A simple interface to the ROSETTA model for predicting hydraulic parameters from soil properties. The ROSETTA API was developed by Dr. Todd Skaggs (USDA-ARS) and links to the work of Zhang and Schaap, (2017). See the related tutorial for additional examples.
ROSETTA(x, vars, v = c("1", "2", "3"), chunkSize = 10000, conf = NULL)a data.frame of required soil properties, may contain other columns, see details
character vector of column names in x containing relevant soil property values, see details
ROSETTA model version number: '1', '2', or '3', see details and references.
number of records per API call
configuration passed to httr::POST() such as verbose().
a data.frame object:
columns present in x
residual volumetric water content (cm^3/cm^3)
saturated volumetric water content (cm^3/cm^3)
related to the inverse of the air entry suction, log10-transformed values with units of cm
index of pore size distribution, log10-transformed values with units of 1/cm
saturated hydraulic conductivity, log10-transformed values with units of cm/day
best-available model selection (-1 signifies that prediction was not possible due to missing values in x)
ROSETTA algorithm version, selected via function argument v
Soil properties supplied in x must be described, in order, via vars argument. The API does not use the names but column ordering must follow: sand, silt, clay, bulk density, volumetric water content at 33kPa (1/3 bar), and volumetric water content at 1500kPa (15 bar).
The ROSETTA model relies on a minimum of 3 soil properties, with increasing (expected) accuracy as additional properties are included:
required, sand, silt, clay: USDA soil texture separates (percentages) that sum to 100%
optional, bulk density (any moisture basis): mass per volume after accounting for >2mm fragments, units of gm/cm3
optional, volumetric water content at 33 kPa: roughly "field capacity" for most soils, units of cm^3/cm^3
optional, volumetric water content at 1500 kPa: roughly "permanent wilting point" for most plants, units of cm^3/cm^3
Column names not specified in vars are retained in the output.
Three versions of the ROSETTA model are available, selected using v = 1, v = 2, or v = 3.
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. doi: 10.1016/S0022-1694(01)00466-8
Schaap, M.G., A. Nemes, and M.T. van Genuchten. 2004. Comparison of Models for Indirect Estimation of Water Retention and Available Water in Surface Soils. Vadose Zone Journal 3(4): 1455-1463. doi: 10.2136/vzj2004.1455
Zhang, Y., and M.G. Schaap. 2017. Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). Journal of Hydrology 547: 39-53. doi: 10.1016/j.jhydrol.2017.01.004
Consider using the interactive version, with copy/paste functionality at: https://www.handbook60.org/rosetta.
Rosetta Model Home Page: https://www.ars.usda.gov/pacific-west-area/riverside-ca/agricultural-water-efficiency-and-salinity-research-unit/docs/model/rosetta-model/.
Python ROSETTA model: http://www.u.arizona.edu/~ygzhang/download.html.
Yonggen Zhang, Marcel G. Schaap. 2017. Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). Journal of Hydrology. 547: 39-53. 10.1016/j.jhydrol.2017.01.004.
Kosugi, K. 1999. General model for unsaturated hydraulic conductivity for soils with lognormal pore-size distribution. Soil Sci. Soc. Am. J. 63:270-277.
Mualem, Y. 1976. A new model predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12:513-522.
Schaap, M.G. and W. Bouten. 1996. Modeling water retention curves of sandy soils using neural networks. Water Resour. Res. 32:3033-3040.
Schaap, M.G., Leij F.J. and van Genuchten M.Th. 1998. Neural network analysis for hierarchical prediction of soil water retention and saturated hydraulic conductivity. Soil Sci. Soc. Am. J. 62:847-855.
Schaap, M.G., and F.J. Leij, 1998. Database Related Accuracy and Uncertainty of Pedotransfer Functions, Soil Science 163:765-779.
Schaap, M.G., F.J. Leij and M. Th. van Genuchten. 1999. A bootstrap-neural network approach to predict soil hydraulic parameters. In: van Genuchten, M.Th., F.J. Leij, and L. Wu (eds), Proc. Int. Workshop, Characterization and Measurements of the Hydraulic Properties of Unsaturated Porous Media, pp 1237-1250, University of California, Riverside, CA.
Schaap, M.G., F.J. Leij, 1999, Improved prediction of unsaturated hydraulic conductivity with the Mualem-van Genuchten, Submitted to Soil Sci. Soc. Am. J.
van Genuchten, M.Th. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Am. J. 44:892-898.
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. doi: 10.1016/S0022-1694(01)00466-8.
Schaap, M.G., A. Nemes, and M.T. van Genuchten. 2004. Comparison of Models for Indirect Estimation of Water Retention and Available Water in Surface Soils. Vadose Zone Journal 3(4): 1455-1463. doi: 10.2136/vzj2004.1455.
Zhang, Y., and M.G. Schaap. 2017. Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). Journal of Hydrology 547: 39-53. doi: 10.1016/j.jhydrol.2017.01.004.