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Determining the Spatial Distribution of Benthos in the Western Arctic Ocean

CE 394K.3 GIS in Water Resources Term Project Presentation. Determining the Spatial Distribution of Benthos in the Western Arctic Ocean. Jon Goodall Environmental and Water Resources Engineering December 6, 2001. Overview. Background Identify Study Region

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Determining the Spatial Distribution of Benthos in the Western Arctic Ocean

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  1. CE 394K.3 GIS in Water Resources Term Project Presentation Determining the Spatial Distribution of Benthos in the Western Arctic Ocean Jon Goodall Environmental and Water Resources Engineering December 6, 2001

  2. Overview • Background • Identify Study Region • Introduce Benthic Biomass Data Sets • Explain Ordinary Kriging • Benthic Biomass in the Western Arctic Ocean • Account for Global Trends • Present Biomass Interpolated Surface • Discuss Results • Conclusions

  3. What is Benthic Biomass? Measurement of amount of living material on the ocean floor (g/m2)

  4. Study Region Western Arctic Ocean Siberia Canada Bering Strait Alaska Pacific Ocean Projection: Lamberts Azimuthal Equal-Area

  5. Stoker (1970 – 1974) Wacasey (1971 –1975) Carey (1971-1976) Broad (1975 – 1981) Feder (1979 – 1986) Grebmier (1984 – 1995) Data Sets Complete Data Set (1970 – 1995) Stoker (1970 – 1974) Wacasey (1971 –1975) Carey (1971-1976) Broad (1975 – 1981) Feder (1979 – 1986) Grebmier (1984 – 1995)

  6. Benthic Biomass at Each Location Siberia Alaska Canada Image created in ArcScene with 3D Analyst

  7. Why Use Geostatistics? “The Geostatistical Analyst uses sample points taken at different locations in a landscape and creates (interpolates) a continuous surface.” -ArcGIS Help Menu Point data Continuous Surface

  8. Source: http://www.aqd.nps.gov/ard/figure3.html

  9. How Ordinary Kriging Works h = Separation Distance Z(i) = Attribute value at i N = # samples separated by distance h You can find value at any location based on known values at neighboring locations

  10. “One of the main issues concerning Ordinary Kriging is whether the assumption of a constant mean is reasonable.” - ArcGIS Help Menu

  11. 68.5 º N

  12. Location of Data Split 68.5 º N

  13. (-43 for overall data set)

  14. (-43 for overall data set)

  15. Prediction StandardContours Biomass (g/m2) 53 0 – 2.3 71 2.3 – 11.6 11.6 – 49.7 89 49.7 – 204.9 107 204.9 - 838 Benthic Biomass Spatial Distribution(Northern Data Set) Legend

  16. Prediction StandardContours Biomass (g/m2) 432 0 – 37 457 37 – 106 106 – 233 482 233 – 469 507 469 - 3222 Benthic Biomass Spatial Distribution(Southern Data Set) Legend

  17. Semivariograms North South

  18. 0 - 105 105- 300 300 - 560 560 - 1000 1000 - 1832 1832 - 3200 Small-Scale Variation a Problem in Southern Data Set Biomass (g/m2) = 1216 = 720 = 405 = 254 Which is it? Biomass (g/m2)

  19. 0 - 105 105- 300 300 - 560 560 - 1000 1000 - 1832 1832 - 3200 Small-Scale Variation in Northern Data Set Biomass (g/m2) = 840 = 270 = 269 Biomass (g/m2)

  20. Conclusions • It was possible to interpolate the biomass on a continuous scale with relatively high certainty for the northern region • This method was not capable of accurately predicting biomass in the southern region due to small-scale variability of biomass measurements Future work: Is small-scale variability a result of measuring errors or is it an inherent property of the benthic biomass?

  21. Acknowledgements Dr. Dunton Marine Science Institute UT-Austin Dr. Maidment Center for Research in Water Resources Susan Schonberg Marine Science Institute UT-Austin Jóna Finndís Jonsdottír Previous M.S. Student Center for Research in Water Resources Dr. Barrett Center for Research in Water Resources

  22. Questions?

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