Joint Sensitivity of Direct Building Asset Loss to Digital Elevation Model Resolution, Rainfall, Infiltration, and Vulnerability Function Choice in a Korean Industrial Complex

Direct flood loss estimation for industrial complexes is jointly sensitive to terrain representation, rainfall magnitude, infiltration assumptions, and depth–damage function selection, yet these uncertainties are rarely evaluated together. We quantify their combined effects for the Gumi National Industrial Complex (GNIC), South Korea, using five DEM resolutions (0.5–10 m), six rainfall return periods (10–200 years plus the observed July 2024 event), and three infiltration regimes (5, 10, 20 mm h −1 ), yielding 90 hydrodynamic realisations from a GPU-accelerated 2D shallow-water model. Each was combined with a harmonised inventory of 16,463 buildings (replacement value 43.07 trillion KRW) and three vulnerability-function families (HAZUS-MH, JRC Huizinga, Korean MD-FDA), producing 270 loss estimates under a common dimensionless transformation. A three-way ANOVA on log-transformed damage confirmed highly significant main effects of resolution, rainfall, and infiltration across all functions, more than an order of magnitude larger than interactions, and robust to heteroscedasticity-consistent and permutation tests. Coarsening the DEM from 0.5 to 10 m reduced expected annual loss (EAL) by 55–57%, while inter-function depth–damage divergence exceeded four-fold at shallow inundation. Validation against the July 2024 event gave the best skill at 2 m resolution (critical success index 0.80, accuracy 0.86). Multi-family residential and heavy industry accounted for 83–89% of total EAL. These results show that terrain resolution and damage-function selection are first-order, statistically independent controls on industrial flood loss, and that omitting any sensitivity axis can bias EAL by more than two-fold.