Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/bg-2017-96
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
17 Mar 2017
Review status
This discussion paper is under review for the journal Biogeosciences (BG).
Constraining a complex biogeochemical model for multi-site greenhouse gas emission simulations by model-data fusion
Tobias Houska1, David Kraus2, Ralf Kiese2, and Lutz Breuer1,3 1Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Giessen, 35392, Germany
2Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, 82467, Germany
3Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Giessen, 35392, Germany
Abstract. This paper presents results of a combined measurement and modelling strategy to analyse N2O and CO2 emissions from adjacent arable, forest and grassland sites in Germany. Measured emissions reveal seasonal patterns and management effects like fertilizer application, tillage, harvest and grazing. Measured annual N2O fluxes are 4.5, 0.4 and 0.1 kg N ha−1 a−1, while CO2 fluxes are 20.0, 12.2 and 3.0 t C ha−1 a−1 for the arable, grassland and forest sites, respectively. An innovative model-data fusion concept based on multi-criteria evaluation (soil moisture in different depths, yield, CO2 and N2O emissions) is used to rigorously test the biogeochemical LandscapeDNDC model. The model is run in a Latin Hypercube based uncertainty analyses framework to constrain model parameter uncertainty and derive behavioral model runs. Results indicate that the model is in general capable to predict the trace gas emissions, evaluated by RMSE as an objective function. The model shows reasonable performance in simulating the ecosystems C and N balances. The model-data fusion concept helps to detect remaining model errors like missing (e.g. freeze-thaw cycling) or incomplete model processes (e.g. respiration amount after harvest). It further elucidates identifying missing model input sources (e.g. uptake of N through shallow groundwater on grassland during the vegetation period) and uncertainty in measured validation data (e.g. forest N2O emissions in winter months). Guidance is provided to improve model structure and field measurements to further advance landscape scale model predictions.

Citation: Houska, T., Kraus, D., Kiese, R., and Breuer, L.: Constraining a complex biogeochemical model for multi-site greenhouse gas emission simulations by model-data fusion, Biogeosciences Discuss., doi:10.5194/bg-2017-96, in review, 2017.
Tobias Houska et al.
Tobias Houska et al.
Tobias Houska et al.

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Short summary
CO2 and N2O are two prominent greenhouse gases (GHG) contributing to global warming. We combined measurement and modelling to quantify GHG emissions from adjacent arable, forest and grassland sites in Germany. Measured emissions reveal seasonal patterns and management effects like fertilizer application, tillage, harvest and grazing. Modelling helps to estimate the magnitude and uncertainty of not measureable C and N fluxes and indicates missing input source, e.g. nitrate uptake from groundwater
CO2 and N2O are two prominent greenhouse gases (GHG) contributing to global warming. We combined...
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