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JOHN W. DAY [1,2]


Abstract. A landscape model was developed to investigate and predict the environmental factors affecting wetland habitat change within the Barataria and Terrebonne basins of coastal Louisiana, USA. The model linked an overland-flooding hydrodynamic module, using cells of 100 [km.sup.2] in size and operating at a 1-h time step, and a spatially articulated ecosystem module, resolving habitat type and change for [1-km.sup.2] cells in daily time steps. Integration across different temporal and spatial scales was accomplished with interpolation routines and averaging algorithms. Forcing functions included dominant regional processes, such as subsidence, sedimentation, and sea-level rise. Hydrologic functions were calibrated against existing climate and hydrologic time series, while habitat information was compared to maps prepared by the United States Fish and Wildlife Service (USFWS) for 1978 and 1988.

Spatial calibration was done by initializing the landscape pattern of the model to a 1978 USFWS habitat map. After a 10-yr simulation, the results were compared against a 1988 USFWS habitat map. Simulated maps had an accuracy of 85-90 (out of a maximum of 100), based on a multiple resolution fit algorithm. For validation, the model was initialized with a 1956 USFWS habitat map, and the results from a 32-yr simulation were compared to the 1988 USFWS habitat map. The landscape model produced reasonable regional agreement, despite the fact that small-scale processes and features were not included. The validation runs produced land-loss rates that matched historical trends with an accuracy fit above 75.

The model simulated 30 years into the future, starting in 1988, testing for long-term climate variability under diverse scenarios. Results indicated that weather variability impacts land-loss rates more than replication of extreme weather years. Even when extreme dry and wet years were repeated, the model predicted lower land loss when compared to historical records. This is indicative of the ability of the simulated plant communities to adapt to repetitive climatic forcing functions.

Key words: Barataria Basin (Louisiana. USA); coastal habitat change; deltaic habitats, response to multiple impacts; landscape model, coastal; Mississippi River Delta (USA); spatial modeling; Terrebonne Basin (Louisiana, USA); watersheds.


Coastal habitats in the Mississippi delta (USA) are changing at unprecedented rates with displacement of freshwater vegetation by more salinity-tolerant communities and massive wetland loss resulting in conversion to open water. Within coastal Louisiana, wetland loss rates have ranged from 73 to 102 [km.sup.2]/yr (Gagliano et al. 1981). The Barataria--Terrebonne Estuarine Complex (Fig. 1) has the highest wetland loss rates along the Louisiana coastline where, under present conditions, coastal wetland life expectancies range from 50 to 200 yr (Gagliano et al. 1981). The general pattern of habitat change over the past 60 yr has been attributed to the interaction among several regional factors (Deegan et al. 1984, Walker et al. 1987, Wells 1996, Roberts 1997) including: (1) deltaic lobe abandonment, (2) eustatic sea-level rise and subsidence, (3) changes in the introduction of freshwater and sediments from the Mississippi and Atchafalaya Rivers, and (4) human modification of internal hydrology (Day and Templet 1989, Baumann and Turner 1990, Boesch et al. 1994, Reed 1995, Day et al. 1997, Roberts 1997, Turner 1997). However, these regional factors have been analyzed mostly at small scale (Salinas et al. 1986, Dozier et al. 1983, Deegan et al. 1984, Nyman et al. 1993). In this paper, we use a landscape model to examine the interactions of the cumulative regional impacts of these factors and variations in weather. We combined geological, hydrological, and meteorological forces to understand the forcing of this climate and its variability on a long-term basis. The deltaic regional factors, such as increased sediment delivery to wetlands and increased marsh flooding with sea-level rise, were mechanistically incorporated into the model.

Wetland elevation gain must equal relative sea-level rise (RSLR) to achieve long-term marsh stability (Chmura et al. 1992, Cahoon et al. 1995). Over the past 6000-7000 yr the Mississippi Deltaic Plain was formed by a series of overlapping delta lobes related to shifts in the course of the river (Penland et al. 1988, Roberts 1997). In abandoned deltaic lobes the land surface loses elevation relative to mean sea level due to compaction and consolidation of sediments (Baumana et al. 1984, Wells 1996). Sea level varies both interannually and long term. Interannual variability can be as high as 10 cm/yr due to changes in circulation patterns in the northern Gulf of Mexico (GoM), variations in river discharge, winter storms, and presence of tropical storms and hurricanes (Penland and Ramsey 1990). Long-term tide readings have shown that RSLR, which accounts for subsidence and eustatic sea-level rise, has averaged about 1.2 cm/yr over the last 30 yr (Penland and Ramsey 1990, Cahoon et al. 1995).

There have been extensive spatial and temporal changes in the hydrology of Mississippi Deltaic Plain. Construction of flood-control levees has isolated most wetlands from the river since the early 1900s (Mossa 1996), eliminating seasonal flooding and the introduction of sediments and nutrients. Currently, the primary source of sediments in most of the delta is resuspended sediment from bay bottoms and the nearshore GoM (Hatton et al. 1983, Baumann et al. 1984). This contribution has not been sufficient to offset RSLR in most of the coastal zone, leading to increased flooding duration and vegetation death (Mendelssohn et al. 1981). Recently, river diversions have been proposed and constructed to restore riverine inputs to preserve and maintain areas of deltaic wetlands (Boesch et al. 1994, Day et al. 1997).

Wetland hydrology has been highly modified by the widespread construction of dredged canals and associated spoilbanks (Turner 1997). Spoilbanks decrease the input of materials to adjacent wetlands and make these areas prone to excessive inundation (Swenson and Turner 1987, Boumans and Day 1993, Reed et al. 1997). Canal dredging increases wetland vulnerability to erosion, as shown by several studies relating canal density to wetland loss (Scaife et al. 1983, Bass and Turner 1997, Turner 1997). Additionally, losses have been caused directly by canal dredging and spoil placement. Such human impacts have had a significant impact on land loss. For example, direct land loss due to canal dredging accounted for more than 30% and 10% of the losses in Barataria and Terrebonne basins, respectively, between 1956 and 1990, with indirect effects (e.g., changes in hydroperiod, edge erosion) estimated to cause twice the land loss (Table 1; Reed 1995).

Modeling wetland habitat at the landscape level allows testing hypotheses about the dynamics of wetland loss, and is critical for understanding long-term effects of proposed restoration alternatives. This was previously done using spatially articulated landscape models for portions of Terrebonne (Sklar et al. 1985, Costanza et al. 1990, White 1991) and Barataria basins (White et al. 1991). These models are known generically as "coastal ecological landscape spatial simulations" (CELSS), and were defined by Sklar and Costanza (1991) as dynamic spatial interaction models with feedback. They incorporated location-specific algorithms to quantify influences from adjacent cells, such as the existence of sub-grid-scale channels or levees affecting material exchange (Boumans and Sklar 1990, Fitz et al. 1996). The explicit feedback between processes and the landscape allowed both the landscape and the intensity of the effecting processes to change throughout time. These models used a water-balance approach to hydrauli c processes. Algorithms incorporating this type of feedback have been used in aquatic modeling programs, such as LAPTER (Reyes et al. 1994), as well as in terrestrial simulation programs such as PATCH-MOD (Wu and Levin 1994), ECOLECON (Liu et al. 1994), and the Frankfurt Biosphere Model (Kindermann et al. 1996).

The objective of this study was to construct a landscape simulation model to predict habitat change in the Mississippi delta for a 30-yr time scale. Using some of the CELSS framework, we incorporated a hydrodynamic module and much enlarged study area (Sklar et al. 1985, Costanza et al. 1990, White 1991), as well as a revision of previous biological algorithms. We investigated the impacts of sea-level changes, Mississippi River discharge, and climate variability on habitat change rates in the Barataria and Terrebonne basins. The Barataria--Terrebonne ecological landscape spatial simulation (BTELSS) model replicated historical trends in land loss and habitat change from 1956 to 1988 for each basin, and then was used to predict trends into the future.


The Barataria--Terrebonne estuarine system includes two of the interdistributary estuarine wetland systems of the Mississippi delta (USA) and is bounded by the Mississippi and Atchafalaya rivers (Fig. 1). The Atchafalaya carries [sim] 30% of the total Mississippi River flow. The Barataria Basin is located between the natural levees of the Mississippi River and Bayou Lafourche, encompassing [sim] 4100 [km.sup.2] of wetlands and waterbodies. The Terrebonne Basin lies to the west of Barataria Basin and occupies [sim] 5300 [km.sup.2] (Fig. 1). Vegetation zones range from freshwater forested wetlands to fresh, brackish, and salt marsh communities (Chabreck 1972, Chabreck and Condrey 1979).

The Barataria Basin has been closed to direct river inflow since the leveeing of the Mississippi River and the closing of the Bayou Lafourche--Mississippi River connection in 1904. The Mississippi River exerts considerable influence on the lower part of the basin through its effect on salinity in the nearshore Gulf of Mexico (GoM; Walker 1996). Precipitation is the main source of fresh water for the basin; however, a small amount of river water enters the basin through the Gulf Intracoastal Waterway. Additional freshwater enters the basin through water runoff that is pumped from agricultural and urban areas.

In contrast, the Terrebonne Basin is strongly influenced by the fresh water from the Atchafalaya River. The western portion of this basin is one of the few locations in southern Louisiana that has experienced net land gain, with the growth of the Atchafalaya Delta (Adams et al. 1976, Adams and Baumann 1980, Roberts 1997). The complex interactions between the fresh water from the Mississippi and Atchafalaya rivers and the saline waters of the GoM are controlled by tides, frontal passages, seasonal sea-level variation, long-term sea-level changes, and shelf topography (Penland et al. 1988, Wiseman et al. 1990, Paille 1997).


The BTELSS (Barataria-Terrebonne estuarine landscape spatial simulation) model linked a hydrodynamics, a plant-production, and a soil-dynamics landscape module. The code utilized the same kind of input files and maps for both basins, allowing the use of the same conceptual framework, modules, and algorithms (Fig. 2). The results of the hydrodynamic and productivity modules were linked to the soil module, and then evaluated with a habitat-switching module that allowed the landscape to evolve on a biannual basis.

