One way to solve environmental problems is through modelling. Humankind developed a series of models, from mental models, physical models to computer simulation models. Building a model assumes abstraction, simplifying the natural system by considering only the essential details and discarding irrelevant ones. Mapping the real worlds to the world of models is done by choosing an abstraction level and the corresponding modelling tool. The right abstraction level is paramount for any modelling project, depending on the real problem being analysed. In modern simulation modelling, there are three methods, each having a particular range of abstraction levels: system dynamics, discrete event (process-centric modelling) and agent-based models. Ecosystems and generally any environmental problems (real world) are complex dynamics that challenge our comprehension. Understanding the significant environmental challenges is vital to adopt adequate policies for a sustainable environment through modelling and simulation. Since our cognitive abilities are limited, we need a simulation of the environmental systems to see the dynamic patterns and how humans interact with the environment. Environmental modelling helps us understand complex systems by building mathematical models and running simulations using a high abstraction level. The system dynamics method of modelling and simulation is used to clarify the representation of the stocks and flows and the feedback process that control the flows and describe the dynamic behaviour (growth, decay, or oscillations) of complex systems over time. Modelling for prediction, understanding across time and spatial scales, and environmental systems disciplines is key for a sustainable future.