USGS Scientists publish article describing the application of regressive modeling techniques to improve marsh unit thickness data in coastal Alabama and Mississippi
SPCMSC Research Geologist Christopher G. Smith recently published a manuscript titled "Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets."
This published research is one of the first studies to assess geomorphic predictors of marsh unit thickness of a fringing coastal brackish marsh. The geomorphic predictors assessed were based largely on relict features related to antecedent geology. The most effective predictors were determined to be distance from upland, distance to shoreline, and distance to water; as well as marsh width for the Grand Bay marsh in coastal Alabama and Mississippi. Specifically, the study site included public lands managed as part of the Grand Bay National Estuarine Research Reserve and the Grand Bay National Wildlife Refuge. These predictors allowed the team to quantitatively map marsh unit thickness and to discuss how the mapped unit contributes to improve coastal sediment budget knowledge, including bulk sediment and sedimentary carbon reservoirs in public lands.
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