A methodology to quantitatively evaluate the effectiveness of groundwater management plans by structuring groundwater management as a systems control problem and simulating sequential management decisions that evolve based on aquifer/management feedback was developed. The method indicated how a plan may proceed in reality under alternate timings and frequencies of management decisions in systems with differing response times. Generally, effectiveness decreased as the interval between management interventions increased and intervals greater than annual showed minimal improvement compared to entitlement only. However, given the high degree of parameter uncertainty typical in groundwater models, a study aim was to determine how much calibration data was necessary to quantitatively evaluate management plan effectiveness. A calibration-constrained predictive uncertainty analysis investigated whether reducing parameter uncertainty decreased predictive uncertainty. A synthetic study was used to evaluate the uncertainty around predictions generated from four different groups of model realisations, created based on increasing amounts of observation data generated by a numerical groundwater model designated as reality. Four simple models of the reality system were built based on prior knowledge, and a calibrated solution to each of the three different observation datasets (three posterior distributions). Each model was used to predict the effectiveness of management decision-making on a monthly basis and both entitlement-only and unmanaged scenarios. The predictive uncertainty was quantified for prior and posterior models, through a calibration-constrained uncertainty analysis using Null-Space Monte Carlo methods. Due to model simplifications, certain parameters, assumed inappropriately high values to compensate, resulting in calibration-induced model bias that caused the models to make erroneous predictions. Even with use of current best practice uncertainty analysis methods, the effectiveness of management could not be determined due to the limitations of the numerical models utilised, which raised serious question over our current ability to model management.