Simulation is critical to the development of effective unmanned ground vehicles (UGVs). Simulation provides the ability to test virtual hardware and software systems in conditions that may be difficult to recreate physically. An important benefit of simulation is that it grants researchers access to simulated hardware, such as sensors and vehicles, that might not be available otherwise. To successfully simulate both hardware and software systems, it is essential to acknowledge the needs and requirements of the simulation platform. In this paper, we investigate two simulation environments being used at Mississippi State University to model and simulate UGVs: the Mississippi State University Autonomous Vehicle Simulator (MAVS) and Gazebo. Within this paper we investigate the specific modeling needs for the Clearpath Robotics Warthog UGV in both simulation environments. We found that Gazebo has more options for vehicle and robot customization. However, Gazebo requires more up-front and explicit information to simulate even basic vehicles. MAVS, in contrast, is a platform that uses pre-defined vehicle and tire models that reduce the informational requirements and better supports rapid prototyping of four-wheeled ground vehicles. The narrower scope of MAVS limits its ability to model complex robots, but it excels at vehicle-terrain interaction and sensor simulation. It is fundamental to understand what level of granularity each system offers regarding simulation creation (i.e., how customizable the vehicle, physics, and environment is) to utilize each simulation environment effectively.