Hydrodynamic module

The two-dimensioned, vertically integrated hydrodynamic module used a finite difference scheme with a time step of 1 h and a spatial cell size of 100 [km.sup.2]. Freshwater inputs included rainfall, pumping from developed areas, and riverine inputs. Tidal boundary exchanges were a source or sink for salt water. Water was also lost by evaporation, whereas infiltration into groundwater was assumed negligible. The effect of wind stress on water flow was modeled with an exponential relationship (Yeh and Chou 1979) as described in the Appendix.

The effect of friction on water flow was modeled using the standard Manning equation (Martin and McCutcheon 1999), where the Manning coefficient was a 100-[km.sup.2] average of a 1-[km.sup.2] habitat-type-dependent Manning coefficient. This 1-[km.sup.2] Manning coefficient depended on the different habitats, and was based on their resistance to flow following both published literature values (Wolanski et al. 1980, Burke and Stolzenbach 1983, Wolanski et al. 1992) and knowledge of the hydraulic characteristics of each habitat. For example, swamps were more resistant to flow than grassy marshes (Freeze and Cherry 1979). Salt marshes had a lower Manning value because they are closest to the coast, more dissected by tidal channels than brackish and fresh marshes, and experience more tidal energy.

Standard hydrodynamic equations required a smaller time step than was practical for prediction of long-term effects, Therefore, the model used the diffusion wave approximation of shallow-water equations in the prediction of long-term water level (Singh and Aravamuthan 1995). This approximation considered local acceleration, uniform flow, and Coriolis force to be negligible. This solution has been used in other modeling efforts linking diverse scales (Baskin 1993, USFWS 1995). Full formulation of the finite difference equations (Casulli 1992) is presented in the Appendix.

A 100-[km.sup.2] average of land elevation and Manning coefficients was used as input to calculate water height and velocity. These resultant 100-[km.sup.2] distributions were then interpolated to 1-[km.sup.2] using a binomial interpolation routine (Press 1992). The interpolated water height and land-elevation values were then used to calculate water depth. Spatially distributed suspended sediment and salinity were computed using the same 1-[km.sup.2] interpolation algorithms.

Hourly water velocity and sediment concentration determined how much deposited sediment was accumulated each day. Once the 1-[km.sup.2] salinity and duration of flooding were computed, these were averaged to daily values. Suspended sediments were distributed in the same way as salinity with two differences: an empirical function (Chmura et al. 1992) was used to resuspend sediments by wind-focused wave action in the bays, and sediments were lost from the water column by deposition on marsh surfaces based on settling velocity.

Productivity module

The biological module simulated net productivity of the macrophyte community for each 1-[km.sup.2] cell on a daily time step. Ecological parameters, such as production and respiration rates, varied according to the cell wetland type. The vegetative communities in each basin reflected the present gradients in elevation, salinity, and soil type. Each wetland type was characterized with algorithms representative of a single dominant species with well-known responses to salinity and flood duration. Salt marshes were characterized by Spartina alterniflora, brackish marshes by Spartina patens, fresh marshes by Panicum hemitomon, and forested wetlands by Taxodium distichium (Chabreck 1972, Conner et al. 1987, Tiner 1993, Visser et al. 1996). Equations of the productivity module are presented in the Appendix.

Macrophytes were modeled with two state variables: belowground and aboveground biomass (in kilograms of organic matter [OM] per square meter, kg OM/[m.sup.2]). Gross production was a function of aboveground biomass, maximum gross production rate (kg OM[cdotp][m.sup.-2][cdotp][d.sup.-1]), and a limitation function. This limiting factor integrated functional responses to water level, salinity, and temperature as a factor ranging from 0 to 1, depending on the synergistic effect of the total environmental conditions (Phipps 1979, Hopkinson et al. 1988, Mitsch 1988). Salinity stress was determined by plant tolerances, depending on habitat types (Howes et al. 1986, Pezeshki et al. 1987). Waterlogging constrained the rate of growth representing different habitat-type tolerances to flooding conditions. Respiration rates were held constant (Pomeroy et al. 1976, Cronk and Mitsch 1994, Dai and Wiegert 1996). Respiration and mortality were differentiated for aboveground biomass and belowground biomass (Pomeroy et al. 1976). Excess fixed carbon calculated from the aboveground biomass was translocated to the belowground storage (Gosselink and Kirby 1974, Howes et al. 1985).

Soil module

The soil module included cumulative storage of inorganic sediments (in kilograms per square meter) and dead belowground organic sediments. Live belowground biomass from the productivity module was added to dead organic sediments for total belowground organic sediments. Inorganic and organic components were divided by bulk density (2.65 and 1.14 g/[cm.sup.3] for inorganic and organic, respectively) and pore-space volume set at 90% (Nyman et al. 1990), and summed to calculate marsh elevation (in meters). While inorganic sediments were conserved, decomposition, calculated as the storage multiplied by a decomposition-rate constant, was lost from belowground organic sediments.

Relative sea-level rise (RSLR) has proven a critical factor in determining relative marsh elevation and habitat changes in the Louisiana coast (Day and Templet 1989, Cahoon 1994, Day et al. 1997). RSLR in Louisiana averages about 1.2 cm/yr with subsidence accounting for 0.84 cm/yr (Swanson and Thurlow 1973, Trahn 1982, Penland and Ramsey 1990). While subsidence was not explicitly included in the soil module, decomposition losses from the organic stock partially simulate shallow subsidence. Deep subsidence of the Holocene layer underlying the study area, which has been identified as the dominant factor contributing to RSLR in Louisiana (Penland and Ramsey 1990), was accounted for by increased rates of eustatic sea-level rise and included in the tidal forcing at the Gulf of Mexico boundary.

Habitat-switching module

A feature of the model was its capacity to keep track of habitat characteristics for each land parcel throughout time. The program not only recognized what type of habitat existed in each 1-[km.sup.2] cell, but also recorded a suite of environmental parameters, such as salinity and duration of flooding, that characterized the cell. The determination of habitat type based on biotic and abiotic factors has been extensively documented (Mitsch 1988, Laurenroth et al. 1993). Each day the module queried the biomass density, salinity, and duration of flooding for each 1-[km.sup.2] cell, and determined what habitat type characterized those values (Table 2). A tally for each daily habitat type was kept per cell, and this habitat counter added one unit to the type registered each day and subtracted one from the previous habitat type.

Habitat counters for each cell were queried by a habitat-switcher algorithm at the end of every two years of simulation. This algorithm evaluated if the environmental conditions of the past two years for each cell had resulted in a habitat change. If more than half of the counts were for open-water conditions, then the cell was assigned an open-water type. If more than half of the counts were for marsh or swamp, the habitat with the highest count was assigned. New habitat-specific productivity rates were then used. Environmental stress was represented by daily salinity values and flooding duration, biomass density as an indicator of vegetation coverage, and the presence or absence of water as an indicator for open-water classification.

Spatial implementation

The boundary conditions for the 100-[km.sup.2] hydrologic model were GoM tide elevation and salinity, Atchafalaya River discharge and suspended-sediment concentration, and various pumping stations and discharge locations at the perimeter of the two basins. A rainfall-dependent discharge, located at the northern-most line of grid cells to account for the effects of runoff from the upper Terrebonne Basin, was set proportional to the actual rainfall for the upper basin.

The model incorporates nine forcing functions, including wind speed and direction, rainfall and evaporation, and tide in hourly time series, while salinity, temperature, river discharge, and inorganic sediment concentrations are daily time series. Wind velocity and direction records started in 1964 for the area. A canonical correlation (SAS Institute 1990) was performed, and missing years were replaced with the highest correlated years to reconstruct records for 1955--1963. Data originally given as daily values (e.g., rainfall and evaporation) were divided by 24 to compute hourly values. Tide stages were acquired from the National Ocean Service (NOS) at Bayou Rigard, Grand Isle, for 1955-1979 and from East Point, Grand Isle, from 1980--1988. To isolate the effects of RSLR all of the tide records were collated, where two distinct longterm trends of mean water level emerged. The period 1955 through 1974 had a linear trend of 1.31 cm/yr RSLR and the period 1975 through 1992 had a linear trend of 1.19 cm/yr. For the future climate-variability simulations, a constant rate of 1.2 cm/yr was applied.

Salinity values from Grand Terre Laboratory (near Grand Isle) collected by Louisiana Department of Wildlife and Fisheries were used as the boundary condition for the Barataria Basin. Because salinity was recorded as conductivity, the practical salinity scale (a conductivity ratio) is assumed. All of the data were missing for 1956 to 1958 and were replaced by the years 1977, 1987, and 1988 respectively, after a canonical correlation analysis (SAS Institute 1990). Hourly time series were comprised of 24 values of the daily salinity. Boundary conditions for Terrebonne Basin were set using the salinity distribution reported by Murray and Donley (1994). Salinity values were lowest at the At-chafalayn Delta and became progressively higher toward the east in the Terrebonne Basin. The daily difference between the two source points ranged from 3 to 9 depending on the season.

Daily maximum temperature records were collected from the National Weather Service tables for New Orleans Airport. Daily river discharge and sediment-load data were obtained from the U.S. Army Corps of Engineers, New Orleans District for the Tabert Landing Station. For continuous data, such as inorganic suspended sediments, the daily reading was used for 24 hours.

The U.S. Fish and Wildlife Service (USFWS) produced digital maps for coastal Louisiana derived from 1956 to 1978 aerial photography and from satellite imagery for 1988 habitat classification and 1990 land-water boundaries. The original map pixels, 25 m on a side, were aggregated to 1 [km.sup.2] by majority rule, then reclassified to four wetland categories (1, swamp; 2, fresh; 3, brackish; and 4, salt marshes), and two other habitats (open water and developed lands).

