Spatial identification and assessment of flood risk for historic buildings in the Minjiang River Basin

Extreme precipitation events increasingly threaten historic buildings in river basins, yet existing studies lack a basin-scale perspective on nonlinear flood risk mechanisms. Taking the Minjiang River Basin as the study area, this research integrates hydrological simulation (SCS-CN), machine learning (XGBoost-SHAP), and spatial autocorrelation analysis to assess flood risk for 5482 historic buildings. Results reveal three key patterns: flood hazard decreases from downstream to upstream with belt-like aggregation along rivers; vulnerability shows a spatial mismatch, being higher upstream and lower downstream; integrated risk exhibits a southeast-high to northwest-low gradient with strong clustering in estuarine and midstream valleys. Key drivers, including impervious surfaces, vegetation cover, and topography, demonstrate significant nonlinear threshold effects and multi-factor coupling. This study provides an accurate and interpretable framework for heritage flood risk management, offering a reference for disaster resilience strategies in similar basins.