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Storm surge caused by tropical cyclones can cause overland flooding and lead to loss of life while damaging homes, businesses, and critical infrastructure. In 2018, Hurricane Michael made landfall near Mexico Beach, Florida, on 10 October with peak wind speeds near 71.9 m s−1 (161 mph) and storm surge over 4.5 m NAVD88. During Hurricane Michael, water levels and waves were predicted near–real time using a deterministic, depth-averaged, high-resolution ADCIRC+SWAN model of the northern Gulf of Mexico. The model was forced with an asymmetrical parametric vortex model [generalized asymmetric Holland model (GAHM)] based on Michael’s National Hurricane Center (NHC) forecast track and strength. The authors report errors between simulated and observed water level time series, peak water level, and timing of peak for NHC advisories. Forecasts of water levels were within 0.5 m of observations, and the timing of peak water levels was within 1 h as early as 48 h before Michael’s eventual landfall. We also examined the effect of adding far-field meteorology in our TC vortex model for use in real-time forecasts. In general, we found that including far-field meteorology by blending the TC vortex with a basin-scale NWP product improved water level forecasts. However, we note that divergence between the NHC forecast track and the forecast track of the meteorological model supplying the far-field winds represents a potential limitation to operationalizing a blended wind field surge product. The approaches and data reported herein provide a transparent assessment of water level forecasts during Hurricane Michael and highlight potential future improvements for more accurate predictions. [ABSTRACT FROM AUTHOR]

A growing concern of coastal communities is increased flood risk and non‐monetary consequences due to climate‐induced impacts such as sea level rise (SLR). Previous efforts have discussed the importance of future flood risk quantification using broad aggregations of monetary loss with "bathtub" SLR models rather than more physically based modeling approaches. Here we quantify actual impacts to coastal communities at the census block level using a dynamic, high‐resolution, bio geophysical modeling framework for four SLR scenarios for the year 2100. This framework accounts for future sea‐levels, landscape change, and urbanization to quantify the 1% and 0.2% annual exceedance probability (AEP) water levels. The computed AEP water levels were used to quantify building damage and populations of displaced people and people requiring long‐term shelter across the Northern Gulf of Mexico (NGOM) (Mississippi, Alabama, and the Florida panhandle). The increase in damaged buildings under SLR is linear, with an increase of 16,367 damaged buildings per 1 m of SLR (R2 = 0.96) for the 1% AEP flood. The rate increases to 24,981 damaged buildings per 1 m of SLR (R2 = 0.96) for the 0.2% AEP, on average. The increase in displaced people across the NGOM is 8,056 people per meter of SLR, and people requiring shelter is 300 per meter of SLR. The results in this work highlight the varying levels of risk across the NGOM and the change in risk under climate change‐induced impacts. Key Points: Building damage rates are 16,367 and 24,981 per meter of sea level rise (SLR) for the 1% and 0.2% annual exceedance probability floodDisplaced people and people requiring shelter increase by 8,056 and 300 per meter of SLR, respectivelyThe number of displaced people per decade increases by 1,208 (or 121 per year from 2020 to 2100) under a SLR increase of 1.2 m [ABSTRACT FROM AUTHOR]