Subsurface Oil Simulator (SOSim) modeling case study: sampling plans for oil response

This case study examines the effectiveness of different field sampling plans in assessing the location of oil submerged in the water column. The Bayesian probabilistic Subsurface Oil Simulator (SOSim) model may be used as an emergency response tool to guide the sampling procedures during the submerged oil response period using both discrete, ship-based sampling equipment and AUVs. The procedures are designed for two phases: the initial sampling and the subsequent sampling when the SOSim model predictions are available. Included are a modified station plan for the initial sampling and the stratified sampling procedures for the subsequent sampling for the ship-based sampling. In addition, three track algorithms have been designed for AUV sampling. Example data from the Deepwater Horizon oil spill are used to illustrate the use of the Python2 scripts. The source code and documentation for SOSim are available in related dataset R6.x812.000:0005 (doi:10.7266/n7-dgsh-kh04), and prediction case studies are available in associated datasets R6.x812.000:0002 (Deepwater Horizon spill, doi:10.7266/n7-qsr6-gr54), R6.x812.000:0008 (T/V Athos 1 spill, doi:10.7266/n7-8rhz-c860), and R6.x812.000:0001 (Tank Barge DBL 152, doi:10.7266/n7-d7cg-md27). This dataset supports the publication: Ji, C., Englehardt, J. D., & Beegle-Krause, C. J. (2020). Design of Real—Time Sampling Strategies for Submerged Oil Based on Probabilistic Model Predictions. Journal of Marine Science and Engineering, 8(12), 984. doi:10.3390/jmse8120984