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

Gang Zhao 1, * Holger Hoffmann 1 Lenny G. J. van Bussel 1, 2 Andreas Enders 1 Xenia Specka 3 Carmen Sosa 4 Jagadeesh Yeluripati 5, 6 Fulu Tao 7 Julie Constantin 8 Helene Raynal 8 Edmar Teixeira 9 Balázs Grosz 10 Luca Doro 11 Zhigan Zhao 12 Claas Nendel 3 Ralf Kiese 13 Henrik Eckersten 14 Edwin Haas 13 Eline Vanuytrecht 15 Enli Wang 12 Matthias Kuhnert 5 Giacomo Trombi 16 Marco Moriondo 17 Marco Bindi 16 Elisabet Lewan 4 Michaela Bach 10 Kurt-Christian Kersebaum 3 Reimund Roetter 7 Pier Paolo Roggero 11 Daniel Wallach 8 Davide Cammarano 18 Senthold Asseng 19 Gunther Krauss 1 Stefan Siebert 1 Thomas Gaiser 1 Frank Ewert 1
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 (Delta), 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., 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.
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Submitted on : Wednesday, May 27, 2020 - 8:02:56 PM
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Gang Zhao, Holger Hoffmann, Lenny G. J. van Bussel, Andreas Enders, Xenia Specka, et al.. Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables. Climate Research, Inter Research, 2015, 65, pp.141-157. ⟨10.3354/cr01301⟩. ⟨hal-02636166⟩



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