Development of a Process‐Based N₂O Emission Model for Natural Forest and Grassland Ecosystems

Abstract

Nitrous oxide (N 2 O) is the third most important greenhouse gas after carbon dioxide (CO 2 ) and methane (CH 4 ), with a long lifetime of ∼120 years in the atmosphere (Fleming et al., 2011). The concentration of atmospheric N 2 O has increased from 270 ppb in the preindustrial period to 332.0 ppb in 2019 (WMO, 2020), which substantially contributes to global warming as its single-molecular global warming potential is about 298 times higher than that of CO 2 (Ramanathan et al., 1985). Anthropogenic activities, such as fertilizer use, animal manure generation, and land cultivation, have greatly increased the global input of reactive nitrogen (N) into natural forest and grassland ecosystems (Fowler et al., 2013), which consequently accelerated the nitrogen transformation processes, including N 2 O production, in these ecosystems. The N 2 O emission from natural forest and grassland ecosystems accounts for approximately 24%-75% of the total global N 2 O emission (H. Tian et al., 2013;R. Xu & Prentice, 2008;Zhuang et al., 2013). Therefore, it is crucial to estimate the magnitude and trend of N 2 O emission accurately from natural forest and grassland ecosystems, and understand its impacts on climate change.

Four decades of full-scale nitrous oxide emission inventory in China

“…1a ) [ 23 ]. Apart from the autotrophic nitrification and denitrifier denitrification considered in the DyN-LPJ model, the IBIS-MicN model included additional N 2 O-producing processes of heterotrophic nitrification and nitrifier denitrification [ 24 ], therefore, the IBIS-MicN model performed with a much smaller inter-annual variability. Both natural N 2 O emissions from forests and grasslands in this study are within the range of previous reports using the direct extrapolation method [ 25–28 ], linear models [ 29 ], random forest regression model [ 30 ], or process-based models [ 31 , 32 ].…”

“…In IBIS-MicN, N 2 O emissions were simulated from four microbial N 2 O-producing processes (i.e. autotrophic nitrification, heterotrophic nitrification, nitrifier denitrification, and denitrifier denitrification) based on the dynamic activities of nitrifiers and denitrifiers, as well as local environmental conditions simulated by IBIS [ 24 ]. While DyN-LPJ simulated N 2 O emissions from autotrophic nitrification and heterotrophic denitrification based on the fully coupled carbon and N dynamics [ 23 ].…”

Response of Soil Nitrogen Components and nirK- and nirS-Type Denitrifying Bacterial Community Structures to Drip Irrigation Systems in the Semi-Arid Area of Northeast China

“…Tao et al [39] also revealed that drip irrigation increased soil NR and NiR activities. Ma et al [40] illustrated that soil NR activity was positively correlated with soil moisture, and the higher the soil moisture, the lower the oxygen content, and the more conducive it was to the improvement of NR activity.…”

A twenty-year dataset of high-resolution maize distribution in China

“…Additionally, a distribution map of maize can be used as a reference for predicting future maize distributions by exploring the driving forces of crop distribution patterns 12 . Because of the use of fertilizers such as nitrogen, a large amount of long-term existed greenhouse gas N 2 O is associated with the maize planting process 13 , 14 . Therefore, the distribution map of maize can also be an important source of data for simulating greenhouse gas emissions from agricultural ecosystems and plays an important role in determining the regional budget for greenhouse gases 15 , 16 .…”