CR 65:141-157 (2015)  -  DOI: https://doi.org/10.3354/cr01301

Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

Gang Zhao1,*, Holger Hoffmann1, Lenny G. J. van Bussel1,2, Andreas Enders1, Xenia Specka3, Carmen Sosa4, Jagadeesh Yeluripati5,19, Fulu Tao6, Julie Constantin7, Helene Raynal7, Edmar Teixeira8, Balázs Grosz9, Luca Doro10, Zhigan Zhao11, Claas Nendel3, Ralf Kiese12, Henrik Eckersten13, Edwin Haas14, Eline Vanuytrecht15, Enli Wang11, Matthias Kuhnert5, Giacomo Trombi18, Marco Moriondo17, Marco Bindi18, Elisabet Lewan4, Michaela Bach9, Kurt-Christian Kersebaum3, Reimund Rötter6, Pier Paolo Roggero10, Daniel Wallach7, Davide Cammarano16, Senthold Asseng16, Gunther Krauss1, Stefan Siebert1, Thomas Gaiser1, Frank Ewert1

1Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
2–19Co-author addresses in the Supplement (www.int-res.com/articles/suppl/c065p141_supp.pdf)
*Corresponding author:

ABSTRACT: We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Δ), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the Δ, especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.


KEY WORDS: Crop model · Model comparison · Spatial resolution · Data aggregation · Spatial heterogeneity · Scaling


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Cite this article as: Zhao G, Hoffmann H, van Bussel LGJ, Enders A and others (2015) Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables. Clim Res 65:141-157. https://doi.org/10.3354/cr01301

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