Effects of climate input data aggregation on modelling regional crop yields

Holger Hoffmann, Gang Zhao, Lenny van Bussel, Andreas Enders, Xenia Specka, Carmen Sosa, Jagadeesh Yeluripati, Fulu Tao, Julie Constantin, Edmar Teixeira, Balasz Grosz, Luca Doro, Claas Nendel, Ralf Kiese, Helene Raynal, Henrik Eckersten, Edwin Haas, Matthias Kuhnert, Elisabet Lewan, Michaela Bach, Kurt-Christian Kersebaum, Pier Paolo Roggero, Reimund Rötter, Daniel Wallach, Gunther Krauss, Stefan Siebert, Thomas Gaiser, Enli Wang, Zhigan Zhao, Frank Ewert


Crop models can be sensitive to climate input data aggregation and this response may differ among models. This should be considered when applying field-scale models for assessment of climate change impacts on larger spatial scales or when coupling models across scales.

In order to evaluate these effects systematically, an ensemble of ten crop models was run with climate input data on different spatial aggregations ranging from 1, 10, 25, 50 and 100 km horizontal resolution for the state of North Rhine-Westphalia, Germany. Models were minimally calibrated to typical sowing and harvest dates, and crop yields observed in the region, subsequently simulating potential, water-limited and nitrogen-limited production of winter wheat and silage maize for 1982-2011. Outputs were analysed for 19 variables (yield, evapotranspiration, soil organic carbon, etc.). In this study the sensitivity of the individual models and the model ensemble in response to input data aggregation is assessed for crop yield.

Results show that the mean yield of the region calculated from climate time series of 1 km horizontal resolution changes only little when using climate input data of higher aggregation levels for most models. However, yield frequency distributions change with aggregation, resembling observed data better with increasing resolution. With few exceptions, these results apply to the two crops and three production situations (potential, water-, nitrogen-limited) and across models including the model ensemble, regardless of differences among models in simulated yield levels and spatial yield patterns. Results of this study improve the confidence of using crop models at varying scales.

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