targetcells: Climate and soil variables for the state of Wyoming

In DrylandEcology/rMultivariateMatching: Multivariate matching for site selection and interpolation using multi-dimensional datasets

Description

A rasterStack containing 21 rasters of climate and soil variables for drylands in the state of Wyoming at 30-arcsecond resolution (WGS84). We defined drylands as cells where a 30-arcsecond (~1 km) resolution aridity index (ratio of precipitation to potential evapotranspiration) defined for the 1970-2000 period (Trabucco & Zomer, 2019) was <0.6.

Usage

targetcells

Format

A rasterStack with 21 layers, each with the following attributes:

dimensions: 482, 841, 405362, 21 (nrow, ncol, ncell, nlayers)
resolution: 0.008333333, 0.008333333 (x, y)
extent: -111.0583, -104.05, 40.99167, 45.00833 (xmin, xmax, ymin, ymax)
crs: +proj=longlat +datum=WGS84 +no_defs

The 21 layers are described below:

bioclim_01

Mean annual temperature, degrees Celsius

bioclim_02

Mean diurnal range (mean of max temp - min temp), degrees Celsius

bioclim_03

Isothermality (bio2/bio7) (* 100)

bioclim_04

Temperature seasonality (standard deviation *100)

bioclim_05

Max temperature of warmest month, degrees Celsius

bioclim_06

Min temperature of coldest month, degrees Celsius

bioclim_07

Temperature annual range (bio5-bio6), degrees Celsius

bioclim_08

Mean temperature of the wettest quarter, degrees Celsius

bioclim_09

Mean temperature of driest quarter, degrees Celsius

bioclim_10

Mean temperature of warmest quarter, degrees Celsius

bioclim_11

Mean temperature of coldest quarter, degrees Celsius

bioclim_12

Total (annual) precipitation, mm

bioclim_13

Precipitation of wettest month, mm

bioclim_14

Precipitation of driest month, mm

bioclim_15

Precipitation seasonality (coefficient of variation)

bioclim_16

Precipitation of wettest quarter, mm

bioclim_17

Precipitation of driest quarter, mm

bioclim_18

Precipitation of warmest quarter, mm

bioclim_19

Precipitation of coldest quarter, mm

sand

Percentage sand content of soil by weight

clay

Percentage clay content of soil by weight

Details

The climate variables ("bioclim_XX") correspond to the 19 bioclim variables described by Hijman (2017) in biovars function (also defined below).

Source

We calculated 30-year (1981 to 2010) normal monthly temperature and precipitation data at 30-arcsecond resolution from DayMet (Thornton et al., 2018) using Google Earth Engine (Gorelick et al., 2017). We then calculated 19 bioclimatic variables using the biovars function in R package dismo (Hijmans, 2017) We derived soils information for each 30-arcsecond cell from Soilgrids+ products (Hengl et al., 2017). We downloaded 250 m resolution global rasters for sand and clay from 0 to 100 cm, cropped each raster to our study area, aggregated the 250 m cells to ~1 km resolution, and reprojected them to a 30-arcsecond resolution to match the bioclim variables. The Soilgrids+ data provide predicted sand and clay content at seven depths: 0 cm, 5 cm, 15 cm, 30 cm, 60 cm, 100 cm, and 200 cm. We derived site specific soil data by calculating depth-weighted sand and clay content for the top 100 cm using the following formula (from Hengl et al., (2017)):

(5(L1+L2)+10(L2+L3)+15(L3+L4)+30(L4+L5)+40(L5+L6))/200

where L1 is the clay or sand content at 0 cm, L2 is the clay or sand content at 5 cm, etc. We then extracted the depth-weighed sand and clay contents for each 30-arcsecond cell. Some cells do not have soils data due to the presence of waterbodies or glaciers.

References
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, pp.18-27. https://doi.org/10.1016/j.rse.2017.06.031

Hijmans, R. J., Phillips, S., Leathwick, J., & Elith, J. (2017). dismo: Species Distribution Modeling. R package version 1.1-4. https://CRAN.R-project.org/package=dismo

Hengl, T., de Jesus, J. M., Heuvelink, G. B., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M.A. Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), p.e0169748. https://doi.org/10.1371/journal.pone.0169748

Thornton, P. E., Thornton, M. M., Mayer, B. W., Wei, Y., Devarakonda, R., Vose, R. S., & Cook, R. B. (2018). Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1328

Trabucco, A., & Zomer, R. (2019). Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. figshare. Fileset. https://doi.org/10.6084/m9.figshare.7504448.v3

DrylandEcology/rMultivariateMatching documentation built on Dec. 17, 2021, 5:30 p.m.