CAREER: Dynamic connectivity: a research and educational frontier for sustainable environmental management under climate and land use uncertainty

动态景观代表了水文、环境和人为特征的网络，这些特征协同工作以赋予生态系统效益并满足社会需求。在土地利用变化和气候变化的压力越来越大的情况下，景观面临越来越大的风险。如果我们要可持续地管理我们共享的水资源，了解景观如何动态地将水、泥沙和营养物质转移到河流以及人类在调节这种连接中所起的作用是至关重要的。因此，这项工作背后的驱动力问题是“人类如何让我们周围的景观变得更糟，我们如何能够将它们变得更好？”该项目将回答这些问题，并通过将农业、市政和雨水专业知识与高频水生遥感、深度学习建模和大样本水质数据集相结合，来推进可持续水管理的研究和教育的前沿。这项研究将产生基础科学进展，以确定不同气候、地貌和人为环境下对河流系统的景观负荷的规模、持续时间和程度。今天的学生将成为明天的利益相关者，他们的教育通过实践经验深深植根于该项目，这些实践经验将使他们具备处理大数据和应对社会最重大的水资源挑战的信心和沟通技能。当代水文科学研究认识到连通性在水循环的大多数方面的重要性；然而，尽管连通性无处不在，但连通性往往被定性地或在静态的结构范围内进行评估。拟议中的研究有可能在走向动态连通性评估方面发挥变革性的作用。该项目将量化美国在时间和空间上的动态连通性。这将通过利用来自150多条河流的硝酸盐和浊度的高频水生传感器来实现，这些传感器作为深度学习模型的训练数据。此外，动态连通性的数学描述将通知连接的主要路径。可解释的机器学习技术将把动态的景观属性如何导致河流水质影响联系起来。此后，将通过为从业人员开发的网络应用程序评估使用动态连接作为管理工具的可能性。结果将直接导致对未来利益相关者的教育和培训，包括通过在高中环境中建立大数据信心和大学生的科学交流技能。该项目由水文科学和既定的激励竞争研究计划(EPSCoR)联合资助。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。

Dynamic landscapes represent a network of hydrologic, environmental, and anthropogenic features that work in tandem to confer ecosystem benefits and provide for societal demands. Increasingly, landscapes are at risk under the growing pressures of land use alteration and climate change. Understanding how landscapes dynamically connect the transfer of water, sediment, and nutrients to rivers and the role humans play in modulating this connectivity is crucial if we are to sustainably manage our shared water resources. Thus, the driving questions behind this work are “how have humans changed the landscapes around us for the worse and how are we able to manage them for the better?” This project will answer these questions and advance the frontiers of research and education for sustainable water management by coupling agricultural, municipal, and stormwater expertise together with high-frequency aquatic sensing, deep learning modeling, and large-sample water quality datasets. This research will generate fundamental scientific advances to identify the magnitude, duration, and extent of landscape loading to river systems across climatological, geomorphic, and anthropogenic settings. The education of today’s students, who will become tomorrow’s stakeholders, is deeply embedded in this project through hands-on experiences that will equip them with the confidence and communication skills to handle big data and tackle society’s grandest water challenges. Contemporary research in hydrologic sciences recognizes the importance of connectivity in most aspects of the water cycle; however, despite its ubiquity, connectivity is often assessed either qualitatively or in a static, structural context. The proposed research has the potential to be transformative in moving toward a dynamic assessment of connectivity. This project will quantify dynamic connectivity through time and across space for the United States. This will be achieved by leveraging high-frequency aquatic sensors for nitrate and turbidity from over 150 rivers, which serve as training data for a deep learning model. Further, a mathematical description of dynamic connectivity will inform dominant pathways of connection. Explainable machine learning techniques will link how dynamic landscape attributes lead to riverine water quality impacts. Thereafter, the potential to use dynamic connectivity as a management tool will be assessed through a web application developed for practitioners. The outcomes will lead directly into the education and training of the stakeholders-of-tomorrow, including through building big data confidence in high school settings and science communication skills in college students.This project is jointly funded by Hydrologic Sciences and the Established Program to Stimulate Competitive Research (EPSCoR).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.