In contrast to the classical critical load (CL) concept, based on long-term steady state conditions, a dynamic deposition threshold (DDT) is introduced. This DDT takes into account all relevant dynamic aspects of vegetation development/forest growth, mineralisation, immobilisation and denitrification, depending on the successional stage of the forest. DDT values for nitrogen were determined for a Douglas fir rotation by two process-based nitrogen models SMART2 and MERLIN using three different criteria for critical nitrogen leaching. During most of the rotation time, the predicted DDT values were higher than the corresponding traditional CL. SMART2 and MERLIN predicted a maximum DDT of 4.9 and 4.6 kmol N ha(-1) yr(-1) (69 and 64 kg N ha(-1) yr(-1)), respectively, when accepting a critical N leaching level of 1.73 kmol N ha(-1) yr(-1) related to impacts on ground water quality. This is due mainly to relatively high tree uptake during the first 50 years of a forest rotation, compared to a long-term estimate, Le, the average tree uptake during a rotation period, used in the traditional CL calculation. At the lowest critical N leaching level of 0.10 kmol N ha(-1) yr(-1), corresponding to a level that might be critical for vegetation changes, the calculated DDT value and related N availability was such that it influenced tree growth, indicated by an increased CN ratio in foliage and organic matter. The two models SMART2 and MERLIN predicted comparable absolute levels of DDT but with a completely different temporal pattern. This was caused by differences in timing of mineralisation in the soil. Both models showed the importance of the soil for supplying N for tree growth in young and productive forests, but the timing of this mobilisation of N from the soil was different. This difference between the two models reflects the lack of knowledge of the mechanisms of the role of soil organic matter in satisfying tree N demand, Nevertheless, this method has a high potential for increasing more detailed insight into the dynamic behaviour of CL, which will make it possible to focus management options on a smaller spatial and temporal scale.