Volumes in Geographic Information Science (GIScience) represent three-dimensional (3D) spatial phenomena that are often dynamic and have hard-to-determine boundaries. Studying their temporal evolution and dynamics is crucial for understanding and managing complex spatial systems such as atmospheric layers, aquatic systems, and subsurface structures. This chapter introduces a comprehensive framework for analyzing 3D volumes, including voxel-based models, Triangulated Irregular Networks (TIN), and boundary representation techniques. Additionally, it explores space-time volume representation, integrating temporal dynamics into 3D models to capture the evolution of phenomena over time through a graph-based spatiotemporal data framework. Practical applications in climate change detection and disaster management are highlighted, showcasing the potential of this framework to enhance predictive accuracy and support strategic decision-making.