1 CLIP Climate-Land Interaction Project US: Campbell, Alagarswamy, Andresen, Heubner, Lofgren,...

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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?


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”


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


• 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


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


Presentation

• Examples of addressing technical and scientific challenges

• How we are completing the loop (it has never been done)


Case studies

PatternsDriversAgents

Extent of Analysis:

Kenya, Tanzania,Uganda, plusRwanda, Burundiand parts of 6other countries.

Need to usedatasets and models at different scales!


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


Delmonte pineapple

Rice paddy

Tea plantation

But high variability in land uses, covers.Example: agriculture


Ivondo

Small scale mixed crop, livestock and agro-forests (90% of all ag. land)

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Multiple Sources of Information


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.”


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.



Approach:

Conduct historical trendsanalysis withinstudy sites, weather generator.

Use new CRUgridded datasetfor crop-climate& LUC modeling.


Cloud cover is our best proxy for spatial rainfall distribution

Used TRMMS satellitedata to initialize RAMS


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.


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Contributing Toward Gaps


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.


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


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.



Global

Regional

Country

District

Town

Family/Farm

Individual/Pixel

Homogenous Zones

IPC

C/G

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SCALE SOURCE

MA

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“Bottom up”

“Top down”

Pot

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Cas

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Resolve

Modeling Scales and DirectionModeling Scales and Direction


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


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?


Constructing the Belief Network

Role Playing Simulation


Intented Decision's Expected Utility

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lativ

e R

ew

ard


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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



-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


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

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Average Cumulative Rewards & 2nd Order Dynamics

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slow learning)

Lower Variability (low uncertainty, faster learning)


2010 2020 2040

Nairobi Nairobi Nairobi

Modeling Agricultural Expansion Based on SRES Scenario 21


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


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).


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.


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.


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.


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.

1 CLIP Climate-Land Interaction Project US: Campbell, Alagarswamy, Andresen, Heubner, Lofgren,...

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Transcript of 1 CLIP Climate-Land Interaction Project US: Campbell, Alagarswamy, Andresen, Heubner, Lofgren,...