Influence of water content and soil properties on unsaturated hydraulic conductivity of some red and black soils

Bhatnagar, D.; Nagarajarao, Y.; Gupta, R. P.

doi:10.1002/jpln.19791420111

Cited by 7 publications

(4 citation statements)

References 12 publications

(10 reference statements)

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“…For Fine Sand, published Campbell parameters generally ranged between 3 and 5 (e.g. Topp, 1969;Haridasan and Jensen, 1972;Bhatnagar et al, 1979;Sisson and vanGenuchten, 1992) and for Coarse Sand between 0.5 and 4 (e.g. Vachaud, 1967;Staple, 1969;Gupta et al, 1977;van Genuchten and Nielsen, 1985).…”

Section: The Amherst Sitementioning

confidence: 99%

Funneled flow mechanisms in layered soil: field investigations

Heilig

Steenhuis

Walter

et al. 2003

Journal of Hydrology

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The movement of water and potential pollutants in the vadose zone of fluvial deposits is often difficult to predict because fineover-coarse layers may behave as capillary barriers, funneling water and dissolved solutes into concentrated preferential flow paths. Capillary barriers have been studied in laboratory experiments and by mathematical analysis with well-defined boundaries but little is known about water and solute movement in naturally layered soils. This paper demonstrates how naturally occurring capillary barriers affect water and solute movement. Experiments were carried-out in a river valley near Amherst, MA and on a prehistoric beach near Cornell University in Ithaca, NY. Dye tracer and chloride were initially applied near the soil surface, followed by intermittent rainfall over several weeks. After 2-6 weeks the blue dye, soil water content, and chloride concentration distributions in the soil profile were examined by excavating a trench and photographing and sampling the exposed soil face. At both sites, the infiltrating water was diverted and flowed laterally over a relatively coarse layer. At the Cornell site, flow broke through the fine -coarse interface into the coarse layer at a few points along the barrier. The primary breakthrough at the Cornell site occurred at a horizontal section of the interface, 2 m down-slope from the point of dye application. The capillary diversions generally agreed with current theory, however, the flow patterns were difficult to predict accurately without more detailed information about the soil layering characteristics. q

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Section: The Amherst Sitementioning

confidence: 99%

Funneled flow mechanisms in layered soil: field investigations

Heilig

Steenhuis

Walter

et al. 2003

Journal of Hydrology

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“…In addition, acceleration due to gravity, water viscosity, ratio of total volume of pores, water density, and the radius of equivalent cylindrical pore size impact the field unsaturated hydraulic conductivity (Amer et al, 2009). Moreover, water quality has an impact on KU, along with soil bulk density and soil moisture content; several studies reported significant effects on unsaturated soil hydraulic conductivity (Bhatnagar et al, 1979;Dec et al, 2008;Xiao et al, 1992;Crescimanno et al, 1995;Springer et al, 1999;Moosavi & Sepaskhah, 2012c). Suction rate also has a strong effect on the unsaturated hydraulic conductivity of soil (Moosavi & Sepaskhah, 2012c;Simunek et al, 1999;Matula et al, 2015).…”

Section: Dimensional Variablesmentioning

confidence: 99%

“…A high positive correlation was noted between soil moisture content and hydraulic soil conductivity values in all investigated soils. It was also recognized that the unsaturated soil hydraulic conductivity drops rapidly with a reduction in soil moisture content, and this decline is influenced by the soil ingredients and properties (Bhatnagar et al, 1979). Nasta et al (2013) predicted the unsaturated relative hydraulic conductivity from Kosugi's water retention function.…”

Section: Introductionmentioning

confidence: 99%

Dimensional Analysis to Estimate the Unsaturated Hydraulic Conductivity of a Sandy Loam Soil in the Agricultural Field

2022

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Hydraulic conductivity in unsaturated soil controls water movement and measuring it in agricultural fields is a challenging task requiring time-consuming, costly, and skilled experimentation. This study was conducted to reduce the cost of experimentation through the development of an estimation model. The developed model is based on dimensional analysis to determine the value of hydraulic conductivity of unsaturated soil as it relates to soil moisture content, irrigation water electrical conductivity, and suction rate (pressure head). Data points were acquired from measurements of cumulative infiltration in the field, using a mini disc infiltrometer. The developed model gave a mean discrepancy ratio of 1.10 (the acceptable range is 0.5-2.0) and a mean percentage of relative errors of 9.96%. These values indicate that the dimensional analysis model is reliable for the prediction of sandy loam soil's unsaturated hydraulic conductivity.

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“…Soil moisture content and soil bulk density have significant effects on soil unsaturated hydraulic conductivity. Bhatnagar et al (1979) determined unsaturated hydraulic conductivity in the laboratory for some red and black soils, following water movement into a horizontal column of homogenous soil with uniform packing. A highly significant positive relationship was found between moisture content and hydraulic conductivity values in all soils studied.…”

Section: Introductionmentioning

confidence: 99%

Modelling the unsaturated hydraulic conductivity of a sandy loam soil using Gaussian process regression

Al-Dosary

Al-Sulaiman

Aboukarima

2019

WSA

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Unsaturated soil hydraulic conductivity is a main parameter in agricultural and environmental studies, necessary for predicting and managing water and solute transport in soils. This parameter is difficult to measure in agricultural fields; thus, a simple and practical estimation method would be preferable, and quantitative methods (analytical and numerical) to predict the field parameters should be developed. Field experiments were conducted to collect water quality data to model the unsaturated hydraulic conductivity of a sandy loam soil. A mini disk infiltrometer (MDI) was used to measure soil infiltration rate. Input variables included electrical conductivity and the sodium adsorption ratio of irrigation water. Suction rate (pressure head), soil bulk density, and soil moisture content acted as inputs, with unsaturated soil hydraulic conductivity as output. The performance of Gaussian process regression (GPR) was analysed, with multiple linear regression (LR) and multi-layer perceptron (MLP) models used for comparison. Three performance criteria were compared: correlation coefficient (r), root mean square error (RMSE), and mean absolute error (MAE). The simulations employed the Waikato environment for knowledge analysis (WEKA) open source tool. The results indicate that the GPR with Pearson VII function-based universal kernel (PUK kernel), cache size 250007, Omega 1.0 and Sigma 1.0 performs better than other kernels when evaluating test split data, with a correlation coefficient of 0.9646. The RMSEs for GPR (PUK kernel), MLP, and LR were 1.16 × 10−04, 1.87 × 10−04, and 2.22 × 10−04 cm·s−1, respectively. Predictive data mining algorithms (DMA) enable an estimate of unknown values based on patterns in a database. Therefore, the present methodology can be put to use in predictive tools to manage water and solute transport in soils, as the GPR model provides much greater accuracy than the LR and MLP models in predicting the unsaturated hydraulic conductivity of a sandy loam soil.

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Influence of water content and soil properties on unsaturated hydraulic conductivity of some red and black soils

Cited by 7 publications

References 12 publications

Funneled flow mechanisms in layered soil: field investigations

Funneled flow mechanisms in layered soil: field investigations

Dimensional Analysis to Estimate the Unsaturated Hydraulic Conductivity of a Sandy Loam Soil in the Agricultural Field

Modelling the unsaturated hydraulic conductivity of a sandy loam soil using Gaussian process regression

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