Abstract: This study evaluates the impacts of projected climate change on irrigation requirements and yields of six crops (winter wheat, winter barley, rapeseed, grain maize, potato, and sugar beet) in Europe. Furthermore, the uncertainty deriving from consideration of irrigation, CO2 effects on crop growth and transpiration, and different climate change scenarios in climate change impact assessments is quantified. Net irrigation requirement (NIR) and yields of the six crops were simulated for a baseline (1982-2006) and three SRES scenarios (B1, B2 and A1B, 2040-2064) under rainfed and irrigated conditions, using a process-based crop model, SIMPLACE <LINTUL5, DRUNIR, HEAT>. We found that projected climate change decreased NIR of the three winter crops in northern Europe (up to 81 mm), but increased NIR of all the six crops in the Mediterranean regions (up to 182 mm yr(-1)). Climate change increased yields of the three winter crops and sugar beet in middle and northern regions (up to 36%), but decreased their yields in Mediterranean countries (up to 81%). Consideration of CO2 effects can alter the direction of change in NIR for irrigated crops in the south and of yields for C3 crops in central and northern Europe. Constraining the model to rainfed conditions for spring crops led to a negative bias in simulating climate change impacts on yields (up to 44%), which was proportional to the irrigation ratio of the simulation unit. Impacts on NIR and yields were generally consistent across the three SRES scenarios for the majority of regions in Europe. We conclude that due to the magnitude of irrigation and CO2 effects, they should both be considered in the simulation of climate change impacts on crop production and water availability, particularly for crops and regions with a high proportion of irrigated crop area.

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.

Abstract: While crop models are widely used to assess the change in crop productivity with climate change, their skill in assessing irrigation water demand or the risk of crop failure in large area impact assessments is relatively unknown. The objective of this study is to investigate which aspects of modeling crop water use (reference crop evapotranspiration (ET0), soil water extraction, soil evaporation, soil water balance and root growth) contributes most to the variability in estimates of maize crop water use and the risk of crop failure, and demonstrate the resulting uncertainty in a climate change impact study for Europe. The SIMPLACE crop modeling framework was used to couple the LINTUL5 crop model in factorial combinations of 2-3 different approaches for simulating the 5 aspects of crop water use, resulting in 51 modeling approaches. Using experiments in France and New Zeland, analysis of total sensitivity revealed that ET0 explained the most variability in both irrigated maize water use and rainfed grain yield levels, with soil evaporation also imporatant in the French experiment. In the European impact study, net irrigation requirement differed by 36% between the Penman and Hargreaves ET0 methods in the baseline period. Average EU grain yields were similar between models, but differences approached 1-2 tonnes in parts of France and Southern Europe. EU wide esimates of crop failure in the historical period ranged between 5.4 years for Priestley-Taylor to every 7.9 years for the Penman ET0 methods. While the uncertainty in absolute values between models was significant, estimates of relative changes were similar between models, confirming the utility of crop models in assessing climate change impacts. If ET0 estimates in crop models can be improved, through the use of appropriate methods, uncertainty in irrigation water demand as well as in yield estimates under drought can be reduced.