Delaware is home to extensive networks of tidal marshes, which provide an array of critical ecosystem services including carbon sequestration. These marshes accumulate carbon due to their relatively high plant productivity and relatively low rates of litter decomposition. Tidal marshes are subjected to a variety of ecological stressors, including relative sea level rise and human activities, which expose marsh soils to erosive forces and variable biogeochemical conditions. Such stressors may negatively impact the ability of marsh soils to sequester carbon, or lead to net carbon loss. The future of carbon cycling in tidal marsh soils is of great concern to climate change researchers and land managers, however large uncertainties still surround estimates of total carbon storage and spatial distributions of carbon storage in these ecosystems.
The DGS team will collect a set of core samples from marsh and fringe community soils from a variety of vegetation types, geomorphic features, and positions within marsh networks at St. Jones and Blackbird Marshes in Delaware. Using emerging techniques in the fields of digital soil mapping and machine learning, the team will develop statistical models relating point observations of soil carbon storage to spatial covariates derived from aerial imagery, satellite data, and high resolution LIDAR data. These models will be used to map spatially continuous estimates of soil carbon storage, which will then be compared to marsh vulnerability indices. The overarching goals of this work are to 1) improve estimates of stored carbon in Delaware tidal marshes, 2) identify hot spots of carbon storage within the marshes, and 3) assess areas of high vulnerability which may help inform marsh preservation and mitigation strategies. This project is funded in partnership with the Delaware National Estuarine Research Reserves.