Research ProjectBlue Carbon: National Estimates

Quantifying Uncertainty in National Estimates of Coastal Wetland Carbon Storage

  • blue carbon map of SERC area

    Blue-carbon map of marshes surrounding SERC

  • field studies

    SERC staff survey vegetation at G-CREW (Photo Credit: Meng Liu)

  • Field studies

    Researchers take a sediment core from a salt marsh (Photo Credit: James Holmquist)

Project Goal

In coastal wetlands, carbon dioxide can be removed from the atmosphere by plants and stored in sediments. We seek to better account for this carbon sink in the United States and to better anticipate how coastal wetlands will respond to sea-level rise.


“Blue Carbon” refers to carbon stored in salt marshes, mangroves, tidal freshwater wetlands, and sea-grass beds. Coastal wetlands have been historically drained, dredged, and developed.  This destruction has reduced Blue Carbon storage as well as other valuable services, such as habitat for endangered species, protection from coastal storm surge, and nursery areas for fish. Coastal wetlands can withstand moderate rates of sea-level rise by gaining soil elevation through vertical soil accumulation, but higher rates of sea-level rise can drown marsh surfaces. Halting wetland loss and restoring degraded wetlands are important tools for climate change mitigation.

The International Panel on Climate Change recognized the importance of Blue Carbon when they published the 2013 Wetland Supplement, a document that outlines three tiers of strategies for national Blue Carbon accounting. We are funded by NASA Carbon Monitoring Systems to develop national estimates of Blue Carbon storage from publicly available data, and to refine estimates using process-based models for six sentinel sites in Puget Sound, San Francisco Bay, the Louisiana Coast, the Everglades, the Chesapeake Bay, and Cape Cod. We will validate these estimates to measure their accuracy, to document sources of error and uncertainty, and to calculate how much additional precision is gained from using sophisticated data-intensive techniques.