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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #314635

Research Project: PATHOGEN FATE AND TRANSPORT IN IRRIGATION WATERS

Location: Environmental Microbial & Food Safety Laboratory

Title: Saturated hydraulic conductivity of US soils grouped according textural class and bulk density

Author
item Pachepsky, Yakov
item Park, Yongeun - University Of Maryland

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/6/2015
Publication Date: 7/24/2015
Citation: Pachepsky, Y.A., Park, Y. 2015. Saturated hydraulic conductivity of U.S. soils grouped according to textural class and bulk density. Soil Science Society of America Journal. 79:1094-1100.

Interpretive Summary: Soil saturated hydraulic conductivity is the essential parameter in a broad class of environmental assessments that include water flow and pollutant transport. Measurement of the soil saturated hydraulic conductivity is time and labor-consuming, and is impractical in large-scale projects. In such projects, saturated hydraulic conductivity has to be estimated from readily available data on soil properties. Soil maps provide data on the spatial distribution of textural classes which show the type of particle size distribution in soils. The objective of this work was to develop and evaluate a grouping-based pedotransfer procedure to estimate Ksat from textural class. We compiled the first nationwide database USKSAT with coupled data on saturated hydraulic conductivity, USDA textural class, and bulk density in the United States. We found that subdividing data from each class into high and low density subgroup allows estimate the saturated hydraulic conductivity with the accuracy comparable to the accuracy of previous works that relied on much more detailed and much less available information of soil properties. Results of this work will be useful to hydrologists, agronomists, and environmental engineers in that they provide a method to estimate saturated hydraulic conductivities in data-poor environments and large scale projects.

Technical Abstract: Importance of the saturated hydraulic conductivity as soil hydraulic property led to the development of multiple pedotransfer functions for estimating it. One approach to estimating Ksat was using textural classes rather than specific textural fraction contents as pedotransfer inputs. The objective of this work was to develop and evaluate a grouping-based pedotransfer procedure to estimate Ksat for samples of sizes used in laboratory measurements. Search in journal publications and technical reports resulted in collection of 1240 datasets with coupled data on Ksat, USDA textural class, and bulk density in the United States into the database dubbed USKSAT. A separate database was assembled for the state of Florida that included 24,566 datasets. References to sources are marked with asterisks in the list of references in this paper. Data in each textural class were split into high bulk density and low bulk density groups using the splitting algorithm that created the most homogeneous groups. Sample diameters and lengths were less than 10 cm. Peaks of the semipartial R2 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 much broader range of Ksat values than the low bulk density groups for clays and loams, but not sandy soils. Inspection superimposed dependences of Ksat on bulk density in the USKSAT database and in the Florida database showed the similarity of those dependencies. When geometric means were used to as estimates of Ksat 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 Ksat from textural class and bulk density may have the advantage of utility in data-poor environments and large scale projects