We illustrate efficient methods to estimate future projected average return intervals (ARIs) of flood depths in coastal regions from storm-tide data and sea-level rise (SLR) projections. A flood-water path-finding algorithm is applied to digital elevation models (DEMs) in coastal regions to determine possible flood depths at interior points, given storm-tide levels at ARIs and local SLR with uncertainty on the coast. We show that the distribution of projected ARIs of a historical baseline flood-depth is truncated log-normal in the Gumbel extreme-value approximation, and we provide analytic expressions for the means. With this approximation, projected change in flood damage over a range of ARIs can be estimated by analysis at any single ARI. Compared to flood-depth distributions, ARI distributions are less directly related to flood damage, but they have the advantage of relative insensitivity to uncertainties in DEMs and other granular details of flood-risk modeling. We illustrate with applications to Miami, Florida, and coastal North Carolina using two different DEMs.