SlideShare una empresa de Scribd logo
1 de 13
Sheldon Waugh
Friday, January 30, 2015
GIS3043
Introduction/Motivation
The BP Oil Spill has been a forefront topic for the Gulf
Coast States and the U.S for more than a month and a half.
With the 2010 Atlantic Hurricane season upon us, many
experts have explained that the oil on the ocean surface
plus a U.S land falling hurricane will wreak havoc on the
coastal environments and wildlife of the Gulf Coast.
The goal here is to find the areas in the Gulf Coast most
likely to be hit by a hurricane, and affected by the
resulting oil pollution.
A analysis like this would be beneficial to Gulf Coast States
regional planning committees, for pinpointing a more
concentrated area to divert their oil pollution prevention
techniques.
Background
The Deepwater Horizon (BP) Oil Spill is currently the
largest offshore oil spill in United States history,
surpassing the Exxon Valdez.
The spill was initially created by the oil rig explosion of
the Deepwater Horizon, killing 11 workers.
Currently the spill has been recorded as 90.1 million
barrels spilled with a spill rate of 40,000 barrels a day (1
Barrel = 60 Gallons).
A stop to the flow of oil from the well has been predicted
in some time in August.
Covers roughly 2,500 Square Miles.
The 2010 Atlantic Hurricane season started June 1st
, 2010
(8-14 Hurricanes predicted, 3-7 Major Hurricanes)
Data Used/Sources
 Hurricane tracks obtained from NOAA(National Oceanic and Atmospheric
Administration), (June-August Hurricanes over the last 140 years)
 U.S, Canada, Mexico and Cuba shapefiles obtained from the CDC (Center for
Disease Control)
 Gulf Coastal Counties shapefiles obtained from the United States Census
Bureau
 The Gulf Coast Sea level and CVI (Coastal Vulnerability Index) shapefiles were
obtained from the USGS (United States Geological Survey).
 The following data was created by myself by goereferencing and digitizing
jpeg images:
 Google Maps (.kml files) NOAA Oil spill location and 72 hour predictions
as of 6/8/2010, the kml were then converted into jpeg images,
georeferenced and digitized.
 The loop currents and gulf streams were obtained from the jpeg image
that came from NASA’s MODIS satellite (5/18/2010)
Analysis
1. I georeferenced and digitized the jpegs for the oil spill (both the actual location, and
the 72 hour locations)
2. I then georeferenced and digitized the jpegs for the loop current and the gulf stream
ocean currents.
3. I then made a buffer from my 72 hour prediction polygon to make it my 2-month
prediction polygon.
4. For the Hurricane data:
 I first selected out tracks that did not fall into my 3 month window (MONTH = June,
July, August).
 I then selected out by location tracks that did not intersect with my 2 month oil spill
prediction buffer.
 Finally I selected out the tracks that did not make landfall onto one of the four Gulf
Coast States I paid attention to (Florida, Louisiana, Alabama, Mississippi) (selected by
location hurricane tracks that intersected with the states layer).
1. I estimated storm surge in each area by combining sea level and CVI risk (Coastal
Vulnerability Index- Index combines: Sea level Change, erosion wave size and
frequency and posts it into a Low to very High index)
2. I then added the necessary regional boundaries
Analysis
7. After inputting my data, I created a fishnet grid that covered
the Gulf Coast on my map. I made the scale to be 1 degree by 1
degree squares.
8. I then used the Feature to Polygon to convert the fishnet grid
into a polygon grid.
9. After that, I executed a spatial join with the hurricane tracks
to the polygon grid.
10. After the spatial join, I opened the attribute table of the
spatial join and checked the field Join_Count. I sorted this
field to show the greatest values. The greater the value in this
field the higher the density of hurricanes is present in that
specific polygon grid square. The data was collected and two
regions were selected as the densest areas for hurricanes.
Conclusion
The two areas (polygons) which had the greatest density
of hurricanes were located at:
Plaquemines and St. Bernard’s Parishes in New Orleans
Santa Rosa, Okaloosa, and Walton Counties in Florida.
These areas according to my analysis will encounter the
most hurricanes that will come in contact will the oil spill.
The effects of theses areas will include:
Wind and rain damage
Oil pollution wherever the storm surge takes the oil inland.
Potentially millions of dollars in oil cleanup and years for the
coastal areas to recuperate from the oil leaching into the
soils and harming wildlife.

