A geospatial information-based framework for assessing water environment vulnerability

This study examined the influence of basin morphometry and land cover characteristics on water quality (BOD, TOC, TN, and TP) across 195 sub-basins of the Nakdong River system and identified structurally vulnerable sub-basins requiring priority management. Local indicators of spatial association (LISA) were used to characterize spatial clustering of morphometric variables (R slm , D d , C c , S f , and R h ) and land cover types (Urban, Agriculture, Forest, and Grassland). Multiscale geographically weighted regression (MGWR) quantified global and local effects and spatial heterogeneity in the relationships between water quality and basin characteristics. MGWR-fitted values were combined with LISA results to identify High–High (HH) clusters wherein elevated values coincided with strong local influences of key drivers. Basin morphology and land cover exhibited pronounced spatial clustering and produced distinct global and local controls for water quality indicators. BOD and TOC vulnerable sub-basins were concentrated in middle and lower reaches, where low drainage density (D d ), low relief ratio (R h ), and high Urban and Agriculture shares form mixed urban–agricultural settings with long pollutant residence times. TN responded to combined urban–agricultural land use and impervious surfaces, whereas TP vulnerability was highest in steep sub-basins with mixed Forest–Agriculture land cover prone to erosion and particulate phosphorus delivery. These results demonstrate variation in water quality vulnerability with geographic setting and land-use composition, highlighting the limitations of uniform basin-wide management. The integrated MGWR–LISA framework prioritizes sub-basins based on hydrological watershed characteristics and can be extended by incorporating soil, flow, and rainfall variables for basin-specific water quality management. • Basin shape and land cover affect water quality across Nakdong sub-basins. • LISA identifies spatial clusters of morphometry and land-use factors. • MGWR exhibits local and global drivers of water-quality variation. • MGWR–LISA framework prioritizes vulnerable sub-basins for management.