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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.
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A data frame with 10630 rows and 34 variables:
Unique identifer: site number (5-digits), with soil type (1-5) designated as a decimal
Unique label used to identify sites in SOILWAT2 simulations
Unique identifier: site number (5-digits)
Longitude coordinate in WGS84
Latitude coordinate in WGS84
Longitude coordinate in Albers Equal Area
Latitude coordinate in Alberrs Equal Area
Mean annual temperature, degrees Celsius
Mean diurnal range (mean of max temp - min temp), degrees Celsius
Isothermality (bio2/bio7) (* 100)
Temperature seasonality (standard deviation *100)
Max temperature of warmest month, degrees Celsius
Min temperature of coldest month, degrees Celsius
Temperature annual range (bio5-bio6), degrees Celsius
Mean temperature of the wettest quarter, degrees Celsius
Mean temperature of driest quarter, degrees Celsius
Mean temperature of warmest quarter, degrees Celsius
Mean temperature of coldest quarter, degrees Celsius
Total (annual) precipitation, mm
Precipitation of wettest month, mm
Precipitation of driest month, mm
Precipitation seasonality (coefficient of variation)
Precipitation of wettest quarter, mm
Precipitation of driest quarter, mm
Precipitation of warmest quarter, mm
Precipitation of coldest quarter, mm
Sand fraction of soil by weight
Clay fraction of soil by weight
Proportion of days that all layers within the MCS are dry when soil temperature at 50 cm >5 degrees Celsius
Number of consecutive days with all MCS layers wet during the winter
Number of consecutive days with all MCS layers dry during the summer
Number of consecutive days with any layer wet when soil temperature at 50 cm depth is >8 degrees Celsius
Number of days with all MCS layers dry
Number of days when any soil layer in the MCS is dry
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).
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
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