UNCERTAINTY ANALYSIS IN ENVIRONMENTAL MODELING UNDER A CHANGE OF SPATIAL SCALE

Although environmental processes at large scales are to a great degree the resultant of processes at smaller scales, models representing the se processes can vary considerably from scale to scale. There are thre e main reasons for this. Firstly, different processes dominate at diff erent scales, and so different processes are ignored in the simplifica tion step of the model development. Secondly, input data are often abs ent or of a much lower quality at larger scales, which results in a te ndency to use simpler, empirical models at the larger scale. Third, th e support of the inputs and outputs of a model changes with change of scale, and this affects the relationships between them. Given these re asons for using different models at different scales, application of a model developed at a specific scale to a larger scale should be treat ed with care. Instead, models should be modified to suit the larger sc ale, and for this purpose uncertainty analyses can be extremely helpfu l. If upscaling disturbed the balance between the contributions of inp ut and model error to the output error, then an uncertainty analysis w ill show this. Uncertainty analysis will also show how to restore the balance. In practice, application of uncertainty analysis is severely hampered by difficulties in the assessment of input and model error. K nowledge of the short distance spatial variability is of paramount imp ortance to input error assessment with a change of support, but curren t geographical databases rarely convey this type of information. Model error can only be estimated reliably by validation, but this is not e asy because the support of model predictions and validation measuremen ts is usually not the same.