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To provide the reasonable basis for scientific management of water resources and certain directive significance for sustaining health of Gufu River and even maintaining the stability of water ecosystem of the Three- Gorge Reservoir of Yangtze River, central China, multiple statistical methods including Cluster Analysis (CA), Discriminant Analysis (DA) and Principal Component Analysis (PCA) were performed to assess the spatial- temporal variations and interpret water quality data. The data were obtained during one year (2010~2011) of monitoring of 13 parameters at 21 different sites (3003 observations), Hierarchical CA classified 11 months into 2 periods (the first and second periods) and 21 sampling sites into 2 clusters, namely, respectively upper reaches with little anthropogenic interference (UR) and lower reaches running through the farming areas and towns that are subjected to some human interference (LR) of the sites, based on similarities in the water quality characteristics. Eight significant parameters (total phosphorus, total nitrogen, temperature, nitrate nitrogen, total organic carbon, total hardness, total alkalinity and silicon dioxide) were identified by DA, affording 100% correct assignations for temporal variation analysis, and five significant parameters (total phosphorus, total nitrogen, ammonia nitrogen, electrical conductivity and total organic carbon) were confirmed with 88% correct assignations for spatial variation analysis. PCA (varimax functionality) was applied to identify potential pollution sources based on the two clustered regions. Four Principal Components (PCs) with 91.19 and 80.57% total variances were obtained for the Upper Reaches (UR) and Lower Reaches (LR) regions, respectively. For the UR region, the rainfall runoff, soil erosion, scouring weathering of crustal materials and forest areas are the main sources of pollution. The pollution sources for the LR region are anthropogenic sources (domestic and agricultural runoff, hydropower exploitation and municipal waste). The study demonstrates the utility of multivariate statistical techniques for river water quality assessment, identification of pollution sources, and exploring spatial and temporal variations of water quality.