Variability of effects of spatial climate data aggregation on regional yield simulation by crop models - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement
Journal Articles Climate Research Year : 2015

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Fulu Tao
  • Function : Author
Luca Doro
Zhigan Zhao
  • Function : Author
Enli Wang
  • Function : Author
Claas Nendel
Reimund Rötter
  • Function : Author
  • PersonId : 970358
Daniel Wallach

Abstract

Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize ) and 3 production situations (potential, waterlimited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha−1, whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.
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Dates and versions

hal-02636784 , version 1 (30-09-2024)

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Holger Hoffmann, Gang Zhao, Lenny G. J. van Bussel, Andreas Enders, Xenia Specka, et al.. Variability of effects of spatial climate data aggregation on regional yield simulation by crop models. Climate Research, 2015, 65, pp.53-69. ⟨10.3354/cr01326⟩. ⟨hal-02636784⟩
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