Optimal partitioning of structured adaptive mesh applications necessitates dynamically determining and optimizing for the most time-inhibiting factor, such as data migration and communication volume. However, a trivial monitoring of an application evaluates the current partitioning rather than the inherent properties of the grid hierarchy. We present a model that given a structured adaptive grid, determines ab initio to what extent the partitioner should focus on reducing the amount of data migration to reduce execution time. This model contributes to the meta-partitioner, our ultimate aim of being able to select and configure the optimal partitioner based on the dynamic properties of the grid hierarchy and the computer. We validate the predictions of this model by comparing them with actual measurements (via traces) from four different adaptive simulations. The results show that the proposed model generally captures the inherent optimization-need in SAMR applications. We conclude that our model is a useful contribution, since tracking and adapting to the dynamic behavior of such applications lead to potentially large decreases in execution times.