Inundation Analysis Using GIS and Hydrodynamic Modeling

Sea level rise and land subsidence have altered and continue to impact the Texas coastline and its ecosystems. Parts of the Gulf of Mexico coast, including the northern portion of the Texas coast, are experiencing some of the highest rates of relative sea level rise due to local subsidence. Small changes in water levels and elevations can cause significant changes in shallow inundated areas making them an excellent laboratory to study the impact of sea level rise. These areas represent important shorebird habitats including a number of migratory species. Changes in tidal dynamic and seasonal effects are important to predict the extent of shallow inundated areas.

The goal of the research is to test the ability of a relatively new hydrodynamic model, the Coastal Modeling System (CMS), to predict water coverage of tidal flats and test the sensitivity of coastal habitats and communities to seasonal differences in water levels. The CMS model was implemented using a 6x6 m resolution cell grid for a 14 km area around Packery Channel which is a small navigational channel between the Gulf of Mexico and Corpus Christi Bay in South Texas. The model is forced from the Gulf of Mexico using a tidal station located nearby along the Gulf coast and from Corpus Christi Bay using a tidal station in the southeastern corner of the bay. A tidal station at the north end of Packery channel is used for wind measurement forcings. A smaller area within the model grid, the Mollie Beattie Coastal Habitat Community tidal flat located along the Packery Channel was used to assess the inundation. A GPS survey (in respect to NAVD88 datum) was conducted to augment existing data and provide a reliable bathymetry/topography for the wet/dry test area's elevations. A current meter located along the western edge of Packery Channel is used to check the model's performance in predicting currents. Through the use of geospatial tools; hydrodynamic model outputs and aerial/satellite image rasters were compared to determine the model's inundation prediction accuracy. Both the CMS output and the corresponding aerial images were converted to binary rasters of equal resolution. These rasters were clipped to fit the study area, avoiding areas which are always flooded or always dry.

The initial analysis consists of three test cases: July 20, 2008, January 12, 2009 and August 4, 2009. These dates were selected primarily due to the availability of reliable aerial imagery, water level and wind data. Comparison between the aerial imagery and the model prediction for the same time showed an accuracy of 91%, 86% and 89% respectively. All three cases showed CMS to be under-predicting the extent of the flooding, the cases showed a bias of .91, .94 and .91 respectively. The model is presently being optimized and the influence of model parameters such as the Manning's roughness coefficient and the depth threshold for drying cells and additional test cases are being assessed and discussed. The accuracy of the inundation analysis will determine if the model can be used to delineate coastal inundation areas and perform as a tool for ongoing coastal operations and planning such as habitat restoration, urban expansion and emergency management.