Mechanisms, Rates, and Patterns of Shallow Subsidence in Coastal Louisiana

Abstract:

It is increasingly clear that the exceptionally high land-surface subsidence rates in coastal Louisiana (as inferred, among others, from tide-gauge records) are primarily due to processes that operate in the uppermost tens of meters of the subsurface. Considerable efforts are needed to better quantify these processes in terms of their rates and spatial patterns. Only then the predictive models can be developed that so far have remained elusive. Here we present shallow stratigraphic data, demonstrating that compaction within the Holocene column can explain a large portion of the subsidence that is observed at the land surface. Loading of water-rich deltaic strata with clastic sediment is an effective mechanism to drive subsidence rates up to at least 5 mm/yr that can persist for centuries if not longer. While stratigraphic studies offer detailed insights in the dominant processes, they have less potential to determine present-day subsidence rates and to predict future subsidence rates and their spatial patterns.

The Coastwide Reference Monitoring System (CRMS), a major effort initiated by the US Geological Survey to monitor Louisiana’s coastal wetlands, offers unique new opportunities to study shallow subsidence on sub-decadal timescales. All of the ~400 CRMS sites include a rod-surface elevation table combined with marker horizons, resulting in detailed records of surface-elevation change and vertical accretion rates. Here we use these data to determine rates of shallow (<45 m) subsidence throughout coastal Louisiana over the past 5-7 years, primarily due to sediment compaction. For the 226 sites with at least 5 years of observation, the range of shallow subsidence rates varies by several tens of mm/yr. Statistical analysis of the relationship between shallow subsidence and other wetland characteristics (e.g., organic matter content, salinity) helps to elucidate the spatial patterns of modern subsidence, with the ultimate objective to improve subsidence predictions.