Looking into the future of water and agriculture in a changing climate (without a crystal ball)

1. Looking into the future of water and agriculture in a changing climate(without a crystal ball) Ana IglesiasDepartment of Agricultural Economics and Social Sciences, Universidad Politécnica de Madrid, Spain CEIGRAM, 21 Octubre 2011

3. Aknowledgements My colleagues: Sonia Quiroga, Luis Garrote, Agustin Diz, Dunia Gonzalez-Zeas Support: MARM, Spain; EU 6th and 7th FP (CIRCE, WasserMed, ClimateCost) Columbia University, GISS/NASA, global analysis McGill University, Africa analysis The World Bank, Eastern Europe, Adaptation project

6. Questions about the future Components of the study Main insights Impacts How can agriculture and water deal with an uncertain future? Global projections of land productivity, water availability (Med) Adaptive capacity How does vulnerability and disparities respond to this uncertain future? Regional adaptive capacity index values and drivers of inequality Adaptation and policy How do we prioritise adaptation to overcome the resulting risks? Assessment and strategy planning process

7. development technology population climate and CO2 changes land productivity water for people trade, prices uncertainty discount rate A view of the problem from the academic side

8. crop models: conceptual framework atmosphere soil management crop

9. model parameter (calibration) uncertainty model theory (arquitecture) complexity 3 issues: discount rate, sustainability, uncertainty

10. ClimateCrop model Output: Database of simulated crop responses to climate, environment, and adaptive management Crop models (DSSAT) 1 2 Agro-climatic regions Agricultural production functions Output: Land productivity and water demand estimated functions 3 Output: Agro-climatic regions that include climate, farm types, irrigated areas, adaptive capacity Output: Land productivity and water demand estimates responding to climate and farm adaptive management Climate change analysis 4 Output: Land productivity and water demand estimates responding to climate and policy (environmental and energy) Climate change policy analysis 5

11. Understanding global uncertainty, land and water (Iglesias et al., 2011) Stations (1141) and agroclimatic zones (73)

12. 9 agro-climatic regions in Europe Mediterranean South

13. 9 climatic regions in China Warm and wet

14. 17 climatic regions in USA Corn Belt

15. RepresentativeEmmissionPahways (RPC) 81 scenarios A1B A balanced emphasis on all energy sources. A1B 2080 = 712 ppm CO2 E1 The so-called global “2 °C-stabilization” scenario is characterized by an atmospheric concentrations of 498 ppmv CO2 in the 2080s) E1 2080 = 498 ppmCO2 RPC8

18. Changes in land productivity (Iglesias et al 2011) HadCM3A2 HadCM3B2

19. Figure 4. (a) Probability density functions (pdf) of A2 and B2 climate change scenario (dashed) and CTL scenario (solid) crop yield variable, vertical lines indicate means, double-headed arrows indicate variance; (b) corresponding cumulative distribution functions (cdf), horizontal arrow indicate changes in the cdf under climate change scenario.

20. Figure 5.SDSeries of deviations of climatechangescenarios (A2 and B2) distributionfunctionswithrespecttoCTL . Black circlesrepresentdecreasedvariability, whitecirclesshow theincreasedvariability and the mean deviations are representedbythemiddlepoint. Also CV, notshown

21. Complexity: need to understand local vulnerabilities

22. Adaptive capacity: components

23. Gini coefficient – Lorenz curves

24. Gini coefficient – Lorenz curves

25. Gini coefficient – Lorenz curves

26. 1 issue: how? “Knowing is not enough; we must apply.Willing is not enough; we must do.” Goethe (1749-1832)

27. Managing the unavoidable(adaptation)

28. Example 1: Cost of action, cost of inaction Source: Iglesias et al., 2007; Ciscar et al 2010

29. Full costs of climate change = cost of inaction economic costs (impacts, baseline) - cost of action + costs of mitigation + costs of adaptation - benefits from mitigation - benefits from adaptation

30. Figure 6. Standard Deviation. Regional disparities outcome for the combined effect of changes in average and variability crop yield for the period 2070-2100 (from 12 A2 and B2 simulations) with respect to 1960-1990 (CTL scenario).

31. Example 2: Practical adaptation questions Management: Can optimal management decrease vulnerability to climate? Technology, biotechnology: What are the characteristics of optimized crop varieties? Water management, infrastructure: Will climate change significantly affect agricultural water demand for agriculture? Can the water/irrigation systems meet the stress of changes in water supply/demand?

33. Adaptation 1 Without adaptation Adaptation 3 Adaptation 2

34. Scenario A1B_av Adaptation 1&3 Irrigation water demand change (% of baseline) 0 2 10 20 30 80

35. Scenario E1_av Adaptation 1&3 Irrigation water demand change (% of baseline) 0 2 10 20 30 80

36. Scenario A1B_av Adaptation 2 Nitrogen fertiliser change (% of baseline) 0 25 50 100 200 300

37. Scenario E1_av Adaptation 2 Nitrogen fertiliser change (% of baseline) 0 25 50 100 200 300

38. 38

39. Very high (AC = 1) no risk high Adaptive capacity medium low very high None (AC = 0) Very positive (more than +30%) Very negative (more than -30%) Impacts

40. potential risk (a synthesis) low medium high or very high

41. Climate change and human displacement • Tuvalu and The Maldives: sea level rise • Nile Delta: desertification, sea level rise • Ganges Delta: migration as a survival strategy • Mekong Delta: floods, resettlement • Mexico, Central America, horn of Africa: drought, disasters • The Sahel: agricultural livelihoods • Asia: glazier melt, irrigation

42. thank you ana.iglesias@upm.es Presentation made at the: CEIGRAM, Madrid 21 October 2011