Natural resources modelling
There are two aspects of modelling of natural resources that I would
emphasise in relation to the Institute for Sustainability. One is the
construction of models that aid in deciding the long-term viability of
organisms susceptible to degradation by external agents. As well as testing
the present state of the organisms, the models aid in making management
decisions to improve their sustainability.
For example, John Boland, Fleur Tiver of ERM and Lynne McArthur, formally of CIAM but now RMIT, are
developing age structured models of populations of native plants of the
rangelands susceptible to grazing by herbivores, both introduced and native.
Not only are these models designed to ascertain the magnitude of the threat
of grazing to these species, but also to help construct sustainable grazing
regimes, if these exist.
There is also a need to construct models of resources that impact on
systems, both technological and natural. One area where this is vital is in
the energy sector. While conventional power systems have not been subjected
to cost benefit analysis, ironically the more sustainable options, that of
generation through renewable sources, undergoes the most stringent scrutiny.
Hence, every effort must be undertaken to provide reliable estimates of
performance. Solar hot water heaters, photovoltaic cells, wind turbines
houses and other devices are susceptible to the influence of climatic
variables. The evaluation of the performance of these systems can be
achieved by various methods.
The system can be erected in situ, tested for its efficacy and adjusted.
Laboratory models can be constructed and testing performed under artificial
conditions.
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The most effective evaluation technique is the construction of mathematical models of the performance of the system. When performing the evaluation, the climatic inputs to the model must represent the climate in which the system will be installed.
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The most effective way of testing such systems is through the construction of synthetic sequences of climatic variables. If the test data sets are created correctly, each data set constructed will be representative of the location's climate, in the sense that each data set provides a possible weather sequence for the location.
There is an added effect from the construction of such models. Sufficient knowledge of the behaviour of a system will make it easier to notice change in the system. For instance, it is easier to detect whether perceived increases in frequencies or intensities of extreme weather events are actually occurring and may be attributed to global warming if one is aware of the statistical and time series characteristics of past events. Similarly, if one knows the characteristics of plant population mechanisms, one can be better able to decide if some new distribution of species is predictive of catastrophe or merely the shift to a different equilibrium state.