It is becoming easier to combine environmental data and models to provide information for problem-solving by environmental policy analysts, decision-makers, and land managers. However, the scale dependencies of each of these (data, model, and problem) can mean that the resulting information is misleading or even invalid. This paper describes the development of a systematic framework (dubbed the 'Scale Matcher') for identifying and matching the scale requirements of a problem with the scale limitations of spatial data and models.

The Scale Matcher framework partitions the complex array of scale issues into more manageable components that can be individually quantified. First, the scale characteristics of data, model, and problem are separated into their scale components of extent, accuracy, and precision, and each is associated with suitable metrics. Second, a comprehensive set of pairwise matches between these components is defined. Third, a procedure is devised to lead the user through a process of systematically comparing or matching each scale component. In some cases, the matches are simple comparisons of the relevant metrics. Others require the combination of data variability and model sensitivity to be investigated by randomly simulating data and model imprecision and inaccuracy. Finally, a conclusion is drawn as to the scale compatibility of the Data-Model-Problem trio based on the overall procedure result. Listing the individual match results as a set of scale assumptions helps to draw attention to them, making users more aware of the limitations of spatial modelling.

Application of the Scale Matcher is briefly illustrated with a case study, in which the scale suitability of two sources of soil map data for identifying areas of vulnerability to groundwater pollution was tested. The Scale Matcher showed that one source of soil map data had unacceptable scale characteristics, and the other was marginal for addressing the problem of nitrate leaching vulnerability. The scale-matching framework successfully partitioned the scale issue into a series of more manageable comparisons and gave the user more confidence in the scale validity of the model output.