To evaluate how any simulated map compared to the USFWS maps, we used a multiple resolution fit index (Costanza 1989), as previously used for similar analyzes (Sklar and Costanza 1991). The index calculation begins with a comparison on a one-to-one cell basis between two maps, and computes the total number of matches. Then, the comparison window increases by a cell per side and recalculates the total number of matches, stopping when the window size is of the same size as the map itself The multiple resolution fit index ([F.sub.t]) is the sum of total matches within the window size, and varies from 0, or no match, to 100, or perfect match.

An elevation base map was developed from a 1994 survey (Alawady and A1-Taha 1996). These measurements were interpolated to derive continuous land surfaces. These values provided information on the degree of variability of the elevation of each habitat, and were used for the 1988 elevation map. Initial 1978 elevations were estimated by incrementally adding elevation to the 1994 land surface and 1978 USFWS habitat map.


Individual module calibration

A calibration strategy was implemented to consider the different scales and modules of the model. Each module was first tested independently, and then combined into the final landscape model. Modules were tested using 1-yr forcing functions repeatedly and, later, with the forcing for the 1978-1988 period.

The hydrodynamic module was tested using the 1994 elevation map with only predicted tidal effects derived from the Naval Ocean Services (NOS) tidal constituents (Shureman 1994). Next, the module was run with the 1988 actual tide. NOS time series from the two basins were extracted where available. These time series were detrended and demeaned, and finally decomposed using a Fourier analysis (Dennis and Long 1978), to compare the magnitude and phase of the primary tidal constituents with the NOS observations (Fig. 3).

Salinity and suspended sediment calibrations were done by iteratively manipulating boundary conditions, such as water inflow and diffusion rates. Salinity results closely matched available data (Murray and Donley 1994) for the lower portions of the basins (Fig. 3). However, the averaging and interpolation procedures distributed salinity more smoothly and farther inland (by about 10 [km.sup.2]) than was generally observed. The actual pattern for these inland reaches is one of uniformly low or zero salinity punctuated by short-lived peaks of higher salinity.

Calibration of the macrophyte module was done by repeatedly running the four wetland habitats using daily time steps for a year, while varying forcing functions within observed limits. Some forcing functions, such as duration of flooding and salinity limits, varied with habitat while others, such as temperature, were held constant for all habitat types.

Spatial calibration

The landscape calibration was done in three steps: First, the model was run repeatedly until matches were produced for land/water ratio for each basin; then, for the habitat-type proportions; finally, for the habitat distribution. For land/water ratios, the BTELSS (Barataria-Terrebonne ecological landscape spatial simulation) model was run using the 1988 forcing functions repeatedly until stable conditions were reached with all modules running concurrently. Then, observed sea-level rise was introduced, and the model was run from 1978 to 1988. A concurrent sensitivity analysis identified those parameters (e.g., Manning's coefficient and initial elevation) most critical to the model output.

To match habitat proportions, the landscape models for each basin were tested by varying several of the spatially distributed parameters (salinity limits, elevation, and Manning's coefficient). For example, changing salinity yielded fluctuations in the total number of cells per habitat (Table 3). These calibration runs indicated a need to obtain more accurate information about the mechanics of vegetation processes, as discussed below (see Sensitivity analysis).

Wetland elevation relative to sea level has been shown to be an important factor affecting productivity and health of vegetation (Nyman et al. 1993, Visser et al. 1996, Venterink and Wassen 1997). However, in both basins wetland elevation relative to mean sea level prior to 1994 was unknown. The measured 1994 surface was used to calibrate the 1988 hydrodynamics (Table 4).

The third step in the spatial calibration was goodness-of-fit analysis (Costanza 1989) between the 1988 model and USFWS maps. The model was repeatedly run, varying the initial spatial parameters (e.g., initial elevation) until the overall fit improved to 85 or better for both basins (Table 4). The 1978-1988 calibrated base case simulations yielded a fit of 89.3 for the Barataria Basin and 85.08 for the Terrebonne Basin (Figs. 4 and 5, Table 5). There was also agreement for total wetland and water areas for the two watersheds ([F.sub.t] = 96 for Barataria and [F.sub.t] = 94 for Terrebonne).

The BTELSS model was designed to simulate ecological processes that produce broad habitat patterns, and calibrated to match these landscape patterns so that all land-loss processes would be implicitly included. By using the USFWS maps for 1978 and 1988, the model incorporated all land-loss factors, since these maps reflect all of the effects impacting the landscape. The BTELSS model only incorporated large-scale factors (salinity, RSLR, and sediment transport) and their impacts having regional effects. The model did not explicitly incorporate local processes ([less than]1-[km.sup.2] cell), and thus it did not accurately recreate historical land changes of a particular cell. Insofar as these processes contributed to overall land loss, they were included implicitly or indirectly since the actual land-loss rates were used for calibration.


To validate the BTELSS (Barataria-Terrebonne ecological landscape spatial simulation) model, simulations were run for 1956-1987 with all parameters set to the 1978-1988 (base case) values, and predicted open-water area from 1956 to 1990 was output annually. The first derivative (wetland loss, in square kilometers per year) of these values was computed to illustrate the variation in wetland loss rates (Fig. 6).

The model predicted annual wetland loss fluctuations of 0-65 [km.sup.2] for Barataria and 0-85 [km.sup.2] for Terrebonne, similar to the values reported by Gagliano and colleagues (1981) of 73 [km.sup.2]/yr. Both basins had a pattern of moderate loss rates in the 1950s and 1960s and high rates in the 1970s (Fig. 6, Table 6). Wetland loss dropped through the 1980s to nearly 0 by 1990. Wetland-loss rates from the model compared favorably to values reported by the U.S. Fish and Wildlife Service (USFWS) and the U.S. Army Corps of Engineers (Table 6, Dunbar et al. 1992). Simulated wetland loss was similar to actual wetland loss for the 32-yr validation period (Table 6). Simulated open water was higher in both basins, indicating that the BTELSS model calculations, based on the 1978-1987 land-loss rate, were around 3% higher than the rates reported for earlier maps (Table 6). The calibration fit, [F.sub.t], was over 70 for both basins when comparing 1988 habitat distribution result to the 1988 USFWS map (Table 5).


For the sensitivity analysis, two parameters--habitat-dependent Manning coefficient ([M.sub.h]) and initial elevation--were chosen because of the scarcity of empirical data and their likely contribution to model instability. Each parameter was varied [+ or -] 1 SD from its mean value to examine the effect on habitat composition and fit index, used as indicators of performance. Two sensitivity indices were used to evaluate the response: the relative change of the total land area by the relative change of the tested parameter ([S.sub.j], Jorgensen 1988), and the coefficient of variation between the land area base case and the sensitivity run (cv, Steel and Torrie 1980).

The first sensitivity experiment increased [M.sub.h] for each habitat type as a measure of how fast water flows through the 100-[km.sup.2]-grid landscape. Using different [M.sub.h] revealed the zone with highest sensitivity as the transition area between fresh and saline environments (Table 7). There was a positive response between [M.sub.h] and cv. The [S.sub.j] had a negative relationship with [M.sub.h] without a clear indication of causality.

For the second sensitivity analysis, the initial elevation was incrementally varied for 10-yr simulations (1978-1987, Table 4). Elevation determined the amount of flooding and how water was distributed throughout the landscape. Varying elevation did not always result in a steady increase of open water, and neither of the two sensitivity indices showed a correlation (Table 4). However, the best fit occurred with an elevation 15 cm above the 1992 datum. Starting below this elevation (negative numbers in Table 4) resulted in an unrealistic increase in land loss due to flooding, while starting above this elevation resulted in land loss due to lack of salinity in brackish and salt-marsh habitats. Running a 30-yr simulation with initial elevation 40 cm above datum showed that the relationship between sea-level rise, subsidence, and land elevation was not linear (last line of Table 4).


Our objective was to understand how climate variability influenced landscape habitat distribution and determine the resilience of Barataria and Terrebonne Wetland habitats to diverse weather conditions as surrogates for potential conditions of global change. The Barataria--Terrebonne ecological landscape spatial simulation (BTELSS) model simulations analyzed the impact of different climate-variability scenarios for both basins from 1988 to 2018. All future simulations included a relative sea-level rise (RSLR) of 1.2 cm/yr over the mean sea-level conditions at the Gulf of Mexico, a value in the middle of the range reported in the literature (Gornitz 1995). Although there had been simulations of global-change effects of Mississippi River discharge (Miller and Russell 1992, Knox 1993) and coastal waters of Louisiana (Justic et al. 1996), none existed for Mississippi Delta wetlands.

The BTELSS model allowed treatment of long-term sea-level rise separate from interannual variation in mean water levels as factors potentially driving habitat change. The first simulation of future conditions was labeled the "normal-conditions scenario" (NCS). Climate variability, including the interactions among weather, river discharge, and changes in mean sea level that produce the observed variation in land loss, was highly dependent on the time series used to drive the model. To test how climate variability influenced the landscape dynamics, experiments were done using various permutations of the time series to generate alternative futures. To evaluate these interactions, comparisons with the NCS 2018 habitat map were made using the goodness-of-fit index and the overall percentage of land change (OLC-the percentage of predicted total land counts divided by 1988 land counts). Five alternative scenarios were simulated: (1) repetition of weather time series, (2) yearly mean sea level and mean river dischar ge conditions, (3) high sea level and high discharge, (4) low sea level and low discharge, and (5) high sea level and low discharge (Table 5).

Normal conditions

The normal-conditions scenario consisted of a 30-yr (1988-2018) simulation for each basin using theoretical time series and boundary conditions. Climate tends to be cyclic in nature (Latif and Barnett 1994, Thomson 1995), and to simulate future conditions we ran the original time series in reverse order. That is, the forcing functions and boundary conditions used data for the years 1955-1992, but when the year 1993 was simulated the weather from 1991 was applied, when 1994 was simulated the weather from 1990 was used.

