An evaluation list as model selection aid: finding models with a balance between modelcomplexity, data availability and model application

The continuous increase in the complexity of models that are being applied for environmental assessments results in increased uncertainty about the quantitative predictions. Classical criteria to find optimal models, such as the Akaike information criterion, do not consider the application. A list that evaluates the balance between model complexity, data support, and application, gives different ‘optimal’ models than classical criteria. This is joint work with P.W. Bogaart.