Geosimulations using computer simulation models provideGI scientists an effective way to study complex geographic phenomena and predict future outcomes. Typically, geosimulations are developed to execute in an HPC environment with parallel and distributed execution capabilities. However, traditional HPC environments limit these simulations to a static runtime environment, where resources for execution must be decided before execution. Traditional simulation approaches such as a data parallel approach assigns fixed computing resources on every unit of data (e.g., a tile or a county). However, in many practical situations, a user may want to assign additional computing resources to speedup or perform more computation in a specific region. For example, in an urban growth model (UGM) simulation, to explore the outcomes of changes due to urban policy in a tile or a group of tiles at a given time-step, an urban geographer may want to assign more computing resources to those group of tiles to quickly determine impacts of policy on urbanization. In the absence of a dynamic resource allocation mechanism, the utility of a geosimulation to explore what-if scenarios on-the-fly is limited to pre-allocated computing resources. Thus, to effectively leverage existing resources, we first design a co-scheduling approach for geosimulations in a resource constrained HPC environment. We then present a second design for a geosimulation which allows dynamic provisioning of resources in an HPC environment based on run-time users' demands. Finally, to demonstrate the utility of the two approaches we modify the FUTURES geosimulation to support computationally expensive high-resolution simulation in regions of interest (ROIs) as specified by a user using the FUTURES-DPE framework.