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Constraining a complex biogeochemical model for CO2 and N2O emission simulations from various land uses by model–data fusion

Houska, Tobias ; Kraus, David ; Kiese, Ralf ; Breuer, Lutz

Originalveröffentlichung: (2017) Biogeosciences 14:3487-3508 doi: 10.5194/bg-14-3487-2017
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URN: urn:nbn:de:hebis:26-opus-138799

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Sammlung: Open Access - Publikationsfonds
Universität Justus-Liebig-Universität Gießen
Institut: Institute for Landscape Ecology and Resources Management (ILR)
Fachgebiet: Agrarwissenschaften und Umweltmanagement
DDC-Sachgruppe: Allgemeines, Wissenschaft
Dokumentart: Aufsatz
Sprache: Englisch
Erstellungsjahr: 2017
Publikationsdatum: 27.11.2018
Kurzfassung auf Englisch: This study presents the results of a combined measurement and modelling strategy to analyse N2O and CO2 emissions from adjacent arable land, forest and grassland sites in Hesse, Germany. The measured emissions reveal seasonal patterns and management effects, including fertilizer application, tillage, harvest and grazing. The measured annual N2O fluxes are 4.5, 0.4 and 0.1?kg N?ha-1?a-1, and the CO2 fluxes are 20.0, 12.2 and 3.0?t C?ha-1?a-1 for the arable land, grassland and forest sites, respectively. An innovative model–data fusion concept based on a multicriteria evaluation (soil moisture at different depths, yield, CO2 and N2O emissions) is used to rigorously test the LandscapeDNDC biogeochemical model. The model is run in a Latin-hypercube-based uncertainty analysis framework to constrain model parameter uncertainty and derive behavioural model runs. The results indicate that the model is generally capable of predicting trace gas emissions, as evaluated with RMSE as the objective function. The model shows a reasonable performance in simulating the ecosystem C and N balances. The model–data fusion concept helps to detect remaining model errors, such as missing (e.g. freeze–thaw cycling) or incomplete model processes (e.g. respiration rates after harvest). This concept further elucidates the identification of missing model input sources (e.g. the uptake of N through shallow groundwater on grassland during the vegetation period) and uncertainty in the measured validation data (e.g. forest N2O emissions in winter months). Guidance is provided to improve the model structure and field measurements to further advance landscape-scale model predictions.
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