The uncertainty in forecasting trends of forest soil acidification

A Regional Soil Acidification Model (RESAM) has been developed to gain insight in long-term impacts of deposition scenarios on forest soils in The Netherlands. Model predictions of such large-scale environmental effects of acid deposition require extrapolation of site specific data to large geographical regions. The major aim of this study is to quantify the uncertainty in model response to a given deposition scenario, due to uncertainty and spatial variability in data. Furthermore, the uncertainty analysis was performed to determine which additional data will most likely improve the reliability of predictions. An efficient Monte Carlo technique was used in combination with regression analysis. The analysis was restricted to one forest soil ecosystem: a leptic podzol with Douglas fir, subject to a reducing deposition scenario. The investigated output variables were pH, Al/Ca ratio and NH₄/K ratio in the root zone, which are generally used as indicators of forest soil acidification and of potential forest damage. Statistical analyses showed that in most cases the relation between the parameters and model output can be satisfactorily described by a linear regression model. The uncertainty contribution of various parameters depends on the considered output variable, soil compartment and time. The uncertainty, as measured by the coefficient of variation, appears to be high for the NH₄/K and Al/Ca ratios, whereas it was relatively low for the pH. Results show that the uncertainty in the depositions of SO_x, NO_x, and NH_x in a receptor area and the uncertainty in the parameters and variables determining the nitrogen and aluminium dynamics contribute most to the resulting uncertainty of the considered model output.