COMPARISON OF METHODS TO ESTIMATE SOIL WATER CHARACTERISTICS FROM SOIL TEXTURE, BULK DENSITY, AND LIMITED DATA

Four approaches used to estimate the soil water characteristic (soil water content-matric potential relationship) were compared on a data set based on 366 cores of Bernow soil (Glossic Paleudalf). Regression equations based on soil texture and bulk density provided poorer estimates of soil water content, with large errors at some matric potentials, compared with other approaches examined. Regression model results were improved when one measured value of soil water content (−1500 kPa) was included as a variable in the equations, and greatly improved when two (−33 and −1500 kPa) measured values were included. A simple log-log interpolation/extrapolation approach, based on two measured values at −33 and −1500 kPa, provided results similar to the regression model with two known values. The similar-media scaling approach, utilizing one measured value at −33 kPa, displayed results similar to the log-log method, but the error was slightly higher. Estimates with the one-parameter model of Gregson, Hector and McGowan (GHM), based on one known value (−33 kPa), was similar to the log-log interpolation/extrapolation when a required generalized slope-intercept relation was calculated for the soils in the study; and the error was slightly higher when using the generalized relationship found by GHM for their data. We conclude that the models which incorporated even one known value of soil water content-matric potential relationship were much better than those based on soil texture and bulk density alone. The simple log-log interpolation/extrapolation and the one-parameter GHM model provided the best estimates of soil water content. The scaling method estimates were only slightly worse than the GHM model estimates. The soil survey data often contain at least one value of the water characteristic. These one point methods should, therefore, be the methods of choice.