Why collect data when we can model it?

The "art of modelling" natural systems has progressed at an enormous rate over the last 10 to 20 years. In particular, during the last decade as computational power increased to the stage where we can now have a super-computer on our desk, the detail and fine scale processes that can be included in models is fantastic. This has opened doors our forebears could only have dreamed of. However, as modelling power has increased, there has been an accompanying reduction in "datapower" in some areas - in particular in the collection of hydrological data. While we undoubtedly have access to huge datasets of extraordinary technological finesse such as the remotely sensed data from satellites, our collection of more basic and traditional datasets suffers woefully. We can read car number plates from outer space, but we still, in the main, measure rainfall by putting a bucket out in a paddock. The argument often mounted by those with the purse is that with current modelling power, data needs are reduced. This is an extraordinarily dangerous and arrogant statement. Our current generation of models are powerful, and do give insights we may not have previously had, but they are only models. Real insight into natural systems comes from observation of them, and the true role of models is to assist this process, not to replace it.