Abstract:
Deterministic models are used to predict the risk of groundwater contamination from non-point sources and to evaluate the effect of alleviation measures. Such model predictions are associated with considerable uncertainty due to uncertainty in the input data used, especially when applied at large scales. The present paper presents a case study related to prediction of nitrate concentrations in groundwater aquifers using a spatially distributed catchment model. Input data were primarily obtained from databases at an European level. The model parameters were all assessed from these data by use of transfer functions, and no model calibration was carried out. The Monte Carlo simulation technique was used to analyse how uncertainty in input data propagates to model output. It appeared that the magnitude of the uncertainty depends significantly on the considered temporal and spatial scale. Thus simulations of flux concentrations leaving the root zone at grid level were associated with large uncertainties, whereas uncertainties in simulated concentrations at aquifer level on catchment scale was much smaller.