Input data for spatial environmental models may have been measured in the field or laboratory, derived from remotely sensed imagery or obtained from expert elicitation. Data are also often digitised, interpolated, classified or generalised prior to submission to a model. In all these cases errors are introduced. Although users may be aware that errors propagate through their models, they rarely pay attention to this problem. However, when the accuracy of the data is insufficient for the intended use then this may result in inaccurate model results, wrong conclusions and poor decisions.

 

In this presentation I review statistical methods for quantification of uncertainty in environmental data and for analysis of uncertainty propagation in spatial environmental modelling. The emphasis is on Monte Carlo simulation methods. We will also discuss geostatistical modelling of spatial interpolation error and address the effects of spatial auto-and cross-correlations on the results of an uncertainty propagation analysis. Quantification of model parameter uncertainty is covered using Bayesian calibration techniques.  The methodology is illustrated with real-world examples on pesticide leaching in soil, coastal inundation mapping and geometric correction of airborne imagery.