Quantitative flood risk assessments rely on rainfall frequency analysis to define Annual exceedance probability (AEP) storms, commonly referred to as design storms, for generating flood hazard maps and expected annual damage curves. Current engineering practice typically employs spatially uniform design storms derived from point-based gauge statistics; however, this approach suppresses the spatial organization and intensity gradients present in observed storms. Stochastic storm transposition (SST) offers an alternative by preserving the spatial structure of observed rainfall and stochastically repositioning full storm fields across a watershed. Although recent work shows that SST-based design storms can alter peak discharge estimates relative to uniform storms, their implications for flood inundation mapping and for estimating structural damage and their resulting monetary losses remain understudied. This study addresses that gap by comparing flood exposure and damages produced by both design-storm approaches in the Vermilion River Basin, a low-gradient inland–coastal watershed in south-central Louisiana. The comparison reveals that SST identifies 11,518 buildings inundated in at least one SST realization but missed entirely by Atlas 14, representing over ∼$110 M in cumulative structural damages. The divergence between the two approaches is concentrated in mid-elevation urban neighborhoods, where spatial rainfall variability activates flooding thresholds that uniform storms cannot trigger. These results demonstrate that uniform design storms systematically underestimate both the catastrophic tail and the breadth of flood exposure in low-gradient basins.