A very significant property of open systems is that elements within them attempt to adjust themselves to the flow of energy and matter through the system towards a condition of equilibrium or steady state. Thus, if we regard a hill slope as an open system, a harmonious relationship will develop over time between the gradient, the infiltration capacity and the size of the sediment particles on the slope. Similarly, in ecosystems, animal populations will adjust closely to plant productivity. The effect of this adjustment is to balance the input of energy and material to the output. However, equilibrium does not mean the system is static, it is performing work all the time, but the opposing forces arc balanced or fluctuating about a mean: the state can be alternatively referred to as dynamic equilibrium.
A fundamental mechanism in maintaining this state of self-regulation is that of feedback. This means that when one of the components in the system changes, perhaps because of some external factor, this leads to a sequence of changes in the other components, which eventually affects the first component again. The most common type of relationship is called negative feedback, whereby the circuit of changes has the result of damping down the first change. Positive feedback, which is much rarer, occurs when the feedback loop aggravates the original change. For instance, in a glacier system, an increase in velocity leads to an increase in erosion and over deepening. The over deepening only serves to accelerate the velocity further. But even here, checks will eventually operate; if the bedrock gradient becomes too steep, this will fundamentally alter the slip-plane in the ice, such that erosion ceases. Positive feedback loops in nature usually operate in short bursts of destructive a
activity, but in the longer term, negative and self-regulation tend to prevail.
In summary, we can say that the systems approach is currently proving useful in physical geography as a framework for process studies. One of the chief values of systems thinking is its flexibility. Systems can be applied at a variety of scales and complexity. On the other hand, the pitfalls of approach are the same as those for models generally. There is always the danger that we might mistake the frame-work for reality, and set off trying to identify system per se rather than use the concept as an aid to understanding.
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