This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).
This project will examine how the interaction of climate, the physical and chemical characteristics of the bedrock, and the action of vegetation, control the movement and storage of water and carbon on Earth's surface. These processes in turn influence climate by altering important factors such as greenhouse gas concentrations like H2O and CO2. Human activities can change these pathways, and this research will enable the forecasting of the possible impacts upon the Earth-surface environment. To achieve this goal requires synthesizing existing datasets, collecting new data, and training teams of people in the fields of water science, geochemistry, soil science, geophysics, ecology, and Earth system modeling. The project will include 28 undergraduate students, four graduate students, and three postdoctoral scholars across seven universities to collectively explore how the interaction of plant roots and bedrock regulate water and carbon movement between the land and atmosphere. The project will also train 45 educators to develop discovery-based learning approaches in their classes, the products of which will be publicly accessible on available web platforms.
This project will investigate when and to what degree bedrock exerts more control than roots on water and carbon fluxes. Using an interdisciplinary approach that incorporates new data collection, data harvesting, machine learning, and numerical modeling, this research will determine the mechanisms by which bedrock and fracture distributions govern the development of preferential flow paths. It will also examine depth, degree, and timing of coupling between the subsurface and atmosphere and its impact on water storage and fluxes. The project will explore how plant roots interact with bedrock to shape the subsurface structure, associated carbon storage, and transpiration rates. Methods will include 3D geophysical surveys and structural soil pore analyses to determine the occurrence of changes in the subsurface and how they govern root water uptake. Global in situ and remotely sensed data will be integrated via machine learning to discern emergent patterns in subsurface structure on larger scales. The project will leverage existing datasets and collect new data from the NSF Critical Zone Cluster Networks (CZCNs), National Ecological Observatory Network (NEON), and Long-Term Ecological Research (LTER) programs. The ultimate outcome will be a comprehensive framework of hydro-biogeochemical linkages to forecast how climatic conditions and subsurface structure regulate hydrological flow and the carbon cycle.
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.
|Effective start/end date||8/1/21 → 7/31/25|
- National Science Foundation: $291,405.00