Estimating Yangtze River basin's riverine N₂O emissions through hybrid modeling of land-river-atmosphere nitrogen flows

Riverine ecosystems are a significant source of nitrous oxide (N₂O) worldwide, but how they respond to human and natural changes remains unknown. In this study, we developed a compound model chain that integrates mechanism-based modeling and machine learning to understand N₂O transfer patterns within land, rivers, and the atmosphere. The findings reveal a decrease in N₂O emissions in the Yangtze River basin from 4.7 Gg yr^-1 in 2000 to 2.8 Gg yr^-1 in 2019, with riverine emissions accounting for 0.28% of anthropogenic nitrogen discharges from land. This unexpected reduction is primarily attributed to improved water quality from human-driven nitrogen control, while natural factors contributed to a 0.23 Gg yr^-1 increase. Notably, urban rivers exhibited a more rapid N₂O efflux ( [Formula: see text] ), with upstream levels nearly 3.1 times higher than rural areas. We also observed nonlinear increases in [Formula: see text] with nitrogen discharge intensity, with urban areas showing a gradual and broader range of increase compared to rural areas, which exhibited a sharper but narrower increase. These nonlinearities imply that nitrogen control measures in urban areas lead to stable reductions in N₂O emissions, while rural areas require innovative nitrogen source management solutions for greater benefits. Our assessment offers fresh insights into interpreting riverine N₂O emissions and the potential for driving regionally differentiated emission reductions.