In many coastal areas, high water tables are present, complicating installation of some stormwater best management practices (BMPs) that rely on infiltration. Regional estimates of the seasonal high water table (SHWT) often rely on sources such as soil surveys taken over a decade ago; these data are static and do not account for groundwater withdrawals or other anthropogenic impacts. To improve estimates of the SHWT, we developed a GIS-based methodology relying on surface water elevations. Data sources included a 1.5-m (5.0 ft) resolution Lidar-derived digital elevation model (DEM), aerial imagery, and publicly available shapefiles of water boundaries. Twenty-six groundwater monitoring wells were screened to eliminate well locations influenced by pumping, yielding 22 wells. In coastal Virginia, tidal water bodies and ditches form terminal boundaries for discharge from the water-table aquifers and permit water table elevations to be fixed at the landward boundaries of surface water bodies. Water table elevations interpolated from well data and boundary elevations were used to create a triangulated irregular network representing the water table elevations for November 2012, which was the date of the DEM. An adjustment factor, calculated from the highest recorded April water table depth from long-term groundwater monitoring data, was added to estimate the SHWT elevation. SHWT elevations were subtracted from the DEM to yield SHWT depth, which was compared with long-term monitoring well data, yielding an R2 value of 0.91. Residual errors were random, although the method underpredicted the highest expected SHWT and overpredicted the median SHWT. The SHWT depth map was validated by using water table depths from 57 soil borings at 10 different sites, and consistently matched observations better than available soil survey estimates. The SHWT depth map could be useful for BMP siting and feasibility studies in similar hydrogeological settings.