The resulting habitat-change maps showed large-scale marsh deterioration. In Barataria 1,105 [km.sup.2] of marsh were converted to open water (Fig. 7), compared to 1204 [km.sup.2] for the Terrebonne Basin (Fig. 8). The water/land ratio increased from 0.94 in 1988 to 1.99 and 0.62 to 1.57 for Barataria and Terrebonne, respectively. The largest decline in the Barataria Basin was for brackish marsh (498 [km.sup.2]), while fresh marsh loss was greater (660 [km.sup.2]) for the Terrebonne Basin. In both basins there were large contiguous losses in the midand upper basins, and fragmentation in the lower, saline areas (Figs. 7 and 8, Table 5). These spatial differential losses were due to differences in initial elevation, salinity, and water levels between the upper and lower basin. However, model predictions for land-loss rates (Table 8) were similar to current estimates (Table 4).

Increasing water levels and flood duration led to lower marsh productivity and marsh conversion to open water at several sites (Table 8). For example, at Bayou L'Ours (Fig. 1), increasing water depth led to habitat change by 2017 (Fig. 9a), while at Bayou Perot (Fig. 9b) the habitat change and consequently land loss occurred in 2015, probably because of the proximity of this site to the coast. The conversion of a freshwater marsh north of Lake de Cade (Fig. 9c) in 2011 was also due to increased flooding and salinity. At the Du-large site (Fig. 9d) flooding duration increased to [greater than]20 h/d, similar to what has been measured in deteriorating coastal marshes in Louisiana (Wang 1997).

Climate variability scenarios

The first experiment ran the weather records in a forward manner (Forward Climate Variability Scenario in Table 5), where the 1956-1992 climate was repeated beginning in 1993. The results for the Barataria and Terrebonne Basins showed 1.3% and 0.4% dissimilarity, respectively, with the 1988-2018 NCS. Both of these values were less than the calibration (1978-1988) variation (3.3% and 1.2%, respectively) and, thus, considered indicators of negligible change.

Model stability and the capability of the marsh communities to endure long-term extreme conditions were tested by repetitively running four representative years with mean and extreme values for sea level and river discharge (Fig. 10). The year 1986 had mean values, in 1983 high levels of both forcing functions occurred, 1964 had low values, and in 1956 a combination of high Gulf of Mexico sea level and low river discharge occurred. The model was run for 30 yr with each of these weather years repeatedly to gauge the overall effect of extreme and average conditions on habitat change.

The effects of river discharge and mean sea level were different for each basin. Barataria Basin had the highest percentage of change (OLC = 15.5%) with the high Gulf-high river scenario. For the same scenario in Terrebonne, the OLC was only 7.8%. Open-water cell counts were minimal for low Gulf-low river, and high Gulf-low river conditions in both basins. The percentage change for low Gulf-low river was 21.5% in Barataria and 21.2% in Terrebonne. The high Gulf-low river scenario yielded 26.8% change for Barataria and 26.4% for Terrebonne.

To measure the influence of the climate variability in a spatial context the multiple resolution fit index was also used (Table 5). Mean Gulf and river-discharge fit indices were the highest (or more similar to the NCS) from all the climate variation simulations, showing that even typical conditions of mean sea level and river discharge did not reduce the current land-loss rate trends. The lowest indices for both basins resulted from the two low-river simulations. These lower indices were consequence of less open-water cell counts on each basin.


Landscape models are one of the few tools that can be used to predict the effects of complex interactions and cumulative, long-term effects of global change both spatially and temporally. The response of deltaic habitats to multiple impacts cannot be simply extrapolated. Historical trends of these responses often do not accurately predict future conditions (Dale and Rauscher 1994), mainly because of the accelerated rates for sea-level rise and global warming, and changes in the composition of the plant assemblages (Dale and Rauscher 1994). A process-based, spatially explicit model can cope with the inherent complexity of such future scenarios (Ruth and Pieper 1994).

The Barataria-Terrebonne ecological landscape spatial simulation (BTELSS) model presented here, was based on the existing CELSS (coastal ecological landscape spatial simulations) methodology (Sklar et al. 1985, Costanza et al. 1988, Costanza et al. 1990). However, this new landscape simulation model included the implementation of an explicit hydrodynamic module, improved ecological algorithms for primary production and habitat switching, and three times the original spatial extent. Much of the present effort was dedicated to refine the algorithms from earlier CELSS versions, and to obtain a similar or better resolution fit than the original CELSS model (CELSS fit was 86, Costanza et al. 1987, Sklar et al. 1991).

The BTELSS model was designed to be forced by and respond to dominant regional coastal processes. However, the model did not simulate plant and soil processes at less than a 1-[km.sup.2] scale, or hydrologic processes at less than 100 [km.sup.2], which can produce localized changes (Salinas et al. 1986, Turner and Rao 1990). Therefore, the calibration method had to compensate for the presence of local effects. Increasing habitat response to weather effects and regional forcing functions resulted in a predicted habitat map that resembled the real 1988 coastal habitat distribution of each basin (Figs. 4 and 5, Table 5).

The validation runs further tested the effects of regional factors, such as subsidence and salt intrusion, on wetland habitat response. The fit index, [F.sub.t], for this run (Table 5) demonstrated the degree of uncertainty of the habitat response algorithms. The [F.sub.t] difference between the maps for the historical 30-yr runs and the 1988 U.S. Fish and Wildlife Service maps was about 30 points (i.e., a variation of about 10 points every 10 years). This decadal 10-point discrepancy may be attributed to interannual variations of sea-level rise (a constant rate for the 30 yr in our model) and other effects such as canal dredging, and marsh impoundment.

A primary objective of this study was to evaluate the usefulness the BTELSS model for predicting landscape responses under varying environmental conditions (Tables 5 and 8). The overall highest fit achieved was under mean sea level and mean discharge conditions. This can be interpreted as a result of maintaining conditions within the normal range of weather variability. Minimum influences (i.e., low Gulf of Mexico sea level--low river-discharge scenario) resulted in less land loss and thus greater differences in habitat distribution when compared to the normal-conditions (NC) scenario. As the weather conditions exerted stress (i.e., high Gulf--high river-discharge scenario) land loss increased, demonstrating further the coupled response of coastal regional change and wetland loss (Table 8).

The model predicted relatively high loss rates, on the order of 50-80 [km.sup.2]/yr for the mid-1990s for all but the high Gulf--high river-discharge scenario, where values nearly double this amount took place (Fig. 6). These results, along with the NC and forward-climate-variability scenarios, suggested interannual variability as responsible for the largest changes in marsh stability. The importance of yearly sea-level rise and its effect on land loss for the two basins was evident (Figs. 6 and 10). Accretion, vegetation productivity, and sediment inputs alone did not compensate for the effects of increased sea-level rise (as high as 10 cm interannually; Penland and Ramsey 1990), acute weather conditions (hurricanes and winter storms), and natural subsidence (Baumann et al. 1984, Coleman 1988, Day and Templet 1989, Cahoon 1994, Dale and Rauscher 1994).

Model limitations

The accuracy of model functioning and predictions could be improved with better input and validation data. For example, elevation is one of the most sensitive parameters affecting marsh survival (Day and Templet 1989, Cahoon 1994, Reed et al. 1997). Yet, there are accurate elevation data for only a few locations and lack of historical records. Elevation of more locations gathered at regular intervals would prove invaluable for model improvement, as well as for coastal management in general (Wells 1996, Reed et al. 1997). Better monitoring of salinity and Water level at a number of stations would also allow much better calibration and validation of the model.

The ecological and habitat-switching modules focused on those factors that directly and predictably influence land elevation and habitat type. One of the most important factors for vegetation production is nutrient availability (Howes et al. 1986, Childers and Day 1990, Nyman et al. 1990). In a landscape context, the influences of river-borne nutrients could not be isolated and distinguished from the effects of freshwater and sediment. A lack of landscape-level nutrient information made it difficult to predict availability, rates of transformations within the estuary, or exchange with the atmosphere, much less the response of plant communities to all of these factors. While nutrient influences affect land elevation, inclusion of nutrients would call for a great deal of data. The productivity module should include nutrient influences to make the model a much more useful tool in predicting eutrophication and inshore nutrient cycling.

Future directions

Although the present model satisfactorily represented by Barataria and Terrebonne systems at a large scale, there are some improvements in the structure that would result in better spatial and temporal resolution. Two of the most important changes would be reducing the scale of the hydrodynamic module and adding a land-building component. The scale of the hydrodynamic module should be reduced to 1 [km.sup.2], the same scale as the ecological and soil modules, to account for smaller-scale features. Inclusion of land-building capability in the model would allow for vegetation colonization. For much of the present effort, this was not a problem because most of the area is not directly affected by riverine sediment input. In the future, modeling river diversions and the influence of the Atchafalaya River will be more accurate with a land-building component.


Financial support for this study was provided by the Barataria-Terrebonne National Estuary Program (BTNEP) through the Louisiana Department of Environmental Quality. The authors would like to acknowledge the assistance of the BTNEP Scientific and Technical Committee members. We are grateful to Hasan Mashriqui, Phillip Atkinson, and James Hyfield for their time and effort with midnight runs and graphics preparation, as well as to Jennifer Purdue for data assembly. Emily Hyfield proved to be invaluable at the word processor and bibliographic research. We also appreciate the comments and suggestions of two anonymous reviewers and W. L. Baker.

(1.) Coastal Ecology Institute, Louisiana State University, Baton Rouge Louisiana 70803 USA

(2.) Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, Louisiana 70803 USA

(3.) Special Programs, Center for Coastal, Estuarine and Environmental Resources, Louisiana State University, Baton Rouge, Louisiana 70803 USA

(4.) Louisiana Water Resources Research Institute, Louisiana State University, Baton Rouge, Louisiana 70803 USA

(5.) E-mail:

(6.) Present address: U.S. EPA Region 5, Mail stop B-19J, 77 West Jackson Boulevard, Chicago, Illinois 60604 USA.


