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This study presents the results of a combined measurement and modelling\udstrategy to analyse N2O and CO2 emissions from adjacent arable\udland, forest and grassland sites in Hesse, Germany. The measured emissions\udreveal seasonal patterns and management effects, including fertilizer\udapplication, tillage, harvest and grazing. The measured annual N2O\udfluxes are 4.5, 0.4 and 0.1 kg N ha−1 a−1, and the CO2\udfluxes are 20.0, 12.2 and 3.0 t C ha−1 a−1 for the arable land,\udgrassland and forest sites, respectively. An innovative model–data fusion\udconcept based on a multicriteria evaluation (soil moisture at different\uddepths, yield, CO2 and N2O emissions) is used to rigorously test\udthe LandscapeDNDC biogeochemical model. The model is run in a\udLatin-hypercube-based uncertainty analysis framework to constrain model\udparameter uncertainty and derive behavioural model runs. The results indicate\udthat the model is generally capable of predicting trace gas emissions, as\udevaluated with RMSE as the objective function. The model shows a reasonable\udperformance in simulating the ecosystem C and N balances. The model–data\udfusion concept helps to detect remaining model errors, such as missing (e.g.\udfreeze–thaw cycling) or incomplete model processes (e.g. respiration rates\udafter harvest). This concept further elucidates the identification of missing\udmodel input sources (e.g. the uptake of N through shallow groundwater on\udgrassland during the vegetation period) and uncertainty in the measured\udvalidation data (e.g. forest N2O emissions in winter months). Guidance\udis provided to improve the model structure and field measurements to further\udadvance landscape-scale model predictions

biogeosciences

Constraining a complex biogeochemical model for CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and N&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O emission simulations from various land uses by model–data fusion