A groundwater flow model for east-central Florida has been developed to help water-resource managers assess the impact of increased groundwater withdrawals from the Floridan aquifer system on heads and spring flows originating from the Upper Floridan Aquifer. The model provides a probabilistic description of predictions of interest to water-resource managers, given the uncertainty associated with system heterogeneity, the large number of input parameters, and a nonunique groundwater flow solution. The uncertainty associated with these predictions can then be considered in decisions with which the model has been designed to assist. The "Null Space Monte Carlo" method is a stochastic probabilistic approach used to generate a suite of several hundred parameter field realizations, each maintaining the model in a calibrated state, and each considered to be hydrogeologically plausible. The results presented herein indicate that the model's capacity to predict changes in heads or spring flows that originate from increased groundwater withdrawals is considerably greater than its capacity to predict the absolute magnitudes of heads or spring flows. Furthermore, the capacity of the model to make predictions that are similar in location and in type to those in the calibration dataset exceeds its capacity to make predictions of different types at different locations. The quantification of these outcomes allows defensible use of the modeling process in support of future water-resources decisions. The model allows the decision-making process to recognize the uncertainties, and the spatial or temporal variability of uncertainties that are associated with predictions of future system behavior in a complex hydrogeological context.

An algorithm was designed to statistically estimate the areal distribution of water-table altitude. The altitude of the water table was bounded below by the minimum water-table surface and above by the land surface. Using lake elevations and stream stages, and interpolating between lakes and streams, the minimum water-table surface was generated. A multiple linear regression among the minimum water-table altitude, the differerence between land-surface and minimum water-table altitudes, and the water-level measurements from surficial aquifier system wells resulted in a consistently high correlation for all groups of physiographic regions in Florida. A simple linear regression between land-surface and water-level measurements resulted in a root-mean-square residual of 4.23 m, with residuals ranging from -8.78 to 41.54 m. A simple linear regression between the minimum water table and the water-level measurements resulted in a root-mean-square residual of 1.45 m, with residuals ranging from -7.39 to 4.10 m. The application of the multiple linear regression presented herein resulted in a root-mean-square residual of 1.05 m, with residuals ranging from -5.24 to 5.63 m. Results from complete and partial F tests rejected the hypothesis of eliminating any of the regressors in the multiple linear regression presented in this study.

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The water-use data from Jill Hood (Southwest Florida Water Management District) and the stream network of the upper St. Johns River from Tom Jobes (St. Johns River Water Management District) are greatly appreciated as well as the expertise provided by hydrologists Brian McGurk (St. Johns River Water Management District), and