subsetcells: Climate, soil, and ecohydrological variables for a 10 x 10 km...

Description Usage Format Details Source

Description

A data frame containing unique ids, geographic coordinates, 19 climate variables, 2 soil variables and 6 ecohydrological variables for drylands in the state of Wyoming at 10-km resolution (Albers Equal Area). We defined drylands as cells where the aridity index (ratio of precipitation to potential evapotranspiration) calculated from SOILWAT2 (Schlaepfer et al., 2012; Schlaepfer & Andrews, 2018; Schlaepfer & Murphy, 2018) was <0.6.

Usage

1

Format

A data frame with 10630 rows and 34 variables:

site_ids

Unique identifer: site number (5-digits), with soil type (1-5) designated as a decimal

Label

Unique label used to identify sites in SOILWAT2 simulations

site_id

Unique identifier: site number (5-digits)

X_WGS84

Longitude coordinate in WGS84

Y_WGS84

Latitude coordinate in WGS84

X_AEANAD83

Longitude coordinate in Albers Equal Area

Y_AEANAD83

Latitude coordinate in Alberrs Equal Area

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

Sand fraction of soil by weight

clay

Clay fraction of soil by weight

Dryprop

Proportion of days that all layers within the MCS are dry when soil temperature at 50 cm >5 degrees Celsius

CwetWinter

Number of consecutive days with all MCS layers wet during the winter

CdrySummer

Number of consecutive days with all MCS layers dry during the summer

Cwet8

Number of consecutive days with any layer wet when soil temperature at 50 cm depth is >8 degrees Celsius

Dryall

Number of days with all MCS layers dry

Dryany

Number of days when any soil layer in the MCS is dry

Details

The climate variables ("bioclim_XX") correspond to the 19 bioclim variables described by Hijman (2017) in biovars function (also defined below). Columns 29-34 contain ecohydrological variables derived from SOILWAT2 simulations and described in Bradford et al. (2019). These variables describe the frequency and seasonality of wet (>-1.5MPa) soil conditions within the moisture control section (MCS: soil layers with depth ranging from 10-30 cm for fine textures to 30–90 cm for coarse textures; Soil Survey Staff, 2014).

Source

The cases in this data frame represent sites that were simulated for Bradford et al. (2019). The simulations were conducted using mean current and future climate conditions at 41,477 dryland sites across western North America defined by a 10-km grid. The data for Wyoming represent 2126 of those cells. For each 10-km cell, Bradford et al. (2019) simulated five different soil types: site-specific soils derived from STATSGO (Miller and White 1998) and four fixed soil types (see 'setsoiltypes' data for details), for a total of 10630 sites with unique location x soil combinations in this dataset. The six output results are from simulations under current climate conditions (1980-2010).

Of the five soil types simulated for each 10 km cell, only the site-specific soils varied with depth (the sand and clay content of the set soil types were constant throughout the soil profile). We calculated depth-weighted sand and clay content for each site-specific soil for the simulated sites.

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). To get bioclim variables for the 10 km simulated sites, we aggregated the 30-arcsecond bioclim variables to ~10 km and reprojected them to Albers Equal Area.

References
Bradford, J. B., Schlaepfer, D. R., Lauenroth, W. K., Palmquist, K. A., Chambers, J. C., Maestas, J. D., & Campbell, S. B. (2019). Climate-driven shifts in soil temperature and moisture regimes suggest opportunities to enhance assessments of dryland resilience and resistance. Front. Ecol. Evol. 7:358. https://doi.org/10.3389/fevo.2019.00358

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

Miller, D. A., & White, R. A. (1998). A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling. Earth Interact. 2, pp.1–26. https://doi.org/10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2

Schlaepfer, D. R., Lauenroth, W. K., & Bradford, J. B. (2012). Ecohydrological niche of sagebrush ecosystems. Ecohydrology 5: 453-466. https://doi.org/10.1002/eco.238

Schlaepfer, D. R., & Andrews, C. A. (2018). rSFSW2: Simulation Framework for SOILWAT2. R package version 3.0.0.

Schlaepfer, D. R., & Murphy, R. (2018). rSOILWAT2: An Ecohydrological Ecosystem-Scale Water Balance Simulation Model. R package version 2.3.2.

Soil Survey Staff. (2014). Keys to Soil Taxonomy, 12th Edn. Washington, DC: USDA-Natural Resources Conservation Service.

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


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