability Index
(Thieler and Hammar-Klose, 1999)
Utilized existing data for six geological and physical process variables (~ 8km grid):
a) Geomorphologyb) Historic shoreline changec) Coastal sloped) Relative sea-level rise ratee) Mean sig. wave heightf) Mean tidal range
Solve this differential equation?
d(state)/dt = funct.(geomorphology, wave-climate, sea level, etc.)
OR, solve this probabilistic version
P(state | inputs) = Bayes Rule
Bayesian method: Predict SLR Bayesian method: Predict SLR impact on coastal erosionimpact on coastal erosion
Data (and uncertainty) are inputData (and uncertainty) are input Prediction of probability of all possible Prediction of probability of all possible
outcomes is outputoutcomes is output Straightforward to evaluate likelihood of Straightforward to evaluate likelihood of
outcome to exceed a user-specified outcome to exceed a user-specified tolerancetolerance Accretion Rate (m/y)
-30 to -10-10 to -2-2 to -1-1 to 11 to 22 to 1010 to 30
6.5522.412.832.87.0412.85.63
-0.843 ± 8.2
prob. erosion>2m/yr =28.95%prob. erosion>2m/yr =28.95%
SLR
inputs
tide
wave
geormorph
Evaluate probability of erosion given different SLR ranges
1. Select SLRcategory
2. ExamineP (SLC < -1 m/yr)
Incr
easi
ng
Sea
Lev
el
For Very High SLR: P(SLC < -1 m/yr) = 56.9%
Coastal data sets Coastal data sets can be evaluated can be evaluated
with Bayesian with Bayesian network.network.
Map probability Map probability of critical of critical scenarios.scenarios.
MappingMappingErosionErosion
ProbabilitiesProbabilities
Atlantic Ocean
Miami
D.C.
New York
Boston
Charleston
Probability of Erosion > 2 m/yr
Prediction Prediction UncertaintyUncertainty We can also map We can also map
uncertainty which can be uncertainty which can be used to identify where used to identify where we need better we need better information.information.
Areas of low confidence Areas of low confidence require:require: better input databetter input data better understanding better understanding
of processesof processes
Use this map to focus Use this map to focus research resourcesresearch resources
low confidence
high confidence
high confidence
Certainty of most likely outcome (probability)
Observational Observational RequirementsRequirements
Data/Observations integrate for us; capturing Data/Observations integrate for us; capturing processes we don’t fully understand and including processes we don’t fully understand and including information on uncertaintyinformation on uncertainty
Data collection should be designed to lead to Data collection should be designed to lead to understanding of processes of understanding of processes of interest/importance, reduce uncertainty in interest/importance, reduce uncertainty in prediction, and inform improved data collectionprediction, and inform improved data collection
This means more data – but it also means more This means more data – but it also means more thoughtful “parameterization” of how we describe thoughtful “parameterization” of how we describe the system, the forcing, and the response; andthe system, the forcing, and the response; and
Observations must be consistent & span scales Observations must be consistent & span scales appropriate for a variety of decision-making appropriate for a variety of decision-making needs.needs.