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1
CLIPClimate-Land Interaction Project
US: Campbell, Alagarswamy, Andresen, Heubner, Lofgren, Lusch, Moore, Olson, Pijanowski, Qi
East Africa: Magezi, Maitima, Misana, Mugisha, N’ganga, Reid, Thornton,Yanda
UK: Conway, Doherty, Hansen, Palutikof
Universityof Nairobi
Open Meeting Oct. 20052/20
Overarching Research Question
What is the nature and magnitude of the interaction between land use and climate change at regional and local scales?
Open Meeting Oct. 20053/20
INTEGRATIVESpatial and temporal scales
Uncertainty analysisFeedbacks and tipping points
Systems paradigmsBroader impacts
CLIMATE DYNAMICS
Regional Local
LAND COVERNPP SIMULATIONS
LAND USE CHANGE
Case Studies Models
Role Playing Games
Crops RangelandRemote Sensing
CaseStudies
Human Systems
Global ClimateThe Climate-Land “Loop”
Open Meeting Oct. 20054/20
Key Points• There are human, technical and scientific challenges to addressing
this question• Human
– “Change is Good”• For researchers to work together, everyone needs to adjust their practice of science
to contribute toward knowledge gaps that require social and biophysical bridges– “Sacrifice for the Good of the Whole”
• Individual researchers often sacrifice advancing their own field in order to fill the gaps
• Younger researchers need to demonstrate productivity and they are making the greatest sacrifices
– “The Big Picture”• System-wide, “big science” questions (e.g., scale, uncertainty, nature of
feedbacks) engage all participants and infuse a sense of intellectual community for all
– “Working is Groups is Hard”• Different personalities require psychological adjustments• Everyone is busy, “buried” and contributing at high levels and long term is
difficult and exhausting
Open Meeting Oct. 20055/20
• Technical Points– “Get Out of Our Box”
• Getting models to “talk to one another” is not easy, researchers are still embroiled in technical aspects of how to run their own model in isolation of the system of models
– “The Cyber Challenge!”• Complex models require cyberinfrastructure which is not easy to use; these
resources are in demand by all scientists (e.g., physicists running models on the ‘big bang’)
– “Talk Isn’t Cheap” • Communication is hard, needs to be frequent for progress to happen
– “We All Do it Differently” • Need to address cultural issues in a project composed of many researchers
from all over the world. What we each consider as important science differs (basic/applied)
– “Data data is everywhere but not a drop to drink”• The data you need is not available but many other surrogates exist that help
you to synthesize what you could use
Key Points
Open Meeting Oct. 20056/20
Key points• Scientific
– “Think Big”• Our overarching question has imbedded many “grand challenge questions” in nearly all natural
and social sciences (e.g., scale, representation, uncertainty)• “Figure 1” is key, it ground us all to the project
– “Start Simple”• Get our “version 1” products to our colleagues quickly
– “We all come up Short”• None of us are experts of the entire system of knowledge, methods, etc
– “Getting Religion” • We need to “blend” practices of science in order to make progress (biophysical scientists have
a lot to learn from social scientists that can improve their science through different practices”– “Coming with a Bias”
• We all have a different impression of what each of us does, sometimes it is not flattering what other disciplines think about each other…especially true of natural science bias of social science (“it is soft and not quantitative and therefore not precise”)
– “Leadership is Key” • We need leadership which has to be shared across subthemes and over time• Provides a common foundation for meeting project timelines
– “The Story Line”• Translating the narrative story into a model is not easy
Open Meeting Oct. 20057/20
Presentation
• Examples of addressing technical and scientific challenges
• How we are completing the loop (it has never been done)
Open Meeting Oct. 20058/20
Case studies
PatternsDriversAgents
Extent of Analysis:
Kenya, Tanzania,Uganda, plusRwanda, Burundiand parts of 6other countries.
Need to usedatasets and models at different scales!
Open Meeting Oct. 20059/20
Multiple MethodsLAND USE
– Long term field work in 8 LUC case study sites:Household surveys, group interviews, LUC analysis, vegetation species
counts, soil sampling, wildlife counts, etc. (LUCID project): historical LUC drivers and patterns
– Expert system analysis, role play: future drivers and patterns– Land use modeling (LTM)– Agent based modeling (MABEL)
LAND COVER– Remote sensing: land surface parameters
CLIMATE– Statistical analysis of meteorological data: historical trends– Comparison of GCM output for region– Regional climate model (RAMS): scenarios under different LULC
NET PRIMARY PRODUCTIVITY– Crop-climate (CERES-Maize & others)– Natural vegetation-climate (LPJ model)
Uncertainty analyses
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Importance of the Storyline
Open Meeting Oct. 200511/20
Delmonte pineapple
Rice paddy
Tea plantation
But high variability in land uses, covers.Example: agriculture
Open Meeting Oct. 200512/20
Ivondo
Small scale mixed crop, livestock and agro-forests (90% of all ag. land)
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Multiple Sources of Information
Open Meeting Oct. 200514/20
Challenge: calibrating models, global datasets to East African situation
1. Needed to choose, or create a “best available” baseline land cover
Statistical analysis of variability within & between cover classes of 3 classifications (“M” statistic)
Use of “ground truthing” information from aerial videography and case studies
Result: hybrid of FAO’s Africover (urban and ag. classes) and GLC2000 (natural veg. classes), called “CLIPcover.”