Más contenido relacionado

La actualidad más candente

MonaNga scrim poster FINAL
MonaNga scrim poster FINALMonaNga scrim poster FINAL
MonaNga scrim poster FINALMona Nga
 
Leseurre, Coraline: CO₂ increase and ocean acidification in the Southern Indi...
Leseurre, Coraline: CO₂ increase and ocean acidification in the Southern Indi...Leseurre, Coraline: CO₂ increase and ocean acidification in the Southern Indi...
Leseurre, Coraline: CO₂ increase and ocean acidification in the Southern Indi...Integrated Carbon Observation System (ICOS)
 
Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster M...
Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster M...Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster M...
Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster M...GIS Colorado
 
California Outcome Likelihood Tool
California Outcome Likelihood ToolCalifornia Outcome Likelihood Tool
California Outcome Likelihood ToolDRIscience
 
Projected Climate Change Impacts for Rhode Island’s Coast: A Summary of the S...
Projected Climate Change Impacts for Rhode Island’s Coast: A Summary of the S...Projected Climate Change Impacts for Rhode Island’s Coast: A Summary of the S...
Projected Climate Change Impacts for Rhode Island’s Coast: A Summary of the S...riseagrant
 
Natural gas leak management
Natural gas leak management  Natural gas leak management
Natural gas leak management Shubham Agrawal
 
Quick Drought Response Index
Quick Drought Response IndexQuick Drought Response Index
Quick Drought Response IndexDRIscience
 
Adding Confidence to Seasonal Drought Forecasts using reference evapotranspir...
Adding Confidence to Seasonal Drought Forecasts using reference evapotranspir...Adding Confidence to Seasonal Drought Forecasts using reference evapotranspir...
Adding Confidence to Seasonal Drought Forecasts using reference evapotranspir...DRIscience
 
Glacier changes and climate trends derived from multiple sources in the data ...
Glacier changes and climate trends derived from multiple sources in the data ...Glacier changes and climate trends derived from multiple sources in the data ...
Glacier changes and climate trends derived from multiple sources in the data ...InfoAndina CONDESAN
 
Using EDINA Datasets in a Hydrology Project - Darius Bazazi
Using EDINA Datasets in a Hydrology Project - Darius BazaziUsing EDINA Datasets in a Hydrology Project - Darius Bazazi
Using EDINA Datasets in a Hydrology Project - Darius BazaziEDINA, University of Edinburgh
 

La actualidad más candente (14)

Exploring_GIS_in_Glaciology
Exploring_GIS_in_GlaciologyExploring_GIS_in_Glaciology
Exploring_GIS_in_Glaciology
 
MonaNga scrim poster FINAL
MonaNga scrim poster FINALMonaNga scrim poster FINAL
MonaNga scrim poster FINAL
 
Leseurre, Coraline: CO₂ increase and ocean acidification in the Southern Indi...
Leseurre, Coraline: CO₂ increase and ocean acidification in the Southern Indi...Leseurre, Coraline: CO₂ increase and ocean acidification in the Southern Indi...
Leseurre, Coraline: CO₂ increase and ocean acidification in the Southern Indi...
 
Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster M...
Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster M...Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster M...
Using GIS to Visualize and Analyze Environmental Time-Series Data as Raster M...
 
California Outcome Likelihood Tool
California Outcome Likelihood ToolCalifornia Outcome Likelihood Tool
California Outcome Likelihood Tool
 
Projected Climate Change Impacts for Rhode Island’s Coast: A Summary of the S...
Projected Climate Change Impacts for Rhode Island’s Coast: A Summary of the S...Projected Climate Change Impacts for Rhode Island’s Coast: A Summary of the S...
Projected Climate Change Impacts for Rhode Island’s Coast: A Summary of the S...
 
