Multimodel Evaluation of Nitrous Oxide Emissions From an Intensively Managed Grassland. Issue 1 (21st January 2020)

• Record Type: Journal Article
• Authors: Fuchs, Kathrin; Merbold, Lutz; Buchmann, Nina; Bretscher, Daniel; Brilli, Lorenzo; Fitton, Nuala; Topp, Cairistiona F. E.; Klumpp, Katja; Lieffering, Mark; Martin, Raphaël; Newton, Paul C. D.; Rees, Robert M.; Rolinski, Susanne; Smith, Pete; Snow, Val
• Is Part Of: Journal of geophysical research. Volume 125:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-21
• Subjects: model validation -- biogeochemical modeling -- eddy covariance -- DayCent -- APSIM -- PaSim; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2019JG005261
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Process‐based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands. They can be used to construct national GHG inventories using a Tier 3 approach. However, accurate simulations of nitrous oxide (N2 O) fluxes remain challenging. Models are limited by our understanding of soil‐plant‐microbe interactions and the impact of uncertainty in measured input parameters on simulated outputs. To improve model performance, thorough evaluations against in situ measurements are needed. Experimental data of N2 O emissions under two management practices (control with typical fertilization versus increased clover and no fertilization) were acquired in a Swiss field experiment. We conducted a multimodel evaluation with three commonly used biogeochemical models (DayCent in two variants, PaSim, APSIM in two variants) comparing four years of data. DayCent was the most accurate model for simulating N2 O fluxes on annual timescales, while APSIM was most accurate for daily N2 O fluxes. The multimodel ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the Intergovernmental Panel on Climate Change (IPCC)‐derived method for the Swiss agricultural GHG inventory (IPCC‐Swiss), but individual models were not systematically more accurate than IPCC‐Swiss. The model ensemble overestimated the N2 O mitigation effect of the clover‐based treatment (measured: 39–45%; ensemble: 52–57%) but was more accurate than IPCC‐Swiss (IPCC‐Swiss: 72–81%). These results suggest that multimodel ensembles are valuable for estimating the impact of climate and management on N2 O emissions. Plain Language Summary: We tested the performance of three dynamic simulation models against measured nitrous oxide (N2 O) fluxes and its driver variables for a Swiss grassland. We showed that DayCent performed best in the prediction of annual N2 O emissions but was outperformed by APSIM for daily N2 O emissions. We identified particular strengths and weaknesses of each model. Further, we compared the individual models against the N2 O flux estimate made with the Intergovernmental Panel on Climate Change (IPCC)‐derived method for the Swiss agricultural greenhouse gas inventory (IPCC‐Swiss). Most individual models were worse than IPCC‐Swiss but the mean of all model predictions was much better than IPCC‐Swiss. All methods overestimated the N2 O mitigation effect of a clover‐based N2 O mitigation. IPCC‐Swiss was worst and the model ensemble was best at estimating the effects of the mitigation. The findings highlight that using multiple models in an ensemble is beneficial for assessing management and climate impacts on N2 O emissions. Key Points: Biogeochemical models are useful to assess N2 O fluxes from grasslands but still need validation against in situ measurements DayCent performed best for annual N2 O emissions while APSIM best represented daily N2 O emissions, particularly after fertilizer application The ensemble‐average improved the estimated N2 O emissions compared to the IPCC‐Swiss estimate