Equifinality, sensitivity and predictive uncertainty in the estimation of critical loads

The impacts of acidifying atmospheric deposition to soil and water resources are commonly calculated utilising predictive mathematical models. The estimation of the predictive uncertainty inherent in these models is important since the model predictions are increasingly being used as a scientific basis for decisions on emission abatement policies and strategies in Europe. When predictive uncertainty is taken into account it may significantly affect conclusions ascertained from model predictions. The Generalised Likelihood Uncertainty Estimation (GLUE) approach is used here in the estimation of predictive uncertainty of PROFILE, a steady-state biogeochemical model. GLUE is based on Monte Carlo simulation and recognises the possible equifinality of parameter sets. With this methodology it is possible to make an assessment of the likelihood of a parameter set being an acceptable simulator of a system when model predictions are compared to measured field data. The GLUE methodology is applied to PROFILE simulations of five European research sites. The results have revealed that the model is unable to reproduce the characteristics of soil water chemistry consistently, and that the resulting predicted critical loads must be associated with significant uncertainty. The study also demonstrates that a wide range of parameter sets exist that give acceptable simulations of site characteristics as well as a broad range of critical load values that are consistent with the site data. A sensitivity analysis is performed for simulations of data sets from each site; this is employed to evaluate the role of the model parameters in forcing the predictions. Results of the sensitivity analyses show that, in general, site predicted soil chemistry is driven by atmospheric inputs and mineral weathering rates are determined by soil physical properties.