Natural gas leak management
Natural gas leak management  Natural gas leak management
Natural gas leak management
 
Quick Drought Response Index
Quick Drought Response IndexQuick Drought Response Index
Quick Drought Response Index
 
The Role of the Oceans in Seamless Prediction, from Days to Seasons
The Role of the Oceans in Seamless Prediction, from Days to SeasonsThe Role of the Oceans in Seamless Prediction, from Days to Seasons
The Role of the Oceans in Seamless Prediction, from Days to Seasons
 
Uncertainty in Climate Change Projections: Results from Different GCMs/RCMs i...
Uncertainty in Climate Change Projections: Results from Different GCMs/RCMs i...Uncertainty in Climate Change Projections: Results from Different GCMs/RCMs i...
Uncertainty in Climate Change Projections: Results from Different GCMs/RCMs i...
 
Adding Confidence to Seasonal Drought Forecasts using reference evapotranspir...
Adding Confidence to Seasonal Drought Forecasts using reference evapotranspir...Adding Confidence to Seasonal Drought Forecasts using reference evapotranspir...
Adding Confidence to Seasonal Drought Forecasts using reference evapotranspir...
 
Glacier changes and climate trends derived from multiple sources in the data ...
Glacier changes and climate trends derived from multiple sources in the data ...Glacier changes and climate trends derived from multiple sources in the data ...
Glacier changes and climate trends derived from multiple sources in the data ...
 
Montana Drought Analysis
Montana Drought Analysis Montana Drought Analysis
Montana Drought Analysis
 
Using EDINA Datasets in a Hydrology Project - Darius Bazazi
Using EDINA Datasets in a Hydrology Project - Darius BazaziUsing EDINA Datasets in a Hydrology Project - Darius Bazazi
Using EDINA Datasets in a Hydrology Project - Darius Bazazi
 

Destacado

Download-manuals-hydrometeorology-data processing-11howtocompilerainfalldata
 Download-manuals-hydrometeorology-data processing-11howtocompilerainfalldata Download-manuals-hydrometeorology-data processing-11howtocompilerainfalldata
Download-manuals-hydrometeorology-data processing-11howtocompilerainfalldatahydrologyproject001
 
6th Year Department Talk
6th Year Department Talk6th Year Department Talk
6th Year Department Talktorloff
 
Recursos técnicos y expresivos
Recursos técnicos y expresivosRecursos técnicos y expresivos
Recursos técnicos y expresivoscv2010
 
Soillimits_Assessment_2009 Irow_Portland
Soillimits_Assessment_2009 Irow_PortlandSoillimits_Assessment_2009 Irow_Portland
Soillimits_Assessment_2009 Irow_Portlandjlarndt_51
 
Internal energy & landforms
Internal energy & landformsInternal energy & landforms
Internal energy & landformsmartalpz
 
Geotrends For 2011 And Beyond
Geotrends For 2011 And BeyondGeotrends For 2011 And Beyond
Geotrends For 2011 And BeyondIan White
 
DojoX GFX Session Eugene Lazutkin SVG Open 2007
DojoX GFX Session Eugene Lazutkin SVG Open 2007DojoX GFX Session Eugene Lazutkin SVG Open 2007
DojoX GFX Session Eugene Lazutkin SVG Open 2007Eugene Lazutkin
 

Destacado (8)

Download-manuals-hydrometeorology-data processing-11howtocompilerainfalldata
 Download-manuals-hydrometeorology-data processing-11howtocompilerainfalldata Download-manuals-hydrometeorology-data processing-11howtocompilerainfalldata
Download-manuals-hydrometeorology-data processing-11howtocompilerainfalldata
 
mramon_AERA2010
mramon_AERA2010mramon_AERA2010
mramon_AERA2010
 
6th Year Department Talk
6th Year Department Talk6th Year Department Talk
6th Year Department Talk
 
