Impacts of nitrogen addition on nitrous oxide emission: Comparison of five nitrous oxide modules or algorithms

The contributions of long-lived nitrous oxide (N2O) to global climate and environment have received increasing attention. Especially, atmospheric nitrogen (N) deposition has substantially increased in recent decades due to the extensive use of fossil fuels in industry, which strongly stimulates the N2O emissions of terrestrial ecosystem. Several models have been developed to simulate the impacts of environmental factors on N2O emission from soil, but there are still large differences in the simulations of N2O emission and their responses to atmospheric deposition over global or regional scales. Using observations from N addition experiments in a subtropical forest, this study compared five widely-used N2O modules or algorithms (i.e. the N2O modules of DayCENT, PnET-NDNDC and DyN, and the algorithm of NOE and NGAS) to investigate their performances for reproducing N2O emission, and especially the impacts of two forms of N additions (i.e. NH4+-N and NO3--N, respectively) of two levels (low and high) on N2O emission. In general, the five modules reproduced the seasonal variations of N2O emission. Under the high levels of N addition compared to low ones for both NH4+-N and NO3--N, however, not all modules can reproduce larger N2O emission. Relatively larger N2O emissions in measurements due to NH4+N compared to NO3--N additions were not indicated neither in all the modules. Moreover, there were substantial differences in simulating the ratios of N2O emission from nitrification and denitrification processes due to disagreements in the structure of these modules or algorithms. The comparison highlights the need to improve the representation of N2O production and diffusion processes. At the same time, it also highlights the application of WFPS in the model methodology as a key scheme that mediates the two microbial processes, i.e. nitrification and denitrification, could probably improve the performances of N2O models in future research.