Uncertainty in spatial decision support systems - Methodology related to prediction of groundwater pollution

Groundwater pollution from non point sources, such as nitrogen from agricultural activities, is a problem of increasing concern. This paper presents a methodology and a case study for large scale simulation of aquifer contamination due to nitrogen leaching. Comprehensive modelling toots of the physically-based type are well proven for small scale applications such as plots or small experimental catchments with good data availability. The two key problems related to large scale simulation are data availability at the large scale and model upscaling to represent conditions at larger scale. In this study readily available data from standard European level data bases such as GISCO and EUROSTAT have been used as the basis of modelling. These data were supplemented by selected readily available national data sources. The model parameters were all assessed from these data by use various transfer functions, and no model calibration was carried out. Furthermore, a statistically based upscaling/aggregation procedure, preserving the areal distribution of soil types, vegetation types etc on a catchment basis has been adopted. Finally, a Monte Carlo simulation technique was used to assess how uncertainty in selected input data propagate through the model and results in uncertainty on the model outputs. The case study from the Karup catchment in Denmark indicate that the resulting uncertainty of the predicted nitrogen concentrations in the aquifer at a scale of some hundreds of km(2) is so relatively small that the methodology appears suitable for large scale policy studies.