In this series of post specially
dedicated to the role playing environmental modelling facing the global
environmental change, we analysed different models approaches and the huge
challenges facing modellers. Probably the most significant analysis that should
carry out regarding to the weaknesses of models, it is the opportunity that those
areas give us to improve models.
The huge environmental threat has
placed to models in a really significant role. As the title of this blog say,
there are enormous challenges for a huge concern. This huge concern has led to
an intense global debate and models are giving us day to day more understanding
and more accuracy in the prediction about our future and the potential
negatives impacts; the threat is real.
As a result of this massive
concern, another challenge arises from the necessity to communicate how the
different models work and how the outcomes obtained by complex methods should
be suitably communicated to stakeholders. This group involves not only decision
makers (authorities) but also the global community. All of us are potential
affected. A suitable way in this direction it is to include the technical
aspect behind environmental modelling in an accessible and clear system for the community and political
authorities. For instance, through simplified and interactive web tools or through
open public presentations. In this task, as Hall et al. (2014) have argued, a major
difficulty has been that students has been educated in technical aspects of
models but the normal tools of education in this field have omitted the role of
communication.
When specialists are presenting
relevant results to communities, the interpretation of results can be
influenced by personal biased concerns which give different point of view from
a non-technical perspective. The experience of knowing the concerns (even from
personal and partial point of view) of potentially affected communities could be highly beneficial for certain elements in the research.
Finally, it is fundamental to
look the future with a clear understanding of models as an abstraction of
reality, with strengths and weaknesses. Although the final result is taken from
the model, this is finally given by the capacity of the modeller to interpret
those outcomes in a particular modelling context.