Programmatic interface to the Harmonized World Soil Database 'HWSD' web services (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1247). Allows for easy downloads of 'HWSD' soil data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded data are provided.

Koen Hufkens. (2022). bluegreen-labs/hwsdr: CRAN release (v1.0). Zenodo. https://doi.org/10.5281/zenodo.6527648

To install the current stable release use a CRAN repository:

install.packages("hwsdr")
library("hwsdr")

Breaking change: as of version 1.1 the order of the coordinates in the location string has changed from (lat, lon, lat, lon) to (lon, lat, lon, lat)!

To install the development releases of the package run the following commands:

if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/hwsdr")
library("hwsdr")

Vignettes are not rendered by default, if you want to include additional documentation please use:

if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/hwsdr", build_vignettes = TRUE)
library("hwsdr")

Get world soil values for a single site using the following format, specifying coordinates as a pair of latitude, longitude coordinates. Here all available soil layers are queried.

all <- ws_subset(
    site = "HWSD",
    location = c(-81, 34),
    param = "ALL"
  )

You can download gridded data by specifying a bounding box c(lat, lon, lat, lon) defined by a bottom left and top right coordinates. Here the call only extracts the top soil fraction of sand (% weight).

t_sand <- ws_subset(
    site = "HWSD",
    location = c(32, -81, 34, -80),
    param = "T_SAND",
    path = tempdir(),
    internal = TRUE
  )

By default all parameters are downloaded, a complete list of the individual parameters is provided on the ORNL webpage (https://daac.ornl.gov/SOILS/guides/HWSD.html). Alternatively you may find a similar list of data in the hwsd_meta_data dataset as provided by the package.

This is an experimental feature, awaiting an update of the ORNL DAAC API to version 2.0 of the HWSD database. Although functionally complete the procedure is more complex as it includes a bulk download of a base map.

The HWSD v2.0 data is distributed as a gridded spatial map where homogeneous regions are indicated with indices (integers). Although the underlying database is included in the package and can be accessed using hwsdr::hwsd2, the spatial data accompanying the database is too large for inclusion in the package. This spatial data needs to be downloaded explicitly to a desired path before any other functions will work.

# set the ws_path variable using a FULL path name
path <- ws_download(
  ws_path = "/your/full/path",
  verbose = TRUE
)

Get world soil values for a single site using the following format, specifying coordinates as a pair of longitude, latitude coordinates (longitude, latitude). Here the call only extracts the top soil (layer = "D1") fraction of sand and silt (% weight) for one specific location. Note that you will need to specify the correct version to be used in processing.

values <- ws_subset(
    site = "HWSD_V2",
    location = c(-81, 34),
    param = c("SAND","SILT"),
    layer = "D1",
    version = "2.0", # set correct HWSD version
    ws_path = "/your/full/path" # specify grid map directory
  )

You can grab gridded data by specifying a bounding box c(lon, lat, lon, lat) defined by a bottom left and top right coordinates. Here the call only extracts the top soil (D1 layer) fraction of sand (%).

sand <- ws_subset(
    location = c(32, -81, 34, -80),
    param = "SAND",
    layer = "D1",
    version = "2.0",
    ws_path = Sys.getenv("WS_PATH"),
    # ws_path = "/your/full/path",
    internal = TRUE
  )

Wieder, W.R., J. Boehnert, G.B. Bonan, and M. Langseth. 2014. Regridded Harmonized World Soil Database v1.2. Data set. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1247).

The hwsdr package is a product of BlueGreen Labs, and has been in part supported by the LEMONTREE project funded through the Schmidt Futures fund, under the umbrella of the Virtual Earth System Research Institute (VESRI).