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

University Of Kansas Center For Research Inc, Lawrence KS

Investigators

Abstract

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.