Improving model prediction of soil NO emissions through Bayesian calibration.

The biogeochemical processes that lead to the production of NO in arable soils are controlled by temporally and spatially varying drivers. The need for prediction of soil NO emissions across scales means that agroecosystem biogeochemistry models are widely used to simulate NO emissions. Due to the parameter-dense nature of agroecosystem models their parameters have to be calibrated according to the soil and climatic conditions of the intended area of application. Bayesian calibration is considered one of the most advanced ways to complete this task. In this study, we calibrate nine parameters of the Landscape-DNDC process-based agroecosystem model, which are key to its NO prediction. The Metropolis-Hastings algorithm is used at four separate implementations in order to estimate parameter posterior distributions at four arable sites in the UK. The results of this process are visualised, summarised and assessed against measured NO data from ten independent arable sites. The study shows that, in many cases, soil NO emission peaks that were not predicted with the default model parameters were predicted after calibration. Overall, the prediction of soil NO fluxes across all the sites that were considered was improved by 33% when using the calibrated parameters.