Comparison of univariate and transfer function models of groundwater fluctuations

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Abstract
Seasonal autoregressive integrated moving average (SARIMA) univariate models and single input‐single output transfer function (SARIMA with externalities or SARIMAX) models of groundwater head fluctuations are developed for 21 Upper Floridan aquifer observation wells in northeast Florida. These models incorporate empirical relationships between rainfall input and head response based on historical correlations and cross correlations between these two time series. The magnitude of the forecast error terms indicates that the SARIMA and SARIMAX models explain an average of 84–87% of the variation observed in the monthly piezometric head levels for 1‐month lead forecasts. Thus the models account for the dominant processes which affect temporal groundwater fluctuations. Both the SARIMA and SARIMAX models provide unbiased forecasts of piezometric head levels; however, the SARIMAX models produce more accurate forecasts (i.e., smaller forecast probability limits) than the SARIMA models, particularly as lead time increases. Modeling efforts reveal consistent model structures over the study region, with local hydrologic and geologic conditions causing site‐specific variability in the time series model parameters.