Abstract. Long-term data series (1996–2009) for eleven acidic headwater streams (< 10 km2) in Sweden were analyzed to determine factors controlling concentrations of trace metals. In-stream chemical data as well climatic, flow, and deposition chemistry data were used to develop models predicting concentrations of chromium (Cr), lead (Pb), and zinc (Zn). Data were initially analyzed using partial least squares to determine a set of variables that could predict metal concentrations across all sites. Organic matter (as absorbance) and iron related positively to Pb and Cr, while pH related negatively to Pb and Zn. Other variables such as conductivity, manganese, and temperature were important as well. Multiple linear regression was then used to determine minimally adequate prediction models which explained an average of 35% (Cr), 52% (Zn), and 72% (Pb) of metal variation across all sites. While models explained at least 50% of variation in the majority of sites for Pb (10) and Zn (8), only three sites met this criterion for Cr. Investigation of variation between site models for each metal revealed geographical (altitude), chemical (sulfate), and land-use (silvaculture) influences on predictive power of the models. Residual analysis revealed seasonal differences in the ability of the models to predict metal concentrations as well. Expected future changes in model variables were applied and results showed the potential for long-term increases (Pb) or decreases (Zn) for trace metal concentrations at these sites.
Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment
