The evaluation and interpretation of the spatio-temporal pattern of surface water quality is crucial for the assessment, restoration and protection of drinking water sources. This case study reports different multivariate statistical techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis, which had been applied for 6 years (2005–2010) water quality data set generated from 19 parameters at 14 different sites within the Fei-Tsui Reservoir basin. Hierarchical cluster analysis grouped 14 sampling sites into three clusters: high-, moderate- and low-pollution regions. This study revealed that water release from the dam outlet will further increase the concentrations of pollutants in the downstream river. PCA/FA did not result in considerable data reduction, as it points to 13 parameters (68 % of original 19) required to explain the 72.8 % of the total variance in the water quality data set. The varifactors obtained from PCA suggested that parameters responsible for water quality variation were mainly related to mineral-related parameters (natural), nutrient group (non-point sources pollution), physical parameters (natural) and organic pollutants (anthropogenic sources). Discriminant analysis used only five parameters: water temperature, dissolved oxygen (DO), calcium, total dissolved solids (TDS) and turbidity; and seven parameters: BOD, DO, nitrate nitrogen, TDS, total alkalinity, turbidity and WT, to discriminate between temporal and spatial with 88 and 90 % correct assignation, respectively. This study illustrated the usefulness of multivariate statistical techniques for designing the sampling and analytical protocol, analysis and interpretation of complex data sets, identification of pollution sources/factors, and provides a reliable guideline for selecting the priorities of possible controlling measures in the sustainable management of Fei-Tsui Reservoir basin.
- Cluster analysis
- Discriminant analysis
- Multivariate statistical techniques
- Principal component analysis
- Water quality