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

Cite this dataset

Xia, Xinghui et al. (2023). Dataset for: Indirect nitrous oxide emission factors of fluvial networks can be predicted by dissolved organic carbon and nitrate from local to global scales [Dataset]. Dryad. https://doi.org/10.5061/dryad.tb2rbp03t

Abstract

Streams and rivers are important sources of nitrous oxide (N2O), a powerful greenhouse gas. Estimating global riverine N2O emissions is critical for the assessment of anthropogenic N2O emission inventories. The indirect N2O emission factor (EF5r) model, one of the bottom-up approaches, adopts a fixed EF5r value to estimate riverine N2O 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 N2O 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 N2O 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 N2O emissions more accurately based on IPCC methodology.

This dataset is a global integrated N2O dataset including data from 4-year (2017-2020) in situ measurements of six large rivers in China, 3-year (2018-2020) in situ measurements of urban river networks in Beijing of China, and 825 measurements from 70 published papers over six continents. The data includes dissolved N2O concentration, biogeochemical (DOC, NO3-, NH4+, temperature, and DO), climatological (climate zones), and geographic (region, location, and land cover) information.

Methods

Methods of data collection/generation are described in the manuscript for details.

Funding

National Natural Science Foundation of China, Award: 52039001

National Natural Science Foundation of China, Award: 92047303

Ministry of Science and Technology of the People's Republic of China, Award: 2017YFA0605001