Improving Flood Simulation Performance of Distributed Hydrological Model in the Plain–Hilly Transition Zone via DEM Stream Burning and PSO

Accurate flood simulation and forecasting in plain–hilly transition zones remain challenging due to limitations of medium- and low-resolution digital elevation models (DEMs), which often produce discontinuous drainage networks and misaligned confluence paths. This study evaluates an integrated improvement framework that combines DEM stream-burning and automatic parameter calibration to enhance the flood-simulation performance of a physically based distributed hydrological model (the Liuxihe Model). The framework was tested in the Beimiaoji Watershed (upper Huaihe River Basin) using 12 observed flood events: one event for parameter calibration via Particle Swarm Optimization (PSO) and 11 events for independent validation. Model performance was assessed using multiple metrics, including the Nash–Sutcliffe Efficiency (NSE), peak error (PE), and peak-timing error (PT). Results indicate that stream-burning substantially improves river-network extraction, and that the combined application of DEM correction and PSO-based calibration markedly enhances model performance. The findings suggest that the proposed, cost-effective correction–calibration pathway can improve operational flood simulations in terrain-sensitive regions without relying on costly high-resolution DEMs, and thus provides a practical reference for similar basins.