Submitted to: Characterization & Measurement of the Hydraulic Properties of Unsaturated P
Publication Type: Proceedings
Publication Acceptance Date: 10/22/1997
Publication Date: N/A
Citation: N/A Interpretive Summary: Computer models of the complex soil-water process and crop growth have demonstrated potential for improving management. However these models require knowledge of the two basic soil hydraulic properties -- the soil water retention curve and the unsaturated hydraulic conductivity. Both of these properties are time consuming and tedious to measure. For this reason these data are often missing from soil databases and need to be estimated from other soil properties which are more easily determined and readily available in the literature. Here we review our work in estimating the soil water retention curve. We have extended the one- parameter model suggested by Gregson. This model is based on the log-log form of the water retention curve. The model requires one know water content value, which can be estimated using soil texture and bulk density, and a generalized slope-intercept relationship. We show how the slope-intercept values can be estimated from textural information in the literature. The one-parameter model is simple to use and can be easily incorporated into various soil-water or crop growth models.
Technical Abstract: The Gregson one-parameter function is based on the log-log form of the soil water retention curve below the air-entry pressure and requires one known value on the soil water retention curve and a general slope-intercept relationship (p and q). Given p and q values for a soil or a group of soils, the known soil water content value is used to calculate the only unknown parameter, b. Previously, values of p and q were developed for four broad textural ranges. Further work has shown that for a texture class p equals the natural log of the mean air-entry pressure, while q equals the absolute value of the natural log of the saturated water content. This relationship provides p and q values for 11 texture classes. When p and q values based on air-entry pressure and saturated soil water content were used, estimation errors were equal to, or less than, the errors using the previously derived p and q values. Both sets of p and q values provide better estimates of soil water content than the regression models based on texture and bulk density. When a value on the soil water retention curve is unknown it can be estimated using a texture and bulk density relationship. Using an estimated soil water retention curve value increases the errors slightly, but overall the one-parameter function estimates the soil water retention curve fairly well.