ORCHIDEE-CROP (v0), a new process based Agro-Land Surface Model: model description and evaluation over Europe

摘要: Abstract. The response of crops to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, impact carbon, water, energy fluxes, causing feedbacks the climate. To simulate temperate [CO2], which accounts for specific phenology mediated by management practice, we describe here development a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), integrates generic crop harvest module, very simple parameterization nitrogen fertilization, into land surface (LSM) ORCHIDEEv196, in order biophysical biochemical interactions croplands, as well plant productivity harvested yield. is applicable range crops, but tested using maize winter wheat, with phenological parameterizations two European varieties originating from STICS agronomical model. We evaluate (v0) against eddy covariance biometric measurements at seven wheat sites Europe. ecosystem variables used evaluation are fluxes (net exchange, NEE), latent heat, sensible heat fluxes. Additional leaf area index (LAI) aboveground biomass yield well. Evaluation results revealed that reproduced observed timing stages amplitude LAI changes. This contrast ORCHIDEEv196 where, default, same grass. A halving root mean square error 2.38 ± 0.77 1.08 ± 0.34 m2 m−2 was obtained when were compared across study sites. Improved carbon allocation led good match between modeled (with normalized squared (NRMSE) 11.0–54.2 %), yield, daily NRMSE  ∼  9.0–20.1  ∼  9.4–22.3 % simulated yields showed three available observations, where average  ∼  8.8 %. data misfit within uncertainties measurements, themselves an incomplete balance closure 80.6–86.3 remaining discrepancies other partly attributable unrealistic representations events has ability capture spatial gradients energy-related variables, such gross primary productivity, NEE, Europe, important requirement future spatially explicit simulations. Further improvement model, nutritional dynamics management, expected improve its predictive croplands Earth system