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Kersebaum, K.; Kroes, J.; Gobin, A.; Takáč, J.; Hlavinka, P.; Trnka, M.; Ventrella, D.; Giglio, L.; Ferrise, R.; Moriondo, M.; Dalla Marta, A.; Luo, Q.; Eitzinger, J.; Mirschel, W.; Weigel, H.-J.; Manderscheid, R.; Hoffmann, M.; Nejedlik, P.; Iqbal, M.; Hösch, J. |
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Title |
Assessing uncertainties of water footprints using an ensemble of crop growth models on winter wheat |
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Journal Article |
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Year |
2016 |
Publication |
Water |
Abbreviated Journal |
Water |
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Volume |
8 |
Issue |
12 |
Pages  |
571 |
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Abstract |
Crop productivity and water consumption form the basis to calculate the water footprint (WF) of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%–19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%–39%. Models responded differently to elevated CO2 in a FACE (Free-Air Carbon Dioxide Enrichment) experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%–49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%–68% of the total actual evapotranspiration. |
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2073-4441 |
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CropM, ft_macsur |
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MA @ admin @ |
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4987 |
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Cammarano, D.; Rötter, P.; Ewert, F.; Palosuo, T.; Bindi, M.; Kersebaum, K.C.; Olesen, J.E.; Trnka, M.; van Ittersum, M.K.; Janssen, S.; Rivington, M.; Semenov, M.; Wallach, D.; Porter, J.R.; Stewart, D.; Verhagen, J.; Angulo, C.; Gaiser, T.; Nendel, C.; Martre, P.; de Wit, A. |
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Title |
Challenges for Agro-Ecosystem Modelling in Climate Change Risk Assessment for major European Crops and Farming systems |
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Conference Article |
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2013 |
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555-564 |
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Impacts World 2013, International Conference on Climate Change Effects, Potsdam, Germany, 2013-05-27 to 2013-05-30 |
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MA @ admin @ |
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2765 |
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Wallach, D.; Mearns, L.O.; Ruane, A.C.; Rötter, R.P.; Asseng, S. |
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Title |
Lessons from climate modeling on the design and use of ensembles for crop modeling |
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Journal Article |
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Year |
2016 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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139 |
Issue |
3-4 |
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551-564 |
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change projections; elevated CO2; uncertainty; wheat; water; soil; simulations; yield; rice; 21st-century; Model ensembles; Crop models; Climate models; Model weighting; Super ensembles |
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Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor. |
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2017-01-06 |
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0165-0009 |
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Article |
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CropM, ft_MACSUR |
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Call Number |
MA @ admin @ |
Serial |
4933 |
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Author |
Müller, C.; Elliott, J.; Levermann, A. |
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Title |
Food security: Fertilizing hidden hunger |
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Journal Article |
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Year |
2014 |
Publication |
Nature Climate Change |
Abbreviated Journal |
Nat. Clim. Change |
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4 |
Issue |
7 |
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540-541 |
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Keywords |
elevated CO2; human-nutrition; climate-change; carbon; face |
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Atmospheric CO2 fertilization may go some way to compensating the negative impact of climatic changes on crop yields, but it comes at the expense of a deterioration of the current nutritional value of food. |
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1758-678x 1758-6798 |
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Editorial Material |
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CropM |
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MA @ admin @ |
Serial |
4537 |
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Author |
Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
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Title |
Estimating model prediction error: Should you treat predictions as fixed or random |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
84 |
Issue |
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Pages |
529-539 |
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Keywords |
Crop model; Uncertainty; Prediction error; Parameter uncertainty; Input uncertainty; Model structure uncertainty |
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Abstract |
Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEPfixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEPuncertain(X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEPuncertain(X) can be estimated using a random effects ANOVA. It is argued that MSEPuncertain(X) is the more informative uncertainty criterion, because it is specific to each prediction situation. |
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1364-8152 |
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CropM, ft_macsur |
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no |
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MA @ admin @ |
Serial |
4773 |
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