These are my Systems Thinking in Spatial Representations: Spatial Simulations projects!

Course Description

This course presents key elements of general system theory and complexity theory. These conceptual foundations shall introduce what it means to "think with the systems lens", and why systems offer a generative and holistic as opposed to an analytical and reductionistic approach to understanding geographical phenomena.

Students shall be able to make use of systems thinking in order to approach the complexity of (geographical) reality. We will discuss strategies and approaches to address complex problems, where a specific focus will be given on how spatial relations structure a system. Several application fields ranging from the physical world, to ecosystems and social spaces shall serve as examples.

Motivation: to digitally represent a model of our complex world with its nested hierarchies and spatial relationships emerging in patterns at various scales ideally takes a systems-driven viewpoint.

A Spatial Simulation Approach to Model Grazing Dynamics on Vierkaser Pasture, Austria

This study employs spatial simulation to model the grazing dynamics on Vierkaser pasture, located on Untersberg, Austria, with the goal of determining the maximum sustainable number of cows that can graze without disrupting the ecosystem. Using geospatial data and ecosystem modeling, the study integrates Agent-Based Modeling (ABM) for simulating cow movement and grazing behavior and Cellular Automata (CA) for grass regrowth dynamics. The 5-meter resolution grid-based model is initialized with realistic environmental parameters, dividing the pasture into distinct areas with varying biomass productivity: lower pasture, Hirschanger, meadow and cutback areas.

The model begins with cows randomly distributed across grassland areas. Cows prioritize grazing spots with higher biomass and reduce grass biomass incrementally with each grazing action. Grass regrowth is parameterized to align with field observations, achieving a balance between grazing intensity and regrowth rates over time. The simulation identifies an equilibrium point at which grass regrowth sustains cow grazing without degrading the pasture ecosystem. Results indicate a carrying capacity of approximately 20 cows for Vierkaser pasture. Beyond this threshold, overgrazing leads to biomass depletion, diminishing ecosystem resilience and causing cow mortality.

Key findings highlight that zones with higher biomass stability, such as the Cleaned 2020 and Cleaned 2021–2023 areas, are better suited for sustainable grazing, while zones like Hirschanger exhibit lower resilience due to limited regrowth potential. These spatial variations emphasize the need for tailored grazing strategies to maintain ecosystem balance. The study underscores the ecological viability of traditional grazing practices when informed by spatial simulation, providing a quantitative framework for sustainable pasture management.

Future research can extend the model by incorporating additional variables such as wildlife interactions, climate variability, and soil dynamics, offering a more comprehensive approach to alpine grazing systems. This research demonstrates the utility of spatial simulation as a decision-support tool for environmental management, enabling informed decisions to preserve the delicate balance of mountain ecosystems.