EVALUATION OF THE GRAPHICAL AND MULTIVARIATE STATISTICAL METHODS USED FOR CLASSIFICATION OF WATER-CHEMISTRY DATA

A robust classification scheme for partitioning water-chemistry samples into homogeneous groups is an important tool for the characterization of hydrologic systems. The chemical composition of natural waters is controlled by many factors including the climate, mineralogy of the aquifers, and topography. In the south Lahontan hydrologic region, there are a wide variety of climatic conditions, hydrologic regimes, and geologic environments. Thus samples from this area represent a variety of water types providing an opportunity to test the performance of many available graphical and statistical methodologies used to classify water samples. A wide variety of classification techniques were employed to classify water samples into hydrochemical facies, also known as "water-type" groups, including: Collins bar diagrams, Pie diagrams, Stiff pattern diagrams, Schoeller plots, Piper diagrams, Icon plots, Q-mode hierarchical cluster analysis, K-means clustering, Principal components analysis, and Fuzzy k-means clustering. All the methods are discussed and compared as to their ability to cluster, ease of use, and ease of interpretation. In addition, several issues related to data preparation, database editing, data-gap filling, data screening, and data quality assurance are discussed and a database construction methodology is presented. The use of graphical techniques proved to have limitations compared to the multivariate methods for large data sets. Principal components analysis is useful for data reduction and to assess the continuity/overlap of clusters or clustering/similarities in the data. The most efficient grouping was achieved by hierarchical cluster analysis and Fuzzy k-means clustering techniques. These techniques also provided the best way to demonstrate spatial and temporal variabilities occurring in the water quality of samples. However, these techniques do not provide information on the chemical makeup of the groups. The combination of graphical and statistical techniques provides a consistent and objective means to classify large numbers of samples using all the available parameters while retaining the classic graphical presentations that provide chemical information.