Hierarchical Bayesian calibration of nitrous oxide (N2O) and nitrogen monoxide (NO) flux module of an agro-ecosystem model: ECOSSE

Abstract

With the position of nitrous oxide (N2O) being the greenhouse gas with the highest global warming potential and its long atmospheric lifetime, the anthropogenic production of N2O is of major concern. The process-based model, ECOSSE, was partly developed to quantify emissions of greenhouse gases with an input data requirement that is readily available at regional scale. Hierarchical Bayesian (HB) methods are potentially used to reduce the uncertainty and to explain the spatial variability of estimated parameters. Here, we used a Hierarchical Bayesian method to calibrate the parameters of the N2O and nitrogen monoxide (NO) sub-model of ECOSSE and to quantify the uncertainty of model simulations and to investigate the model extrapolation using soil information. The sub model simulated N2O emission from nitrification and denitrification, while the simulated NO from nitrification. The HB calibration reduced the uncertainty in the N2O and NO simulations. The model's root mean square error (RMSE) was decreased by 18% and 29% for N2O and NO across field sites compared to an uncalibrated model. Parameters for nitrification could be considered universal, while parameters for denitrification challenged the assumption that these parameters may be considered universal constant values across sites. Parameters of the NO module could be considered constant for model extrapolation to regional scale. The calibrated parameters derived from soil-specific calibration could be served as default values for the N2O module extrapolation for similar soil types. Otherwise, the mean value of posterior distribution of calibration parameters in multi-dataset could be served as the parameter for model up scaling at regional scale.