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

Abstract

Co-author addresses are provided in the Supplement (www. int-res. com/ articles/ suppl/ c065 p053 _ supp. pdf) 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.

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

“…The groups of models with medium and strong aggregation effects are all models that determine NPP based on the radiation use efficiency, while the other models use temperature based approaches. Hoffmann et al (2015) investigated the aggregation effect on yields for the same set of models and the same region and suggested that the aggregation effect on radiation may not be much higher than on temperature, but the models might be sensitive to changes on radiation values. Despite trends of decreasing temperature and solar radiation at coarser resolution ( Fig.…”

“…The models were not calibrated for the study area, but were adjusted based on 30-year yield averages of about 8 t ha −1 for winter wheat and 14 t ha −1 for silage maize. The weather data, presented and discussed by Zhao et al (2015) and Hoffmann et al (2015), show a 30 year average temperature of 9.7 • C, an average annual precipitation of 899 mm and mean annual global radiation of 3758 MJ m −2 a −1 with the standard deviations of 1.2 • C, 214.0 mm a −1 and 169.4 MJ m −2 a −1 , respectively. The coldest year was 2004, with an average temperature of 8.9 • C and the warmest year was 1983 (11.2 • C).…”

“…The settings for management are presented by Hoffmann et al (2015). The sowing date is fixed for all models, but the harvest date can vary for the phenological models.…”

“…The chosen soil type is a sandy loam, which is typical for this region and the management includes ploughing, sowing, harvest and three (130, 52, 26 kg ha −1 ) and two (30, 208 kg ha −1 ) fertilizer applications during spring for wheat and maize, respectively (for details see Hoffmann et al, 2015). The models were not calibrated for the study area, but were adjusted based on 30-year yield averages of about 8 t ha −1 for winter wheat and 14 t ha −1 for silage maize.…”

“…de Wit et al (2005) and Hoffmann et al (2015) investigated the impact of climate data aggregation on crop yields. While de Wit et al (2005) varied precipitation and solar radiation only on the resolutions 10 km and 50 km, Hoffmann et al (2015) differentiated between five different resolutions between 1 and 100 km and also considered aggregation effects of temperature for 13 models. Both studies found only slight impacts of data aggregation on simulated yield over longer time periods at a regional scale.…”