1. 1 CLIP Climate-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 University of Nairobi

2. Open Meeting Oct. 2005 2/20 Overarching Research Question What is the nature and magnitude of the interaction between land use and climate change at regional and local scales?

3. Open Meeting Oct. 2005 3/20 INTEGRATIVE Spatial and temporal scales Uncertainty analysis Feedbacks and tipping points Systems paradigms Broader impacts CLIMATE DYNAMICS RegionalLocal LAND COVER NPP SIMULATIONS LAND USE CHANGE Case Studies Models Role Playing Games CropsRangeland Remote Sensing Case Studies Human Systems Global Climate The Climate-Land “Loop”

4. Open Meeting Oct. 2005 4/20 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

5. Open Meeting Oct. 2005 5/20 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

6. Open Meeting Oct. 2005 6/20 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

7. Open Meeting Oct. 2005 7/20 Presentation Examples of addressing technical and scientific challenges How we are completing the loop (it has never been done)

8. Open Meeting Oct. 2005 8/20 Case studies Patterns Drivers Agents Extent of Analysis: Kenya, Tanzania, Uganda, plus Rwanda, Burundi and parts of 6 other countries. Need to use datasets and models at different scales!

9. Open Meeting Oct. 2005 9/20 Multiple Methods LAND 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

10. 10 Importance of the Storyline

11. Open Meeting Oct. 2005 11/20 Delmonte pineapple Rice paddy Tea plantation But high variability in land uses, covers. Example: agriculture

12. Open Meeting Oct. 2005 12/20 Ivondo Small scale mixed crop, livestock and agro-forests (90% of all ag. land)

13. 13 Multiple Sources of Information

14. Open Meeting Oct. 2005 14/20 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.”

15. Open Meeting Oct. 2005 15/20 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

16. Open Meeting Oct. 2005 16/20 Approach: Conduct historical trends analysis within study sites, weather generator. Use new CRU gridded dataset for crop-climate & LUC modeling.

17. Open Meeting Oct. 2005 17/20 Cloud cover is our best proxy for spatial rainfall distribution Used TRMMS satellite data to initialize RAMS

18. Open Meeting Oct. 2005 18/20 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

19. 19 Contributing Toward Gaps

20. Open Meeting Oct. 2005 20/20 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.

21. Open Meeting Oct. 2005 21/20 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!

22. 22 Blending Practices of Science

23. Open Meeting Oct. 2005 23/20 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

24. Open Meeting Oct. 2005 24/20 Global Regional Country District Town Family/Farm Individual/Pixel Homogenous Zones IPCC/GTAP Experts/ Demography RPS/Case Studies SCALE SOURCE MABEL LTM “Bottom up” “Top down” Potential Case Studies Resolve Modeling Scales and Direction

25. Open Meeting Oct. 2005 25/20 Role Playing Simulation: Kenya How do farmer and herder groups decide who gets what land? How do they respond to government mandates? What is the land use outcome of the competition over land? Representatives from the Kenya Government and universities

26. Open Meeting Oct. 2005 26/20 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?

27. Open Meeting Oct. 2005 27/20 Constructing the Belief Network Role Playing Simulation

28. Open Meeting Oct. 2005 28/20 Step 0 Prior Probabilities of the FDS Belief Network (Initial State)

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93. Open Meeting Oct. 2005 93/20 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)

94. Open Meeting Oct. 2005 94/20 2010 2020 2040 Nairobi Modeling Agricultural Expansion Based on SRES Scenario 21

95. Open Meeting Oct. 2005 95/20 Preliminary results 1 Rapid 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

96. Open Meeting Oct. 2005 96/20 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).

97. Open Meeting Oct. 2005 97/20 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.

98. Open Meeting Oct. 2005 98/20 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.

99. Open Meeting Oct. 2005 99/20 Results 4 Maize 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.

100. Open Meeting Oct. 2005 100/20 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.