CAREER: Characterizing Climate Change Feedbacks in Arctic Ponds while Incorporating Next-Generation Technologies and Arctic Field Experiences in Education

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

Wetlands represent a significant portion of the Arctic landscape and are characterized by their numerous polygonal thaw ponds. These Arctic pond habitats are hotspots for biodiversity and carbon cycling. Particularly, ponds are key emitters of methane (CH4), a potent greenhouse gas that enhances climate change. This project will characterize the role of Arctic wetland ponds in regional land-atmosphere carbon exchange, estimate their contributions of CH4 to the atmosphere, and assess how they have changed over the past 50 years to better anticipate their future role in Arctic carbon cycling and feedbacks to climate. This project also has two major educational components: (1) train the next generation of scientists from underrepresented groups to design and lead Arctic fieldwork intended to deepen understanding of the Arctic system and (2) incorporate virtual reality, drone sensors, and machine learning into education to improve engagement in STEM and Polar Sciences. These student-technology interactions will spur creativity and innovation, providing them with a competitive edge for academic and industry positions. The broader impacts of the project include summer outreach activities with Alaskan Indigenous communities and a photo book design by students that portrays Arctic landscapes, Indigenous communities, and the threats of climate change through a cartographic perspective. The book will communicate and promote awareness about the Arctic region to a general audience. Despite their importance to regional CH4 budgets, ponds have been generally understudied and thus, underrepresented in Arctic and global CH4 estimates and earth system models. Therefore, it is unknown how their evolution will impact future land-atmosphere carbon exchange and potential feedbacks to climate. This study will (1) establish a foundational understanding of surface-atmosphere carbon dynamics of polygonal ponds, (2) characterize the timing and pathways of CH4 emissions, and (3) unravel the evolution of ponds under climate change and its carbon implications. The use of cutting-edge technologies including eddy-covariance flux system, drone & airborne imaging spectroscopy, drone LIDAR, and deep-learning Artificial Intelligence will allow us to characterize bottom-up and top-down regional scale CH4 emissions from arctic ponds. In addition, this study will rescue historical records to better understand biogeochemical changes of ponds over the past 50 years to answer where, when, and how the evolution of these aquatic systems has influenced surface-atmosphere carbon feedbacks to climate. 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.