Abstract
Climate change brings intense hurricanes and storm surges to the US Atlantic coast. These disruptive meteorological events, combined with sea level rise (SLR), inundate coastal areas and adversely impact infrastructure and environmental assets. Thus, storm surge projection and associated risk quantification are needed in coastal adaptation planning and emergency management. However, the projections can have large uncertainties depending on the planning time horizon. Excessive uncertainties arise from inadequately quantified ocean-climatic processes that control hurricane formation, storm track, and SLR in time of climate change. For this challenge, we propose an objective-based analytical-statistical approach using the National Oceanic and Atmospheric Administration’s (NOAA)’s Sea, Lake, and Overland Surge from Hurricanes (SLOSH) model in scenario analysis of the storm surge impacts. In this approach, synthetic hurricanes (wind profile and track direction) are simulated to yield the likely range of the maximum envelope of water (MEOW), the maximum of the maximum (MOM), local wind speed, and directions. The surge height and time progression at a location are analyzed using a validated SLOSH model for a given adaptation or planning objective with a set of uncertainty tolerance. We further illustrate the approach in three case studies at Mattapoisett (MA), Bridgeport (CT), and Lower Chesapeake Bay along the US Atlantic coast. Simulated MOMs as the worst-case surge scenarios defined the long-term climate risk to the shoreside wastewater plants in Bridgeport and environmental assets in the Lower Chesapeake Bay. The wind-surge probability envelopes in simulated MEOWs provide location-specific estimates of the storm surge probability for local adaptation analysis at four locations in Lower Chesapeake Bay and at Mattapoisett of the southeastern Massachusetts coast. Using the constraints of local bathymetry and topography, the wind-surge probability curves and time progression also provide quantitative probability estimates for emergency response planning, as illustrated in the Mattapoisett case study.
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Data Availability Statement
SLOSH modeling and summary data for each of the case studies are available upon request from the corresponding author.
Acknowledgments
The research underpinning this paper is funded and conducted under the Air-Climate-Energy (ACE) National Research Program managed by the EPA Office of Research and Development (ORD). Any opinions expressed in this manuscript are those of the authors and do not necessarily reflect the views of the USEPA; therefore, no official endorsement should be inferred. The authors also thank anonymous peer reviewers for their constructive comments, Jerri Weiss (EPA Region 1) and Regina Poeske (EPA Region 3) for coordination in the case studies, and William (Quin) Robertson, formerly at Aptim Inc. for assistance in SLOSH modeling.
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Published In
Journal of Water Resources Planning and Management
Volume 150 • Issue 6 • June 2024
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© 2024 American Society of Civil Engineers.
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Received: Jul 21, 2021
Accepted: Dec 26, 2023
Published online: Mar 27, 2024
Published in print: Jun 1, 2024
Discussion open until: Aug 27, 2024
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