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Understanding the limitations of environmental data is essential both for managing environmental systems effectively and for encouraging the responsible use of scientific research when knowledge is limited and priorities are varied. Using a combination of quantitative and qualitative techniques for assessing probabilities, and acknowledging the importance of scenarios where probabilities cannot be determined, an integrated methodology is presented for handling uncertainties about environmental data. The methodology is based on a fourfold distinction between the empirical quality of data (and the ancillary information, such as 'scale', required to interpret this), the sources of uncertainty in data, the 'fitness for use' of the data, and the quality or 'goodness' of an uncertainty model. [ABSTRACT FROM AUTHOR]