Affiliations: [a] College of Environment, Hohai University, Nanjing, 210098, P.R. China | [b] College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, P.R. China | [c] Center for Integrated Mountain Research, University of the Punjab, Lahore, Pakistan | [d] Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education Hohai University, Nanjing, 210098, P.R. China
Abstract: Lakes depth is a most important component to evaluate the impacts on water quality scenarios. Present study discusses the impacts of depth on water quality by using multivariate statistical analysis. This well established phenomenon of correlation between lake depth and water is firstly proved by using multivariate statistical techniques. Depth of Rawal Lake was divided into three groups of surface, middle and bottom to analyze impacts between these stages on water quality. There were sixteen parameters (physico-chemical, bacteriological and metals) analyzed for which samples were collected and analyzed from a fresh water Rawal lake for four seasons in 2012-2013. The statistical correlation was developed between the water quality parameters and between the layers, by using multivariate scatterplot, cluster analysis and discriminant analysis. Results of these statistical techniques revealed a strong correlation (positive or negative) among most of the water quality parameters. Aluminum was found to be with medium variability and temperature at high variability in cluster analysis. Statistically significant correlation was found between the two dimensions of canonical discriminant functions with canonical correlation of 0.957 and 0.586. Therefore, these statistical analyses validated the high impact of depth on different water quality parameters.
Keywords: Water quality, statistical analysis, lake depth, discriminant analysis, cluster analysis
