Tag Archives: spatial aggregation

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

authors: Holger Hofmann, Gang Zhao, Lenny van Bussel, Andreas Enders, Xenia Specka, Carmen Sosa, Jagadeesh Yeluripati, Fulu Tao, Julie Constantin, Helene Raynal, Edmar Teixeira, Balázs Grosz, Luca Doro, Zhigan Zhao, Enli Wang,  Claas Nendel, Kurt-Christian Kersebaum, Edwin Haas, Ralf Kiese, Steffen Klatt, Henrik Eckersten,  Eline Vanuytrecht, Matthias Kuhnert, Elisabet Lewan,  Reimund Rötter, Pier Paolo Roggero, Daniel Wallach, Davide Cammarano, Senthold Asseng, Gunther Krauss, Stefan Siebert, Thomas Gaiser, Frank Ewert.

abstract: Field-scale crop models can be applied at different spatial resolutions but little is known on the response of models to input data aggregation and why these responses can differ across models.  We therefore evaluated 13 crop models which were supplied with climate input data of different  spatial aggregation. Spatial resolution of climate input data ranged from 1 to 100 km raster and was  used with two crops (winter wheat and silage maize) and three production situations (potential, water  limited  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 to determining the model-specific input data aggregation on  simulated yields (aggregation effects) were mainly related to changes in radiation (winter wheat) and  temperature (silage maize). Additionally, aggregation effects were systematic since models differed in  the systematic fraction of the aggregation effect, regardless of the extent of the effect (20 to 66 % as  compared to 1.7 % for random effects). Climate input data aggregation changed the mean simulated  yield over the region 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  on  average  but  can  be systematically  biased  at  higher  temporal or  spatial  resolutions,  depending on the model and its parametrization.

journal: Climate Research