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

Abstract

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 (FN2O), with upstream levels nearly 3.1 times higher than rural areas. We also observed nonlinear increases in FN2O 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.