Continental-Scale ModEx to Understand Sediment Respiration Using ICON Science

Authors

James Stegen^1,2* ([email protected]), Amy Goldman¹, Stefan Gary³, Brieanne Forbes¹, Vanessa Garayburu-Caruso¹, Brianna Gonzalez¹, Sophia McKever¹, Emily Rexer¹, Lupita Renteria¹, Tim Scheibe¹, Matthew Shaxted³, Alvaro Vidal Torreira³, Michael Wilde³

Institutions

¹Pacific Northwest National Laboratory, Richland, WA; ²Washington State University, Pullman, WA; ³Parallel Works, Inc., Chicago, IL

URLs

• https://www.pnnl.gov/projects/river-corridor

Abstract

In river corridors, sediments from the hyporheic zone can dominate channel metabolism, but it can also be a minor contributor, with literature estimates ranging from 4–96% of stream metabolism coming from the hyporheic zone. At present, no models can explain among-stream-reach variation in the hyporheic zone contribution to stream metabolism. This significant gap in river corridor science needs to be addressed to advance collective ability to understand and predict the future state of river corridor hydro-biogeochemical function (e.g., greenhouse gas emission rates). The research team approached this challenge with a continental-scale effort aimed at (1) producing knowledge and models that are transferable and generalizable across diverse river corridor settings and (2) generating science outcomes, data products, and modeling infrastructure that is mutually beneficial across a broad range of stakeholders.

To achieve these goals, ICON science principles are being used to conduct an ongoing study that is integrated across disciplines, coordinated via use of consistent protocols, open throughout the research lifecycle, and networked with multiple stakeholders to understand and respond to diverse needs. Through globally open engagement prior to initiating the study, the research team received feedback and modified the study design so that project outcomes would be beneficial to as many stakeholders as possible. Initial engagement was followed by crowdsourcing samples across the contiguous United States, with sampling locations guided by machine learning (ML) models. Resulting estimates of hyporheic zone respiration were used to test the ML models, update those models, and generate new ML-based guidance on where to sample next. This feedback between models and data generation is ongoing monthly, with significant changes to the spatial distribution of prioritized sampling locations. The engagement process is also approached as an iterative loop, with follow-on engagement in educational settings with direct student participation. Intentional use of ICON principles and iterative feedback between models and data are providing new opportunities for a broad range of researchers, offering unprecedented abilities to predict and understand hyporheic zone biogeochemistry, and generating FAIR products from which all can benefit.