Ontario Benthos Biomonitoring Network Participants’ Training
Determining the Spatial Distribution of Benthos in the Western Arctic Ocean
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Transcript of Determining the Spatial Distribution of Benthos in the Western Arctic Ocean
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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
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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
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What is Benthic Biomass?
Measurement of amount of living materialon the ocean floor (g/m2)
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Study Region
Projection: Lamberts Azimuthal Equal-Area
Alaska
CanadaSiberia
Bering Strait
Western Arctic Ocean
Pacific Ocean
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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)
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Benthic Biomass at Each Location
Image created in ArcScene with 3D Analyst
AlaskaCanada
Siberia
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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
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Source: http://www.aqd.nps.gov/ard/figure3.html
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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
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“One of the main issues concerning Ordinary Kriging is whether the assumption of a constant mean is reasonable.”
- ArcGIS Help Menu
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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
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Location of Data Split
68.5 º N
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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)
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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)
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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
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Benthic Biomass Spatial Distribution(Southern Data Set)
Legend
Prediction Standard Contours432
457
482
507
Biomass (g/m2)
0 – 3737 – 106106 – 233
233 – 469469 - 3222
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Semivariograms
North South
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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
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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
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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?
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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
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Questions?