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

Gang Zhao*, Holger Hoffmann, L.G.J. Van Bussel, Andreas Enders, Xenia Specka, Carmen Sosa, Jagadeesh Yeluripati, Fulu Tao, Julie Constantin, Helene Raynal, Edmar Teixeira, Balázs Grosz, Luca Doro, Zhigan Zhao, Claas Nendel, Ralf Kiese, Henrik Eckersten, Edwin Haas, Eline Vanuytrecht, Enli WangMatthias Kuhnert, Giacomo Trombi, Marco Moriondo, Marco Bindi, Elisabet Lewan, Michaela Bach, Kurt Christian Kersebaum, Reimund Rötter, Pier Paolo Roggero, Daniel Wallach, Davide Cammarano, Senthold Asseng, Gunther Krauss, Stefan Siebert

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)

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 processbased 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.

Original languageEnglish
Pages (from-to)141-157
JournalClimate Research
Volume65
DOIs
Publication statusPublished - 2015

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Keywords

  • Crop model
  • Data aggregation
  • Model comparison
  • Scaling
  • Spatial heterogeneity
  • Spatial resolution

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