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Measuring and predicting steady state infiltration rates for Arizona irrigated soils

Mohammed H. Bagour

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Abstract

Five methods to measure the saturated hydraulic conductivity of Arizona irrigated soils were evaluated using the in-situ single ring, double ring, compact constant head permeameter methods, and with tempe cells (soil cores) in the laboratory. Ten Arizona irrigated soils were studied, and the textures of these soils ranged from sand to silty clay. Three water qualities were evaluated, namely the local water, gypsum in local water, and gypsum water (0.005M CaSO₄ · 2H₂O solution). Sites were selected to provide soils having a wide range of soil characteristics and detailed laboratory and field morphology data were measured including soil texture, bulk density, soil aggregation, stickiness, plasticity, moisture retention at various tensions, ECₑ, and pHₑ. The results showed that the double ring method was the better in-situ method. The relationships between soil properties and K(sat) were evaluated, and soil properties were examined as predictor variables for K(sat) in stepwise multiple-regression. Stepwise multiple-regression criteria were set at a probability of F to enter ≤ 0.5 and probability of F to remove ≥ 0.1. Six models are presented that can be used in predicting K(sat). Each model has a subset of field and laboratory predictor variables selected based on stepwise multiple-regression criteria, and with some personal judgment. Casewise diagnostics were used to test model performances.

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What this paper is about

Five methods to measure the saturated hydraulic conductivity of Arizona irrigated soils were evaluated using the in-situ single ring, double ring, compact constant head permeameter methods, and with tempe cells (soil cores) in the laboratory. Ten Arizona irrigated soils were studied, and the textures of these soils ranged from sand to silty clay. Three water qualities were evaluated, namely the local water, gypsum in local water, and gypsum water (0.005M CaSO₄ · 2H₂O solution). Sites were selected to provide soils having a wide range of soil characteristics and detailed laboratory and field morphology data were measured including soil texture, bulk density, soil aggregation, stickiness, plasticity, moisture retention at various tensions, ECₑ, and pHₑ. The results showed that the double ring method was the better in-situ method. The relationships between soil properties and K(sat) were evaluated, and soil properties were examined as predictor variables for K(sat) in stepwise multiple-regression. Stepwise multiple-regression criteria were set at a probability of F to enter ≤ 0.5 and probability of F to remove ≥ 0.1. Six models are presented that can be used in predicting K(sat). Each model has a subset of field and laboratory predictor variables selected based on stepwise multiple-regression criteria, and with some personal judgment. Casewise diagnostics were used to test model performances.

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Available abstract

Five methods to measure the saturated hydraulic conductivity of Arizona irrigated soils were evaluated using the in-situ single ring, double ring, compact constant head permeameter methods, and with tempe cells (soil cores) in the laboratory. Ten Arizona irrigated soils were studied, and the textures of these soils ranged from sand to silty clay. Three water qualities were evaluated, namely the local water, gypsum in local water, and gypsum water (0.005M CaSO₄ · 2H₂O solution). Sites were selected to provide soils having a wide range of soil characteristics and detailed laboratory and field morphology data were measured including soil texture, bulk density, soil aggregation, stickiness, plasticity, moisture retention at various tensions, ECₑ, and pHₑ. The results showed that the double ring method was the better in-situ method. The relationships between soil properties and K(sat) were evaluated, and soil properties were examined as predictor variables for K(sat) in stepwise multiple-regression. Stepwise multiple-regression criteria were set at a probability of F to enter ≤ 0.5 and probability of F to remove ≥ 0.1. Six models are presented that can be used in predicting K(sat). Each model has a subset of field and laboratory predictor variables selected based on stepwise multiple-regression criteria, and with some personal judgment. Casewise diagnostics were used to test model performances.

Key concepts: Soil water, Permeameter, Hydraulic conductivity, Soil texture, Soil science, Environmental science, Water content, Infiltration (HVAC)

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