Simulating space-time uncertainty in continental-scale gridded precipitation fields for agrometeorological modelling

Previous analyses of the effects of uncertainty in precipitation fields on the output of EU Crop Growth Monitoring System (CGMS) demonstrated that the influence on simulated crop yield was limited at national scale, but considerable at local and regional scales. We aim to propagate uncertainty due to precipitation in the crop model by Monte Carlo sampling of the precipitation field. We use an error model fitted to a highly accurate precipitation dataset (ELDAS) which was available for the year 2000. Our error model consisted of two components. The first is an additive component generating precipitation residues over the entire spatial domain. The residues are generated by quantile-based back transformation of standard Gaussian fields using a set of histograms for different CGMS precipitation bins. The second component is multiplicative and generates binary rain/no-rain events on locations where the CGMS precipitation records report nil precipitation. Our results demonstrate that the model generates realistic patterns of precipitation and reproduces the histograms of the reference precipitation dataset well. A remaining problem is the inability to model prolonged dry spells which is due to our model choice. The precipitation realizations were used as input in a crop growth model. The first results indicate that the uncertainty in precipitation is sufficient to sustain divergence in the soil moisture ensemble, but not in the leaf area index ensemble.