Average Hydraulic Parameters from the ROSETTA Model by USDA Soil Texture Class

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
)


}