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WorldClim - Climate Change Data Analysis

Historical & future Precipitation/Temperature Climatologies (1970-2100) - Maps plotting and time series extraction

MAPS

This python notebook uses data from the wordclim web site to plot future climatologies maps on a given area. The outpus are stored as image files (png) and raster files (geotif).

How to PROCEED ?

The data files must be divided into different sub-folders:

  • a folder containing the 12 tif files with historical monthly climatologies (1 file per month of the year)
  • a folder with sub-folders for the various future periods (e.g. 1921-1940, 1961-1981, 1981-2100). Each subfolder contains 4 tif files for each model (and each file contains 12 bands for the the different months).
  • Optionnaly, the global DEM file can be downloaded from WorldClim to plot the elevation map on the area

Run the first script, then one of the following ones:

  1. Customization
  2. Plot maps for one scenario, one period & one model (12 months and annual)
  3. Generate a bunch of maps of all scenarios/periods/models (annual maps in png files)
  4. Plot the elevation map

GRAPHS

4 SSP scenarios and 9 Global Climatic Models

This python notebook uses data from the wordclim web site to compute the evolution of future precipitation/temperature on a given watershed (average of the pixels inside the polygon). The ouputs are stored in an excel file.

How to PROCEED ?

The data files must be divided into different sub-folders:

  • a folder containing the 12 tif files of historical monthly climatologies (1 file per month of the year)
  • a folder with 4 sub-folders for the various future periods (1921-1940, 1961-1981, 1981-2100). Each folder contains 4 tif files for each model (and each file contains 12 bands for the the different months).

Then run cells in order:

  1. Historical monthly climatologies 1970-2000
  2. Future climatologies
  3. Multi index by Model, Scenario and Period
  4. Concatenate historical and future precipitation in a single dataframe
  5. Plot annual evolution of annual precipitation in future, according to all SSP scenarios and all GCM
  6. Plot future evolution for each month, according to 4 SSP scenarios and 9 GCMs
  7. Store statistics in a dataframe
  8. Plot montly projections (all SSP scenarios and all GCMs)
  9. Store all outputs in an excel file