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United States Department of Agriculture

Agricultural Research Service

ROSETTA Model
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The demo version of the program, and examples and manual,
can be downloaded from the software download area.

Year: 1999
Version: 1.0

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Index

Abstract

Mathematical models have become increasingly popular in both research and management problems involving flow and transport processes in the subsurface. The unsaturated hydraulic functions are key input data in numerical models of vadose zone processes. These functions may be either measured directly or estimated indirectly through prediction from more easily measured data based using quasi-empirical models. Rosetta V1.0 is a Windows 95/98 program to estimate unsaturated hydraulic properties from surrogate soil data such as soil texture data and bulk density. Models of this type are called pedotransfer functions (PTFs) since they translate basic soil data into hydraulic properties. Rosetta can be used to estimate the following properties:

  • Water retention parameters according to van Genuchten (1980)
  • Saturated hydraulic conductivity
  • Unsaturated hydraulic conductivity parameters according to van Genuchten (1980) and Mualem (1976)
Detailed description of the hydraulic functions Rosetta offers five PTFs that allow prediction of the hydraulic properties with limited or more extended sets of input data. This hierarchical approach is of a great practical value because it permits optimal use of available input data. The models use the following hierarchical sequence of input data

  • Soil textural class
  • Sand, silt and clay percentages
  • Sand, silt and clay percentages and bulk density
  • Sand, silt and clay percentages, bulk density
    and a water retention point at 330 cm (33 kPa).
  • Sand, silt and clay percentages, bulk density
    and water retention points at 330 and 15000 cm (33 and 1500 kPa)
The first model is based on a lookup table that provides class average hydraulic parameters for each USDA soil textural class. The other four models are based on neural network analyses and provide more accurate predictions when more input variables are used. In addition to the hierarchical approach, we also offer a model that allows prediction of the unsaturated hydraulic conductivity parameters from fitted van Genuchten (1980) retention parameters (Schaap and Leij, 1999). This model is also used in the hierarchical approach such that it automatically uses the predicted retention parameters as input, instead of measured (fitted) retention parameters.

All estimated hydraulic parameters are accompanied by uncertainty estimates that permit an assessment of the reliability of Rosetta's predictions. These uncertainty estimates were generated by combining the neural networks with the bootstrap method (see Schaap and Leij (1998) and Schaap et al. (1999) for more information).

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Data input and output

Rosetta is based on ACCESS-97 database tables which allow efficient handling and lookup of small and large volumes of data. Data can be either manually entered or read from ASCII files. The maximum amount of samples (records) that Rosetta can handle is limited by the available hard disk space. Estimated hydraulic properties can be exported in ASCII files and used in other programs. ACCESS-97 is not required to run Rosetta; however, ACCESS-97 can be used to manage Rosetta's predictions in a larger project, provided that the tables created by Rosetta are not altered.

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Downloading and installing Rosetta

The compressed ROSETTA.EXE file can be downloaded from our sofware download site. Download ROSETTA.EXE (approximately 3 MB), store this file in a temporary directory and run it from the Windows Start menu (Start, Run). Go to the Start menu again, and run SETUP.EXE from the same directory used for ROSETTA.EXE. This will install Rosetta on your PC. Rosetta will take up less than 6 MB of disk space when installed.

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Help system and tutorials

Rosetta contains extensive help files that explain how to use the various menu options and screens. The help system also contains two tutorials that illustrate most functions in Rosetta. Furthermore, the help system contains extensive information about the background of Rosetta (data used for calibration, calibration results, neural networks and the bootstrap method).

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References

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.

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Disclaimer

Rosetta has been tested for a large number of cases. However, no warranty is given that the program is completely error-free. If you do encounter problems with the code, find errors, or have suggestions for improvement, please contact:

Walter Russell
USDA-ARS U.S. Salinity Laboratory
450 W Big Springs Road
Riverside, CA 92507

Tel: 951-369-4850
Fax: 951-342-4964
Walt.Russell@ars.usda.gov

Technical contact:
Todd Skaggs
USDA-ARS U.S. Salinity Laboratory
450 W Big Springs Road
Riverside, CA 92507-4617

Tel: 951-369-4853
Fax: 951-342-4964
Todd.Skaggs@ars.usda.gov


Last Modified: 4/16/2013