Work

GPU-Accelerated High-Resolution Dam-Break Flood Simulation Using 0.5 m Airborne LiDAR for Sustainable Disaster Risk Reduction in Ageing Reservoirs: Application to Geumosan Reservoir, South Korea

Year: 2026
Type: article
Abstract: Ensuring the sustainability of ageing water-storage infrastructure is an increasingly urgent challenge under climate-driven hydrological extremes. In the Republic of Korea, approximately 18,000 small and medium-sized agricultural reservoirs—many several decades old—pose escalating risks to downstream communities and threaten progress toward SDGs 6, 11, and 13. This study presents a 0.5 m airborne LiDAR-based, GPU-accelerated two-dimensional shallow-water simulation of a hypothetical breach of the Geumosan Reservoir, South Korea, using a MUSCL + HLL solver verified against the Ritter (1892) and Stoker (1957) analytical dam-break solutions. Two scenarios are compared: Run A with a uniform Manning coefficient (n = 0.035) and Run B with spatially variable roughness derived from the Korean Ministry of Environment land-cover map (mean n = 0.0711). Mass conservation is preserved to within 0.01% during the closed-domain phase. Spatially variable roughness expands the total inundated area by 8.5% (3.05 → 3.31 km2) while reducing the Extreme-hazard zone, defined by the DEFRA hazard rating HR = h(v + 0.5), by 24% (1.49 → 1.14 km2); arrival times in the downstream urban corridor are delayed by up to 30 min. Uniform Manning assumptions therefore systematically overestimate extreme-hazard extents while underestimating the broader shallow-inundation footprint—biases comparable in magnitude to breach-parameter uncertainty. By delivering reproducible, georeferenced hazard, arrival-time, and damage-class maps for emergency action planning, the proposed framework supports risk-informed and sustainable management of ageing reservoir infrastructure and community-level disaster resilience aligned with the Sendai Framework and SDGs 6, 11, and 13.
Language: English
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7161651840
FWCI:
Cites: 41
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