Prepare data from raw to form needed for calculating the index. See the NatureServe Guidelines for details on how the data is prepared.
prep_clim_data(
mat_norm,
mat_fut,
cmd_norm,
cmd_fut,
ccei = NULL,
map = NULL,
mwmt = NULL,
mcmt = NULL,
clim_poly = NULL,
in_folder = NULL,
out_folder,
reproject = FALSE,
overwrite = FALSE,
scenario_name = "",
brks_mat = NULL,
brks_cmd = NULL,
brks_ccei = NULL
)file paths to find data if in_folder is not given
file path where files are stored. Files must be named according to the convention described in details
file path where the processed files will be saved
should the data be re-projected to lat/long? Not recommended.
should existing files in out_folder be overwritten?
a string identifying the climate change scenario that will be used as a suffix for the output files.
a matrix containing breaks to use for
classifying mat, cmd and ccei into 6, 6 and 4 classes, respectively. See
reclassify for details on the matrix format. If NULL,
the default, the breaks will be determined using the median and half the
interquartile range
Returns a list of matrices with the breaks used to classify mat, cmd
and ccei. This list can be supplied to a future call to
prep_clim_data in order to use the same breaks for multiple climate
data sets. Processed data is saved in out_folder
Definition of input data sets and file names required in in_folder:
"MAT" mean annual temperature for the historical normal period
"MAT_2050" mean annual temperature for the future under climate change it can be any number eg 2050, 2100
"CMD" climate moisture deficit for the historical normal period
"CMD_2050" climate moisture deficit for the future under climate change it can be any number eg 2050, 2100
"CCEI" Climate Change Exposure Index from NatureServe website
"MAP" mean annual precipitation for the historical normal period
"MWMT" mean warmest month temperature for the historical normal period
"MCMT" mean coldest month temperature for the historical normal period
An optional shapefile with a polygon of the range of the climate data. It will be created from the climate data if it is missing but it is faster to provide it.
Accepted raster file types are ".asc", ".tif", ".nc", ".grd" and ".img"
get_clim_vars for loading the processed data.
in_folder <- system.file("extdata/clim_files/raw", package = "ccviR")
pth_out <- system.file("extdata/clim_files/processed", package = "ccviR")
# use first scenario to set breaks
brks_out <- prep_clim_data(mat_norm = file.path(in_folder, "NB_norm_MAT.tif"),
mat_fut = file.path(in_folder, "NB_RCP.4.5_MAT.tif"),
cmd_norm = file.path(in_folder, "NB_norm_CMD.tif"),
cmd_fut = file.path(in_folder, "NB_RCP.4.5_CMD.tif"),
map = file.path(in_folder, "NB_norm_MAP.tif"),
mwmt = file.path(in_folder, "NB_norm_MWMT.tif"),
mcmt = file.path(in_folder, "NB_norm_MCMT.tif"),
out_folder = pth_out,
clim_poly = file.path(system.file("extdata", package = "ccviR"),
"assess_poly.shp"),
overwrite = TRUE,
scenario_name = "RCP 4.5")
#> processing MAT
#> processing CMD
#> processing MAP
#> processing MWMT and MCMT
#> finished processing
prep_clim_data(mat_norm = file.path(in_folder, "NB_norm_MAT.tif"),
mat_fut = file.path(in_folder, "NB_RCP.8.5_MAT.tif"),
cmd_norm = file.path(in_folder, "NB_norm_CMD.tif"),
cmd_fut = file.path(in_folder, "NB_RCP.8.5_CMD.tif"),
out_folder = pth_out,
clim_poly = file.path(system.file("extdata", package = "ccviR"),
"assess_poly.shp"),
overwrite = TRUE,
scenario_name = "RCP 8.5",
brks_mat = brks_out$brks_mat, brks_cmd = brks_out$brks_cmd,
brks_ccei = brks_out$brks_ccei)
#> processing MAT
#> processing CMD
#> finished processing