DisC-PP · Groundwater DIScharge of Carbon in Permafrost Peatlands (DisC-PP)

A better understanding of high-latitude terrestrial carbon (C) cycling is essential for predicting global climate futures. This urgency arises from the large C stocks present in permafrost soils and the rapid regional warming observed during the past decades. Complexity introduced by strong coupling between hydrology, soil temperature, and biogeochemical processes in these systems impedes efforts to close the C budget and understand the fate of newly thawed soil organic C. Notably, groundwater discharge of C (GDC) stands out as a significant source of uncertainty in the high-latitude C budget because these fluxes are difficult to measure, highly heterogeneous, and strongly impacted by permafrost thaw.The main objective of the DisC-PP project is to improve understanding of GDC fluxes within permafrost peatlands. Groundwater plays a key but uncertain role in the functioning of these distinctive, C-rich wetland ecosystems. By combining field measurements with ecosystem modeling, DisC-PP will address three knowledge gaps: (1) what are dominant drivers responsible for spatial and seasonal fluctuations in GDC from permafrost peatlands?, (2) how will climate-induced changes to hydrology and C cycling impact GDC?, and (3) what is the contribution of GDC to the overall C balance of permafrost peatlands? The project will be conducted at Stordalen Mire, an intensively characterized permafrost peatland in subarctic Sweden. The modeling component will employ ecosys, a validated process-rich mechanistic ecosystem model that has demonstrated applicability in high-latitude ecosystems.DisC-PP would significantly advance my career via relevant skill development, exposure to different high-latitude ecosystems, and establishment of new relationships with leading experts in ecohydrology. Additionally, the project will enhance comprehension of how lateral C fluxes via groundwater discharge impact high-latitude C cycle and climate feedbacks – a current gap in global climate models.