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。持续的营养物质污染已经在全球范围内造成了富营养化和有害的藻类大量繁殖，据估计，仅在美国每年就会造成超过40亿美元的损失。营养物污染威胁生态系统和粮食生产。随着全球城市人口的增加，土壤侵蚀将继续加剧。自1972年以来，美国在改善水质方面已经花费了超过1万亿美元，相当于每个美国人每年花费100美元，这使得清洁水可以说是最昂贵的环境投资之一，超过了清洁空气的成本。了解水质动态是至关重要的，但仍然是一项重大挑战，部分原因是水质的复杂性和数据稀缺。该项目旨在通过开发预报工具和增进关于水质在不同条件和地点下如何变化以及为什么变化的知识，提高对水质动态的了解。结果将帮助政策制定者、水资源管理者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的进展和努力，但水质测量仍然困难且昂贵，导致数据覆盖范围不一致。因此，对水质动态的了解往往仅限于个别地点。该项目旨在确定美国各地水质变量的浓度-排放关系的模式和过程。该项目将侧重于常见的水质变量，包括硝酸盐、总磷和浊度(总悬浮泥沙的替代指标)。该项目将测试浓度-排放关系的空间模式是否主要由土地利用(相对于其他驱动因素)驱动，这些土地利用调节水文流动路径和源水生物地球化学。这些假设将通过过程引导的深度学习来验证，该学习将传统的长期短期记忆模型与反应性传输模型相结合。整合将解决数据稀缺的局限性和深度学习模型的“黑箱”性质，并提高预测精度。该项目还将1)公开重建的数据；2)共享训练后的模型，用于在不受监控的时间、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；以及4)扩大对人工智能/机器学习领域的参与。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.