Effects of data uncertainty in the Swedish critical load assessment for forest soils

The effects of input data uncertainty on the critical loads and exceedance estimates for Swedish forest soils was assessed using Monte Carlo simulations of the PROFILE model. The study focuses on the effects of data uncertainty on the 5%-ile critical load at 150 x 150 km resolution and the 95%-ile exceedance at 150 x 150 km and 50 x 50 km resolution. The results indicate that data uncertainty limits the possibility to differentiate grid cells on 150 x 150 km resolution. The confidence interval for a given percentile can generally be reduced if the uncertainties in calculated critical loads are addressed simultaneously for all sites in a grid cell. The resulting best estimates of the 5%-ile critical load were found to be lowered, thereby advocating larger deposition reductions to comply with a given gap closure of exceedance. The results further indicate that the number of sites within the grid cells is important for the range of the confidence interval for a given percentile. Re-aggregation of exceedance estimate in 50 x 50 km grid cells showed that differentiation may be improved as compared to 150 x 150 km resolution. For 70% of the grid cells on 50 x 50 km resolution, the confidence interval for calculated exceedance covers both negative and positive values.