Sensitivity analyses of MAGIC modelled predictions of future impacts of whole-tree harvest on soil calcium supply and stream acid neutralizing capacity.

Forest biofuel is a main provider of energy in Sweden and the market is expected to grow even further in the future. Removal of logging residues via harvest can lead to short-term acidification but the long-term effects are largely unknown. The objectives of this study were to 1) model the long-term effect of whole-tree harvest (WTH) on soil and stream water acidity and 2) perform sensitivity analyses by varying the amounts of logging residues, calcium (Ca(2+)) concentrations in tree biomass and site productivity in nine alternate scenarios. Data from three Swedish forested catchments and the Model of Acidification of Groundwater in Catchments (MAGIC) were used to simulate changes in forest soil exchangeable Ca(2+) pools and stream water acid neutralizing capacity (ANC) at Gammtratten, Kindla and Aneboda. Large depletions in soil Ca(2+) supply and a reversal of the positive trend in stream ANC were predicted for all three sites after WTH. However, the magnitude of impact on stream ANC varied depending on site and the concentration of mobile strong acid anions. Contrary to common beliefs, the largest decrease in modelled ANC was observed at the well-buffered site Gammtratten. The effects at Kindla and Aneboda were much more limited and not large enough to offset the general recovery from acidification. Varying the tree biomass Ca(2+) concentrations exerted the largest impact on modelled outcome. Site productivity was the second most important variable whereas changing biomass amounts left on site only marginally affected the results. The outcome from the sensitivity analyses pointed in the same direction of change as in the base scenario, except for Kindla where soil Ca(2+) pools were predicted to be replenished under a given set of input data. The reliability of modelled outcome would increase by using site-specific Ca(2+) concentrations in tree biomass and field determined identification of site productivity.