High-Resolution Flood Risk Assessment in Small Streams Using DSM–DEM Integration and Airborne LiDAR Data

Seung-Jun Lee, Yong-Sik Han, Ji-Sung Kim and Hong-Sik Yun ()
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Seung-Jun Lee: Geodesy Laboratory, Civil & Architectural and Environmental System Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Gyeonggi, Republic of Korea
Yong-Sik Han: Disaster & Risk Management Laboratory, Interdisciplinary Program in Crisis & Disaster and Risk Management, Sungkyunkwan University (SKKU), Suwon 16419, Gyeonggi, Republic of Korea
Ji-Sung Kim: School of Geography, Faculty of Environment, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK
Hong-Sik Yun: Geodesy Laboratory, Civil & Architectural and Environmental System Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Gyeonggi, Republic of Korea

Sustainability, 2025, vol. 17, issue 21, 1-20

Abstract: Flood risk in small streams is rising under climate change, as small catchments are highly vulnerable to short, intense storms. We develop a high-resolution assessment that integrates a Digital Surface Model (DSM), a Digital Elevation Model (DEM), and airborne LiDAR within a MATLAB (2025b) hydraulic workflow. A hybrid elevation model uses the DEM as baseline and selectively retains DSM-derived structures (levees, bridges, embankments), while filtering vegetation via DSM–DEM differencing with a 1.0 m threshold and a 2-pixel kernel. We simulate 10-, 30-, 50-, 100-, and 200-year return periods and calibrate the 200-year case to the July 2025 Sancheong event (793.5 mm over 105 h; peak 100 mm h −1 ). The hybrid approach improves predictions over DEM-only runs, capturing localized depth increases of 1.5–2.0 m behind embankments and reducing false positives in vegetated areas by 12–18% relative to raw DSM use. Multi-frequency maps show progressive expansion of inundation; in the 100-year scenario, 68% of the inundated area exceeds 2.0 m depth, while 0–1.0 m zones comprise only 13% of the footprint. Unlike previous DSM–DEM studies, this work introduces a selective integration approach that distinguishes structural and vegetative features to improve the physical realism of small-stream flood modeling. This transferable framework supports climate adaptation, emergency response planning, and sustainable watershed management in small-stream basins.

Keywords: airborne LiDAR; flood risk assessment; hybrid elevation modeling; hydraulic simulation; small-stream flooding; sustainable water management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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