Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.

ORCID

https://orcid.org/0000-0003-2773-7726

Access Type

Open Access Thesis

Document Type

thesis

Embargo Period

1-15-2023

Degree Program

Geography

Degree Type

Master of Science (M.S.)

Year Degree Awarded

2023

Month Degree Awarded

February

Abstract

Tidal marshes serve as important “blue carbon” ecosystems that accrete large amounts of carbon with limited area. While much attention has been paid to the spatial variability of sedimentation within salt marshes, less work has been done to characterize spatial variability in marsh carbon density. Driven by tidal inundation, surface topography, and sediment supply, soil properties in marshes vary spatially with several parameters, including marsh platform elevation and proximity to the marsh edge and tidal creek network. We used lidar to extract these morphometric parameters from tidal marshes to map soil organic carbon (SOC) at the meter scale. Fixed volume soil samples were collected at four northeast U.S. tidal marshes with distinctive morphologies to aid in building our predictive models. Tidal creek networks were delineated from 1-m resolution topo-bathy lidar data using a semi-automated workflow in GIS. Sample distance to tidal creeks and flow distance to the marsh edge were then determined. Log-linear multivariate regression models were developed to predict soil organic content, bulk density, and carbon density as a function of these predictive metrics at each site and across sites. Results show that modeling salt marsh soil characteristics with morphometric inputs works best in marshes with simple, single creek morphologies. Distance from tidal creeks was the most significant model predictor. Addition of distance to the inlet and tidal range as regional metrics significantly improves cross-site modeling. Our process-based approach results in predicted total marsh carbon stocks comparable to previous studies but provides trade-offs to existing simplistic carbon mapping methods. Further, we provide motivation to continue rigorous mapping of soil carbon at fine spatial resolutions and to use these results to guide salt marsh restoration projects and aid in the development of carbon markets.

First Advisor

Qian Yu

Second Advisor

Brian Yellen

Third Advisor

Jonathan Woodruff

Download

Share

COinS