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

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/m 2 )

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

5. Data Sets Stoker (1970 – 1974)Wacasey (1971 –1975)Carey (1971-1976)Broad (1975 – 1981)Feder (1979 – 1986)Grebmier (1984 – 1995) 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 Image created in ArcScene with 3D Analyst Alaska Canada Siberia

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

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)

15. Benthic Biomass Spatial Distribution (Northern Data Set) Legend Prediction Standard Contours 53 71 89 107 Biomass (g/m 2 ) 0 – 2.3 2.3 – 11.6 11.6 – 49.7 49.7 – 204.9 204.9 - 838

16. Benthic Biomass Spatial Distribution (Southern Data Set) Legend Prediction Standard Contours 432 457 482 507 Biomass (g/m 2 ) 0 – 37 37 – 106 106 – 233 233 – 469 469 - 3222

17. Semivariograms NorthSouth

18. Small-Scale Variation a Problem in Southern Data Set Biomass (g/m 2 ) Biomass (g/m2) = 1216 = 720 = 405 = 254 Which is it? 0 - 105 105- 300 300 - 560 560 - 1000 1000 - 1832 1832 - 3200

19. Small-Scale Variation in Northern Data Set Biomass (g/m2) = 840 = 270 = 269 Biomass (g/m 2 ) 0 - 105 105- 300 300 - 560 560 - 1000 1000 - 1832 1832 - 3200

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

22. Questions?