RII Track-4: NSF:Assessing Dynamic Connectivity of Streams and Wetlands across Spatial and Human Gradients with Deep Learning

University Of Kansas Center For Research Inc, Lawrence KS

Investigators

Abstract

Wetlandscapes, the interconnected tapestries of streams and wetlands, are crucial for maintaining water quality standards, yet their coverage has dramatically decreased in recent centuries due to development and agricultural expansion. The extent to which these systems remove pollutants, such as nitrate, can greatly impact downstream aquatic systems. There exist major gaps in understanding the dynamic connections that act to intercept, retain, and transmit nitrate through wetlandscapes and the extent to which natural and human features control the strength of these connections. These gaps have limited ability to predict the function of these landscapes and manage them effectively. This research will provide an opportunity for a tenure-track assistant professor and a graduate student to propel research in the area of modeling wetlandscape connectivity and water quality through collaborations with the USEPA Center for Environmental Measurement and Modeling (CEMM) in Cincinnati, Ohio. The PI and a student will work with CEMM scientists, who have established state-of-the-science process-based models, to advance the modeling frontier with a deep learning modeling framework that can overcome the limitations of existing approaches. The PI’s home jurisdiction, Kansas, has lost 48% of its wetland coverage. Thus, this effort will improve infrastructure for modeling, managing, and maintaining these landscape features through the education of students and the development of open access tools to be shared with watershed scientists, managers, and stakeholders. This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4:NSF) project would provide a fellowship to an Assistant Professor and training for a graduate student at the University of Kansas. This work would be conducted in collaboration with researchers at the USEPA Center for Environmental Measurement and Modeling (CEMM). Historically, wetlandscape connectivity and contaminant removal efficiency has been viewed through a static lens whereby, due to data and computational limitations, the question often posed is “What is the net removal of nitrate over seasonal or annual periods?”. However, streams and wetlands are highly dynamic and there exist moments where disproportionate removal occurs. Further, it is often assumed that wetlandscape efficacy is controlled by hydrology, which determines the residence time of nitrate, but recent work shows a disconnect between efficacy at the individual wetland-scale, where removal appears high, compared to the catchment scale where overall removal decreases. Two knowledge gaps exist that the proposed research aims to close. The first is a move beyond a static assessment of connectivity and the second is an evaluation of the natural and human controls of wetlandscape removal efficiency. The research team will collaborate with researchers at the USEPA Center for Environmental Measurement and Modeling (CEMM) who have developed state-of-the-science, process-based wetland models. While process-based models can be effective tools, they are often highly parameterized to a single location and difficult to transfer across scales. Thus, the research objectives of this project are to (1) improve the representation of dynamic wetlandscape connectivity with a deep learning model and benchmark that model performance against existing process-based models and (2) quantify the hydrologic, anthropogenic, and geomorphic controls on nitrate removal efficacy using deep learning modeling across regions and scales. This RII Track-4:NSF fellowship will provide an opportunity for the PI to generate fundamental advances to identify, quantify, and predict wetlandscape behavior and to develop tools that stakeholders can use for improved land management outcomes. 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.