Adams, R. D., B. B. Barrett, J. H. Blackmon, B. W. Gane, and W. G. McIntire. 1976. Barataria Basin: geologic processes and framework. Sea Grant publication number LSU-T-76-006. Louisiana State University Center for Wetland Resources, Baton Rouge, Louisiana, USA.

Adams, R. D., and R. H. Baumann. 1980. Land building in Coastal Louisiana: emergence of the Atchafalaya Bay Delta. Louisiana State University, Baton Rouge, Louisiana, USA.

Alawady, M., and K. Al-Taha. 1996. Elevation data gathering-Barataria--Terrebonne National Estuary Program (BTNEP). Department of Civil and Environmental Engineering and Remote Sensing and Image Processing Laboratory, Louisiana State University, Baton Rouge, Louisiana, USA.

Baskin, Y. 1993. Ecologists put some life into models of a changing world. Science 259:1694-1696.

Bass, A. S., and R. E. Turner. 1997. Relationships between salt marsh loss and dredged canals in three south Louisiana estuaries. Journal of Coastal Research 13:895-903.

Baumann, R. H., J. W. Day, and C. A. Miller. 1984. Mississippi deltaic wetland survival: sedimentation versus coastal submergence. Science 224:1093-1095.

Baumann, R. H., and R. E. Turner. 1990. Direct impacts of outer continental shelf activities on wetland loss in the central Gulf of Mexico. Environmental, Geological and Water Resources 15:189-198.

Blum, U., E. D. Seneca, and L. M. Stroud. 1978. Photosynthesis and respiration of Spartina and Juncus salt marshes in North Carolina: some models. Estuaries 1:228-238.

Boesch, D. F, M. N. Josselyn, A. J. Mehta, J. T. Morris, W. K. Nuttle, C. A. Simenstad, and D. J. P. Swift. 1994. Scientific assessment of coastal wetland loss, restoration and management in Louisiana. Journal of Coastal Research 20: (Special Issue).

Boumans, R. M. J., and J. W. Day. 1993. Effects of two Louisiana marsh management plans on water and material flux and short-term sedimentation. Wetlands 14:247-261.

Boumans, R. M. J., and F. H. Sklar. 1990. A polygon-based spatial (PBS) model for simulating landscape change. Landscape Ecology 4:(2/3)83-97.

Burke, R. W., and K. H. Stolzenbach. 1983. Free surface flow through salt marsh grass. Sea Grant College Program publication Number MITSG 83-16. Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Cahoon, D. R. 1994. Recent accretion in two managed marsh impoundments in coastal Louisiana. Ecological Applications 4:166-176.

Cahoon, D. R., D. J. Reed, and J. W. Day. 1995. Estimating shallow subsidence in microtidal salt marshes of the southeastern United States: Kaye and Barghoorn revisited. Marine Geology 128:1-9.

Casulli, V. 1992. Semi-implicit finite difference methods for two-dimensional shallow water flow. International Journal for Numerical Methods in Fluids 15:629-648.

Chabreck, R. H. 1972. Vegetation, water and soil characteristics of the Louisiana coastal region. Louisiana Agricultural Experiment Station Bulletin 664.

Chabreck, R. H., and R. E. Condrey. 1979. Common vascular plants of the Louisiana marsh. Sea Grant, Baton Rouge, Louisiana, USA.

Childers, D. L., and J. W. Day. 1990. Marsh-water column interactions in two Louisiana estuaries. 2. Nutrients dynamics. Estuaries 13:404-417.

Chmura, G. L., R. Costanza, and E. C. Kosters. 1992. Modelling coastal marsh stability in response to sea level rise: a case study in coastal Louisiana, USA. Ecological Modelling 64:47-64.

Coleman, J. M. 1988. Dynamic changes and processes in the Mississippi River delta. Geological Society of America Bulletin 100:999-1015.

Conner, W. H., J. W. Day, J. G. Gosselink, C. S. Hopkinson, and W. C. Stowe. 1987. Vegetation: composition and production. Pages 31-47 in W. H. Conner and J. W. Day, editors. The ecology of Barataria Basin, Louisiana: an estuarine profile. Biological report 85(7.13). U.S. Fish & Wildlife Service, Washington, D.C., USA.

Costanza, R. 1989. Model goodness of fit: a multiple resolution procedure. Ecological Modelling 47:199-215.

Costanza, R., F. H. Sklar, and J. W. Day. 1987. Using the coastal ecological landscape spatial simulation (CELSS) model for wetland management. Pages 3879-3890 in Proceedings of the Fifth Symposium on Coastal and Ocean Management--Coastal Zone '87. Volume 4. American Society of Civil Engineers, Seattle, Washington, USA.

Costanza, R., F. H. Sklar, and M. L. White. 1990. Modeling coastal landscape dynamics. BioScience 40:91-107.

Costanza, R., F. H. Sklar, M. L. White, and J. W. Day. 1988. A dynamic spatial simulation model of land loss and marsh succession in coastal Louisiana. Pages 99-114 in W. J. Mitsch, M. Staskraba, and S. E. Jorgensen, editors. Wetland modelling. Elsevier, Amsterdam, The Netherlands.

Cronk, J. K., and W. J. Mitsch. 1994. Aquatic metabolism in four newly constructed freshwater wetlands with different hydrologic inputs. Ecological Engineering 3:449-468.

Dai, T., and R. G. Wiegert. 1996. Estimation of the primary productivity of Spartina alterniflora using a canopy model. Ecography 19:410-423.

Dale, V. H., and H. M. Rauscher. 1994. Assessing impacts of climate change on forests: the state of biological modeling. Climatic Change 28:65-90.

Day, J. W., J. E Martin, L. C. Cardoch, and P H. Templet. 1997. System functioning as a basis for sustainable management of deltaic ecosystems. Coastal Management 25: 115-153.

Day, J. W., and P. H. Templet. 1989. Consequences of sea level rise: implications from the Mississippi Delta. Coastal Management 17:2411-257.

Deegan, L. A., H. M. Kennedy, and C. Neill. 1984. Natural factors and human modifications contributing to marsh loss in Louisiana's Mississippi River deltaic plain. Environmental Management 8:519-528.

Dennis, R. E., and E. E. Long. 1978. A user's guide to a computer program for harmonic analysis of data at tidal frequencies NOAA Technical Report number NOS 41. National Oceanic and Atmospheric Administration, Rockville, Maryland, USA.

Dozier, M. D., J. G. Gossselink, C. E. Sasser, and J. M. Hill. 1983. Wetland change in southwestern Barataria Basin, Louisiana, 1945-1980. LSU-CEL-83-11. Louisiana State University Center for Wetland Resources, Baton Rouge, Louisiana, USA.

Dunbar, J. B., L. D. Britsch, and E. B. I. Kemp. 1992. Land loss rates. Report 3 of a series Number GL-90-2. Louisiana Coastal Plain. U.S. Army Corps of Engineers, New Orleans, Louisiana, USA.

Fitz, C. H., R. Costanza, E. DeBellevue, T. Maxwell, L. Waigner, and R. Boumann. 1996. Development of a general ecosystem model for a range of scales and ecosystems. Ecological Modelling 88:263-295.

Freeze, R. A., and J. A. Cherry. 1979. Groundwater. Prentice-Hall, Englewood Cliffs, New Jersey, USA.

Gagliano, S. M., K. J. Meyer-Arendt, and K. M. Wicker. 1981. Land loss in the Mississippi River deltaic plain. Transactions of the Gulf Coast Association of the Geological Societies 31:295-300.

Gleason, M. L., and E. L. Dunn. 1982. Effects of hypoxia on root and shoot respiration of Spartina alternifolra. Pages 243-253 in V. S. Kennedy, editor. Estuarine comparisons. Academic Press, New York, New York, USA.

Gornitz, V. 1995. Sea-level rise: a review of recent past and near-future trends. Earth Surface Processes and Landforms 20:7-20.

Gosselink, J. G., and C. J. Kirby. 1974. Decomposition of salt marsh grass. Spartina alterniflora (Loisel). Limnology and Oceanography 19:825-832.

Hatton, R. S., R. D. DeLaune, and W. H. Patrick. 1983. Sedimentation, accretion, and subsidence in marshes of Barataria Basin, Louisiana. Limnology and Oceanography 28:494-502.

Hopkinson, C. S., J. W. Day, and B. T. Gael. 1978. Respiration studies in a Louisiana salt marsh. Anales del Centro de Ciencias del Mar y Limnologia UNAM 5:(l)225-238.

Hopkinson, C. S., R. L. Wetzel, and J. W. Day. 1988. Simulation models of coastal wetland and estuarine systems: realization of goals. Pages 67-96 in W. J. Mitsch, M. Staskraba, and S. E. Jorgensen, editors. Wetland modelling. Developments in environmental modelling 12. Elsevier Science, Amsterdam, The Netherlands.

Howes, B. L., W. H. Dacey, and D. D. Goehringer. 1986. Factors controlling the growth form of Spartina alterniflora: feedbacks between above-ground production, sediment oxidation, nitrogen and salinity. Journal of Ecology 74:881-898.

Howes, B. L., J. W. H. Dacey, and J. M. Teal. 1985. Annual carbon mineralization and belowground production of Spartina alterniflora in a New England salt marsh. Ecology 66:595-605.

Jorgensen, S. E. 1988. Fundamentals of ecological modelling. Elsevier, Amsterdam, The Netherlands.

Justic, D., N. N. Rabalais, and R. E. Turner. 1996. Effects of climate change on hypoxia in coastal waters: a doubled [CO.sub.2] scenario for the northern Gulf of Mexico. Limnology and Oceanography 41:992-1003.

Kindermann, J., G. Wurth, G. H. Kohlmaier, and F. W. Badeck. 1996. Interannual variation of carbon exchange fluxes in terrestrial ecosystems. Global Biogeochemical Cycles 10:737-755.

Kirby, C. J., and J. G. Gosselink. 1976. Primary production in a Louisiana gulf coast Spartina alterniflora marsh. Ecology 57:1052-1059.

Knox, J. C. 1993. Large increases in flood magnitude in response to modest changes in climate. Nature 361:430-432.

