Dynamic terrain is useful for enhancing realism and immersion in VR simulation. Current approaches typically limit the resolution or size of the editable region of terrain, or handle one or few clients simultaneously. Our primary goals are to simulate large regions of terrain with dynamic attributes and to handle many clients simultaneously. To achieve these goals, we present two models of distributed dynamic-terrain databases, which we're utilizing the resources of a high-performance computing cluster (HPCC) to prototype. The first model breaks the terrain into mutually exclusive pieces, which are distributed among compute nodes, while the second model uses total terrain-data replication across multiple compute nodes. We compare the advantages and disadvantages of each model and discuss optimization techniques for each.