Recursos técnicos y expresivos
Recursos técnicos y expresivosRecursos técnicos y expresivos
Recursos técnicos y expresivos
 
Soillimits_Assessment_2009 Irow_Portland
Soillimits_Assessment_2009 Irow_PortlandSoillimits_Assessment_2009 Irow_Portland
Soillimits_Assessment_2009 Irow_Portland
 
Internal energy & landforms
Internal energy & landformsInternal energy & landforms
Internal energy & landforms
 
Geotrends For 2011 And Beyond
Geotrends For 2011 And BeyondGeotrends For 2011 And Beyond
Geotrends For 2011 And Beyond
 
DojoX GFX Session Eugene Lazutkin SVG Open 2007
DojoX GFX Session Eugene Lazutkin SVG Open 2007DojoX GFX Session Eugene Lazutkin SVG Open 2007
DojoX GFX Session Eugene Lazutkin SVG Open 2007
 

Similar a Final Project

Final Project
Final ProjectFinal Project
Final Projectwaughsh
 
Sea level rise and storm surge tools and datasets supporting Municipal Resili...
Sea level rise and storm surge tools and datasets supporting Municipal Resili...Sea level rise and storm surge tools and datasets supporting Municipal Resili...
Sea level rise and storm surge tools and datasets supporting Municipal Resili...GrowSmart Maine
 
Cle International Nepa Conference Presentation 2011 January 17 Presentation...
Cle International Nepa Conference Presentation 2011   January 17 Presentation...Cle International Nepa Conference Presentation 2011   January 17 Presentation...
Cle International Nepa Conference Presentation 2011 January 17 Presentation...awaltner
 
HU Research Day Presentation
HU Research Day PresentationHU Research Day Presentation
HU Research Day PresentationMussie Kebede
 
Gulf.report.dec.01.2010
Gulf.report.dec.01.2010Gulf.report.dec.01.2010
Gulf.report.dec.01.2010John Hutchison
 
Gulf.report.dec.01.2010
Gulf.report.dec.01.2010Gulf.report.dec.01.2010
Gulf.report.dec.01.2010John Hutchison
 
Gulf.report.dec.01.2010
Gulf.report.dec.01.2010Gulf.report.dec.01.2010
Gulf.report.dec.01.2010John Hutchison
 
Climate change preparedness and engagement in southwest florida 10 21-19
Climate change preparedness and engagement in southwest florida 10 21-19Climate change preparedness and engagement in southwest florida 10 21-19
Climate change preparedness and engagement in southwest florida 10 21-19David Silverberg
 
Investigation of Observed Seismicity in the Horn River Basin
Investigation of Observed Seismicity in the Horn River BasinInvestigation of Observed Seismicity in the Horn River Basin
Investigation of Observed Seismicity in the Horn River BasinMarcellus Drilling News
 
Slovinsky Coastal Resiliency
Slovinsky Coastal ResiliencySlovinsky Coastal Resiliency
Slovinsky Coastal ResiliencyWellsReserve
 
Developing a Model to Validate the Use of Landsat and MODIS Data to Monitor C...
Developing a Model to Validate the Use of Landsat and MODIS Data to Monitor C...Developing a Model to Validate the Use of Landsat and MODIS Data to Monitor C...
Developing a Model to Validate the Use of Landsat and MODIS Data to Monitor C...daileya
 
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docxStudent Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docxdeanmtaylor1545
 
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docxStudent Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docxcpatriciarpatricia
 
Sea Level Rise Models for Ocean Beach_kw.ed
Sea Level Rise Models for Ocean Beach_kw.edSea Level Rise Models for Ocean Beach_kw.ed
Sea Level Rise Models for Ocean Beach_kw.edEllen Doudna
 
Nichi.11 12-13.nasa.cynthia rosenzweig
Nichi.11 12-13.nasa.cynthia rosenzweigNichi.11 12-13.nasa.cynthia rosenzweig
Nichi.11 12-13.nasa.cynthia rosenzweigNICHI_USA
 
NISAR Oil, Gas, and Water Underground Reservoirs
NISAR Oil, Gas, and Water Underground ReservoirsNISAR Oil, Gas, and Water Underground Reservoirs
NISAR Oil, Gas, and Water Underground ReservoirsDr. Pankaj Dhussa
 

Similar a Final Project (20)

Final Project
Final ProjectFinal Project
Final Project
 
Sea level rise and storm surge tools and datasets supporting Municipal Resili...
Sea level rise and storm surge tools and datasets supporting Municipal Resili...Sea level rise and storm surge tools and datasets supporting Municipal Resili...
Sea level rise and storm surge tools and datasets supporting Municipal Resili...
 
