The Future of Indiana’s Landscape Bryan C. Pijanowski Department of Forestry and Natural Resources Purdue University West Lafayette, IN ana Farm Policy Study Group Presentation
The Future of Indiana’s Landscape Bryan C. Pijanowski Department of Forestry and Natural Resources Purdue University West Lafayette, IN Indiana Farm Policy.
Jan 21, 2016
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The Future of Indiana’s Landscape
Bryan C. PijanowskiDepartment of Forestry and Natural Resources
Purdue UniversityWest Lafayette, IN
Indiana Farm Policy Study Group Presentation
Presentation Topics
1. Patterns of Land Use/Cover Change in Indiana
2. Review of Modeling Tools We Have Developed– Special focus on a Michigan Land
Resources Based Industries Study 2001
3. Sample Land Use Change Impact Assessments (EPA, NASA and NSF)
1. Patterns of Land Use Change
Land Use Change in Central Indiana
1980
Land Use Change in Central Indiana
1990
2000
Land Use Change in Central Indiana
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
high
den
sity
low
den
sity
bare
soi
ls
exca
vatio
ns
fore
st
herb
aceo
us
agric
ultu
re
wet
land
for
est
wet
land
oth
er
wet
land
bar
e
Land Use/Cover Class
Percent Use/Cover by Year
1980
1990
2000
Central Indiana Land Use Change Analysis
Percent Change in Use/Cover (1980-2000)
-80
-60
-40
-20
0
20
40
60
80
100
120
high d
ensit
y
low de
nsity
bare
soils
exca
vatio
ns
fore
st
herb
aceo
us
agric
ultur
e
wetland
fores
t
wetland
other
wetland
bare
Central Indiana Land Use Change Analysis
0
5000
10000
15000
20000
25000
30000
35000
40000
agriculture bare soils w etland other w etland forest herbaceous forest low density high density w etland bare low density
Land Use/Cover Class
Patches of Use/Cover
1980
1990
2000
Central Indiana Land Use Change Analysis
Landscape is becoming more fragmented in uses
2. Land Use Change Modeling
Different Land Use Change Models
• Land Transformation Model (LTM)– Artificial neural network
based– Modeling of spatial
patterns– Based on AI tools that
simulate the way the mammalian brain works
– Couples neural nets and GIS; cell based
• Multi-agent based models (MABEL)– Modeling of individuals and
their behavior– Uses an integrated
behavioral-economic-utility– Parameterization is
empirical, land transactions on parcels
– Couples SPSS, GIS and SWARM
Modeling trends and spatial relationships
Modeling processes and studying
emergent properties
Michigan Land Resources Project
Economic Impact to Natural Resources
Participants in the Study
• Oversight: Public Sector Consultants, Inc., Lansing
• Land Use Projections: Michigan State University
– Stuart Gage– Bryan Pijanowski– David Skole
• Economic Impact Assessments:– Agriculture: Jake Ferris (MSU)– Forestry: Michael Moore (Moore and Associates)– Mining: Mark Roberts & Gary Campbell (MTU)– Tourism/Recreation: Charles Nelson (MSU)
• Communication: Pace & Partners, Inc., Lansing
• Evaluation: Planning & Zoning Center, Inc., Lansing
Introduction-Setting the Tone• Michigan’s land-based industries – agriculture, forestry,
mining, tourism and recreation – contribute $63 Billion (30%) to the state’s economy
• Land use change is a significant issue, which could have a profound impact on these industries in the future
• Land-based industries contribute to our scenic landscape and quality of life
• Polls show that citizens care about land use change: 72% are concerned about loss of agriculture land, 65% believe that loss of forests is a serious problem
Advisors to the StudyMichigan Economic & Environmental Roundtable Members
• MEER President - James DuBay
• AFL/CIO – Tim Hughes
• East Michigan Environmental Action Council – Elizabeth Harris
• Greater Downtown Partnership, Inc. – Larry Marantette
• Hanson Cold Storage Company – Jordan Tatter
• Michigan Environmental Council – Lana Pollack and Conan Smith
• Michigan Farm Bureau – Al Almy
• Michigan Municipal League – George Goodman
• Michigan Retailers Association – Larry Meyer
• Michigan State University – Gordon Guyer and William Taylor
• Michigan United Conservation Clubs – Dennis Fox and Jim Goodheart
• The Dow Chemical Company – Joy Hutchison
• The Nature Conservancy – Helen Taylor
• Traverse City Area Chamber of Commerce– Hal VanSumeren
• Washtenaw County Drain Commissioner – Janis Bobrin
Other Participants in the StudyAdvisory Council
• Crystal Mountain Resort – Chris MacInnes
• International Paper – Mark Pontti
• Michigan Chamber of Commerce – Jim Barrett and Kevin
Korpi
• Michigan Farm Bureau – Al Almy
• Michigan State University – Gordon Guyer and William
Taylor
• Michigan United Conservation Clubs – Dennis Fox and Jim
Goodheart
• The Frey Foundation – John Frey and Milt Rohwer
Goals of the Study of Land Use Change
– Provide, through quantitative measurement, a credible estimate of the present and future land use patterns for the entire state
– Evaluate the impact of land use change on Michigan’s land- based industries, if current trends continue, for 2020 and 2040
– Determine what would be the cumulative impacts of current land use trends for Michigan’s economy (and ecosystems)
1980
2020
2040
Built
Agriculture
