Abstract
We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.
Highlights
Process-based crop models are increasingly used for large-scale simulations at regional [1] and global scale [2]
That the spatial yield variability could be explained by 68% and 81% based on four variables (Figure A in S1 Fig): growing season precipitation, plant available water capacity of the soil profile, soil profile depth and topsoil awc in the case of wheat and growing season mean daily temperature, awc, soil profile depth and topsoil awc in the case of silage maize
Data aggregation effects on regional soil and climate statistics Input data were modified by aggregation, affecting the regional mean and variability of the data
Summary
Process-based crop models are increasingly used for large-scale simulations at regional [1] and global scale [2]. These models have typically been developed at the field scale, where model driving variables are obtained [3,4]. Changing the spatial resolution by aggregating or disaggregating data bears the risk of biased simulations due to modified data [8,9,10]. This is the so-called nonlinear aggregation error [8] or aggregation effect [11,12]. The error associated with this practice is, rarely considered when assessing model validity [15,16]
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