Water Quality Models Using the Box-Jenkins Method

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Abstract
The Box-Jenkins method, a time-based technique for time series analysis, is shown to be successful in modeling chloride and dissolved oxygen data for the St. Clair River near Corunna, Ontario. This is thought to be the first application of the method to water quality data. The technique is demonstrated to be superior in this situation to either a frequency-based approach or a deterministic causative model. The description of the model building process includes the identification, estimation, and diagnostic checking stages. Forecasting and interpretation follow the derivation of the successful models. It is found that an autoregressive type of model best represents the chloride data, and a moving average process the dissolved oxygen following removal of nonstationarity. Similar causative mechanisms appear to influence June and December chloride and June dissolved oxygen.

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