Indirect nitrous oxide emission factors of fluvial networks can be predicted by dissolved organic carbon and nitrate from local to global scales.

Abstract

Streams and rivers are important sources of nitrous oxide (N2 O), a powerful greenhouse gas. Estimating global riverine N2 O emissions is critical for the assessment of anthropogenic N2 O emission inventories. The indirect N2 O emission factor (EF5r ) model, one of the bottom-up approaches, adopts a fixed EF5r value to estimate riverine N2 O emissions based on IPCC methodology. However, the estimates have considerable uncertainty due to the large spatiotemporal variations in EF5r values. Factors regulating EF5r are poorly understood at the global scale. Here, we combine 4-year in situ observations across rivers of different land use types in China, with a global meta-analysis over six continents, to explore the spatiotemporal variations and controls on EF5r values. Our results show that the EF5r values in China and other regions with high N loads are lower than those for regions with lower N loads. Although the global mean EF5r value is comparable to the IPCC default value, the global EF5r values are highly skewed with large variations, indicating that adopting region-specific EF5r values rather than revising the fixed default value is more appropriate for the estimation of regional and global riverine N2 O emissions. The ratio of dissolved organic carbon to nitrate (DOC/NO3 - ) and NO3 - concentration are identified as the dominant predictors of region-specific EF5r values at both regional and global scales because stoichiometry and nutrients strictly regulate denitrification and N2 O production efficiency in rivers. A multiple linear regression model using DOC/NO3 - and NO3 - is proposed to predict region-specific EF5r values. The good fit of the model associated with easily obtained water quality variables allows its widespread application. This study fills a key knowledge gap in predicting region-specific EF5r values at the global scale and provides a pathway to estimate global riverine N2 O emissions more accurately based on IPCC methodology.