ScholarWorks@성균관대학교

초록

Riverine and pluvial flooding triggered by extreme monsoon rainfall is intensifying under climate change, yet flood-risk products in many coastal municipalities remain too coarse for parcel-scale prevention and climate-adaptive planning. This study presents a 1 m LiDAR-GIS flood susceptibility framework validated against consecutive record-breaking floods in Dangjin City, South Korea (July 2024: 214.6 mm; July 2025: 377.4 mm). Five terrain parameters-elevation, slope, topographic wetness index, flow accumulation, and distance to stream-were integrated into a weighted Flood Susceptibility Index (FSI=0.20 & sdot;E<^>+0.30 & sdot;S<^>+0.25 & sdot;T<^>+0.15 & sdot;F<^>+0.10 & sdot;D<^>) and classified into four risk strata using K-means clustering (k = 4), identifying a high-risk zone of 0.3119 km2 (5.00% of the 6.18 km2 analysis domain). A Monte Carlo sensitivity analysis (n = 5000; +/- 0.10 weight perturbation) confirmed classification robustness (CV = 5.21%, mean Pearson r = 0.992). Static bathtub inundation scenarios (Delta h = 0.5-2.0 m above the 5th-percentile baseline elevation of 13.29 m AMSL) produced footprint expansion from 0.370 to 0.572 km2, capturing all nine observed flood inventory points at the 2.0 m threshold, with flow-connectivity analysis confirming that 91.7-93.1% of predicted inundation is hydraulically connected to the D8-derived stream network. Spatial validation yielded a combined IoU of 6.51%, with a progressive increase from 3.33% (2024) to 6.50% (2025) confirming that the FSI correctly tracks flood-extent expansion with increasing rainfall intensity. Relying exclusively on topographic data and standard GIS algorithms, the framework supports scientifically grounded flood risk governance in data-limited municipalities, directly aligned with SDG 11, SDG 13, and Sendai Framework Target B.

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