Average soil hydraulic parameters generated by the first stage predictions of the ROSETTA model by USDA soil texture class. These data were extracted from ROSETTA documentation and re-formatted for ease of use.

data(ROSETTA.centroids)

Format

A data frame:

texture

soil texture class, ordered from low to high available water holding capacity

theta_r

average saturated water content

theta_s

average residual water content

alpha

average value, related to the inverse of the air entry suction, log10-transformed values with units of cm

npar

average value, index of pore size distribution, log10-transformed values with units of 1/cm

theta_r_sd

1 standard deviation of theta_r

theta_s_sd

1 standard deviation of theta_s

alpha_sd

1 standard deviation of alpha

npar_sd

1 standard deviation of npar

sat

approximate volumetric water content at which soil material is saturated

fc

approximate volumetric water content at which matrix potential = -33kPa

pwp

approximate volumetric water content at which matrix potential = -1500kPa

awc

approximate available water holding capacity: VWC(-33kPa)

  • VWC(-1500kPa)

Source

ROSETTA Class Average Hydraulic Parameters

Details

Theoretical water-retention parameters for uniform soil material of each texture class have been estimated via van Genuchten model.

See the related tutorial

References

van Genuchten, M.Th. (1980). "A closed-form equation for predicting the hydraulic conductivity of unsaturated soils". Soil Science Society of America Journal. 44 (5): 892-898.

Schaap, M.G., F.J. Leij, and M.Th. van Genuchten. 2001. ROSETTA: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology 251(3–4): 163-176.

Examples



if (FALSE) {

library(aqp)
library(soilDB)
library(latticeExtra)

data("ROSETTA.centroids")

# iterate over horizons and generate VG model curve
res <- lapply(1:nrow(ROSETTA.centroids), function(i) {
  m <- KSSL_VG_model(VG_params = ROSETTA.centroids[i, ], phi_min = 10^-3, phi_max=10^6)$VG_curve
  # copy generalized hz label
  m$hz <- ROSETTA.centroids$hz[i]
  # copy ID
  m$texture_class <- ROSETTA.centroids$texture[i]
  return(m)
})

# copy over lab sample number as ID
res <- do.call('rbind', res)

# check: OK
str(res)


# visual check: OK
xyplot(
  phi ~ theta | texture_class, data=res,
  type=c('l', 'g'),
  scales=list(alternating=3, x=list(tick.number=10), y=list(log=10, tick.number=10)),
  yscale.components=yscale.components.logpower,
  ylab=expression(Suction~~(kPa)),
  xlab=expression(Volumetric~Water~Content~~(cm^3/cm^3)),
  par.settings = list(superpose.line=list(col='RoyalBlue', lwd=2)),
  strip=strip.custom(bg=grey(0.85)),
  as.table=TRUE
)


}