Characteristic ‘fingerprints’ of crop model responses to weather input data at different spatial resolutions |
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| Institution: | 1. Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, D-53115 Bonn, Germany;2. MTT Agrifood Research Finland, Lönnrotinkatu 5, FI-50100 Mikkeli, Finland;3. Institute of Agrosystems and Bioclimatology, Mendel University Brno, Zemedelska 1, 61300 Brno, Czech Republic;4. Global Change Research Center AS CR, v.v.i., B?lidla 986/4a, 603 00, Brno, Czech Republic;5. Finnish Environment Institute (SYKE), Box 140, FI-00251 Helsinki, Finland;1. International Maize and Wheat Improvement Center, Apdo. Postal 6-641 06600 Mexico, D.F., Mexico;2. Department of Agricultural and Biological Sciences, University of Florida, FL 110570, USA;3. International Food Policy Research Institute, DC 20006-1002, USA;4. World Agroforestry Center, P.O. Box 1041-00621, Kenya;5. International Center for Agricultural Research in the Dry Areas, P.O. Box 114/5055, Beirut, Lebanon;6. Current affiliation: The James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland UK;7. Current affiliation: APEC Climate Center, 12, Centum 7-ro, Haeundae-gu, Busan, 48058, Korea;8. Current affiliation: CIRAD, s/c IRD antenne de Bobo Dioulasso, 01 BP 171, Bobo Dioulasso, Burkina Faso;9. Current affiliation: IEG, World Bank, 1818, H Street, NW Washington DC 20433, USA;10. Current affiliation: Professor Emeritus, University of Illinois at Urbana-Champaign (UIUC), USA;1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China;2. National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing, 100048, China;1. University of Chicago Computation Institute, 5735 S. Ellis Avenue, Chicago, IL 60637, USA;2. Argonne National Laboratory Math & Comp. Science Division, Argonne, IL 60439, USA;3. Columbia University Center for Climate Systems Research, 2880 Broadway, NY, NY 10025, USA;4. University of Chicago Department of Geophysical Sciences, 5734 S. Ellis Avenue, Chicago, IL 60637, USA;5. New Zealand Landcare Research, 231 Morrin Road, St Johns, Auckland 1072, New Zealand;6. Department of Computer Science, University of Chicago, Chicago, IL 60637, USA;1. Crop Science Group, INRES, University of Bonn, Germany;2. Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany;3. Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Sweden;4. Department of Agri-Food Production and Environmental Science (DISPAA), University of Florence, Italy;5. CNR-Ibimet, Florence, Italy;6. Thünen-Institute of Climate-Smart-Agriculture, Braunschweig, Germany;7. Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, Scotland, UK;8. Desertification Research Centre, University of Sassari, Viale Italia, Sassari, Italy;9. Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany;10. AGIR, Université de Toulouse, INRA, Castanet-Tolosan, France;11. MIAT, Université de Toulouse, INRA, Castanet-Tolosan, France;12. Texas A&M AgriLife Research, Blackland Research and Extension Center, Temple, TX, USA;1. The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, UK;2. Grassland Ecosystem Research Unit, French National Institute of Agricultural Research, 5 Chemin de Beaulieu, 63039 Clermont-Ferrand, France |
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| Abstract: | Crop growth simulation models are increasingly used for regionally assessing the effects of climate change and variability on crop yields. These models require spatially and temporally detailed, location-specific, environmental (weather and soil) and management data as inputs, which are often difficult to obtain consistently for larger regions. Aggregating the resolution of input data for crop model applications may increase the uncertainty of simulations to an extent that is not well understood. The present study aims to systematically analyse the effect of changes in the spatial resolution of weather input data on yields simulated by four crop models (LINTUL-SLIM, DSSAT-CSM, EPIC and WOFOST) which were utilized to test possible interactions between weather input data resolution and specific modelling approaches representing different degrees of complexity. The models were applied to simulate grain yield of spring barley in Finland for 12 years between 1994 and 2005 considering five spatial resolutions of daily weather data: weather station (point) and grid-based interpolated data at resolutions of 10 km × 10 km; 20 km × 20 km; 50 km × 50 km and 100 km × 100 km. Our results show that the differences between models were larger than the effect of the chosen spatial resolution of weather data for the considered years and region. When displaying model results graphically, each model exhibits a characteristic ‘fingerprint’ of simulated yield frequency distributions. These characteristic distributions in response to the inter-annual weather variability were independent of the spatial resolution of weather input data. Using one model (LINTUL-SLIM), we analysed how the aggregation strategy, i.e. aggregating model input versus model output data, influences the simulated yield frequency distribution. Results show that aggregating weather data has a smaller effect on the yield distribution than aggregating simulated yields which causes a deformation of the model fingerprint. We conclude that changes in the spatial resolution of weather input data introduce less uncertainty to the simulations than the use of different crop models but that more evaluation is required for other regions with a higher spatial heterogeneity in weather conditions, and for other input data related to soil and crop management to substantiate our findings. Our results provide further evidence to support other studies stressing the importance of using not just one, but different crop models in climate assessment studies. |
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| Keywords: | Crop model Weather data resolution Aggregation Yield distribution |
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