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Storm Surge Projection and Objective-Based Risk Management for Climate Change Adaptation along the US Atlantic Coast.

Marissa S Liang1, Zhifei Dong2, Susan Julius3

  • 1USEPA, Office of Chemical Safety and Pollution Prevention, 1200 Pennsylvania Ave. NW, Washington, DC 20460-0001.

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Summary
This summary is machine-generated.

Climate change intensifies hurricanes and sea level rise, increasing coastal storm surge risks. This study uses the SLOSH model to quantify these risks for better adaptation and emergency planning along the US Atlantic coast.

Keywords:
Coastal adaptationInundationModeling uncertaintyOceanic climate processRisk assessmentStorm surge

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Area of Science:

  • Coastal engineering
  • Climate change adaptation
  • Hydrodynamic modeling

Background:

  • Climate change exacerbates hurricane intensity and sea level rise (SLR) along the US Atlantic coast.
  • Coastal inundation from storm surges threatens infrastructure and environmental assets.
  • Accurate storm surge projections are critical for coastal adaptation and emergency management but face uncertainties.

Purpose of the Study:

  • To develop an objective-based analytical-statistical approach for storm surge risk quantification.
  • To reduce uncertainties in storm surge projections for climate change adaptation planning.
  • To provide location-specific storm surge probability estimates for risk assessment and emergency response.

Main Methods:

  • Utilized the National Oceanic and Atmospheric Administration's (NOAA) Sea, Lake, and Overland Surge from Hurricanes (SLOSH) model.
  • Simulated synthetic hurricanes to generate Maximum Envelope of Water (MEOW) and Maximum of the Maximum (MOM) values.
  • Analyzed surge height and temporal progression using validated SLOSH model outputs for specific adaptation objectives and uncertainty tolerances.

Main Results:

  • Simulated MOMs identified long-term climate risks to infrastructure (e.g., wastewater plants) and environmental assets.
  • MEOWs provided location-specific storm surge probability estimates for adaptation analysis.
  • Wind-surge probability curves and temporal progression offered quantitative probabilities for emergency planning.

Conclusions:

  • The proposed approach effectively quantifies storm surge risks under climate change scenarios.
  • The methodology supports informed coastal adaptation planning and enhances emergency response preparedness.
  • Case studies demonstrated the practical application of the approach for diverse coastal assets and planning horizons.