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.