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United States Department of Agriculture

Agricultural Research Service

Title: Calculating Soil Water Content with a One-Parameter Model Using Air-Entry Pressure and Porosity Values

Authors
item Williams, Robert
item Ahuja, Lajpat

Submitted to: Agronomy Society of America, Crop Science Society of America, Soil Science Society of America Meeting
Publication Type: Abstract Only
Publication Acceptance Date: July 25, 2001
Publication Date: November 1, 2001
Citation: WILLIAMS, R.D., AHUJA, L.R. 2001. CALCULATING SOIL WATER CONTENT WITH A ONE-PARAMETER MODEL USING AIR-ENTRY PRESSURE AND POROSITY VALUES. SOIL SCIENCE SOCIETY OF AMERICA ANNUAL MEETING. v. 92. Abstract p. 201.

Interpretive Summary: Abstract Only.

Technical Abstract: The soil matric potential, soil water content relationship, is an important soil hydraulic property is often missing in databases and is frequently estimated from other soil properties. One estimation method, the one- parameter model, is based on the log-log form of the soil water retention curve below the air-entry matric potential it requires one known matric potential - water content value, psi(theta), and a generalized slope- intercept relationship (p and q). This provides the general form of the model: In(psi) + p + b(In(theta)+q). Given p and q values for a soil, or group of soils, the known psi(theta) value is used to calculate the unknown parameter, b. Previously it was demonstrated that p equals the natural log of the air-entry pressure and q equals the natural of the saturated water content. Here these p and q values are tested with thirty U.S. soils of varying textures and results compared to those obtained with a regression model based on texture, bulk density and organic matter. The one-paramete model estimated the soil water content remarkably well. Calculated mean errors and root-mean-square errors, regardless of matric potential, ranged from 0.001 to 0.052 m3/m3 (absolute value) and 0.019 to 0.089 m3/m3, respectively. These errors were generally less than, or equal to, those obtained with the regression model.

Last Modified: 12/22/2014