Landscape Modeling of Coastal Habitat Change in the Mississippi Delta

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 km2 in size and operating at a 1‐h time step, and a spatially articulated ecosystem module, resolving habitat type and change for 1‐km2 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.

Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors

“…[73] Subsequent efforts using the same direct-calculation approach resulted in the Barataria-Terrebonne Ecological Landscape Spatial Simulation model (BTELSS) [Reyes et al, 2000] which focused on historical trends in land loss and habitat change for coastal Louisiana. The BTELSS model consists of an explicit hydrodynamic module with water and particle flow and ecological algorithms modeling critical environmental parameters.…”

Does more mean less? The value of information for conservation planning under sea level rise

“…Therefore, an Inundation model was used to represent the 'simple' model (Table 1). Alternatively, more sophisticated complex models can be employed that account for dynamic processes, but require more data and expertise to apply (such as the Sea Level Affecting Marshes Model (SLAMM) (Park et al, 1993;Craft et al, 2009), the Barataria-Terrebonne ecological landscape spatial simulation model (Reyes et al, 2000), the Mississippi Delta Model (Penland et al, 1988), or the Dynamic Interactive Vulnerability Assessment model (Hinkel & Klein, 2009). SLAMM (version 6) is an example of a more complex, process-based coastal impact model which allows for the prediction of shifts in ecosystems, due to the inclusion of some key ecological processes and abiotic factors (Craft et al, 2009;McLeod et al, 2010).…”

“…The uncertainty inherent in elevation data inputs into coastal impact models can limit the accuracy of predictions and consequently their usefulness for management and planning, yet a range of elevation data sets have been used in previous studies as inputs into coastal impact models (Gesch, 2009;McLeod et al, 2010). The horizontal resolution of these data sets ranges from a coarse resolution of approximately one kilometre, with a vertical accuracy of 5 m for regional and global studies (Reyes et al, 2000;Martin et al, 2002;Small & Nicholls, 2003;Li et al, 2009), to a finer horizontal resolution of about 10 cm, with a vertical accuracy of 11-30 cm for site-specific analyses (Geselbracht et al, 2011;Traill et al, 2011). However, this fine resolution data are far more expensive to obtain and require more expertise to apply (Johansen et al, 2010).…”

Quantifying ecological filters: the relative impact of herbivory, neighbours, and sediment on an oligohaline marsh

“…The addition of sediment had no effect on any species in our experiment. In the long term, of course, lack of sedimentation combined with rising sea levels might lead to the loss of the entire marsh while increased sedimentation might allow invasion of woody species and reversion to swamp (Barras et al 1994, Boesch et al 1994, Reyes et al 2000. In wetlands where natural sedimentation rates have been increased by land-use change, sediment as a filter may be significant.…”