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Refsgaard, J. C., Madsen, H., Andréassian, V., Arnbjerg-Nielsen, K., Davidson, T. A., Drews, M., et al. (2014). A framework for testing the ability of models to project climate change and its impacts. Clim. Change, 122(1-2), 271–282.
Abstract: Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections.
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Angulo, C., Rötter, R., Lock, R., Enders, A., Fronzek, S., & Ewert, F. (2013). Implication of crop model calibration strategies for assessing regional impacts of climate change in Europe. Agricultural and Forest Meteorology, 170, 32–46.
Abstract: Process-based crop simulation models are increasingly used in regional climate change impact studies, but little is known about the implications of different calibration strategies on simulated yields. This study aims to assess the importance of region-specific calibration of five important field crops (winter wheat, winter barley, potato, sugar beet and maize) across 25 member countries of the European Union (EU25). We examine three calibration strategies and their implications on spatial and temporal yield variability in response to climate change: (i) calculation of phenology parameters only, (ii) consideration of both phenology calibration and a yield correction factor and (iii) calibration of phenology and selected growth processes. The analysis is conducted for 533 climate zones, considering 24 years of observed yield data (1983-2006). The best performing strategy is used to estimate the impacts of climate change, increasing CO2 concentration and technology development on yields for the five crops across EU25, using seven climate change scenarios for the period 2041-2064. Simulations and calibrations are performed with the crop model LINTUL2 combined with a calibration routine implemented in the modelling interface LINTUL-FAST. The results show that yield simulations improve if growth parameters are considered in the calibration for individual regions (strategy 3); e.g. RMSE values for simulated winter wheat yield are 2.36, 1.10 and 0.70 Mg ha(-1) for calibration strategies 1, 2 and 3, respectively. The calibration strategy did not only affect the model simulations under reference climate but also the extent of the simulated climate change impacts. Applying the calibrated model for impact assessment revealed that climatic change alone will reduce crop yields. Consideration of the effects of increasing CO2 concentration and technology development resulted in yield increases for all crops except maize (i.e. the negative effects of climate change were outbalanced by the positive effects of CO2 and technology change), with considerable differences between scenarios and regions. Our simulations also suggest some increase in yield variability due to climate change which, however, is less pronounced than the differences among scenarios which are particularly large when the effects of CO2 concentration and technology development are considered. Our results stress the need for region-specific calibration of crop models used for Europe-wide assessments. Limitations of the considered strategies are discussed. We recommend that future work should focus on obtaining more comprehensive, high quality data with a finer resolution allowing application of improved strategies for model calibration that better account for spatial differences and changes over time in the growth and development parameters used in crop models. (c) 2012 Elsevier B.V. All rights reserved.
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Coucheney, E., Buis, S., Launay, M., Constantin, J., Mary, B., García de Cortázar-Atauri, I., et al. (2015). Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France. Env. Model. Softw., 64, 177–190.
Abstract: Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. (C) 2014 Elsevier Ltd. All rights reserved.
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