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Download and process Köppen-Geiger climate classification maps

Source: R/climatezones.R
climatezones.Rd

This function downloads, processes, and extracts variables from the High-resolution (1 km) Köppen-Geiger maps dataset. Each variable corresponds to a global GeoTIFF representing climate classification zones based on historical data or future CMIP6 projections.

Usage

climatezones(x, vars = "zones", years = "1991-2020", ssp = NULL, ...)

Arguments

x

The output from `par_set()` defining the area or locations for extraction, the reference system, and the buffer. Leave this empty and use `par_set()` to define parameters for download.

vars

Character vector. Defaults to "zones". Accepted aliases include: "koppengeiger", "climate", "climatezones", "koppen".

years

Character vector of time periods. Defaults to "1991-2020". Accepts formats with underscores or hyphens (e.g., "1901-1930" or "1901_1930").

ssp

Numeric or character vector of Shared Socioeconomic Pathways. Required for future projections (e.g., 126, 585).

...

Additional arguments (currently unused).

Value

If `par_set()` contained a raster/polygon/points with buffer: a `SpatRaster` stack of processed variables. If `par_set()` contained spatial points or data.frame of points without buffer: a `data.frame` of x, y, and extracted values.

Details

Available variables (working synonyms in parentheses):

  • 1 - "zones" ("koppengeiger", "climate", "climatezones", "koppen", "koppen geiger")

Time Periods (years argument):

Historical

  • "1901-1930"

  • "1931-1960"

  • "1961-1990"

  • "1991-2020" (default)

Future

  • "2041-2070"

  • "2071-2099"

SSP Scenarios (ssp argument, required for future periods):

  • 119 (SSP1-1.9)

  • 126 (SSP1-2.6)

  • 245 (SSP2-4.5)

  • 370 (SSP3-7.0)

  • 434 (SSP4-3.4)

  • 460 (SSP4-6.0)

  • 585 (SSP5-8.5)

Citation:
Beck HE, McVicar TR, Vergopolan N, Berg A, Lutsko NJ, Dufour A, Zeng Z, Jiang X, van Dijk AIJM, Miralles DG (2023). "High-resolution (1 km) Köppen-Geiger maps for 1901-2099 based on constrained CMIP6 projections." Scientific Data 10, 724. https://doi.org/10.1038/s41597-023-02549-6

Examples

# \donttest{
processed <- par_set(country= "Italy", crs=3035) %>% 
climatezones(vars="zones", years="1991-2020")
  # }