daymetr functionality

Downloading single pixel data

A common application of daymet data is the extraction of local climate data, at a given location. This data will be extracted from a pixel within the gridded daymet dataset corresponding to a location provided in latitude / longitude format. You can download a dataset for a single location using the following format:

df <- download_daymet(
  site = "Oak Ridge National Laboratories",
  lat = 36.0133,
  lon = -84.2625,
  start = 2000,
  end = 2010,
  internal = TRUE,
  simplify = TRUE # return tidy data !!
  ) 

With the internal parameter flag set to TRUE the data will be exported as a nested list to your terminal or a variable if assigned. If this parameter is set to FALSE the data will be written to a file on disk in your current working directory (i.e. getwd()). If you want to change the location where files are written to disk use the path parameter. For a full list of parameters see the table below.

There also is a batch mode which uses similar parameters but you provide a comma separated file with site names and latitude longitude which are sequentially downloaded. The format of the comma separated file is as such: site name, latitude, longitude. This function is helpful when downloading large number of locations. Once more if the internal flag is set to true the data will be read in as a nested list of lists.

# code is not run
download_daymet_batch(
  file_location = 'my_sites.csv',
  start = 1980,
  end = 2010,
  internal = TRUE
  )

If only the first set of coordinates is provided the tile in which these reside is downloaded. If your region of interest falls outside the scope of the DAYMET data coverage a warning is issued. If both top left and bottom right coordinates are provided all tiles covering the region of interst are downloaded. I would caution against downloading too much data, as file sizes do add up. So be careful how you specify your region of interest.

Data downloaded into the workspace, e.g. the df data frame in the above example, resides in a nested list. In addition to the climate data this list includes various meta-data parameters relevant to the query made, and at times helpful in subsequent analysis.

str(df)
#> tibble [28,105 × 9] (S3: tbl_df/tbl/data.frame)
#>  $ site       : chr [1:28105] "tmp" "tmp" "tmp" "tmp" ...
#>  $ tile       : num [1:28105] 11388 11388 11388 11388 11388 ...
#>  $ latitude   : num [1:28105] 36 36 36 36 36 ...
#>  $ longitude  : num [1:28105] -84.3 -84.3 -84.3 -84.3 -84.3 ...
#>  $ altitude   : num [1:28105] 279 279 279 279 279 279 279 279 279 279 ...
#>  $ year       : num [1:28105] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
#>  $ yday       : num [1:28105] 1 2 3 4 5 6 7 8 9 10 ...
#>  $ measurement: chr [1:28105] "dayl..s." "dayl..s." "dayl..s." "dayl..s." ...
#>  $ value      : num [1:28105] 34560 34560 34560 34560 34560 ...

The package maintains the original data header names in order to provide a transparent way of dealing with both data downloaded through daymetr or directly from the Daymet website. You can access the climate data using the df$data sub-list. An example of a plot of 10 years of maximum temperature values is provided below.

# simple graph of Daymet data
df %>%
  mutate(date = as.Date(paste(year, yday, sep = "-"), "%Y-%j")) %>%
  filter(measurement == "tmax..deg.c.") %>%
  ggplot() +
  geom_line(aes(x = date, y = value)) +
  facet_wrap(~ measurement, ncol = 2)

You can further summarize the data frame by year / month or day using either the by(), aggregate() or tidyverse based mutate() or summarize() operations. The code below (not run) uses the tidyverse (ddplyr) syntax to calculate daily mean temperature values from the minimum and maximum values provided by Daymet.

# code not run
df %>%
  mutate(date = as.Date(paste(year, yday, sep = "-"), "%Y-%j")) %>%
  group_by(date) %>%
  filter(measurement == "tmax..deg.c." | measurement == "tmin..deg.c.") %>%
  summarize(mean_temp = mean(value))

Gridded data downloads

For gridded data use either download_daymet_tiles() for individual tiles or download_daymet_ncss() for a netCDF subset which is not bound by tile limits (but restricted to a 6GB query size). Due to the (default) larger size of tiled data I’ll demonstrate the use of the gridded data with a smaller dataset of monthly averaged data subset for a small (sub-tile sized) region. The below queries illustrate a variety of ways on how to download tile based data and subsets of the available data. You can query data based upon tile number, or a geographic location and required climate variables. A full description of the function parameter is provided in the table below.

For the netCDF data subsets keep in mind that the bounding box is defined by the minimum (square) bounding box in a Lambert Conformal Conic (LCC) projection as defined by the provided geographic coordinates. In general the query area will be larger than the requested location. For more information I refer to Daymet documentation (https://daymet.ornl.gov/web_services.html) on the web service.

The daymet package also includes spatial polygon data frame outlining all the tiles in the Daymet repository (tile_outlines). This data can be used to extract all relevant tiles for an analysis by calculating the intersect between a geographic region of interest and this file.

# plot the tile outlines
# roughly painting a picture of North America
p <- ggplot(tile_outlines)+ 
  geom_sf() +
  theme_void()

print(p)

# code not run

# Download tiled data for multiple years (1980 - 2012)
# based upon a geographic location.
download_daymet_tiles(location = c(36.0133,-84.2625),
                      tiles = NULL,
                      start = 1980,
                      end = 2012,
                      param = "ALL")

# Download tiled data for multiple years (1980 - 2012)
# based upon a tile number (11207) restricted to the
# minimum temperature data only.
download_daymet_tiles(tiles = 11207,
                      start = 1980,
                      end = 2012,
                      param = "tmin")

# download monthly
download_daymet_ncss(location = c(34, -82, 33.75, -81.75),
                     start = 1980,
                     end = 1980,
                     frequency = "monthly",
                     param = c("tmin","tmax"),
                     path = tempdir(),
                     silent = TRUE)

# convert nc files to geotiff
nc2tif(tempdir())

# read in the demo data from the package for speed
r <- terra::rast(
  system.file(package = "daymetr","extdata/tmin_monavg_1980_ncss.tif")
  )

# set the correct projection
r <- terra::project(
    r,
    "+proj=lcc +lat_1=25 +lat_2=60 +lat_0=42.5 +lon_0=-100 +x_0=0 +y_0=0 +ellps=WGS84 +units=km +no_defs"
  )

plot(r)

# reproject to lat lon
r <- terra::project(r, "+init=epsg:4326")

# plot the monthly mean minimum temperature for 1980
plot(r)

# plot the monthly mean minimum temperature for 1980
r_tmean <- daymet_grid_tmean(
  path = system.file(package = "daymetr","extdata"),
  product = "monavg",
  year = 1980,
  internal = TRUE
  )

Reference

Hufkens K., Basler J. D., Milliman T. Melaas E., Richardson A.D. (2018) An integrated phenology modelling framework in R: Phenology modelling with phenor. Methods in Ecology & Evolution, 9:1-10.

Acknowledgements

This project was is supported by the National Science Foundation’s Macro-system Biology Program (awards EF-1065029 and EF-1702697).