Illuminating patterns and processes of water quality in U.S. rivers using physics-guided deep learning

水质问题是社会面临的根本性、普遍性挑战。持续的营养物污染已经在全球范围内造成了富营养化和有害藻类大量繁殖，仅在美国，估计每年的损失就超过40亿美元。营养污染威胁着生态系统和粮食生产。土壤侵蚀将随着全球城市人口的增加而继续增长。自1972年以来，美国已经花费了超过1万亿美元来改善水质，相当于每个美国人每年花费100美元，使清洁水可以说是最昂贵的环境投资之一，超过清洁空气的成本。了解水质动态至关重要，但仍是一项重大挑战，部分原因是其复杂性和数据稀缺。该项目旨在通过开发预测工具和提高对不同条件和地点下水质变化的方式和原因的认识，提高对水质动态的理解。这些成果将帮助决策者、水资源管理者和广大公众做出明智的决策，确保水资源的可持续性。尽管在过去几十年中取得了巨大的进步和努力，但水质测量仍然艰巨而昂贵，导致数据覆盖范围不一致。因此，对水质动态的了解往往局限于个别地点。该项目的目的是确定模式和过程，调节浓度排放关系的水质变量在美国各地。该项目将侧重于常见的水质变量，包括硝酸盐、总磷和浊度（悬浮沉积物总量的代表）。该项目将测试浓度-排放关系的空间格局是否主要由调节水文流动路径和源水生态地球化学的土地利用（相对于其他驱动因素）驱动。这些假设将使用过程引导的深度学习进行测试，将传统的长短期记忆模型与反应性传输模型相结合。该集成将解决数据稀缺的局限性和深度学习模型的“黑匣子”性质，并提高预测准确性。该项目还将：1）公开重建的数据; 2）共享训练好的模型，用于在未监测的时间、空间和未来场景中进行预测; 3）制作视频，教育利益相关者如何使用模型; 4）扩大人工智能领域的参与;该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

Water quality problems are fundamental, universal challenges in society. Persistent nutrient pollution has caused eutrophication and harmful algal blooms globally, estimated to cost more than 4 billion dollars annually in the United States alone. Nutrient pollution threatens ecosystems and food production. Soil erosion will continue to grow with the global urban population. The United States has spent more than a trillion dollars to improve water quality since 1972, equivalent to annual spending of $100 per American, making clean water arguably one of the most expensive environmental investments, more than the cost of clean air. Understanding water quality dynamics is essential yet has remained a major challenge, partly due to its complex nature and data scarcity. This project aims to improve understanding of water quality dynamics by developing forecasting tools and advancing knowledge on how and why water quality changes under different conditions and places. The outcomes will help policymakers, water managers, and the broader public to make informed decisions that ensure the sustainability of water resources.Despite tremendous progress and efforts in the past decades, water quality measurements have remained arduous and expensive, leading to inconsistent data coverage. Understanding of water quality dynamics therefore is often limited to individual sites. The project aims to determine the patterns of and processes that regulate concentration-discharge relationships of water quality variables across the United States. The project will focus on common water quality variables, including nitrate, total phosphorus, and turbidity (a proxy for total suspended sediment). The project will test whether spatial patterns of concentration-discharge relationships are driven predominantly by land use (relative to other drivers) that regulates hydrological flow paths and source water biogeochemistry. The hypotheses will be tested using Process-Guided Deep Learning integrating traditional Long Short-Term Memory models with reactive transport models. The integration will address the limitations of data scarcity and the "black box" nature of deep learning models, and advance predictive accuracy. The project will also 1) make the reconstructed data publicly available; 2) share the trained models for prediction in unmonitored time, space and future scenarios; 3) create videos to educate stakeholders on how to use the models; and 4) broaden participation in the field of artificial intelligence/machine learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.