Regional yield simulations with process-based models often rely on input data of coarse spatial resolution (Ewert et al., 2015; Zhao et al., 2015). Using aggregated data as input for process-based models entails the risks of introducing so-called aggregation errors (AE). Such AE depend on the model structure in combination with the aggregation method, the type of aggregated data as well as its spatial heterogeneity. While the regional crop yield bias is usually <5 % on average over all years, it may increase in single years (Hoffmann et al. 2015), depending on the model. Here we present a model intercomparison on AE for a range of environmental conditions with varying combinations of aggregated climate, soil and crop management data for two crops grown under varying production situations. Multi-model ensemble runs were conducted with soil, climate and crop management input data at resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Climate data was spatially averaged. Soil data was aggregated by area majority. Aggregated crop management data was obtained by applying management rules on aggregated climate data. Winter wheat and silage maize yields of 1982-2011 were simulated with 11 models for potential, water-limited and water-nitrogen-limited production after calibration to average regional sowing date, harvest date and crop yield. Regional yields were reproduced by the models on average, regardless of input data type and resolution. However, large AE were observed in dry years as well as due to soil aggregation. AE due to aggregated management data were comparatively lower. Finally, models differed considerably in AE. The results highlight the interactions between model, data and aggregation method with AE, emphasizing the importance of models intercomparison analyses.
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