Date of Award

Spring 6-14-2026

Document Type

Thesis (Undergraduate)

Department

Earth Sciences

First Advisor

Meredith Kelly

Second Advisor

Meagan Eagle

Abstract

Preserving “Blue Carbon”, or the carbon buried in suboxic or anoxic soil in coastal environments, is emerging as a potential natural climate solution (Fargione et al., 2018; Mcleod et al., 2011). Tidal wetlands act as both carbon sinks through uptake by vegetation and soil storage and serve as carbon sources via lateral transport of carbon through tidal flushing and emission of greenhouse gases (GHGs), including carbon dioxide (CO₂) and methane (CH₄) (Bansal et al., 2023). The C-NEWTS (“Carbon New England Wetland Time Series”) project, an NSF-funded collaboration between the USGS Woods Hole Coastal and Marine Science Center, Woods Hole Oceanographic Institution, and Old Dominion University, examines carbon cycling and carbon storage in wetlands through in situ measurements and data analysis. My specific role in this project contributes to the model of carbon cycling in Cape Cod, MA, wetlands by investigating CO₂ and CH₄ fluxes into and out of salt marshes in the 2024 and 2025 extended growing seasons. Through GHG data collection by static and floating chamber methods and a LiCor-7810 GHG analyzer, my project quantifies and models these fluxes across five field sites. In addition, data collected by the USGS regarding soil temperature, photosynthetically active radiation (PAR), water table depth, and salinity identifies what environmental factors may be driving GHG fluxes most significantly. I also use environmental data to test and evaluate an existing scaling model which predicts GHG fluxes based on soil temperature, PAR, and salinity. While regional models provide a baseline for this research, micro-geographic and environmental variations are not well understood. These five field sites demonstrate a net uptake of CO2 by marsh vegetation, and a net emission of CO2 by surface water channels. Both marsh vegetation and surface water channels show a very slow release of CH4. The existing scaling model is best suited to predict ecosystem respiration, as the predictive equation only relies on soil temperature, which demonstrates the strongest relationship with GHG fluxes. The addition of more sensitive, site-specific variables, like PAR and soil salinity, into the predictive equations for net ecosystem exchange and CH4 flux result in the model having a harder time predicting flux values.

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