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 ) }