Introduction — Spatially Distributed Dynamic Modelling

It is perhaps no coincidence that an emerging interest in the use of spatially distributed dynamic modelling (hereafter SDDM) to simulate geographical phenomena has coincided with a shift in the nature of the "big" questions facing the Earth and environmental science communities. Until comparatively recently most of these "big" questions have tended to focus on the identification and characterisation of land cover and climate change processes — an area of research in which remote sensing has made numerous important contributions. However, the net results of these studies have served to focus the attention of policy makers on the fact that global change processes are real, and that they have tangible, large-scale, environmental, and socioeconomic impacts. At the same time, a scientific consensus has now essentially been reached regarding the significance and nature of future climate change. Accordingly, it can be argued that the core research agenda is now moving away from identification towards impacts assessment. SDDM has therefore emerged as an important discipline in its own right because it represents a means of predicting the response of a range (examples in this section comprise hydrological, geomorphological, and ecological applications) of environmental systems to environmental change felt from the local to the global scale.