U.S.-Ireland R&D Partnership: Sensor Application to Peatland Hydrology in Remote Environments (SAPHIRE)

This is a project that is jointly funded by the National Science Foundation’s Directorate of Geosciences (NSF/GEO) and both the via an NSF/Republic of Ireland/Northern Ireland Lead Agency Agreement. This Agreement allows a single proposal to be submitted and merit reviewed by NSF, for which there are PIs from each of the three participating countries.

Upon determination of an award by NSF, the Agency in each country funds the proportion of the budget and the investigators associated with its own investigators and component of the work. Peatlands make up approximately 3% of surface land globally but peatlands contain as much as 40% of the total global soil carbon, making bogs an important component of the Earth’s carbon system.

Yet there is little information on the relationship between the impact of changing water flows and climate with the chemical processes that store and transport elements such as carbon from peatland environments. This work will apply off-the-shelf sensor technology to assess the impact of changing hydrological conditions on peatland water quality in a remote area in Ireland.

The understanding of these processes can be applied to other important peatland regions such as those in the United States and Canada. This project is joint between the Ohio State University group and collaborators from Northern Ireland and the Republic of Ireland. The collaboration will allow an early career scientist from the United States to develop international research connections.

In addition, outreach activities will be developed to communicate the importance of peatlands on the global carbon cycle.

The project will integrate a state-of-the-art sensor network with conventional water sampling techniques to develop an improved, more quantitative relationship between hydrological variations and peatland biogeochemical processes and elemental fluxes. This will include evaluating the relative importance of high energy events like stream floods on bog hydrology and chemodynamics.

The project will also use machine learning to integrate data on stream flow collected by the sensor network to grow knowledge about how hydrology influences the flux of chemicals from bogs. Overall, the information generated will lead to an enhanced understanding of the influence of hydrological variations on water quality and watershed scale processes in areas dominated by peatland landscapes.

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.