This project generates probability-based maps of passive (hydrostatic) flooding under incremental sea-level rise using a NOAA-style “confidence area” bathtub modeling approach. The method incorporates uncertainty in both digital elevation models (DEMs) and tidal water levels by treating them as normally distributed variables and propagating their combined error into inundation estimates. Using a mean higher high water (MHHW) reference surface derived by NOAA with the tidal constituent interpolation method, the model computes the likelihood that water levels exceed ground elevation at each pixel, adopting an 80% confidence threshold to define high-confidence flooding.Future sea-level rise scenarios are simulated by incrementally raising the MHHW surface in 1 ft (~0.3 m) increments up to 10 ft (3.05 m). The resulting inundation extents are further classified based on topographic connectivity using an eight-neighbor algorithm, distinguishing between marine flooding (areas connected to the ocean) and groundwater inundation (disconnected low-lying areas). Isolated clusters smaller than 100 m^2 are excluded. This probability-based framework enables more robust flood projections over large regions by explicitly accounting for elevation and water level uncertainties while also differentiating key flooding mechanisms relevant to coastal environments.