| Abstract: | Environmental models constructed with a spatial domain require choices about the representation of space. Decisions in the adaptation of a spatial data model can have significant consequences on the ability to predict environmental function as a result of changes to levels of aggregation of input parameters and scaling issues in the processes being modelled. In some cases, it is possible to construct a systematic framework to evaluate the uncertainty in predictions using different spatial models; in other cases, the realm of possibilities plus the complexity of the environmental model in question may inhibit numeric uncertainty estimates. We demonstrate a range of potential spatial data models to parameterize a landscape‐level hydroecological model (RHESSys). The effects of data model choice are illustrated, both in terms of input parameter distributions and resulting ecophysiological predictions. Predicted productivity varied widely, as a function of both the number of modelling units, and of arbitrary decisions such as the origin of a raster grid. It is therefore important to use as much information about the modelled environment as possible. Combinations of adaptive methods to evaluate distributions of input data, plus knowledge of dominant controls of ecosystem processes, can help evaluate potential representations. In this case, variance‐based delineation of vegetation patches is shown to improve the ability to intelligently choose a patch distribution that minimizes the number of patches, while maintaining a degree of aggregation that does not overly bias the predictions. |
