On modelling the effects of afforestation on acidification in heterogeneous catchments at different spatial and temporal scales

A modelling approach is presented for simulating and predicting future changes in streamwater Gran alkalinity throughout a large, heterogeneous river system. The methodology is based on integrating End Member Mixing Analysis (EMMA), the Model of Acidification of Groundwater in Catchments (MAGIC) and spatial data describing the catchment characteristics stored on a Geographical Information System (GIS). These are integrated within a Functional Unit Network (FUN) to predict the changes in Gran alkalinity resulting from possible future changes in atmospheric deposition and land use (low intensity afforestation) in the River Dee catchment, NE Scotland. Model results indicate that declining sulphate and constant nitrogen deposition, combined with low intensity Scots pine (Pinus sylvestris) afforestation are unlikely to contribute significantly to streamwater acidification.