Effect of changing size and composition of a crop model ensemble on impact and adaptation response surfaces

A Rodríguez, M Ruiz-Ramos, T Palosuo, R Ferrise, I.J. Lorite, M Bindi, T.R. Carter, S Fronzek, N Pirttioja, P Baranowski, S Buis, D Cammarano, Y Chen, B Dumont, F Ewert, T Gaiser, P Hlavinka, H Hoffmann, J.G. Höhn, F Jurecka, K.C. Kersebaum, J Krzyszczak, M Lana, A Mechiche-Alami, J Minet, M Montesino, C Nendel, J.R. Porter, F Ruget, M.A. Semenov, Z Steinmetz, P Stratonovitch, I Supit, F Tao, M Trnka, A.D. Wit, R.P. Rötter


Climate change is expected to generate severe impacts in cropping systems and food production. Because of that, successful local adaptation is needed.Impact response surfaces (IRSs) are tools that allow assessing responses of studied variables (yields) to systematic changes in two explanatory variables (e.g. precipitation, P, and temperature, T). Adaptation response surfaces (ARSs) show the impacts or efficiencies of adaptation measures within the same T and P change space. To quantify some important aspects of uncertainties of model simulations, the use of an ensemble of crop simulation models is recommended. Yet properties of climate model ensembles have been analyzed in depth, this is not the case for crop model ensembles.Changes in ensemble composition and size can occur when the ensemble is extended to include new members, or when some are excluded (e.g. members giving implausible results). These changes can make an important difference on the results for both impact and/or adaptation simulations and affect the conclusions or management recommendations made based on them.For this study we are utilizing simulations from an ensemble of crop models that were applied to simulate wheat growth in Lleida, northwest of Spain. The outputs of this ensemble have been used to create IRSs and ARSs, to analyze the response of wheat yield to a range of T and P perturbations, under different CO2 levels.The objective of this study is to establish a methodology to show the effects of changing the ensemble composition and size on impact and adaptation assessment. The methodology developed here allows measuring the impact of the ensemble members' selection on the ensemble central tendency measures and on the IRSs and ARSs main features and allows for analysis of robustness of conclusions.

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