HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Saxton, K. E. Articles by Rawls, W. J. Search for Related Content PubMed Articles by Saxton, K. E. Articles by Rawls, W. J. Agricola Articles by Saxton, K. E. Articles by Rawls, W. J. Related Collections Soil Physics Surface Hydrology Soil Hydrology

Soil & Water Management & Conservation

Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions

^a Saxton Engineering and Associates, 1250 SW Campus View, Pullman WA 99163
^b USDA-ARS Hydrology and Remote Sensing Lab, Bldg. 007, Rm. 104, BARC-W, Beltsville, MD 20705

^* Corresponding author (wrawls{at}hydrolab.arsusda.gov)

Hydrologic analyses often involve the evaluation of soil water infiltration, conductivity, storage, and plant-water relationships. To define the hydrologic soil water effects requires estimating soil water characteristics for water potential and hydraulic conductivity using soil variables such as texture, organic matter (OM), and structure. Field or laboratory measurements are difficult, costly, and often impractical for many hydrologic analyses. Statistical correlations between soil texture, soil water potential, and hydraulic conductivity can provide estimates sufficiently accurate for many analyses and decisions. This study developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and OM. These equations are similar to those previously reported by Saxton et al. but include more variables and application range. They were combined with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses. Verification was performed using independent data sets for a wide range of soil textures. The predictive system was programmed for a graphical computerized model to provide easy application and rapid solutions and is available at http://hydrolab.arsusda.gov/soilwater/Index.htm.

This article has been cited by other articles:

				A. Lilly, A. Nemes, W. J. Rawls, and Ya. A. Pachepsky Probabilistic Approach to the Identification of Input Variables to Estimate Hydraulic Conductivity Soil Sci. Soc. Am. J., January 11, 2008; 72(1): 16 - 24. [Abstract] [Full Text] [PDF]