Skip to main content
ARS Home » Pacific West Area » Pullman, Washington » Northwest Sustainable Agroecosystems Research » Research » Publications at this Location » Publication #175395
Title: SOIL WATER CHARACTERISTIC ESTIMATES BY TEXTURE AND ORGANIC MATTER FOR HYDROLOGIC SOLUTIONS

Author
item SAXTON, KEITH - USDA,ARS RETIRED
item RAWLS, WALTER - USDA,ARS

Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/28/2004
Publication Date: 11/1/2004
Citation: Saxton, K.D., Rawls, W. 2004. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Agronomy Abstracts.

Interpretive Summary:

Technical Abstract: Hydrologic analyses frequently involve the evaluation of soil water infiltration, conductivity and storage, and plant-water relationships. To define variable soil water effects requires estimating relationships of soil-water potential and hydraulic conductivity with soil water content. These relationships are quite variable depending on soil characteristics such as texture, organic matter and structure. Measurement of these relationships is costly, difficult, and often impractical. For many purposes, estimates based on the generalities developed from numerous reported data are adequate. Previous studies have shown that statistical correlations among soil texture, soil water potential and hydraulic conductivity have provided estimation methods sufficiently accurate to be very useful in applied hydrologic analyses. Extensive data sets based on improved laboratory methods have been assembled and analyzed to provide more accurate and reliable correlations. These new equations represent a wide range of soil textures and have been combined with modern computer methodology to provide rapid and graphical solutions. Adjustments to texture solutions include effects of organic matter, compaction, gravel and salinity. These new equations provide reasonably accurate solutions and computer efficiency, however, they represent the statistical average of large data sets and thus the results may have some variation for specific soils.