| description 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. | |
