Improving Oil Spill Impact Communication and Readiness with GIS

Transcription

1 Improving Oil Spill Impact Communication and Readiness with GIS 2016 ESRI UC - San Diego, California Session: GIS for Hazmat and Spill Response Thursday, June 30, 2016 Jake Nelson, Lucy Romeo, Jen Bauer, Dori Dick, Kelly Rose

2 Overview Introduction Motivation Spill Model Impact Communication Approach Application Conclusions 2

3 Introduction History of U.S. oil spills The three most recent events Exxon Valdez, Deepwater Horizon, Refugio 100 s of millions to over a billion dollars worth of damage Most affected industries: oil and gas, housing, aquaculture, tourism, and fishing with emphasis on coastal communities. High level analyses focus on large-scale economic factors. While useful, these analyses may fail to recognize the extent and degree of damage to the smaller coastal communities Disasters can display heterogeneous temporal and geographic footprints of damage. 3

4 Introduction Most common way to model spills is by simulation. Varied approaches Oil plume is modeled as a set of individual points Undergo separate environmental processes unique characteristics. Computationally intensive Uncertainty built into models Ocean is a stochastic environment, modelled as such Results change from simulation to simulation. Droplet Distribution Control Volume 4

5 Motivation Accurate Oiling Predictions: The tourism industry is a critical economic engine for the United States Value is greater than the U.S. agriculture, airline, and motion picture industry combined (Sydnor-Bousso et al., 2011). Tourism is especially integral to coastal communities. Success for all facets of the tourism industry is intrinsically tied to the conditions of the area. In the wake of extreme events, tourism is most vulnerable to economic hardship (Pforr, 2009). The hospitality industry is disproportionately vulnerable to disasters and is easily affected by consumer perception in the wake of a disaster (Jauhari, Malhotra, & Venkatesh, 2009) 5

6 Objectives Because of the negative perceptions would-be tourists may develop following an event like an oil spill, it is important to both communicate potential oiling and minimize uncertainty regarding the predictions. Simulation 1 Objective 1: Develop a method for rapid estimation of uncertainty and prediction of oiling. Objective 2: Using the BLOSOM model, apply the method to enhance uncertainty and communication of oiling location and severity given a hypothetical spill. Simulation 2 6

7 Objectives Because of the negative perceptions would-be tourists may develop following an event like an oil spill, it is important to both communicate potential oiling and minimize uncertainty regarding the predictions. Simulation 1 Objective 1: Develop a method for rapid estimation of uncertainty and prediction of oiling. Objective 2: Using the BLOSOM model, apply the method to enhance uncertainty and communication of oiling location and severity given a hypothetical spill. Simulation 2 7

8 BLOSOM Using the Blowout and Spill Occurrence Model (BLOSOM) new model developed at the National Energy Technology Lab Multi-component deterministic model, 3D spatially explicit Advection by wind, wave, and currents Movement using random walk/flight with diffusion (function of diffusion coefficient) and deflection by wind velocity and direction 8

9 Impact Impact Communication Approach Mitigate uncertainty Repeated sampling approach Assign mean value to impact area 95% confidence interval Values for Cell x following 20 simulations ,000 Normal distribution of values for Cell x following repeated sampling Simulation More Impact 9

10 Application in the Gulf of Mexico o N, o W - Western Gulf of Mexico - 82 miles off the coast - 25 active well platforms in the area - 196ft water depth - 1 week blowout, 2 month duration **20 simulations 10

11 Application in the Gulf of Mexico Recorded the total amount of oil for each cell following the 20 simulations. Exported to CSV. 10/20 values extracted and used to calculate a mean. Repeated 1000 times/cell. Mean value from the 1000 simulations assigned to the cell Impact 11

12 Results Values based on repeated sampling procedure. Distributed most heavily in the Freeport, TX area. Some distribution within the Galveston area. 12

13 Results Contrary to some of the simulations there is a chance for oiling in the Galveston area based on simulation sample. More hotels but less oil (up to 9 barrels in most locations) Targets for protection Up to 233 barrels of potential oil along Freeport beaches. Much less hotel/accommodation industry presence. Specific beaches can be targeted. 13

14 Results Test of performance: Five additional simulations performed, independent from original 20. Oiling from independent samples compared to MC results and results from single simulations. RMSE calculated RMSE MC Surface Run 2 Surface Run 6 surface Run 10 surface Run 15 surface Run 18 surface Sample Sample Sample Sample Sample

15 Concluding Remarks Enhance the potential for impact minimization by targeting areas of high oiling Communicate where and how much oil will make landfall provides targets for protection and alleviates the potential for falsely assuming oiling. Monte Carlo simulations can enhance understanding of the potential for oiling along the shoreline Repeated sampling Generation of confidence intervals for each reported value In a timely situation, this approach offers decision makers and responders a way to quickly address uncertainty in spill models to help optimize equipment and personnel dispatch. Stakeholders able to determine potential for damage and protection need 15

16 Thank You Jake Nelson ORISE Post-Graduate Research Fellow For more information on the larger offshore project visit: 16