Analysis of temporal patterns of high-dimensional time-series water quality data is essential in informing better pollution management. In this study, Dynamic Factor Analysis (DFA) and Cluster Analysis (CA) were adopted to analyze time-series water quality data monitored at five stations SB1, SB2, SB3, SB4 and SB5 on La Buong river in the Southern Vietnam. Application of DFA identified two temporal patterns in SB1 and SB2 and three temporal patterns in SB3, SB4 and SB5. Analysis of factor loadings of water variables revealed run-off-driven patterns with the contribution of Total Suspended Solid (TSS), turbidity or Fe at all stations. The association of other variables like BOD5, COD at SB1, SB2, SB4, and SB5 to this run-off pattern exposed their sharing of common driver. On the contrary, separation of variables like Phosphate (PO43−) in SB3, SB4 and SB5 from run-off pattern suggested their local point-source origin. The derived factors from DFA were later used in time-point CA to explore temporal distribution of pollution intensities. Comparisons between clusters’ value and two regulatory benchmarks A2 and B1 for drinking and irrigation water respectively suggested land-use approach for abating TSS, Fe and BOD5, COD at most sites. The control of point sources of BOD5 and COD pollutants is needed at SB3 along with PO43−, Ammonium (NH4+) and Escherichia coli (E.coli) at SB1 and SB4.