22 research outputs found
Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model
Nitrous oxide (N2O) is the main biogenic greenhouse gas contributing to the global warming potential
(GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity
to predict N2O emissions in relation to environmental conditions and crop management. Biophysical
models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to
explore these relationships, but are fraught with high uncertainties in their parameters due to their
variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N2O
submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N2O emissions from the
nitrification and denitrification processes, which are modelled as the product of a potential rate with
three dimensionless factors related to soil water content, nitrogen content and temperature. These
equations involve a total set of 15 parameters, four of which are site-specific and should be measured on
site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior
information on the model parameters based on the literature review, and assigned them uniform
probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was
subsequently developed to update the parameter distributions against a database of seven different
field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm.
This site-specific calibration significantly reduced the spread in parameter distribution, and the
uncertainty in the N2O simulations. The model’s root mean square error (RMSE) was also abated by 73%
across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently
applied simultaneously to all data sets, to obtain better global estimates for the parameters initially
deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the
uncalibrated model. These global parameter values may be used to obtain more realistic estimates of
N2O emissions from arable soils at regional or continental scales
Using a crop model to account for the effects of local factors on the LCA of sugar beet ethanol in Picardy region, France
Analysis of two years of ASCAT-and SMOS-derived soil moisture estimates over Europe and North Africa
Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO2 and H2O fluxes at five European maize (Zea mays L.) sites
International audienceThis paper is a modelling study of crop management impacts on carbon and water fluxes at a range of European sites. The model is a crop growth model (STICS) coupled with a process-based land surface model (ORCHIDEE). The data are online eddy-covariance observations of CO2 and H2O fluxes at five European maize cultivation sites. The results show that the ORCHIDEE-STICS model explains up to 75% of the observed daily net CO2 ecosystem exchange (NEE) variance, and up to 79% of the latent heat flux (LE) variance at five sites. The model is better able to reproduce gross primary production (GPP) variations than terrestrial ecosystem respiration (TER) variations. We conclude that structural deficiencies in the model parameterizations of leaf area index (LAI) and TER are the main sources of error in simulating CO2 and H2O fluxes. A number of sensitivity tests, with variable crop variety, nitrogen fertilization, irrigation, and planting date, indicate that any of these management factors is able to change NEE by more than 15%, but that the response of NEE to management parameters is highly site-dependent. Changes in management parameters are found to impact not only the daily values of NEE and LE, but also the cumulative yearly values. In addition, LE is shown to be less sensitive to management parameters than NEE. Multi-site model evaluations, coupled with sensitivity analysis to management parameters, thus provide important information about model errors, which helps to improve the simulation of CO2 and H2O fluxes across European croplands