Cle International Nepa Conference Presentation 2011 January 17 Presentation...
Cle International Nepa Conference Presentation 2011   January 17 Presentation...Cle International Nepa Conference Presentation 2011   January 17 Presentation...
Cle International Nepa Conference Presentation 2011 January 17 Presentation...
 
HU Research Day Presentation
HU Research Day PresentationHU Research Day Presentation
HU Research Day Presentation
 
Gulf.report.dec.01.2010
Gulf.report.dec.01.2010Gulf.report.dec.01.2010
Gulf.report.dec.01.2010
 
Gulf.report.dec.01.2010
Gulf.report.dec.01.2010Gulf.report.dec.01.2010
Gulf.report.dec.01.2010
 
Gulf.report.dec.01.2010
Gulf.report.dec.01.2010Gulf.report.dec.01.2010
Gulf.report.dec.01.2010
 
Tusanami
TusanamiTusanami
Tusanami
 
DA 2 NDMM.pptx
DA 2 NDMM.pptxDA 2 NDMM.pptx
DA 2 NDMM.pptx
 
Climate change preparedness and engagement in southwest florida 10 21-19
Climate change preparedness and engagement in southwest florida 10 21-19Climate change preparedness and engagement in southwest florida 10 21-19
Climate change preparedness and engagement in southwest florida 10 21-19
 
Investigation of Observed Seismicity in the Horn River Basin
Investigation of Observed Seismicity in the Horn River BasinInvestigation of Observed Seismicity in the Horn River Basin
Investigation of Observed Seismicity in the Horn River Basin
 
Slovinsky Coastal Resiliency
Slovinsky Coastal ResiliencySlovinsky Coastal Resiliency
Slovinsky Coastal Resiliency
 
Developing a Model to Validate the Use of Landsat and MODIS Data to Monitor C...
Developing a Model to Validate the Use of Landsat and MODIS Data to Monitor C...Developing a Model to Validate the Use of Landsat and MODIS Data to Monitor C...
Developing a Model to Validate the Use of Landsat and MODIS Data to Monitor C...
 
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docxStudent Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
 
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docxStudent Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
Student Name Bud BennemanGeology 105 Spring 2020Paper Outline.docx
 
hurricanes-math
hurricanes-mathhurricanes-math
hurricanes-math
 
Sea Level Rise Models for Ocean Beach_kw.ed
Sea Level Rise Models for Ocean Beach_kw.edSea Level Rise Models for Ocean Beach_kw.ed
Sea Level Rise Models for Ocean Beach_kw.ed
 
Nichi.11 12-13.nasa.cynthia rosenzweig
Nichi.11 12-13.nasa.cynthia rosenzweigNichi.11 12-13.nasa.cynthia rosenzweig
Nichi.11 12-13.nasa.cynthia rosenzweig
 
NISAR Oil, Gas, and Water Underground Reservoirs
NISAR Oil, Gas, and Water Underground ReservoirsNISAR Oil, Gas, and Water Underground Reservoirs
NISAR Oil, Gas, and Water Underground Reservoirs
 
