This work reports on the design and implementation of advanced geospatial simulations using an Agent-Based Model (ABM) integrated with an augmented reality solution for interactive and immersive modeling exploration. The multi-scenario modeling framework allows for emergent phenomena and provides flexible representation of biological and physical environmental factors associated with natural and man-made systems. Augmented reality is provided by a sandbox running Tangible Landscape, based on a customization of GRASS GIS. An integrated Microsoft Kinect sensor mounted over the sandbox captures real-time topography produced by physical interactions with sand and resulting digital elevation models are ingested into the Recursive Porous Agent Simulation Toolkit (Repast) as landscape definition input. We illustrate the implementation by presenting a model system that includes a classic predator-prey relationship over a grassland habitat where sheep and wolves coexist as agents. Food sources for sheep are scattered over the landscape and are consumed as agents forage. Wolves control sheep population by actively searching for sheep and chasing individuals when their presence is detected. We simulate natural conditions by defining that the presence and movement of agents over the landscape is controlled by elevation provided by the sandbox. For instance, the presence of agents and resources can be limited to specific elevation ranges and slope is used to incorporate movement cost (energy loss) while individual agents travel over the landscape. Ecological conditions are further simulated by the consumption and regrowth of food resources. Users interact with the sandbox and the modeling effort by manually moving sand and altering landforms. This effort brings together multiple technologies and data manipulation/visualization strategies and allows for feature-rich experimentation by supporting multiple co-located and georeferenced layers (e.g., land use/land cover, soil, hydrography).