TerraClimate is a monthly climate dataset
for global terrestrial surfaces from 1958-2021. This function enables you to extract
climate variables from the
hosting server
provided by the Idaho University for a given coordinate(s) without
a need to download the whole raster files in the netCDF format (~100MB per variable for each year)
and provide them in a standard data frame format ready to use in your code. It also calculates the
bioclimatic variables using the calc_biovars
function derivative from the dismo R package.
TerraClimate vs. WorldClim
1958-2021 vs. 1970-2000
14 vs. 7 climate variables
~4 km vs. ~1 km spatial resolution
need to calculate vs. pre-calculated 19 bioclimatic variables
Usage
get_terraclimate(
lat,
lon,
from = "1958-01-01",
to = "2022-12-31",
clim_vars = NULL,
month_mask = NULL,
offline = FALSE,
data_path = "./data/"
)
Arguments
- lat
Vector of Latitude(s) in decimal degree format.
- lon
Vector of Longitude(s) in decimal degree format.
- from
Start date as a string in the 'YYYY-MM-DD' format.
- to
End date as a string in the 'YYYY-MM-DD' format.
- clim_vars
List of all climate variables to be imported. Valid list includes: aet, def, pet, ppt, q, soil, srad, swe, tmax, tmin, vap, ws, vpd, and PDSI. Default is NULL for all.
- month_mask
A list of all months of interest (e.g., planting season:
c(10:12,1:5)
). Default is NULL for all.- offline
Extract TerraClimate data from downloaded netCDF files (default is FALSE)
- data_path
String contains the directory path where downloaded netCDF files exists (default is './data/')
References
Abatzoglou, J., Dobrowski, S., Parks, S. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci Data 5, 170191 (2018). doi:10.1038/sdata.2017.191
Author
Khaled Al-Shamaa, k.el-shamaa@cgiar.org
Examples
if (interactive()) {
# data <- get_terraclimate(36.016, 36.943,
# '1979-09-01', '2012-06-30',
# c('ppt', 'tmin', 'tmax'), c(10:12,1:5))
data <- get_terraclimate(36.016, 36.943, '1979-09-01', '2012-06-30')
View(data$climate[[1]])
View(data$biovars[[1]])
}