WRDSeminar_rmartyr
WRDSeminar_rmartyrWRDSeminar_rmartyr
WRDSeminar_rmartyr
 

Final Project

  • 1. Sheldon Waugh Friday, January 30, 2015 GIS3043
  • 2. Introduction/Motivation The BP Oil Spill has been a forefront topic for the Gulf Coast States and the U.S for more than a month and a half. With the 2010 Atlantic Hurricane season upon us, many experts have explained that the oil on the ocean surface plus a U.S land falling hurricane will wreak havoc on the coastal environments and wildlife of the Gulf Coast. The goal here is to find the areas in the Gulf Coast most likely to be hit by a hurricane, and affected by the resulting oil pollution. A analysis like this would be beneficial to Gulf Coast States regional planning committees, for pinpointing a more concentrated area to divert their oil pollution prevention techniques.
  • 3. Background The Deepwater Horizon (BP) Oil Spill is currently the largest offshore oil spill in United States history, surpassing the Exxon Valdez. The spill was initially created by the oil rig explosion of the Deepwater Horizon, killing 11 workers. Currently the spill has been recorded as 90.1 million barrels spilled with a spill rate of 40,000 barrels a day (1 Barrel = 60 Gallons). A stop to the flow of oil from the well has been predicted in some time in August. Covers roughly 2,500 Square Miles. The 2010 Atlantic Hurricane season started June 1st , 2010 (8-14 Hurricanes predicted, 3-7 Major Hurricanes)
  • 4. Data Used/Sources  Hurricane tracks obtained from NOAA(National Oceanic and Atmospheric Administration), (June-August Hurricanes over the last 140 years)  U.S, Canada, Mexico and Cuba shapefiles obtained from the CDC (Center for Disease Control)  Gulf Coastal Counties shapefiles obtained from the United States Census Bureau  The Gulf Coast Sea level and CVI (Coastal Vulnerability Index) shapefiles were obtained from the USGS (United States Geological Survey).  The following data was created by myself by goereferencing and digitizing jpeg images:  Google Maps (.kml files) NOAA Oil spill location and 72 hour predictions as of 6/8/2010, the kml were then converted into jpeg images, georeferenced and digitized.  The loop currents and gulf streams were obtained from the jpeg image that came from NASA’s MODIS satellite (5/18/2010)
  • 5. Analysis 1. I georeferenced and digitized the jpegs for the oil spill (both the actual location, and the 72 hour locations) 2. I then georeferenced and digitized the jpegs for the loop current and the gulf stream ocean currents. 3. I then made a buffer from my 72 hour prediction polygon to make it my 2-month prediction polygon. 4. For the Hurricane data:  I first selected out tracks that did not fall into my 3 month window (MONTH = June, July, August).  I then selected out by location tracks that did not intersect with my 2 month oil spill prediction buffer.  Finally I selected out the tracks that did not make landfall onto one of the four Gulf Coast States I paid attention to (Florida, Louisiana, Alabama, Mississippi) (selected by location hurricane tracks that intersected with the states layer). 1. I estimated storm surge in each area by combining sea level and CVI risk (Coastal Vulnerability Index- Index combines: Sea level Change, erosion wave size and frequency and posts it into a Low to very High index) 2. I then added the necessary regional boundaries
  • 6. Analysis 7. After inputting my data, I created a fishnet grid that covered the Gulf Coast on my map. I made the scale to be 1 degree by 1 degree squares. 8. I then used the Feature to Polygon to convert the fishnet grid into a polygon grid. 9. After that, I executed a spatial join with the hurricane tracks to the polygon grid. 10. After the spatial join, I opened the attribute table of the spatial join and checked the field Join_Count. I sorted this field to show the greatest values. The greater the value in this field the higher the density of hurricanes is present in that specific polygon grid square. The data was collected and two regions were selected as the densest areas for hurricanes.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13. Conclusion The two areas (polygons) which had the greatest density of hurricanes were located at: Plaquemines and St. Bernard’s Parishes in New Orleans Santa Rosa, Okaloosa, and Walton Counties in Florida. These areas according to my analysis will encounter the most hurricanes that will come in contact will the oil spill. The effects of theses areas will include: Wind and rain damage Oil pollution wherever the storm surge takes the oil inland. Potentially millions of dollars in oil cleanup and years for the coastal areas to recuperate from the oil leaching into the soils and harming wildlife.