Spatiotemporal Analysis of Water Quality in the Upper Watershed of Guanting Reservoir Based on Multivariate Statistical Analysis

Exploring the spatiotemporal pattern of water quality and identifying pollution sources is crucial for achieving precise management of reservoir watersheds. This study is based on monthly water quality data from 9 monitoring stations in the upstream watershed of Guanting Reservoir in 2024, combined with an improved water quality index method (WQI) and multivariate statistical analysis (clustering, discrimination, principal component and factor analysis), to reveal the spatiotemporal variation characteristics of water quality and pollution sources. The results show (1) significant spatiotemporal heterogeneity. In terms of time, the water quality is worst during the summer rainy season (June August), indicating that the pollution load input from surface runoff exceeds the dilution effect of rainfall. In terms of space, the water quality deteriorates significantly downstream along the river network, with the most prominent pollution occurring in the entrance area. (2) The results also show clear identification of key indicators and dominant pollution sources. Discriminant analysis shows that BOD5 and DO are key indicators for distinguishing rainy and dry seasons, while TN, TP, COD, CODMn, and F can effectively distinguish spatial clusters. Factor analysis further revealed that organic pollution (originating from domestic and industrial wastewater) and nutrient pollution (mainly from agricultural non-point sources) are the dominant factors. This study confirms that pollution input during rainfall is the primary driving factor for water quality deterioration, and human activities have led to the cumulative effect of pollutants along the river network. Based on this suggestion, differentiated and precise governance strategies should be implemented according to the spatiotemporal differentiation characteristics to improve the water environment quality of the upstream watershed of Guanting Reservoir.