Many integrated assessments inevitably require the use of a number of different models: to represent different environmental compartments or media, to simulate different environmental processes, and to provide a means of analysing the different links within the causal chain (from source to impact).  Assessment thus involves the use of a range of linked models, with outputs from one model becoming inputs to the next. 

Because integrated assessments are rarely conducted in a routine and repetitive way, ready-made systems that provide an integrated suite of models are rarely available.  As a consequence, the various models have to be linked and made to operate together specifically for the purpose of the assessment.  This can generate a number of problems and uncertainties, not all of which may be immediately apparent.

The most obvious problems are those due to differences in data format, which may make it difficult to pass data between the various models.  This is a problem, however, that can usually be readily fixed.  For a one-off application, it can be solved by passing the data through a purpose-designed intermediary program, which restructures the data; a more permanent solution can be achieved by setting up clear data protocols at the start, and by implementing some form of data warehouse, which manages storage, integration and retrieval of data.   

More difficult are the subtle discrepancies that may occur within the data used and generated by the different models, and in the assumptions on which they are based.  Amongst others, these may involve differences in:

• definition (e.g. of the variables), which may mean that different elements of the analysis are modelling different (and inconsistent) versions of reality;
• temporal or spatial scale, such that the data become more generalised at certain stages in the analysis system, thereby removing important variability from the assessment;
• statistical characteristics of, or requirements for, the data (e.g. regarding spatial or temporal autocorrelation, normality of the distribution, heteroscedasticity), which may invalidate some elements of the analysis;
• data handling and reporting (e.g. treatment of outliers, rounding, averaging), which may mean that uncertainties are generated at the interface between different models.

Detecting, and keeping track of, the resulting uncertainties is not easy, and cannot be done in a post hoc way (e.g. by trying to analyse the errors after the results ahve been generated).  Instead, methods for dealing with these issues need to be developed during the Design stage.  These might include, for example:

• reprogramming some of the models to remove the inconsistencies;
• running the analysis at a different spatial or temporal resolution to remove scale discrepancies;
• introducing reporting steps (e.,g. indicators) at crucial intermediary steps in the analysis so that more detailed information is not wholly lost as a result of subsequent generalisation.

In order to identify these problems, and to develop solutions, it is also often essential to test the effects of model linkage before the real analysis begins.  This can be done with a sub-set of the data to be used in the analysis, or with an independent trial data set from a comparable setting.  The difficulties with these approaches, however, are to know what the 'truth' really is (amidst inevitable uncertainties in the data), and to identify the additional uncertainties introduced by modelling.  Often, therefore, a more powerful alternative is to use simulated data to trial model linkage, for these provide total control of the testing.  The SIENA urban simulator (see panel to the left) provides a ready-made environment for this purpose.