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Spatial variations of the water quality parameters of the Merbok estuary were interpreted by multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). Data from January to December 2011 were collected to monitor 13 parameters at six sampling stations along the river stretch (two stations at each river section: upstream, midstream, and downstream). Cluster analysis results revealed two different groups between the sampling stations, reflecting different physicochemical properties and pollution levels in the study area. Factor analysis was used for the parameters of the surface and bottom water quality, yielding four factors that were responsible for 72.93 and 68.90 % of the total variance of data sets. PCA also found conductivity, salinity, dissolved oxygen, chlorophyll a, and NO 3 -to be the most important parameters contributing to the fluctuations of surface water and bottom water quality in the Merbok estuary. This study presents the usefulness of multivariate statistical techniques for assessing water quality data sets and for understanding spatial variations in water quality parameters to effectively manage water quality in estuaries.

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