A Methodology for Clustering Lakes in Alberta on the basis of Water Quality Parameters

Abstract

In this study, a methodology for clustering 18 lakes in Alberta, Canada using the data of 19 water quality parameters for a period of 11 years (1988-2002) is presented. The methods consist of (i) principal component analysis (PCA) to determine the dominant water quality parameters, (ii) cluster analysis techniques to develop the characteristics of the clusters, and (iii) pattern-match lakes to determine the appropriate cluster for each of the lakes. The PCA revealed that three principal components (PCs) were able to explain $88% of the variability and the dominant water quality parameters were total dissolved solids, total phosphorus, and chlorophyll-a. We obtained five clusters for the period 1994-1997 by using the dominant parameters with water quality deteriorating as the cluster number increased from 1 to 5. Upon matching cluster patterns with the entire dataset, it was observed that some of the lakes belonged to the same cluster all the time (e.g., cluster 1 for lakes Elkwater, Gregg, and Jarvis; cluster 3 for Sturgeon; cluster 4 for Moonshine; and cluster 5 for Saskatoon), while others changed with time. This methodology could be applied in other regions of the world to identify the most suitable source waters and prioritize their management. It could be helpful to analyze the natural controlling processes, pollution types, impact of seasonal changes and overall quality of source waters. This methodology could be used for monitoring water bodies in a cost effective and efficient way by sampling only less number of dominant parameters instead of using a large set of parameters.

Effect of Water Quality on Phytoplankton Structure in Oxbow Lakes under Anthropogenic and Non‐Anthropogenic Impacts

“…PCA is an effective method for separating variables in subgroups and it is ordered to apply to the monthly, seasonal or yearly water quality sampling data . An appropriation of using RDA for the analyzed data set was checked using DCA.…”

Clusterization of Surface Water Quality and Its Relation to Climate and Land Use/Cover

“…In the second step, we used PCA to identify the major PCs and obtain the dominant parameters using the normalized data [24]. The numbers of PCs were decided by setting eigenvalue to 0.5 and the loading values of parameters were obtained using varimax normalized rotation [8].…”

Impactos do uso do solo nos recursos hídricos da bacia de contribuição do Lago dos Manacás, Minas Gerais, Brasil

“…Dentre eles, podem-se citar o aumento de salinidade, alterações de temperatura e contaminação por componentes químicos industriais e agrícolas. O monitoramento da qualidade da água se faz através do acompanhamento periódico dos parâmetros físico-químicos, e fornece subsídios para avaliar as condições do recurso hídrico para tomada de decisões no gerenciamento do corpo d'água (Akbar et al, 2011;Bhattacharyya e Kapil, 2010;Marotta et al, 2008;Rocha et al, 2014;Rocha e Costa, 2015;Sheela et al, 2012 (Minas Gerais, 1996), a BCLM e o córrego Independência são enquadrados na Classe 2, devido à localização de sua foz em trecho do rio Paraibuna, que é enquadrado dessa forma.…”