Latif, M., and T. P. Barnett. 1994. Causes of decadal climate variability over the North Pacific and North America. Science 266:634-637.

Laurenroth, W. K., D. L. Urban, D. P. Coffin, W. J. Parton, H. H. Shugart, T. B. Kirchner, and T. M. Smith. 1993. Modeling vegetation structure-ecosystem process interaction across sites and ecosystems. Ecological Modelling 67:49-80.

Liu, J., F. W. Cubbage, and R. H. Pulliam. 1994. Ecological and economic effects of forest landscape structure and rotation length: simulation studies using ECOLECON. Ecological Economics 10:249-263.

Martin, J. L., and S. C. McCutcheon. 1999. Hydrodynamics and transport for water quality modeling. Lewis, Boca Raton, Florida, USA.

Mendelssohn, I. A., K. L. McKee, and W. H. Patrick. 1981. Oxygen deficiency in Spartina alterniflora roots: metabolic adapation to anoxia. Science 214:439-441.

Miller, J. R., and G. L. Russell. 1992. The impact of global warming on river runoff. Journal of Geophysical Research 97:(D3)2757-2764.

Mitsch, W. J. 1988. Productivity-hydrology-nutrient models of forested wetlands. In M. S. Mitsch, W. J., S. E. Jorgensen, editors. Wetland Modelling, Developments in Environmental Modelling 12. Elsevier Science Publishers. Amsterdam.

Mitsch, W. J., and E. C. Reeder. 1991. Modelling nutrient retention of a freshwater coastal wetland: estimating the roles of primary productivity, sedimentation, resuspension and hydrology. Ecology Modelling 54:151-187.

Morris, J. T., R. A. Houghton, and D. B. Botkin. 1984. Theoretical limits of belowground production by Spartina alterniflora: an analysis through modelling. Ecological Modelling 26:155-175.

Mossa, J. 1996. Sediment dynamics in the lowermost Mississippi River. Engineering Geology 45:457-479.

Murray, S. P., and J. Donley. 1994. Mississippi River plume hydrography: annual report. MMS 94-0028. Louisiana State University Coastal Studies Institute, Baton Rouge, Louisiana, USA.

Nyman, J. A., R. D. DeLaune, and W. H. Patrick. 1990. Wetland soil formation in the rapidly subsiding Mississippi River deltaic plain: mineral and organic matter relationships. Estuarine, Coastal and Shelf Science 30:1-13.

Nyman, J. A., R. D. DeLaune, H. H. Roberts, and W. H. Patrick, Jr. 1993. Relationship between vegetation and soil formation in a rapidly submerging coastal marsh. Marine Ecology Progress Series 96:269-279.

Paille, R. 1997. Lower Atchafalaya Basin re-evaluation study: planning aid. Report on freshwater inflows to the Terrebonne Basin. U.S. Fish & Wildlife Service, Lafayette, Louisiana, USA.

Penland, S., R. Boyd, and J. R. Suter. 1988. Transgressive depositional systems of the Mississippi delta plain: a model for barrier shoreline and shelf sand development. Journal of Sedimentary Petrology 58:932-949.

Penland, S. and K. E. Ramsey. 1990. Relative sea-level rise in Louisiana and the Gulf of Mexico: 1908-1988. Journal of Coastal Research 6:323-342.

Pezeshki, S. R., R. D. DeLaune, and W. H. Patrick. 1987. Response of Spartina patens to increasing levels of salinity in rapidly subsiding marshes of the Mississippi River deltaic plain. Estuarine, Coastal and Shelf Science 24:389-399.

Phipps, R. L. 1979. Simulation of wetlands forest vegetation dynamics. Ecological Modelling 7:257-288.

Pomeroy, L. R., K. Bancroft, J. Breed, R. R. Christian, D. Frankenberg, J. R. Hall, L. G. Maurer, J. W. Wiebe, R. G. Wiegert, and R. L. Wetzel. 1976. Flux of organic matter through a salt marsh. Pages 270-279 in M. Wiley, editor. Estuarine processes. Academic Press, New York, New York, USA.

Press, W. H. 1992. Numerical recipes in FORTRAN: the art of scientific computing. Second edition. Cambridge University Press, New York, New York, USA.

Reed, D. J. 1995. Status and trends of hydrologic modification, reduction in sediment availability, and habitat loss modification in the Barataria-Terrebonne estuarine system. Report number 20. Barataria-Terrebonne National Estuary Program, Thibodaux, Louisiana, USA.

Reed, D., N. de Luca, and A. L. Foote. 1997. Effect of hydrologic management on marsh surface sedimentation deposition in coastal Louisiana. Estuaries 20:301-311.

Reycs, E., I. W. Day, and F. H. Sklar. 1994. Ecosystem models of aquatic primary production and fish migration in Laguna de Terminos, Mexico. Pages 519-536 in W. J. Mitsch, editor. Global wetlands: Old World and New. Elsevier Scientific, Amsterdam, The Netherlands.

Roberts, H. H. 1997. Dynamic changes of the Holocene Mississippi River delta plain: the delta cycle. Journal of Coastal Research 13:605-627.

Ruth, M., and F. Pieper. 1994. Modeling spatial dynamics of sea-level rise in a coastal area. System Dynamics Review 10:375-389.

Salinas, L. M., R. D. DeLaune, and W. H. Patrick, Jr. 1986. Changes occurring along a rapidly submerging coastal area: Louisiana, USA. Journal of Coastal Research 2:269-284.

SAS Institute 1990. SAS language: reference, version 6 edition. SAS Institute, Cary, North Carolina, USA.

Scaife, W. W., R. E. Turner, and R. Costanza. 1983. Coastal Louisiana recent land loss and canal impacts. Environmental Management 7:433-442.

Shureman, P. 1994. Manual of harmonic analysis and prediction of tides. Special publication number 98). U.S. Department of Commerce, Coast and Geodetic Survey, Washington, D.C., USA.

Singh, V. P., and V. Aravamuthan. 1995. Errors of kinematic-wave and diffusion-wave approximations for time-independent flows. Water Resources Management 9:175-202.

Sklar, F. H., and R. Costanza. 1991. The development of dynamic spatial models for landscape ecology: a review and prognosis. Pages 239-288 in M. G. Turner and R. H. Gardner, editors. Quantitative methods in landscape ecology. Springer-Verlag, New York, New York, USA.

Sklar, F. H., R. Costanza, and J. W. Day, Jr. 1985. Dynamic spatial simulation modelling of coastal wetland habitat succession. Ecological Modelling 29:261-281.

Sklar, F. H., M. L. White, and R. Costanza. 1991. The coastal ecological landscape spatial simulation (CELSS) model: users guide and results for the Atchafalaya/Terrebonne study area. Open file report 91-04. National Wetlands Research Center, U.S. Fish & Wildlife Service, Slidell, Louisiana, USA.

Steel, R. G. D., and J. H. Torrie. 1980. Principles and procedures of statistics: a biometrical approach. Second edition. MacGraw Hill, New York, New York, USA.

Swanson, R. L., and C. I. Thurlow. 1973. Recent subsidence rates along the Texas and Louisiana coasts as determined from tide measurements. Journal of Geophysical Research 78:2665-2671.

Swenson, E. M., and R. E. Turner. 1987. Spoil banks: effects on coastal marsh water-level regime. Estuarine, Coastal and Shelf Science 24:599-609.

Thomson, D. J. 1995. The seasons, global temperature, and precession. Science 268:59-68.

Tiner, R. W. 1993. Field guide to coastal wetland plants of the Southeastern United States. University of Massachusetts Press, Amherst, Massachusetts, USA.

Trahn, D. B. 1982. Monitoring local subsidence in areas of potential geopressured fluid withdrawal, southwestern Louisiana. Transactions of the Gulf Coast Association of Geological Societies 32:23 1-236.

Turner, R. E. 1976. Geographic variations in salt marsh macrophyte production: a review. Contributions in Marine Science 20:47-68.

Turner, R. E. 1997. Wetland loss in the northern Gulf of Mexico: multiple working hypotheses. Estuaries 20:1-13.

Turner, R. E., and T. S. Rao. 1990. Relationships between wetland fragmentation and recent hydrologic changes in a deltaic coast. Estuaries 13:272-281.

USFWS (United States Fish & Wildlife Service). 1995. Working in a world dominated by humans. Part I. University of Minnesota, Minneapolis, Minnesota, USA.

Venterink, H. O., and M. J. Wassen. 1997. A comparison of six models predicting vegetation response to hydrological habitat change. Ecological Modelling 101:347-361.

Visser, J. M., C. E. Sasser, R. H. Chabreck, and R. G. Linscombe. 1996. Marsh vegetation types of Barataria and Terrebonne Estuaries. LSU-CEI-96-11. Barataria-Terrebonne National Estuary Program, Thibodaux, Louisiana, USA.

Walker, H. J., J. M. Coleman, H. H. Roberts, and R. S. Tye. 1987. Wetland loss in Louisiana. Annales de Geographic 69:189-200.

Walker, N. D. 1996. Satellite assessment of Mississippi River plume variability: cases and predictability. Remote Sensing of the Environment 58:21-35.

Wang, E C. 1997. Dynamics of intertidal marshes near shallow estuaries in Louisiana. Wetlands Ecology and Management 5:131-143.

Wells, J. T. 1996. Subsidence, sea-level rise, and wetland loss in the lower Mississippi River delta. Pages 281-311 in J. D. Milliman and B. U. Haq, editors. Sea-level rise and coastal subsidence. Kluwer Academic Publishers, Dordrecht, The Netherlands.

White, M. L. 1991. Spatial modelling in coastal Louisiana. Pages 367-376 in S. Mathies, Editor. Data Inventory Workshop 5. Barataria-Terrebonne National Estuary Program, Thibodaux, Louisiana, USA.

White, M. L., T. Maxwell, R. Constanza, and T. W. Doyle. 1991. Ecosystem modeling of Barataria Basin, Louisiana: utilizing desktop parallel technology. American Water Resources Association.

