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

This Article Figures Only Full Text Full Text (PDF) 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 Google Scholar Articles by Arya, L. M. Articles by Cassel, D. K. PubMed Articles by Arya, L. M. Articles by Cassel, D. K. Agricola Articles by Arya, L. M. Articles by Cassel, D. K. Related Collections Soil Physics Water Content Scaling

SOIL PHYSICS

Scaling Soil Water Characteristics of Golf Course and Athletic Field Sands from Particle-Size Distribution

^a 770 El Caballo Dr., Oceanside, CA 92057
^b Crop Science Dep., North Carolina State Univ., Raleigh, NC 27695
^c Soil Science Dep., North Carolina State Univ., Raleigh, NC 27695

^* Corresponding author (larya{at}sbcglobal.net).

The soil water characteristic (SWC) of sands is an important hydraulic parameter in designing golf courses and athletic fields. A modified version of the Arya–Paris model of the soil water characteristic was adapted to 14 golf course media that contained no to minor amounts of clay and silt. In this model, the particle-size distribution curve is divided into a number of fractions and the natural pore length, L_i(n), is scaled using the diameter of spherical particles as the length unit. The scaled pore length is given by 2R_in_iⁱ, where n_i is the number of spherical particles in the ith fraction, 2R_i is the particle diameter, and _i is the scaling parameter, which is calculated using the relationship logn_iⁱ= a + blogn_i. Although the model adapted well, there were concerns about the sensitivity of predicted SWCs to uncertainties in parameters a and b. Consequently, we developed and evaluated a procedure to predict L_i(n) directly from straight pore lengths, L_i(c) in counterpart cubic close-packed assemblages of spherical particles, using the relationship logL_i(n) = c + dlogL_i(c). Predicted pressure heads using both procedures were similar with best-fit parameters. When uncertainties were imposed on Parameters a, b and c, d, however, SWCs using the latter procedure showed far less sensitivity, as measured by the root mean square residuals (RMSRs). In addition, for sand materials grouped together on the basis of similarity in particle-size distribution and bulk density, replacing individual best-fit parameters by the group mean parameters did not have significant effects on predicted pressure heads.

Abbreviations: AP, Arya–Paris model • PSD, particle-size distribution • RMSR, root mean square residual • SWC, soil water characteristic