Abstract
Correlation matrix, principal component analysis (PCA), and cluster analysis were used to improve understanding of complex groundwater systems using scant monitoring data. The applicability of these statistical techniques was assessed using groundwater monitoring data from the Gaza Coastal Aquifer (GCA), which is complex and highly heterogeneous. Principal component analysis and cluster analysis results identified two groundwater contamination patterns, (1) salinization, and (2) interaction between anthropogenic and natural (mineralization) processes. Cluster analysis grouped the study wells into three clusters of similar water quality trends. Analysis of the spatiotemporal trends of chloride and nitrate, the most important groundwater quality parameters, were also performed. This study demonstrates the reliability of these statistical techniques in capturing a basic yet comprehensive view of groundwater quality trends and their influencing variables, and which can subsequently form the basis for groundwater management schemes.
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