Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions.
Rui Xiong, Yi Zheng, Nengwang Chen, Qing Tian, Wei Liu, Feng Han, Shijie Jiang, Mengqian Lu, Yan Zheng
Author Information
Rui Xiong: School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Yi Zheng: School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China. ORCID
Nengwang Chen: Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China. ORCID
Qing Tian: Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.
Wei Liu: School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Feng Han: School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Shijie Jiang: School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Mengqian Lu: Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR 999077, China.
Yan Zheng: School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Terrestrial export of nitrogen is a critical Earth system process, but its global dynamics remain difficult to predict at a high spatiotemporal resolution. Here, we use deep learning (DL) to model daily riverine nitrogen export in response to hydrometeorological and anthropogenic drivers. Long short-term memory (LSTM) models for the daily concentration and flux of dissolved inorganic nitrogen (DIN) were built in a coastal watershed in southeastern China with a typical subtropical monsoon climate. The DL models exhibited excellent accuracy for both DIN concentration and flux, with Nash-Sutcliffe efficiency coefficients (NSEs) up to 0.67 and 0.92, respectively, a performance unlikely to be achieved by generic process-based models with comparable data quality. The flux model ensemble, without retraining, performed well (mean NSE = 0.32-0.84) in seven distinct watersheds in Asia, Europe, and North America, and retraining with multi-watershed data further improved the lowest NSE from 0.32 to 0.68. DL interpretation confirmed that interbasin consistency of riverine nitrogen export exists across different continents, which stems from the similarities in rainfall-runoff relationships. The multi-watershed flux model projects 0.60-12.4% increases in the nitrogen export to oceans from the studied watersheds under a 20% increase in fertilizer consumption, which rises to 6.7-20.1% with a 10% increase in runoff, indicating the synergistic effect of human activities and climate change. The DL-based method represents a successful case of explainable artificial intelligence in environmental science, providing a potential shortcut to a consistent understanding of the global daily-resolution dynamics of riverine nitrogen export under the currently limited data conditions.
