Florida Bay (Fla.)

Iron and manganese redox chemistry are important drivers of sulfur cycling in marine sediments. Florida Bay sediments are extremely sulfidic, having been attributed to mass mortality of seagrass and oxygen depletion in the water column. This research used conventional sediment analyses and a diagenetic model to infer the overall capacity for Florida Bay sediments to eliminate hydrogen sulfide and prevent high rates of sediment dissolved oxygen consumption via hydrogen sulfide reoxidation. Previous studies have suggested that iron is important for buffering hydrogen sulfide in Florida Bay sediments, while the results of this project show for the first time that this phenomenon is relevant only in specific locations and times of the year. However, my research indicates that Fe has the potential to sequester sulfides and minimize hypoxia in the Everglades system. Thus, under a scenario that greater amounts of Fe are delivered to Florida Bay sediments from freshwater flows under Everglades restoration, Fe could be a component of ecosystem management.

One of the largest restoration programs in the world, the Comprehensive Everglades Restoration Plan (CERP) aims to restore freshwater flows to the Everglades and Florida Bay estuary. Coupled with climate change, future changes from restoration highlight the need to implement an ecosystem-based fisheries management (EBFM) approach in Florida Bay. The Ecopath framework was used to develop and apply a mass-balanced food web model to the spatiotemporal dynamics of hydrological restoration and climate change through time. Results suggest Florida Bay is stabilized through large detrital energy pathways and low nutrient inputs, but subject to species distribution shifts due primarily to sea-level rise and salinity variation. A suite of winners and losers predicted provide an opportunity to ensure management strategies are designed appropriately to best achieve desired results for the future of the Florida Bay ecosystem.

Understanding and resolving the water quality problems that Florida Bay has
endured requires an understanding of its salinity drivers. Because salinity is the prime
factor that drives estuarine ecosystem, Florida Bay’s ecosystem health depends on the
correct salinity balance of the Bay. In this thesis, the Regional Oceanic Modeling System
- a hydrodynamic prognostic model -was implemented on Florida Bay and it was tailored
for shallow waters. Results show that the model captures most of the salinity spatial and
temporal variability of Florida Bay. Furthermore, it establishes the role of the major
drivers like evaporation, precipitation, and runoff on Florida Bay’s salinity. The model
resolves region specific salinity drivers in all four areas of Florida Bay characterized by
their own salinity regimes. The model was also able to reveal the impact of surface runoff
on salinity in the later part of the year when evaporation increases. A new technique was
developed to estimate the discharge and salinity of unmonitored small creeks north of
Florida Bay. Those data were estimated from the relationship between net freshwater flux, runoff, and salinity. Model results revealed the importance of accounting for these
small creeks to accurately simulate Florida Bay’s salinity.

Human activities in the past century have caused a variety of environmental
problems in South Florida. In 2000, Congress authorized the Comprehensive Everglades
Restoration Plan (CERP), a $10.5-billion mission to restore the South Florida ecosystem.
Environmental projects in CERP require salinity monitoring in Florida Bay to provide
measures of the effects of restoration on the Everglades ecosystem. However current
salinity monitoring cannot cover large areas and is costly, time-consuming, and laborintensive.
The purpose of this dissertation is to model salinity, detect salinity changes, and
evaluate the impact of salinity in Florida Bay using remote sensing and geospatial
information sciences (GIS) techniques. The specific objectives are to: 1) examine the
capability of Landsat multispectral imagery for salinity modeling and monitoring; 2)
detect salinity changes by building a series of salinity maps using archived Landsat images; and 3) assess the capability of spectroscopy techniques in characterizing plant
stress / canopy water content (CWC) with varying salinity, sea level rise (SLR), and
nutrient levels.
Geographic weighted regression (GWR) models created using the first three
imagery components with atmospheric and sun glint corrections proved to be more
correlated (R^2 = 0.458) to salinity data versus ordinary least squares (OLS) regression
models (R^2 = 0.158) and therefore GWR was the ideal regression model for continued
Florida Bay salinity assessment. J. roemerianus was also examined to assess the coastal
Everglades where salinity modeling is important to the water-land interface. Multivariate
greenhouse studies determined the impact of nutrients to be inconsequential but increases
in salinity and sea level rise both negatively affected J. roemerianus. Field spectroscopic
data was then used to ascertain correlations between CWC and reflectance spectra using
spectral indices and derivative analysis. It was determined that established spectral
indices (max R^2 = 0.195) and continuum removal (max R^2= 0.331) were not significantly
correlated to CWC but derivative analysis showed a higher correlation (R^2 = 0.515 using
the first derivative at 948.5 nm). These models can be input into future imagery to
predict the salinity of the South Florida water ecosystem.