Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 9, 857-873, 2016
http://www.geosci-model-dev.net/9/857/2016/
doi:10.5194/gmd-9-857-2016
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Development and technical paper
01 Mar 2016
ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe
X. Wu1,2, N. Vuichard1, P. Ciais1, N. Viovy1, N. de Noblet-Ducoudré1, X. Wang3, V. Magliulo4, M. Wattenbach5, L. Vitale4, P. Di Tommasi4, E. J. Moors6, W. Jans6, J. Elbers6, E. Ceschia7, T. Tallec7, C. Bernhofer8, T. Grünwald8, C. Moureaux9, T. Manise9, A. Ligne9, P. Cellier10, B. Loubet10, E. Larmanou10, and D. Ripoche11 1CEA-CNRS-UVSQ, UMR8212-Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Orme des Merisiers, 91191 Gif-Sur-Yvette, France
2College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
3Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
4Istituto per i Sistemi Agricoli e Forestali del Mediterraneo, CNR, Via C. Patacca 85, 80056 Ercolano (Napoli), Italy
5Helmholtz Centre Potsdam GFZ German Research Centre For Geosciences, Deutsches GeoForschungsZentrum GFZ, Telegrafenberg, 14473 Potsdam, Germany
6Wageningen UR, Alterra, Earth System Science and Climate Change Group, P.O. Box 47, 6700 AA Wageningen, the Netherlands
7CESBIO, UMR5126 – CNES-CNRS-UPS-IRD – 18 avenue Edouard Belin 31401 Toulouse CEDEX 9, France
8Technische Universität Dresden, Institute of Hydrology and Meteorology, Pienner Str. 23, 01737 Tharandt, Germany
9Université de Liège – Gembloux Agro-Bio Tech, Crops Management Unit, 5030 Gembloux, Belgium
10INRA, UMR INRA-AgroParisTech ECOSYS (Ecologie fonctionnelle et écotoxicologie des agro-écosystèmes), 78850 Thiverval-Grignon, France
11INRA, US1116 AgroClim, Avignon, France
Abstract. The response of crops to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water, and energy fluxes, causing feedbacks to the climate. To simulate the response of temperate crops to changing climate and [CO2], which accounts for the specific phenology of crops mediated by management practice, we describe here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module, and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but is tested here using maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at seven winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (net ecosystem exchange, NEE), latent heat, and sensible heat fluxes. Additional measurements of leaf area index (LAI) and aboveground biomass and yield are used as well. Evaluation results revealed that ORCHIDEE-CROP (v0) reproduced the observed timing of crop development stages and the amplitude of the LAI changes. This is in contrast to ORCHIDEEv196 where, by default, crops have the same phenology as grass. A halving of the root mean square error for LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 was obtained when ORCHIDEEv196 and ORCHIDEE-CROP (v0) were compared across the seven study sites. Improved crop phenology and carbon allocation led to a good match between modeled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0–54.2 %), crop yield, daily carbon and energy fluxes (with a NRMSE of  ∼  9.0–20.1 and  ∼  9.4–22.3 % for NEE), and sensible and latent heat fluxes. The simulated yields for winter wheat and maize from ORCHIDEE-CROP (v0) showed a good match with the simulated results from STICS for three sites with available crop yield observations, where the average NRMSE was  ∼  8.8 %. The model data misfit for energy fluxes were within the uncertainties of the measurements, which themselves showed an incomplete energy balance closure within the range 80.6–86.3 %. The remaining discrepancies between the modeled and observed LAI and other variables at specific sites were partly attributable to unrealistic representations of management events by the model. ORCHIDEE-CROP (v0) has the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, and sensible and latent heat fluxes across the sites in Europe, which is an important requirement for future spatially explicit simulations. Further improvement of the model, with an explicit parameterization of nutritional dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth system model.

Citation: Wu, X., Vuichard, N., Ciais, P., Viovy, N., de Noblet-Ducoudré, N., Wang, X., Magliulo, V., Wattenbach, M., Vitale, L., Di Tommasi, P., Moors, E. J., Jans, W., Elbers, J., Ceschia, E., Tallec, T., Bernhofer, C., Grünwald, T., Moureaux, C., Manise, T., Ligne, A., Cellier, P., Loubet, B., Larmanou, E., and Ripoche, D.: ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe, Geosci. Model Dev., 9, 857-873, doi:10.5194/gmd-9-857-2016, 2016.
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The response of crops to changing climate and atmospheric CO2 could have large effects on food production, terrestrial carbon, water, energy fluxes and the climate feedbacks. We developed a new process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module into the land surface model ORCHIDEE. Our model has good ability to capture the spatial gradients of crop phenology, carbon and energy-related variables across Europe.
The response of crops to changing climate and atmospheric CO2 could have large effects on food...
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