Open Meeting Oct. 200515/20
2. Critical lack of long-term, consistent meteorological data. Stations tend to be clustered in wetter areas,
almost absent in semi-arid zones.
Much missing data.
Insufficient radiosonde data.
Challenge: calibrating models, global datasets to East African situation
Open Meeting Oct. 200516/20
Approach:
Conduct historical trendsanalysis withinstudy sites, weather generator.
Use new CRUgridded datasetfor crop-climate& LUC modeling.
Open Meeting Oct. 200517/20
Cloud cover is our best proxy for spatial rainfall distribution
Used TRMMS satellitedata to initialize RAMS
Open Meeting Oct. 200518/20
3. Generic, “look-up table” of land cover parameters such as albedo and leaf area index (LAI) in RAMS are different from our region, yet these parameters are critical for our research question.
East Africa is drier and has more seasonal variation in vegetation than look up table values would indicate.
Challenge: calibrating models, global datasets to East African situation
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Contributing Toward Gaps
Open Meeting Oct. 200520/20
RAMS’ LAI phenological curves vs. observed (MODIS) LAI Curves
So, calculated new phenological LAI curves from MODIS data with polynominal splines for each land cover class at altitudinal belts.
The red dotted curves are phenologies at the equator, green solid curves are for north 5 degree, the blue dashed curves for south 5 degree, and the black dot-dashed line is the zero comparison line.
Open Meeting Oct. 200521/20
Did it make any difference?
LAI shown in:1. RAMS default land cover,
and default cover parameters.
2. CLIPcover, with default land cover parameters.
3. CLIPcover with new spline functions
4. MODIS observed LAI for that period.
Conclusion: YES, it made a difference!
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Blending Practices of Science
Open Meeting Oct. 200523/20
4. For land use modeling: Limited spatial data, especially socio-economic, that covers the entire study domain, yet high spatial and temporal variability in patterns and drivers.
Policy is a major driver, and changes often.
Migration from distant areas an important driver in some places.
Some drivers, such as war, result in reverses of LUC with land abandonment.
Need to learn of future drivers and patterns beyond historical trends, from people who would know.
Challenge: calibrating models, global datasets to East African situation
Open Meeting Oct. 200524/20
Global
Regional
Country
District
Town
Family/Farm
Individual/Pixel
Homogenous Zones
IPC
C/G
TA
PE
xper
ts/
Dem
ogra
phy
RP
S/C
ase
Stu
dies
SCALE SOURCE
MA
BE
L
LTM
“Bottom up”
“Top down”
Pot
entia
l
Cas
e S
tudi
es
Resolve
Modeling Scales and DirectionModeling Scales and Direction
Open Meeting Oct. 200525/20
Role Playing Simulation: Kenya
How do farmer and herder groupsdecide who gets what land?
How do they respond to government mandates?
What is the land use outcome of thecompetition over land?
Representatives from the Kenya Government and universities
Open Meeting Oct. 200526/20
Land Use Expert Workshop: Tanzania
Representatives from the government and
from academic units at UDSM, NSF
Major LUC is expected in certain zones due to:– Transport infrastructure development:
roads, bridges
– Government’s agricultural modernisation policy
– Growth of urban market for fuelwood, meat, milk and crop production
– Structural adjustment
– Rural migration
– Sedentarisation of pastoralists.
Large role of policies—how incorporate into models?
