Selecting seasonal streamflow models

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Abstract
Seasonal autoregressive integrated moving average models have become an important tool for modeling and forecasting streamflows. The first step in building such models is identifying candidate model structures. Some common methods for selecting a final model from the candidate set are not applicable when different transformations of the observations are used by some candidates. Akaike's information criterion and Kashyap's posterior probability criterion can be used in such cases. These criteria are used to model monthly average streamflow on the Boise River. The results indicate that such ‘Objective’ model selection criteria must be used with care or an invalid model may be selected.

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