Management watershed identification of sub-watersheds using spatial pattern analysis of watershed morphometric variables
Year: 2025
Type: article
Abstract: Study Region This study was conducted across 195 sub-watersheds within the Nakdong River Basin in South Korea. The basin encompasses diverse topographic and hydrological characteristics, making it a suitable for morphometric and spatial analysis. Study Focus Sixteen morphometric parameters were derived using a geographic information system (GIS). Principal component analysis (PCA) was applied to extract eight key variables: drainage density (D d ), drainage intensity (D i ), stream frequency (F s ), infiltration number (I f ), length of overland flow (L of ), circularity ratio (R c ), relative relief (R r ), and compactness constant (C c ). Spatial autocorrelation and clustering were assessed using Local Moran’s I and Local Geary’s C to identify localized patterns and anomalies in watershed morphology. New Hydrological Insights for the Region Watersheds with high D d and F s values exhibited dense stream networks and were prone to rapid runoff. Elevated I f , D i , and R r indicated high hydrologic reactivity and erosion susceptibility. High L of values suggested greater potential for pollutant retention, while R c and C c reflected circular basin shapes associated with concentrated runoff and flood risk. Spatial clusters of these variables were found in the upper and middle Nakdong reaches, highlighting zones vulnerable to environmental stress. Local Geary’s C revealed morphological similarities in northeastern and central sub-watersheds, while dissimilarities were interpreted as hydrological anomalies. The integrated PCA–LISA framework offers a robust approach for identifying water environment vulnerability and guiding targeted watershed management.
Authors: Seon Yeon Choi, Chang Dae Jo, Heon Gak Kwon
Institution: National Institute of Environmental Research
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,600
Award: G217733147
OpenAlex ID: W7083578717
