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Prepare Climate Data

prep_climate_data.Rd

Download and prepare climate data for use with LANDIS-II simulations:

Usage

prep_daily_weather(vars = NULL, years = NULL, studyArea = NULL, id = NULL)

prep_monthly_weather(vars = NULL, years = NULL, studyArea = NULL, id = NULL)

Arguments

vars

character specifying the climate variables.

years

integer vector specifying the years.

studyArea

sf polygons object delineating e.g., ecoregions or fire zones.

id

character specifying the name of the column/field to use for zonal summaries.

Value

tbl_df

Details

  1. Climate data for the studyArea are downloaded and converted to SpatRaster using the climateR package;

  2. These are then summarized by zone (specified by id) using the zonal package;

  3. The tabular data are pivoted as required for ingestion by LANDIS-II Climate Library.

Historical daily weather

Daymet provides daily North American weather 1980-present (https://daymet.ornl.gov/). Daymet variables: dayl, prcp, srad, swe, tmax, tmin, vp. Use prep_daily_weather() for Daymet weather data.

Historical monthly weather

Terra Climate provides monthly North American weather 1980-present (https://www.climatologylab.org/terraclimate.html). TerraClim variables: aet, def, PDSI, pet, ppt, q, soil, srad, swe, tmax, tmin, vap, vpd, ws. Use prep_monthly_weather() for TerraClim weather data.

Climate projections

TODO

Caching

Caching is enabled by default, with the cache location configurable by setting the landisutils.cache.path option.

Examples

if (requireNamespace("climateR", quietly = TRUE) &&
  requireNamespace("zonal", quietly = TRUE)) {
  ## use BEC zones in random study area in BC
  # ecoregionPolys <- landisutils::test_ecoregionPolys

  # if (interactive()) plot(frpFRT["PolyID"])

  # clim_years <- 2011:2012 ## availability is 1980 to last year

  ## get historic daily weather data from Daymet
  # daily_climvars <- c("prcp", "tmax", "tmin")
  # daily_weather <- prep_daily_weather(
  #   vars = daily_climvars,
  #   years = clim_years,
  #   studyArea = ecoregionPolys,
  #   id = "PolyID"
  # )

  # head(daily_weather)

  ## get historic monthly weather from TerraClim
  # monthly_climvars <- c("ppt", "tmax", "tmin")
  # monthly_weather <- prep_monthly_weather(
  #   vars = monthly_climvars,
  #   years = clim_years,
  #   studyArea = ecoregionPolys,
  #   id = "PolyID"
  # )

  # head(monthly_weather)
}
#> NULL