School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR 999077, China.
Environ Sci Technol. 2022 Jul 19;56(14):10530-10542. doi: 10.1021/acs.est.2c02232. Epub 2022 Jun 30.
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.
陆地氮素输出是一个关键的地球系统过程，但在高时空分辨率下，其全球动态仍难以预测。在这里，我们使用深度学习（DL）来模拟每日河流水体中氮素的输出，以响应水文气象和人为驱动因素。我们在中国东南部一个具有典型亚热带季风气候的沿海流域中建立了长短期记忆（LSTM）模型，用于模拟每日溶解无机氮（DIN）的浓度和通量。DL 模型在 DIN 浓度和通量方面均表现出优异的准确性，其纳什-苏特克里夫效率系数（NSE）分别高达 0.67 和 0.92，这一性能不太可能通过具有可比数据质量的通用过程模型实现。通量模型集合，无需重新训练，在亚洲、欧洲和北美的七个不同流域中表现良好（平均 NSE = 0.32-0.84），并且使用多流域数据重新训练进一步将最低 NSE 从 0.32 提高到 0.68。DL 解释证实，不同大陆之间存在河流水体氮素输出的流域间一致性，这源于降雨-径流关系的相似性。多流域通量模型预测，在肥料消耗增加 20%的情况下，研究流域的氮素向海洋的输出将增加 0.60-12.4%，而在径流量增加 10%的情况下，氮素向海洋的输出将增加 6.7-20.1%，这表明人类活动和气候变化的协同效应。基于 DL 的方法代表了环境科学中可解释人工智能的一个成功案例，为在目前有限的数据条件下，一致理解全球每日分辨率河流水体氮素输出的动态提供了一种潜在的捷径。
