Our ability to numerically model natural systems has progressed enormously over the last 10e20 years. During the last decade computational power has increased to the stage where we can now have a super-computer on our desk, and the detail and fine scale processes that can be included in models are fantastic. The computational tools available for analysis and display have opened doors beyond the dreams of our forebears. However, as modelling power has increased there has been a concurrent reduction in ``data power'', particularly in the collection of hydrological data. While we undoubtedly have access to large datasets of extraordinary technological finesse such as the remotely sensed data from satellites, our collection of more basic and traditional datasets suffers. 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. This paper discusses the growth in sophistication of hydrological modelling throu...
R. P. Silberstein