1. John Haines 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

2. 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.

3. 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

4. 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 +
Infrastructure Risk
Habitat Risk
Low
High
Med.
Weather
high
medium
Observations
Processes
Response
Probability
Risk Analysis
Areas for collaboration
1. Nested modeling using national observation resources and large scale models to support high resolution models—we need accurate Boundary Condition inputs
2. Scenario exploration for likely climate changes and extreme storms
wave/water OBS
wave/water MODELS

5. 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

6. National Observations, Research & Products

7. National Observations, Research & Products
Storm Vulnerability Assessment
Coastal Change Assessment
Surge Monitoring
Sea-level Rise Vulnerability Index

8. Regional Observations, Research & Products - Focus on Geologic Setting & Processes, Sediment Inventory and Budget A A’ A A’

9. Cape Fear
Cape
Romain
Regional Observations, Research & Products - Modeling Sediment Transport and Coastal Evolution
Cold
Front
net flux
to the NE
Tropical
Storm
net flux
to the SW

10. Regional Observations, Research & Products Chandeleur Islands
XBEACH simulations
Sediment Volumes
Subaerial Change
STWAVE/ADCIRC

11. 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

12. Coastal Vulnerability Index
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 version P(state | inputs) = Bayes Rule

13. 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. erosion>2m/yr =28.95%
inputs
SLR
tide
wave
geormorph

14. Evaluate probability of erosion given different SLR ranges
1. Select SLR
category
Increasing Sea Level
2. Examine
P (SLC < -1 m/yr)
For Very High SLR: P(SLC < -1 m/yr) = 56.9%

15. Mapping Erosion Probabilities
Probability of Erosion > 2 m/yr
Mapping
Erosion
Probabilities
Boston
New York
D.C.
Coastal data sets can be evaluated with Bayesian network. Map probability of critical scenarios.
Atlantic Ocean
Charleston
Miami

16. Prediction Uncertainty
Certainty of most likely outcome (probability)
Prediction Uncertainty
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

17. Observational Requirements
Data/Observations integrate for us; capturing processes we don’t 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.