Search USGSSearch

USGS · 70185534

Hydraulic conductivity of a sandy soil at low water content after compaction by various methods

Abstract

To investigate the degree to which compaction of a sandy soil influences its unsaturated hydraulic conductivity K , samples of Oakley sand (now in the Delhi series; mixed, thermic, Typic Xeropsamments) were packed to various densities and K was measured by the steady-state centrifuge method. The air-dry, machine packing was followed by centrifugal compression with the soil wet to about one-third saturation. Variations in (i) the impact frequency and (ii) the impact force during packing, and (iii) the amount of centrifugal force applied after packing, produced a range of porosity from 0.333 to 0.380. With volumetric water content θ between 0.06 and 0.12, K values were between 7 × 10 −11 and 2 × 10 −8 m/s. Comparisons of K at a single θ value for samples differing in porosity by about 3% showed as much as fivefold variation for samples prepared by different packing procedures, while there generally was negligible variation (within experimental error of 8%) where the porosity difference resulted from a difference in centrifugal force. Analysis involving capillary-theory models suggests that the differences in K can be related to differences in pore-space geometry inferred from water retention curves measured for the various samples.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

John R. Nimmo, Katherine C. Akstin. 1988-03-01. Hydraulic conductivity of a sandy soil at low water content after compaction by various methods. https://doi.org/10.2136/sssaj1988.03615995005200020001x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

The processes of preferential flow in the unsaturated zone

Preferential flow, a major influence in unsaturated soil and rock almost everywhere, occurs by multiple phenomenologically distinct hydraulic processes. For the mode known as funneled flow, concentrated in particularly conductive portions of the medium, the surface-tension/viscous-flow processes of traditional unsaturated flow theory predominate. Fingered flow, through conductive paths of higher water content than surrounding material, requires amendments to traditional theory concerning instabilities and dynamic flow-regime boundaries. Macropore flow, the most recognized preferential flow mode, poses unanswered questions and major difficulties in practice. Accumulated evidence shows that water flows preferentially mostly through macropores that are (a) only partially filled with water, and (b) surrounded by matrix material that is drier, sometimes much drier, than saturation. With partial filling, geometric characteristics such as aperture have much less influence than was previously thought, and the intra-macropore configuration of the flowing water phase, about which little is conclusively known, is then a dominant controlling influence. With unsaturated surroundings, macropore/matrix exchange interactions control, for given input and medium, the initiating circumstances, conveyed flux, and duration of macropore flow. The multiple processes in play during such interactions have different sensitivities to the matrix water state and different directions of influence. The net influence of matrix water content on macropore flow is thus highly complex and a major research need. Additional high-priority topics are: flowpath connectivity, for watersheds as well as small scales; intra-macropore processes, to discern their importance and possible means of quantification; and the identification of measurable soil and rock properties that can be utilized predictively.

Soil Science Society of America Journal

A hybrid approach for predictive soil property mapping using conventional soil survey data

Soil property maps are important for land management and earth systems modeling. A new hybrid point-disaggregation predictive soil property mapping strategy improved mapping in the Colorado River Basin, and can be applied to other areas with similar data (e.g. conterminous United States). This new approach increased sample size ~6-fold over past efforts. Random forests related environmental raster layers representing soil forming factors to samples to predict 15 soil properties (pH, texture fractions, rock, electrical conductivity, gypsum, CaCO3, sodium adsorption ratio, available water capacity, bulk density, erodibility, organic matter) at 7 depths, depth to restrictive layer, and surface rock size and cover. Cross-validations resulted in coefficient of determinations averaging 0.52, with a range of 0.20 to 0.76; and mean absolute errors ranged from 3% to 98% of training data averages with a mean of 41%. Uncertainty estimates were also developed by creating relative prediction intervals (RPIs) for the entire study area, which allow end users to evaluate uncertainty relative to original data distributions. Average error increased with higher RPI values (higher uncertainty), and areas with the highest RPI are consistently under-sampled, suggesting that additional sampling in these areas may improve prediction accuracy. Greater uncertainty was also observed in areas with shale parent materials and physiographic settings uncommon relative to the broader study area.

New Mexico, Colorado, Wyoming, Utah, Nevada, Arizo

Approaches for improving field soil identification

Use of soil survey information by non-soil-scientists is often limited by their inability to select the correct soil map unit component (COMP). Here, we developed two approaches that can be deployed to smartphones for non-soil-scientists to identify COMP from the location alone or location together with easily observed field data (i.e., slope, depth to the restrictive layer, and soil texture by depth). In addition, we also compared the two newly developed approaches with a traditional approach identifying COMP based on the dominant COMP (DC-based approach). All three approaches were tested with the Rapid Assessment of US Soil Carbon database and the combined USDA- NRCS Soil Survey Geographic database and the USDA-NRCS State Soil Geographic Database. The results indicated that the observation-based approach performed significantly better than the other two approaches, suggesting that a small set of easy-to-measure site-specific observations could significantly improve COMP identification. The location- and DC-based approaches had similar low performance overall. However, the location-based approach slightly improved identifications over the DC-based approach for cases where (i) there were multiple possible components within the soil map unit and (ii) the components were located in close proximity to a boundary of a different soil map unit polygon. The benefit of using the location-based approach may be greater in specific soil survey areas where topography was the major factor leading to the creation of the map unit legend.

Soil Science Society of America Journal