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

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.

水质问题是社会中的基本挑战。持续的营养污染引起了全球富营养化和有害藻类的开花，仅在美国，每年估计每年耗资超过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发展而继续增长。自1972年以来，美国已经花费了超过一万亿美元来提高水质，相当于每位美国人的年支出100美元，这可以说是最昂贵的环境投资之一，而不是清洁空气的成本。了解水质动态是必不可少的，但仍然是一个重大挑战，部分是由于其复杂的性质和数据稀缺。该项目旨在通过开发预测工具并促进有关如何以及为什么在不同条件和地点变化的知识来提高对水质动态的理解。这些结果将有助于决策者，水管理人员和更广泛的公众做出明智的决定，以确保水资源的可持续性。尽管在过去几十年中取得了巨大进展和努力，但水质测量仍然艰巨而昂贵，导致数据覆盖率不一致。因此，了解水质动态通常仅限于单个站点。该项目旨在确定在美国水质变量的调节集中释放关系的模式和过程。该项目将着重于常见的水质变量，包括硝酸盐，总磷和浊度（代理总悬浮沉积物）。该项目将测试浓度分离关系的空间模式是否主要由调节水文流道路和源水生物地球化学的土地使用（相对于其他驱动因素）驱动。假设将使用过程指导的深度学习进行测试，将传统的长期短期记忆模型与反应性传输模型相结合。集成将解决数据稀缺性的局限性和深度学习模型的“黑匣子”性质，并提高预测精度。该项目还将1）公开可用的重建数据； 2）在不受监视的时间，空间和未来情况下共享训练有素的预测模型； 3）创建视频，以教育利益相关者如何使用模型； 4）扩大参与人工智能/机器学习领域。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准被认为值得通过评估来获得支持。