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Saturated Hydraulic Conductivity of US Soils Grouped According to Textural Class and Bulk Density
Author(s) -
Pachepsky Yakov,
Park Yongeun
Publication year - 2015
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2015.02.0067
Subject(s) - pedotransfer function , loam , hydraulic conductivity , soil water , bulk density , soil science , soil texture , environmental science , database , mathematics , computer science
The importance of saturated hydraulic conductivity ( K sat ) as a soil hydraulic property led to the development of multiple pedotransfer functions for estimating it. One approach to estimating K sat uses textural classes rather than specific textural fraction contents as a pedotransfer input. The objective of this work was to develop and evaluate a grouping‐based pedotransfer procedure to estimate K sat for sample sizes used in laboratory measurements. A search of publications and reports resulted in the collection of 1245 data sets with coupled data on K sat , USDA textural class, and bulk density in the United States into a database called USKSAT. A separate database was assembled for the state of Florida that included 24,566 data sets. Data in each textural class were split into high and low bulk density groups using the splitting algorithm that created the most homogeneous groups. Sample diameters and lengths were <10 cm. Peaks of the semi‐partial R 2 were well defined for loamy soils. The threshold bulk density separating high and low bulk density groups is 1.24 g cm −3 for clay soils, about 1.33 g cm −3 for loamy soils, and about 1.65 g cm −3 for sandy soils. The high bulk density groups included a much broader range of K sat values than the low bulk density groups for clays and loams but not sandy soils. Inspection of superimposed dependencies of K sat on bulk density in the USKSAT database and in the Florida database showed their similarity. When geometric means were used as estimates of K sat within groups, the accuracy was not high and yet was comparable with estimates obtained from far more detailed soil information using sophisticated machine learning methods. Estimating K sat from textural class and bulk density may have the advantage of utility in data‐poor environments and large‐scale projects.