Effects of shoreline sensitivity on oil spill trajectory modeling of the Lower Mississippi River.
OBJECTIVE
The Lower Mississippi River is a major transportation route for commercial goods and petroleum products produced and refined locally. Oil spills caused by vessel accidents and equipment failure at refineries are a serious threat to the drinking water supply of Southern Louisiana, as well as to the many natural, economic, and social resources supported by the river. Providing accurate trajectory modeling to contingency planners is critical to protecting the local environment. The majority of trajectory model results, assuming a uniform shoreline, show 60-70% of spilled oil can be retained. This study examines the impact of detailed shoreline mapping that captures spatial and temporal changes in shoreline type on oil spill trajectory modeling.


METHODS
Detailed shoreline maps based on recent remote sensing imagery were generated to identify spatial changes in shoreline. A hydrodynamic model of the 78 mile reach from Convent, Louisiana to West Pointe a la Hache was developed to obtain the stage levels and velocity fields of four river discharges. Based on river stage level, another layer was added to the shoreline maps, so that shoreline type was accurately represented at each river discharge, a feature not included in previous mapping. An oil spill trajectory model was then used to investigate the effect of implementing different re-floatation half-lives that correlate to the shoreline maps developed for this study at four river discharges.


RESULTS
Detailed shoreline mapping showed the Lower Mississippi River has four major shoreline types each with different oil re-floatation half-lives: muddy clay, sand, low vegetation, and high vegetation. As flow rate changed, the shoreline spatial variability also changed, from 84% mud/sand and 16% vegetation at low flow rates to 4% mud and 96% vegetation at higher flow rates. At flow rates with large variability in shoreline type, the distribution of oil attached to the shore was significantly different from results of simulations that used a constant shoreline type and re-floatation half-life.


CONCLUSIONS
At low flow rates, simulations with the detailed delineation of shoreline type predicted that approximately 30% of the oil would be beached/retained because the oil was able to travel further down the reach and interact with the shoreline in multiple locations. Simulations at the low flow rates with the existing shoreline mapping predicted approximately 65% of the oil would be retained as did all the simulations at the highest flow rates. At high flow rates, the oil interacted mostly with vegetation and results were very similar to those obtained with a single re-floatation half-life of 1 year. In addition to shoreline type, river geometry and the hydrodynamics were major factors influencing the distribution of oil along the river reach.


CONCLUSIONS
Shoreline re-floatation half-lives have a major impact on simulating the distribution of oil along the shore after a spill, especially in areas with a high variability of shoreline type as in the lower Mississippi River. Assigning the correct re-floatation half-life and retention capacity is only possible when shoreline types have been correctly identified. The maps developed for this study provided an important level of detail and incorporated the change in shoreline type with flow rate, resulting in more detailed trajectory modeling of the study reach.


CONCLUSIONS
Shoreline maps should include as much detail about shoreline type as possible. When developing shoreline maps or environmental sensitivity assessments, the focus should include specific characteristics of the study area; using standardized maps or methods of assessment may leave out detail that could negatively impact modeling efforts. Finally, shoreline sensitivity to oiling is an important area of research that will benefit from an improved understanding of oil retention by vegetation.
Danchuk
Samantha
S
Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA. sdanch1@lsu.edu
Willson
Clinton S
CS
D055884:Chemical Hazard Release; D004784:Environmental Monitoring / Q000379:methods; D005843:Geography; D007700:Kinetics; D008145:Louisiana; D008956:Models, Chemical; D010578:Petroleum / Q000032:analysis*; D045483:Rivers / Q000737:chemistry*; D014872:Water Movements; D014874:Water Pollutants, Chemical / Q000032:analysis*
D010578:Petroleum; D014874:Water Pollutants, Chemical