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
Feb 05, 2016
Determining the Spatial Distribution of Benthos
in the Western Arctic Ocean
Jon GoodallEnvironmental and Water Resources Engineering
December 6, 2001
CE 394K.3 GIS in Water ResourcesTerm Project Presentation
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
What is Benthic Biomass?
Measurement of amount of living materialon the ocean floor (g/m2)
Study Region
Projection: Lamberts Azimuthal Equal-Area
Alaska
CanadaSiberia
Bering Strait
Western Arctic Ocean
Pacific Ocean
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)
Benthic Biomass at Each Location
Image created in ArcScene with 3D Analyst
AlaskaCanada
Siberia
Why Use Geostatistics?
Point data Continuous Surface
“The Geostatistical Analyst uses sample points taken at different locations in a landscape and creates (interpolates) a continuous surface.”
-ArcGIS Help Menu
Source: http://www.aqd.nps.gov/ard/figure3.html
How Ordinary Kriging Works
2
1
)()(2
1)(
n
iii hXZXZ
nh
h = Separation Distance Z(i) = Attribute value at iN = # samples separated by distance h
Separation Distance
Square difference of attribute Values
100 m 225
150 m 64
100 m 200
75 m 50… …
You can find value atany location based on known values at neighboring locations
“One of the main issues concerning Ordinary Kriging is whether the assumption of a constant mean is reasonable.”
- ArcGIS Help Menu
All Data
y = -43.331x + 3138.2
R2 = 0.2004
0
500
1000
1500
2000
2500
3000
3500
61 63 65 67 69 71 73 75
Latitude
Bio
mas
s (g
/m2 )
68.5 º N
Location of Data Split
68.5 º N
North Data ( > 68.4 N )
y = 3.1665x + 174.43
0
500
1000
1500
2000
2500
3000
3500
61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0
Latitude
Bio
mas
s (g
/m2 )
(-43 for overall data set)
South Data ( < 68.4 N)
y = 13.602x - 902.29
0
100
200
300
400
500
600
700
800
900
68.0 69.0 70.0 71.0 72.0 73.0 74.0
Latitude
Bio
mas
s (g
/m2 )
(-43 for overall data set)
Benthic Biomass Spatial Distribution(Northern Data Set)
Legend
Prediction Standard Contours53
71
89
107
Biomass (g/m2)
0 – 2.32.3 – 11.611.6 – 49.7
49.7 – 204.9204.9 - 838
Benthic Biomass Spatial Distribution(Southern Data Set)
Legend
Prediction Standard Contours432
457
482
507
Biomass (g/m2)
0 – 3737 – 106106 – 233
233 – 469469 - 3222
Semivariograms
North South
Small-Scale Variation a Problem in Southern Data Set
Biomass (g/m2)
Biomass (g/m2) = 1216 = 720 = 405 = 254
Which is it?
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)
0 - 105
105- 300
300 - 560
560 - 1000
1000 - 1832
1832 - 3200
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?
Acknowledgements
Dr. MaidmentCenter for Research in Water Resources
Dr. BarrettCenter for Research in Water Resources
Dr. DuntonMarine Science Institute
UT-Austin
Susan SchonbergMarine Science Institute
UT-Austin
Jóna Finndís JonsdottírPrevious M.S. StudentCenter for Research in Water Resources
Questions?