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Understanding, communicating and managing uncertainty and risk related to future changes in catchments.

Model Fusion and Making Real Decisions

This conference at the Geological Society in London (28/29th November 2011) was primarily driven by the British Geological Survey, in part to present the BGS vision of integrated modelling, including a new scoping study on Data and Research for Environmental Applications and Models (DREAM).  Dennis Peach, Chief Scientist at BGS, opened the conference with a talk on the BGS Vision of the future.  Being BGS of course, their vision was based on new methods for presenting geological structures.  Other visions were also presented including the Open Platform being developed by the Met Office, the NERC funded Environment Virtual Observatory project, and the Community Surface Dynamics Modelling System (CSDMS) from the USA.  Naturally there was much discussion about the need for standards in exchanging data and model outputs, including OpenMI.  There was a recognition that model fusion / integrated environmental modelling was still in its early stages, but an analogy was drawn with the progress that had been made from the early days of aviation to the current range of sophisticated aircraft, though whether the military and public perceive an equivalent need for integrated environmental modelling is a moot point.  This was revealed by the need for a “killer app” to really drive funding and development being mentioned several times.

 

Interaction of stakeholders and users with such integrated models was also the subject of some discussion, as it has been in the CCN project.  Uncertainty and risk are important components of this interaction, and in my talk I suggested that the type of Guidelines or Decision Tree Framework used in CCN might be a useful way forward here, facilitating decisions about assumptions and communication of meaning. 

 

One interesting question that was raised in response was when would a consideration of uncertainty make a difference to the decision that was made.   There are academic responses to this (when the risk function is highly nonlinear etc) but I think that misses the point.  Evaluating uncertainty and risk is about being more scientifically honest and about understanding the role of risk in the process of prioritising spending in being more or less risk accepting.  The decision maker might then choose to make the same “deterministic” decision but should at least be much more aware of what the significance of that might be in relation to how well the science can characterise the system.

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