Other vegetation
Forest
Lake
Wetland
ProjectedLand Use
Trend
Michigan Land Resources Project
1980
1995
2020
2040
Sprawl Index
0 5 10 15 20 25 30
Twin Cities
Michigan Statewide
Saginaw MI
Bay City MI
Ann Arbor MI
Lansing MI
Detroit MI
Indiana StatewideS
pra
wl I
nd
ex
Comparison of Urbanization Across the Midwest
1980
2000
2030
Preliminary forecasts of new urban from the Land Transformation Model for Central Indiana
Multi-Agent Based Behavioral-Economic
Landscape Model (MABEL)
Constructing the Belief Network
Role Playing Simulation
Intented Decision's Expected Utility
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(Initial State)
Intented Decision's Expected Utility
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69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 18
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 19
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 20
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 21
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 22
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 23
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 24
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 25
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 26
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 27
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 28
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 29
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 30
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 31
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 32
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 33
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 34
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 35
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 36
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 37
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 38
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 39
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 40
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 41
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 42
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 43
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 44
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 45
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 46
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 47
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 48
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 49
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 50
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 51
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 52
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 53
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 54
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 55
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 56
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 57
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 58
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 59
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 60
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 61
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 62
Cumulative Reward Value
-300
-200
-100
0
100
200
300
400
500
600
700
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cu
mu
lativ
e R
ew
ard
Intented Decision's Expected Utility
60
65
70
75
80
85
90
95
100
01 2 3 4 5
67
89
1011
1213
1415
16
1718
1920
21
22
23
24
25
2627
2829
3031
32
3334
3536
3738
3940
4142
434445
464748495051525354555657
5859
6061
6263
64
6566
6768
69
7071
7273
7475
76
77
78
79
80
8182
8384
85
8687
8889
9091
9293
9495
96 97 98 99100
Step 100
3. Impact AssessmentEnvironmental
Zoom of GW Model
withLand Use,
Surface Water and
Roads
June Lake
Hess Lake
Utley Lake
Abeys Lake
Cold Creek
Sylvan Lake
Pettit Lake
Brooks Lake
Emerald Lake
Brooks Creek
Pickerel Lake
Penoyer CreekBigelow Creek
John Ford Lake
Butterfield Lake
Little Lake Placid
Flow paths (arrow) and
fluxes (width)
Forest
Agriculture
Residential
Muskegon River Watershed – MichiganCoupling of two Purdue University ModelsExamining the Impact of Urbanization on Water Quality
Percent changes in metrics between 1980 and 2040
0 20 40 60 80 1000
1 104
2 104
3 104
4 104
5 104
6 104
5.188 104
0
c1978vrow SRPload frq
c2040vrow SRPload frq
c2040svrow SRPload frq
c1830vrow SRPload frq
955 ExFreqfrq
0 20 40 60 80 1000
0.01
0.02
0.03
c1978vrow SRPconc frq
c2040vrow SRPconc frq
c2040svrow SRPconc frq
c1830vrow SRPconc frq
ExFreqfrq
c.a. 1830
2040
1978
c.a. 1830
2040
1978
grams SRP /day @Q10
.005 – 6.5 6.5 – 13 13 – 19 19 – 26 26 – 32 32 – 39 39 - 45
Land Use Trends and Phosphate Loading
Work of Mike Wiley, University of Michigan
Mega-Model Runs target the entire watershed and provide a time-dependent context for understanding ourCurrent conditions, identifying risks that lie ahead, and a testing ground for alternate Management
Scenarios.
For more information
• http://human-environment.org
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