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The Analogues Methodology - Ramirez-Villegas
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Presentation by Julian Ramirez-Villegas. …

Presentation by Julian Ramirez-Villegas.

CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.

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  • 1. The Analogues Methodology
    Julian Ramirez-Villegas
  • 2. The method
    Jointly developed by the Walker Institute (University of Reading, UK) and the Decision and Policy Analysis program (DAPA) at CIAT
    Funded by CCAFS Theme 1: Adaptation to progressive climate change
    And collaborating with some other people in different institutions
  • 3. Which questions can we answer?
    • Where can I find my site in the future? (FORWARD)
    • 4. Where can I find a place that currently looks like how my site would be in the future? (BACKWARD)
    • 5. Where can I find similar areas to my site currently or in the future? (NO-DIRECTION)
  • Guiding principles and final aims
    • Facilitate farmer-to-farmer exchange of knowledge
    • 6. Permit validation of computational models and trialing of new technologies and techniques
    • 7. Learning from history
    • 8. Ground climate change impacts and adaptation studies
    • 9. Quantify the real effects of uncertainty in the process of adaptation
  • How do we calculate dissimilarity?
    In reality, this is a n-dimensional
    space of m time-steps and v variables
  • 10. How do we calculate dissimilarity?
    The CCAFS measure: a modified version of the Euclidean distance
    m: time-step (e.g. month, day, quarter)
    v: number of variables
    V: Variable (i.e. temperature, rainfall, etc)
    W: Weight (any number or variable)
    f: reference scenario (e.g. future climate)
    p: target scenario (e.g. current climate)
    z: exponent (we normally assume 2)
    Lagging
  • 11. How do we calculate dissimilarity?
    Accounting for lag
    Site A (southern)
    Site B (northern)
  • 12. How do we calculate dissimilarity?
    The CCAFS measure: weights with other variables
    W: Weight value
    X: Variable value
    i: a given time step
    j: a given variable
  • 13. How do we calculate dissimilarity?
    As with the example data,
    m: month (from 1 to 12)
    f: reference value (e.g. future climate)
    p: target value (e.g. present climate)
    z: exponent (we normally assume 2)
    T: Temperature
    P: Precipitation
    DTR: Diurnal temperature range
    m-lag: month that produces a maximum match of climate parameters (precip, temp)
  • 14. How do we calculate dissimilarity?
    Hallegatte et al. (2007): a rule based dissimilarity measure
    Relative difference between total values less than “a”
    Mean absolute relative differences between mean values for m steps less than “b”
    Mean absolute difference between total values for m steps less than “c”
    m: time-step (e.g. month, day, quarter)
    V: Variable (i.e. temperature, rainfall, etc)
    W: Weight (any number or variable)
    f: reference scenario (e.g. future climate)
    p: target scenario (e.g. current climate)
    a, b, c: user defined parameters
  • 15. Do our two measures agree?
    • Could depend on the site, but so far, they do
  • Analogues of what?
    • Of a site within all land areas of a given geographic domain
  • Analogues of what?
    Of one site to many others
    Dissimilarity from a site in Ghana (future) to 35 other sites at present
    (bars are the distribution of 24 GCMs)
  • 16. Analogues of what?
    • Of many points one versus the other (n-by-n sites matrix)
  • How analogues?
    • On seasonal temperature?
  • How analogues?
    • On seasonal rainfall?
  • How analogues?
    • On seasonal combination of both?
  • How certain are we?
    • Counting GCMs that choose a site as analogue
  • Not so fast, though
    Only certain variables are key for crop dev.
    Storage in organs
  • 17. Not so fast, though
    So we need to do it “through the eyes of the crop”
    Temperature and CO2
    Avail. water and solar radiation
    Source: Bates, 2002
    Source: Isdo et al., 1995 (sour orange trees)
  • 18. Agricultural analogues
    • How much of this difference is due to the environment?
    West et al. 2010, based on Monfreda et al. 2008
  • 19. Agricultural analogues
    • Dissimilarities and agricultural yields
    Very similar high yield
    Very dissimilar and low yields
  • 20. Important science questions
    • Could we relate our dissimilarity index with growth parameters and yields?
    • 21. If so, which variables do we need to use?
    • 22. Rainfall + temperature?
    • 23. Rainy days?
    • 24. Days with extreme (very low, very high) temperatures?
    • 25. Evapotranspiration?
    • 26. Total thermal times?
    • 27. Soils?
    • 28. Varietal targeting and testing?
    • 29. Cropland expansion/optimisation/migration?
    • 30. Coupling with crop models for validation and adaptation-strategy testing?
  • Messages
    • Grounding results is required due to the large local variability
    • 31. Decision support tool, but must be validated
  • In this course you will learn…
    • The main principles of the analogues method and its potential use
    • 32. How to calculate climate analogues online and via R
    • 33. The different sensitivities of this method (variables, weights, direction)
    • 34. How should we validate the results of the analogues tool
  • A couple of clarifications…
    Impacts on Sorghum suitability by 2030, re-calibrated
  • 35. A couple of clarifications…
    Similarity between EcoCrop response and other models