Wiseman, W. 1., E. M. Swenson, and J. Power. 1990. Salinity trends in Louisiana estuaries. Estuaries 13:265-271.

Wolanski, E., M. Jones, and J. S. Bunt. 1980. Hydrodynamics of a tidal creek-mangrove swamp system. Australian Journal of Marine and Freshwater Research 31:43 1-450.

Wolanski, E., Y. Mazda, and P. Ridd. 1992. Chapter 3. Mangrove hydrodynamics. Pages 43-62 in A. I. Robertson and D. M. Alongi, editors. Tropical mangrove ecosystems. American Geophysical Union, Washington, D.C., USA.

Wu, J. G., and S. A. Levin. 1994. A spatial patch dynamic modeling approach to pattern and process in an annual grassland. Ecological Monographs 64:447-464.

Yeh, G.-T., and F-K. Chou. 1979. Moving boundary numerical surge model. Journal of The Waterway Port Coastal and Ocean Division (August):247-267.
                 Area of wetland and aquatic habitats for
              Barataria and Terrebonne Basins (USA) in 1956,
               1978, and 1988 (Reed 1995). Habitat areas are
               based on U.S. Fish and Wildlife Service maps.
                    Area ([km.sup.2])
                        Barataria               Terrebonne
Habitat                   1956        1978 1988    1956    1978 1988
Swamp                      306         324  319     293     352  316
Upland                     256         363  350      83     104  117
All marsh                 2137        1570 1243    2769    2142 1849
Water                     1735        2178 2523    2458    2999 3323
Total land loss [+]                    442  345             547  316
(+.)Total land loss is the difference of all
terrestrial habitats of one period minus the same
summation for the following sample year.
                          Habitat-type definition
                      by salinity and biomass for the
                       habitat-switching algorithm.
                  Mimima                 Maxima
                           Biomass                    Biomass
Habitat type     Salinity  (kg OM/      Salinity      (kg OM/
                          [m.sup.2])                 [m.sup.2])
Freshwater marsh   0.0       0.9      [less than]4.5    4.6
Swamp              0.0      20.3                 9.0   45.2
Brackish marsh     4.5       0.4     [less than]12.5    2.2
Saltwater marsh   12.5       1.2                40.0    6.0
Notes: Salinity is reported
in terms of the practical salinity
scale, a conductivity ratio.
For biomass, OM = organic
matter (dry mass). The open-
water habitat type resulted when
any habitat was inundated for
24 h each day and biomass
was less than minimum.
               Salinity tolerance used for brackish marshes
                during the habitat-switcher calibration for
                             Barataria Basin.
 upper   Brackish cells   Habitat fit
 limit    ([km.sup.2])  index, [F.sub.t]
  12.5        865            85.08
  13.0        851            84.52
  13.5        896            84.60
Note: Salinity is reported in terms of the
practical salinity scale, a conductivity radio.
                 Sensitivity analysis results for changes
                  in the 1992 initial elevation base map
                           for Barataria Basin.
                  Habitat-type cell counts
elevation                Freshwater              Brackish Saltwater
   (m)                     marsh           Swamp  marsh     marsh
  -0.2                      1284            990    357       150
  -0.15                     1103            990    580       340
  -0.1                      1172            990    222       500
  -0.05                     1257            955    250       430
  -0.01                     1250            990    252       520
   0.0                      1259            990    259       530
   0.05                      887            991    671       267
   0.15                      842            995    719       386
   0.2                       843            995    753       391
   0.4 [parallel]           1002            781    894       175
 Initial                        Fit         Sensitivity index
elevation                     index,
   (m)            Open water [F.sub.t] [++]     [S.sub.j] [++]
  -0.2               3154      80.52              -460
  -0.15              2922      83.35               237
  -0.1               3051      83.71              -232
  -0.05              3043      83.63              -250
  -0.01              2923      83.75               438
   0.0               2897      83.72               640
   0.05              3119      88.16             -1260
   0.15              2993      88.99
   0.2               2953      88.94              -800
   0.4 [parallel]    3083      78.16               nc [paragraph]
   (m)            CV [ss]
  -0.2            105
  -0.15            98
  -0.1            102
  -0.05           102
  -0.01            98
   0.0             97
   0.05           104
   0.2             99
   0.4 [parallel] nc [paragraph]
Notes: The elevation base value
(0.15 m) is boldfaced. All indices use
the results from the base run for
comparison purposes.
All runs are for the 1987 weather
repeated 10 times except is
noted; nc = not calculated.
(+.)See Costanza (1989).
(++.)Jorgensen's (1988) sensitivity index,
[S.sub.j] = [W.sub.i] - [W.sub.0.15]/[E.sub.i]
- [E.sub.0.15] where [W.sub.i] is the
resulting water area and [E.sub.i] the initial
elevation of each run.
(ss.)Coefficient of variation
(Steel and Torrie 1980.)
                        Summary of scenario results
                         performed in each basin.
                                           Resulting habitat
                                         coverage ([km.sup.2])
                                              Freshwater             Brackish
Scenario name                                    marsh         Swamp  marsh
Barataria Basin
 USFWS 1988 map                                   755          1022    734
 Base case (calibration, 1977-1988)               723          1002    722
 Base case (validation, 1956-1988)               1191           795    577
 Normal conditions (1988-2018)                    396          1017    236
 Forward climate variability (1988-2018)          363          1017    294
 Mean Gulf and mean river (1988-2018)             682          1022    520
 High Gulf and high river (1988-2018)            1022          1022    293
 Low Gulf and low river (1988-2018)               859           817    773
 High Gulf and low river (1988-2018)             1099           817    770
Terrebonne Basin
 USFWS 1988 map                                  1170           432    828
 Base case (calibration, 1977-1987)              1100           516    865
 Base case (validation, 1956-1987)                847           657    596
 Normal conditions (1988-2018)                    510           428    499
 Forward climate variability (1988-2018)          496           428    535
 Mean Gulf and mean river (1988-2018)             737           426    600
 High Gulf and high river (1988-2018)             817           429    538
 Low Gulf and low river (1988-2018)               640           609    629
 High Gulf and low river (1988-2018)              847           429    840
                                         water Open  Calibration fit [+]
Scenario name                            marsh water     Land/water
Barataria Basin
 USFWS 1988 map                           460  2952
 Base case (calibration, 1977-1988)       634  2854         95.97
 Base case (validation, 1956-1988)        288  3084         86.48
 Normal conditions (1988-2018)            217  4057
 Forward climate variability (1988-2018)  247  4002         97.00
 Mean Gulf and mean river (1988-2018)     312  3387         89.78
 High Gulf and high river (1988-2018)     159  3427         88.75
 Low Gulf and low river (1988-2018)       303  3183         86.08
 High Gulf and low river (1988-2018)      279  2970         83.56
Terrebonne Basin
 USFWS 1988 map                           576  2106
 Base case (calibration, 1977-1987)       551  2080         94.33
 Base case (validation, 1956-1987)        842  2170         86.64
 Normal conditions (1988-2018)            365  3310
 Forward climate variability (1988-2018)  356  3297         98.55
 Mean Gulf and mean river (1988-2018)     360  2989         96.85
 High Gulf and high river (1988-2018)     278  3050         94.37
 Low Gulf and low river (1988-2018)       627  2607         90.40
 High Gulf and low river (1988-2018)      425  2571         91.52
Scenario name                            Habitat
Barataria Basin
 USFWS 1988 map
 Base case (calibration, 1977-1988)       89.32
 Base case (validation, 1956-1988)        74.39
 Normal conditions (1988-2018)
 Forward climate variability (1988-2018)  96.69
 Mean Gulf and mean river (1988-2018)     88.07
 High Gulf and high river (1988-2018)     85.63
 Low Gulf and low river (1988-2018)       76.74
 High Gulf and low river (1988-2018)      73.35
Terrebonne Basin
 USFWS 1988 map
 Base case (calibration, 1977-1987)       85.08
 Base case (validation, 1956-1987)        72.74
 Normal conditions (1988-2018)
 Forward climate variability (1988-2018)  98.30
 Mean Gulf and mean river (1988-2018)     95.93
 High Gulf and high river (1988-2018)     92.63
 Low Gulf and low river (1988-2018)       88.76
 High Gulf and low river (1988-2018)      86.20
Notes: Scenarios assumed different
sea-level elevations for
the gulf of Mexico and different
Mississippi River discharge
amounts. "Forward climate
variability" means taking the 1956
-1992 climate records and
repeating them beginning in 1993.
(+.)Fit values for base-case
scenarios were computer against
1988 USFWS habitat map; fit
values for weather patterns were
computed against the 2018
normal-conditions habitat map.
        Comparison of estimates of land-loss rates ([km.sup.2]/yr)
         for several intervals in Barataria and Terrebonne basins.
          Barataria                       Terrebonne
          Dunbar et   Reed  USFWS  Model  Dunbar et   Reed  USFWS  Model
Interval  al. (1992) (1995) (1988) output al. (1992) (1995) (1988) output
1931-1958      7      ...    ...    ...        5      ...    ...    ...
1956-1978    ...     20.09  21.55  25.50     ...      24.86 21.27  26.73
1974-1983     22      ...    ...    ...       20      ...    ...    ...
1978-1988    ...     34.40  35.40  27.11     ...      31.6  33.10  33.89
Note: USFWS (1988) = U.S. Fish and Wildlife Service 1988 habitat map.
      Sensitivity analysis results for the habitat-dependent Manning
                          coefficient, [M.sub.h].
                 Habitat-type cell counts ([km.sup.2])
                              Freshwater                     Brackish
[M.sub.h]                        marsh                 Swamp  marsh
Barataria Basin
0.0125                            767                  1001    745
0.025                             750                  1001    722
0.05                              732                  1001    696
0.1                               726                  1001    662
0.2                               721                  1002    632
Terrebonne Basin
0.0125                           1103                   516    917
0.025                            1101                   516    877
0.05                             1094                   516    832
0.1                              1096                   513    756
0.2                              1105                   512    701
                 Saltwater Open  Sensitivity index
[M.sub.h]          marsh   water   [S.sub.j] [+]   CV [++]
Barataria Basin
0.0125              653    2769        3680           98
0.025               647    2815
0.05                622    2884        2760          102
0.1                 603    2943        1707          105
0.2                 573    3007        1097          107
Terrebonne Basin
0.0125              629    1947        5920           96
0.025               597    2021
0.05                564    2106        3400          104
0.1                 505    2242        2947          111
0.2                 458    2336        1800          116
Notes: Higher coefficients indicate higher friction. The base value
is 0.025 (boldfaced). All indices use the results from the base run
for comparison purposes.
(+.)Jorgensen's (1988) sensitivity index, [S.sub.j] = [W.sub.i] -
[M.sub.0.025]/[M.sub.i] - [M.sub.0.025] where [W.sub.i] is the
resulting water area and [M.sub.i] is the Manning coefficient of each
(++.)CV = coefficient of variation.
        Predicted rates of land loss for the 1988-2018 interval in
      Barataria and Terrebonne basins of coastal Louisiana, USA, with
    varying conditions in the Gulf of Mexico and the Mississippi River.
                                             Land-loss rates ([km.sup.2]/yr)
Scenario name                                           Barataria
Normal conditions, 1988-2018                              36.83
Forward climate variability, 1988-2018 [+]                29.67
Mean Gulf sea level and river discharge,
 1988-2018                                                15.33
High Gulf sea level and river discharge,
 1988-2018                                                15.83
Low Gulf sea level and river discharge,
 1988-2018                                                14.10
High Gulf sea level and low river discharge,
 1988-2018                                                15.23
Scenario name                                Terrebonne
Normal conditions, 1988-2018                   40.13
Forward climate variability, 1988-2018 [+]     38.03
Mean Gulf sea level and river discharge,
 1988-2018                                     32.23
High Gulf sea level and river discharge,
 1988-2018                                     31.83
Low Gulf sea level and river discharge,
 1988-2018                                     16.87
High Gulf sea level and low river discharge,
 1988-2018                                     15.50
(+.)The 1956-1992 climate records were repeated beginning in 1993.
                Comparison of Barataria 1988 U.S. Fish and
                Wildlife Service habitat map and calibrated
                           BTELSS model output.
               Area ([km.sup.2])
Habitat Type         USFWS       BTELSS
fresh marsh           755          723
swamp                1022         1002
brackish marsh        734          720
salt marsh            460          624
open water           2952         2854
uplands               178          178 (imposed)
                Comparison of Terrebonne 1988 U.S. Fish and
                Wildlife Service habitat map and calibrated
                           BTELSS model output.
               Area ([km.sup.2])
Habitat Type         USFWS       BTELSS
fresh marsh          1170         1100
swamp                 432          514
brackish marsh        828          866
salt marsh            576          552
open water           2106         2080
uplands               395          395 (imposed)
                    Simulated output under the normal-
                    conditions scenario and comparison
                     with Barataria 1988 U.S. Fish and
                       Wildlife Service habitat map.
                          The output map is for a
                         30-yr run from 1988-2018.
Habitat Type               Area ([km.sup.2])
fresh marsh                       396
swamp                            1017
brackish marsh                    236
salt marsh                        217
open water                       4057
uplands                           178 (imposed)
land changes to open water       1105
land changes to land              120
               Simulated output under the normal-conditions
                  scenario and comparison with Terrebonne
                    1988 U.S. Fish and Wildlife Service
                      habitat map. The output map is
                      for a 30-yr run from 1988-2018.
Habitat Type               Area ([km.sup.2])
fresh marsh                   510
swamp                         428
brackish marsh                499
salt marsh                    365
open water                   3310
uplands                       395 (imposed)
land changes to open water    1204
land changes to land            73


