Determination of thermal stratification and its effects on water quality in dams using analytical methods

摘要

Data analysis should be viewed as an integral component of the water quality management process [1]. Statistical techniques such as factor analysis (FA), cluster analysis (CA) and T test were applied to get information about the similarities or dissimilarities, to detect thermal stratification and to interpret Amir Kabir Dam water quality data. Analyses were based on 150 total samples from 5 depths during about a two year period (from April 2008 to April 2010) which were measured for 29 (19 physicochemical and 10 biological) parameters resulting in 4350 observations. Using PCA/FA for physicochemical variables, five Varifactors were obtained with eigenvalues >1 contributing to almost 82.342% of total variance in water dataset which are included on the components. Moreover, FA is a helpful method for physicochemical data reduction, although it is not as good for biological data reduction. Thermal stratification is the biggest problem at the majority of deep reservoirs resulting from the surface warming. It results in water quality detritions in the bottom layers. Finding the depths affected by this occurrence is an important problem in the management of reservoirs. In this study CA and t-test were applied to identify thermal stratification and its consequences on water quality. Sampling stations were grouped into two main classes using CA during each season. After CA, T test was applied to detect the statistical importance of differences between clusters. 0 and 10 depths differ from lower depths during spring and summer. These layers contain more physicochemical and less biological parameters compared to lower depths. This study revealed the effectiveness of CA and T test for thermal stratification detection, which provides some important benefits including enhanced water quality management, greater confidence in water treatment processes, and improved efficiency in reservoir management.