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The freshwater and coastal ecosystems of the Everglades provide many socioeconomic benefits including important recreational and tourism opportunities, key fishery habitat, water quality improvements, flood and erosion mitigation, and mitigation of greenhouse gases through carbon storage and sequestration. Increasing pressures from sea-level rise (SLR) will influence the number, types, and value of ecosystem services expected in the future. These pressures combined with the geological history of Florida put the Everglades at the “forefront of transition.” In the peat-based portions of the Everglades, where water depths can be altered either by changes in water elevation due to sea level and rainfall or by changes in elevation of the peat surface due to biophysical processes, slight changes in the depth and period of inundation can influence the presence and distributions of plant species and communities. As a flat, low-lying landscape, the conventional thinking is that Everglades coastal habitats will gradually migrate upslope with increases in sea level as a transgressive transition of saltwater into freshwater sawgrass marshes. Inland transgression of mangroves has been suggested as a means by which subtropical and tropical coastal landscapes will “adapt” to increasing sea-level rise (SLR). However, erosion of coastal peats, inundation ponding, and overstep (salt water encroachment too fast for all communities to retreat) has already been observed in the southeast saline Everglades. In several areas of coastal Everglades National Park, freshwater and oligohaline Everglades wetlands are expected to be exposed to increased duration and inundation of seawater, impacting processes that may lead to peat collapse. In some cases, the freshwater marsh has collapsed by some 0.5 m over a period of a few decades and has converted to an open water, mangrove-free environment. If coastal communities cannot adapt to the salinity changes associated with increasing sea levels, then significant coastal wetland loss may occur, dramatically altering and increasing the vulnerability of the south Florida coastline. Without restoration of freshwater flow to the Everglades, saltwater intrusion-induced peat collapse may be enhanced and landward migration of mangroves into freshwater peat soils stymied.

Measurements of tree growth, leaf litter production, aquatic respiration, decomposition rates, benthic invertebrates and floating communities of plants and animals were taken from an impounded cypress-tupelo swamp of the Upper Barataria Basin. A dynamic computer simulation model was designed to identify the significant associations among the aquatic components as a function of environmental forcing functions (i.e. temperature, surplus precipitation, water flow, extent of dry season), and biological parameters (i.e. growth rates, predator-prey relations, assimilation efficiences). It was also developed to predict the response of an impounded swamp habitat to various management options, especially manipulation of the hydrologic regimes. Swamp impoundments, for example, were found to increase their annual energy exports by 43% when spoil banks were removed. Results indicate the sensitivity of swamp ecosystem response to changes in hydrology and the options for management of swamp ecosystems.

A spatial simulation model was constructed to help understand the historical changes in the Atchafalaya/Terrebonne marsh/estuarine complex in south Louisiana and to project impacts of proposed human modifications. The model consists of almost 3,000 interconnected cells each representing 1 km 2. Each cell in the model contains a dynamic, nonlinear, simulation model. Variables include water volume and flow, sediment and salt concentrations, organic standing crop, and productivity. The balance between sediment deposition and erosion as influenced by these variables in this rapidly subsiding area is particularly critical to habitat succession and the productivity of the area. Primary input data for the model were detailed, digitized habitat maps prepared by the U.S. Fish and Wildlife Service for 1956 and 1978. In addition, long time series of field measurements are available for some variables. The data base assembled for the model includes annual changes in environmental forcing functions, such as river discharge, and human modifications, such as canals and levees. At present, we are in the preliminary stages of running and calibrating the model. Our current results mimic the spatial seasonal patterns of sediment, salinity and water flow reasonably well. Starting with the 1956 initial conditions the model correctly predicts the habitat type in 1978 of 68% of the almost 3000 cells. Changes in salinity zones were accurately predicted but the model predicts higher land loss rates in the fresh zone than those actually observed and does not do well at predicting the growth of the Atchafalaya delta. The former problem may have to do with the prevalence of floating marsh in the fresh zone, which the model does not adequately consider at present. This paper discusses: (1) the structure of the model; (2) the spatial data base necessary to run the model; (3) some preliminary results concerning the temporal and spatial distributions of some of the state variables; and (4) future directions for the model. In general, this approach seems to be applicable to modeling spatial ecosystem dynamics, but the size and computational complexity of the model makes calibration and verification difficult.

A spatial simulation model was constructed to help understand the historical changes in the Atchafalaya/Terrebonne marsh/estuarine complex in south Louisiana and to project impacts of proposed human modifications. The model consists of 2,479 interconnected “lcells,” each representing 1 square km. Each cell in the model contains a dynamic, nonlinear, simulation model that has evolved out of the modelling work mentioned in the previous chapter. Variables include water volume and flow, relative elevation, sediment, nutrient, and salt concentrations, organic standing crop, and productivity. The model produces weekly maps of all the state variables and habitat types. Habitat succession occurs in a cell in the model when physical conditions change sufficiently, so that the new conditions better match the “signature” of another habitat. In this chapter we: 1) summarize the history of the Louisiana coastal land loss problem and suggested solutions; 2) briefly discuss the model's structure, data base, and degree of fit with historical data; 3) discuss the uses and implications of the model, particularly as regards the estimation of the impacts of canals and levees on coastal marsh systems; and 4) outline the potential interface between the model and management agencies to provide solutions to pressing coastal management problems.