Spatial and temporal variations in surface water quality of the dam reservoirs in the Tigris River basin, Turkey

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied to evaluate the temporal/spatial variations of water quality data sets for Kralkızı, Dicle and Batman dam reservoirs in the Tigris River basin, obtained during 1 year (2008–2009) of monitoring. This study highlights the usefulness of multivariate statistical techniques for the evaluation and interpretation of complex water quality data sets, apportionment of pollution sources/factors and the design of a monitoring network for the effective management of water resources. Hierarchical CA grouped 12 months into two clusters (wet and dry seasons) and classified ten monitoring sites into four clusters based on similarities in the water quality characteristics. PCA/FA identified five factors in the data structure that explained 80% of the total variance of the data set. The PCA/FA grouped the selected parameters according to common features to help evaluate the influence of each group on the overall variation in water quality. Discriminant analysis showed better results for data reduction and pattern recognition during both spatial and temporal analysis. Temporal DA revealed nine parameters (water temperature, dissolved oxygen, total alkalinity, total hardness, nitrate nitrogen, ammonia nitrogen, total phosphorus, chloride and calcium), affording 100% correct assignations. Spatial DA revealed eight parameters (water temperature, pH, dissolved oxygen, electrical conductivity, nitrate nitrogen, orthophosphate phosphorus, sodium and total suspended solids), affording 92.5% correct assignations. Therefore, DA allowed a reduction in the dimensionality of the large data set and indicated a few significant parameters responsible for large variations in water quality that could reduce the number of sampling parameters.

► All water quality parameters studied showed significant temporal differences.
► Hierarchical CA grouped 12 months into two clusters (wet and dry seasons).
► PCA/FA grouped the selected parameters according to common features.
► DA allowed a reduction in the dimensionality of the large data set.