Here, we present equations for several modules of the BTELSS (Barataria--Terrebonne ecological-landscape spatial simulation) model.


The governing equations for two-dimensional water movement are the continuity equation

[delta]z/[delta]t + [delta]/[delta]x[(h + z)U] + [delta]/[delta]y[(h + z)V] = sources - sinks

where h = mean water elevation (m), t = time (s), U = depth-averaged velocity in x direction (m/s), V = depth-averaged velocity in y direction (m/s), and z = water level variation (m)

and the x and y momentum equations

[delta]u/[delta]t + u[delta]u/[delta]x + v[delta]u/[delta]y - fv = -g[delta]z/[delta]x - [[gamma].sub.x]u + [[[tau].sup.w].sub.x]/[[rho].sub.w]H and

[delta]v/[delta]t + u[delta]v/[delta]x + v[delta]v/[delta]y + fv = -g[delta]z/[delta]y - [[gamma].sub.y]v + [[[tau].sup.w].sub.y]/[[rho].sub.w]H

where f = Coriolis parameter (dimensionless), g = acceleration due to gravity (m/[s.sup.2]), H = h + z total water elevation (m), [gamma] = friction coefficients (dimensionless), [tau] = wind stress in horizontal direction (N/[m.sup.2]), and [[rho].sub.w] = density of water (kg/[m.sup.3]).

The effect of friction is accounted for by the relationship

[gamma] = g[M.sup.2]/[H.sup.4/3] [([u.sup.2] + [v.sub.2]).sup.1/2]

where M = Manning coefficient averaged at a 10 [km.sup.2] scale. The effect of wind stress is:

[tau] = [[rho].sub.a][C.sub.d]W


[C.sub.d] = wind-sheer stress coefficient based on wind speed

= 1.25 X [10.sup.-6] + 1.75 X [l0.sup.-6]sin([[pi].sub.w-[w.sub.1]]/2[W.sub.2] - [W.sub.1])

for [W.sub.1] [less than] W [less than or equal to] [W.sub.2]

= 3.0 X [10.sup.-6] for W [greater than] [W.sub.2]

and where [W.sub.1] = 5.1 m/s, [W.sub.2] = 15.0 m/s, W = wind speed (m/s), and [[rho].sub.a] = density of air (kg/[m.sup.3]).

These equations in finite difference form require a smaller time step than is practically useful in the prediction of long-term effects (Casulli 1992). The use of these equations in the prediction of long-term water level requires some simplification. Diffusion wave approximation of shallow-water equations (Singh and Aravamuthan 1995) were used, requiring that (1) local acceleration, (2) uniform flow, and (3) Coriolis force are negligible--i.e., for (1) local acceleration, [delta]u/[delta]t = [delta]v/[delta]t [congruent] 0; (2) uniform flow, [delta]u/[delta]x = [delta]u/[delta]y = [delta]v/[delta]x = [delta]v/[delta]x = 0); and (3) Coriolis force fv [congruent] fu [congruent] 0).


The continuity equation for salinity is

[delta]S/[delta]t + u[delta]S/[delta]x + v[delta]S/[delta]y + [D.sub.s][[delta].sup.2]S/[delta][t.sup.2] = 0

where [D.sub.s] = diffusion coefficient for salinity and inorganic sediment (m/[s.sup.2]) and S = salinity (in terms of the practical salinity scale, a conductivity ratio).

The continuity equation for inorganic suspended sediments is

[delta]I/[delta]t + u[delta]I/[delta]x + v[delta]I/[delta]y + [D.sub.s][[delta].sup.2]I/[delta][t.sup.2] + d[z.sub.i]/dt A = 0

where A = area of cell ([m.sup.2]), I = inorganic suspended sediment concentration (kg/[m.sup.3]), and [z.sub.i] = depth of deposited inorganic sediment (m).


Aboveground macrophyte production is given by

dB/dt = pB

where B is aboveground biomass in grams of organic matter (gOM), and p is the actual production rate, with

p = P - ([phi]B + [lambda]B + [gamma]B)

where the coefficients [phi] = translocation rate from aboveground biomass, [lambda] = detritus or litterfall rate (Turner 1976), and [gamma] = the habitat-specific aboveground respiration rate (Blum et al. 1978, Hopkinson et al. 1978, Mitsch and Reeder 1991, Dai and Wiegert 1996), all in g OM/d, and the specific gross production, P. is

P = [micro]P X F(S X J X [C/[C.sub.max]])

where [micro]P = the maximum gross production rate per habitat (Dai and Wiegert 1996). Environmental external factors include: salinity tolerance ([S.sub.t]) as an empirical function per habitat (Pezeshki et al. 1987), water-level stress tolerance (J) in hours tolerance also as an empirical function where tolerances for brackish and salt marshes are from 0.00 to 11.00 h and for swamp and fresh marsh are from 0.00 to 24.00 h (Nyman et al. 1993). The last term of the production equation is a function of temperature (C in centigrade) and [C.sub.max] is the air temperature maximum for a 30-yr record (Morris et al. 1984).

The belowground macrophyte production function (g OM) is defined as

dG/dt = kG

with initial biomass values being habitat determined (Kirby and Gosselink 1976, Childers and Day 1990), and where

k = T - ([eta]G + [sigma]G)

i.e., the belowground increment rate (k), per habitat, is a function of the translocated aboveground biomass (T = -[phi]B, Howes et al. 1985) minus belowground mortality rate ([eta]; in g OM/d) and [sigma] is the belowground respiration rate (g OM/d, Gleason and Dunn 1982).


The contribution of soil pore space to elevation was calculated using the following equation

poreht = pore %/1 - pore % X (ht. inorg + ht. org)

Total sediment elevation (poreht) results from adding belowground inorganic height (ht. inorg), organic storage (ht. org), and percentage of pore space (pore %). Total sediment height is the sum of organic, inorganic, and pore height for each 1-[km.sup.2] cell. Annual inorganic deposition (kg/[m.sup.2]) from the hydrodynamic module and belowground mortality (kg/[m.sup.2]) are inputs to inorganic sediments and dead belowground organic sediments, respectively.
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Geographic Code:1U7LA
Date:Aug 1, 2000

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