Author(s): Xihua Wang; Qinya Lv; Y. Jun Xu

Linked Author(s): Yi Jun Xu

Keywords: BiLSTM; China; Deep learning; Greenhouse gas; Nitrous oxide; Waterbodies

Abstract: Many studies have been conducted on the prediction of nitrous oxide (N2O) emissions from soils. Comparably, prediction of N2O water–air emissions is much more limited, especially at the national level. Here, we collected published N2O emission data across China's watersheds and analyzed spatiotemporal patterns during dry and wet seasons. We predicted N2O emission fluxes from these waterbodies for 2026–2028 using a traditional gray prediction model (GM) coupled with several deep learning models: LSTM, GRU, and BiLSTM. The study showed large regional variation in emissions from subtropical to boreal watersheds. Average emission rates varied from 13.95 (±27.15) μg m− 2 h− 1 in the Yellow River Basin to 68.71 (±102.62) μg m− 2 h− 1 in Southwest China. N2O emissions were clearly higher in the dry season than the wet season in all regions except the Yellow River Basin, indicating strong influence from wetland vegetation. Regarding model performance, higher accuracy was achieved by GRU and BiLSTM, which successfully predicted fluctuating increases of N2O emission fluxes in most regions from 2026 to 2028. These findings provide insights into national spatiotemporal patterns of N2O emissions and can guide regional and national mitigation strategies as well as future research.

DOI:

Year: 2026