Open Meeting Oct. 200527/20
Constructing the Belief Network
Role Playing Simulation
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8889
9091
9293
9495
96 97 98 99100
Step 15
Open Meeting Oct. 200545/20
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 16
Open Meeting Oct. 200546/20
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 17
Open Meeting Oct. 200547/20
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 18
Open Meeting Oct. 200548/20
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 19
Open Meeting Oct. 200549/20
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 20
Open Meeting Oct. 200550/20
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
Open Meeting Oct. 200551/20
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 22
Open Meeting Oct. 200552/20
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 23
Open Meeting Oct. 200553/20
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 24
Open Meeting Oct. 200554/20
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 25
Open Meeting Oct. 200555/20
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 26
Open Meeting Oct. 200556/20
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 27
Open Meeting Oct. 200557/20
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 28
Open Meeting Oct. 200558/20
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 29
Open Meeting Oct. 200559/20
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 30
Open Meeting Oct. 200560/20
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 31
Open Meeting Oct. 200561/20
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 32
Open Meeting Oct. 200562/20
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 33
Open Meeting Oct. 200563/20
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 34
Open Meeting Oct. 200564/20
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 35
Open Meeting Oct. 200565/20
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 36
Open Meeting Oct. 200566/20
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 37
Open Meeting Oct. 200567/20
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 38
Open Meeting Oct. 200568/20
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 39
Open Meeting Oct. 200569/20
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 40
Open Meeting Oct. 200570/20
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 41
Open Meeting Oct. 200571/20
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 42
Open Meeting Oct. 200572/20
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 43
Open Meeting Oct. 200573/20
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 44
Open Meeting Oct. 200574/20
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 45
Open Meeting Oct. 200575/20
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 46
Open Meeting Oct. 200576/20
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 47
Open Meeting Oct. 200577/20
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 48
Open Meeting Oct. 200578/20
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 49
Open Meeting Oct. 200579/20
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 50
Open Meeting Oct. 200580/20
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 51
Open Meeting Oct. 200581/20
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 52
Open Meeting Oct. 200582/20
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 53
Open Meeting Oct. 200583/20
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 54
Open Meeting Oct. 200584/20
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 55
Open Meeting Oct. 200585/20
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 56
Open Meeting Oct. 200586/20
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 57
Open Meeting Oct. 200587/20
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 58
Open Meeting Oct. 200588/20
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 59
Open Meeting Oct. 200589/20
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 60
Open Meeting Oct. 200590/20
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 61
Open Meeting Oct. 200591/20
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 62
Open Meeting Oct. 200592/20
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 100
Open Meeting Oct. 200593/20
Average Cumulative Rewards & 2nd Order Dynamics
y = 0.0235x2 - 0.0112x - 48.023R2 = 0.9646
-100
-50
0
50
100
150
200
250
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Case Number
Cum
ula
tive R
ew
ard
s
Average Cumulative Rewards
Poly. (Average Cumulative Rewards)
Losses Period (decreasing rate)
Break-even Period (steady rate)
Gains Period (increasing rate)
Higher Variability (high uncertainty,
slow learning)
Lower Variability (low uncertainty, faster learning)
Open Meeting Oct. 200594/20
2010 2020 2040
Nairobi Nairobi Nairobi
Modeling Agricultural Expansion Based on SRES Scenario 21
Open Meeting Oct. 200595/20
Preliminary results 1Rapid historic and current LUC
patterns, especially from higher agric. potential towards lower potential zones. In general:
1) an expansion of cropping into grazing areas, particularly in semi-arid to sub-humid areas,
2) an expansion of rainfed and irrigated agriculture in wetlands or along streams in semi-arid areas,
3) a reduction in size of many unprotected woodlands and forests,
4) an intensification of land use in areas already under crops in the more humid areas, and
5) the maintenance of natural vegetation in most protected areas.
Semi Arid Ivondo, Mbeere, Kenya
Open Meeting Oct. 200596/20
Preliminary results 2
• Result:– Higher number of people being supported on
land,– But rapid soil degradation,– Competition over land and water resources,– And, coincidence of the poorest people
(migrating farmers) moving to the most marginal environments (degraded soil, prone to drought).
Open Meeting Oct. 200597/20
Recent historical trends show complex, mixed results.Annual time series of average monthly rainfall anomalies (mm) for the Kenya/Tanzania site stations. Red line represents linear regression over the full record. The blue line represents smoothing from an 11-point Gaussian filter. Stations are plotted in latitudinal order (from left to right, north to south). As can be seen, it is difficult to generalise and summarise the character of rainfall within the study site.
Open Meeting Oct. 200598/20
Preliminary results 3
• Meanwhile, decreasing vegetated cover from LUC is expected to lead to drier, warmer conditions (RAMS results).
• From GCM, can expect increasing temperatures and an increase in rainfall variability. This will lead to new pressure on the soil, water and vegetation. The results will reverberate through the human and biophysical systems.
Open Meeting Oct. 200599/20
Results 4Maize productivity simulations show high sensitivity to water availability. In a low rainfall site (on right) where farmers are settling, productivity is highly variable with several years with no harvest. The risk of no production is expected to increase.
Open Meeting Oct. 2005100/20
Summary
• Challenges of working in an area with less available data requires careful adaptation of globally available datasets.
• In East Africa, it appears that climate change impacts of land use/cover change, combined with global climate changes, may increase risk of agricultural drought especially where land use and social changes are now rapidly occurring.