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The Analogues R-Package - 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 R-Package
    Julian Ramirez-Villegas
  • 2. The tool
    Entirely coded as an package
    Optimised for large datasets with GRASS-GIS (experimental)
    Example data at 0.5-degree (~100km), globally, for 24 GCMs, and the SRES-A1B emission scenario, but any other data can be integrated
    Implemented using the raster, rgdal, sp, and maptoolspackages, so that it is easy to handle GIS formats, and export outputs
    Dissimilarity is calculated via two measures (CCAFS and Hallegatte), and uncertainty is provided as the SD and CV among individual GCMs, but, R is flexible
    Calculations can be done and outputs generated for any geographic region at any resolution.
  • 3. What do you need?Set up: just download and install
    R >= 2.13.0 (http://www.r-project.org), and packages:
    raster, sp, rgdal, maps, spgrass6, stringr, maptools, foreign, lattice, akima, plotrix, rimage, XML
    GRASS GIS >= 6.4 (http://grass.fbk.eu/) (exp)
    Quantum GIS >= 1.6 (http://www.qgis.org/) (opt)
  • 4. What do you need?Set up: just download and install
    http://code.google.com/p/ccafs-analogues/
  • 5. Analogues of what?
    • Of a site within all land areas of a given geographic domain (gridded dissimilarity)
  • Gridded analyses Inputs/Outputs
  • 6. Initial set up: climate data
    Climate data for gridded analyses
    • Must be gridded data (rasters)
    • 7. At least one variable, for a given area, with any time-step (from whole year to daily)
    • 8. Is uniform in spatial coverage (i.e. extent) and resolution
    • 9. Represents one or more given (climate) scenario(s)
    • 10. Is stored in the same folder
    • 11. Is named in a way the tool can understand
    • 12. Is in a GIS format supported by GDAL (Geographic Data Abstraction Library)
  • We provide some data
    Periods: 2030(2020_2049)
    Extent: Global
    Emissions scenario: SRES-A1B (a1b)
    Naming structure:
    [CURRENT]_[DTR | MEAN | PREC | BIO]_[STEP].ASC
    [SRES]_[YEAR]_[GCM]_[DTR | TMEAN | PREC | BIO]_[STEP].ASC
    Resolution: 0.5 degree (~50km)
    • But we also have 1km downscaled datasets for the same GCMs and for SRES-A2, SRES-B1 and SRES-A1B itself (http://www.ccafs-climate.org)
  • We provide some data
  • 13. We provide some data
    For instance: BCCR-BCM2.0, precipitation
  • 14. Gridded-analyses: creating a basic report
    After an analysis, you could print a simple report showing results
  • 15. Point-based analyses Inputs/Outputs
    Similar to gridded, but not equal!
  • 16. Initial set up: climate data
    Climate data for point analyses
    • Can be in any format, but you need to load them into R (as matrices) beforehand
    • 17. Ensure quality and zero NODATA by yourself beforehand
    • 18. One matrix per variable, with columns being time-steps and rows sites
    • 19. Objects named in R as [VARIABLE].[SCENARIO]
    • 20. Uniform in time-step for all variables
  • Point analyses: using R afterwards
    If you know how to use R, you could do your further data analyses with it
    Dissimilarity from a site in Ghana (future) to 35 other sites at present
    (bars are the distribution of 24 GCMs)
  • 21. In both cases…
    • Results can be exported from R in any GIS (gridded) or table (points) format
    • 22. Further operations can be done in R, upon your needs and knowledge
    • 23. The R-workspace can be saved and then loaded at any time in the future