Modeling Spatial Distributions of Salt Marsh Blue Carbon Using Morphometric Parameters From Lidar

Abstract

Salt marshes sequester large amounts of carbon, mainly within their deep soils. Several nationwide assessments have indicated that spatial variability of marsh soil carbon is minimal, however there's a need to further reduce carbon stock uncertainties by exploring finer-scale variation using a process-based modeling approach. Marsh soil properties vary spatially with several parameters, including marsh platform elevation, which controls inundation depth, and proximity to the marsh edge and tidal creek network, which control variability in relative sediment supply. We used lidar to extract these morphometric parameters from salt marshes to map soil organic carbon across a marsh at the meter scale. Soil samples were collected in 2021 from four northeast U.S. salts marshes with distinctive geomorphologies. Tidal creeks were delineated from 1-m resolution topobathy lidar data using a semi-automated workflow in GIS. Log-linear multivariate regression models were developed to predict soil organic matter, bulk density, and carbon density as a function of predictive metrics at each site and across sites. Distance from tidal creeks was the most significant model predictor. Modeling marsh soil characteristics worked best in marshes with single channel hydrology. Addition of distance to the inlet and tidal range as regional metrics significantly improved cross-site modeling. Our mechanistic approach reveals important meter-level variation in soil characteristics across a marsh and provides motivation to continue rigorous mapping of soil carbon at fine spatial resolutions. Furthermore, carbon density values used to calculate total marsh carbon stocks should be carefully selected depending on project scale, marsh geomorphology, and desired accuracy.