Big Data in the Critical Zone: Deciphering Controls on Hydro-Biogeochemical Processes Across Scales

The Critical Zone (CZ), extending from the top of the canopy to the bottom of circulating groundwater, is shaped by a myriad of hydro-biogeochemical processes. Historically, understanding the long-term and large-scale drivers of these processes has been challenging due to data limitations. However, the increasing availability of comprehensive CZ data—spanning multiple sites, years, variables, and disciplines—has opened new possibilities for uncovering these complex processes.

"Big data" approaches, including machine learning and deep learning, can illuminate the controls on surface and subsurface processes across extensive environmental gradients, and illustrate how CZ processes respond to changing climate and land use. These advanced techniques enable analysis across large spatiotemporal scales, offering new insights into CZ dynamics.

The Big Data in the Critical Zone: Deciphering Controls on Hydro-Biogeochemical Processes Across Scales AGU24 session welcomes contributions that employ innovative methods such as machine learning, deep learning, or other statistical approaches to understand CZ processes using large datasets. We are particularly interested in interdisciplinary or cross-disciplinary studies that advance our understanding of the Critical Zone in novel ways. Join us at AGU24 to share your groundbreaking research.

July 31, 2024 is the deadline to propose an abstract to include in this session.

Primary Convener

• Samuel Shaheen
  • Yale University

Conveners

• Kayalvizhi Sadayappan
  • Pennsylvania State University Main Campus
• Keira Johnson
  • Oregon State University
• Valerie Smykalov
  • Pennsylvania State University Main Campus

Student/Early Career Convener

• Ijaz UL HAQ
  • University of Vermont