Measuring and predicting steady state infiltration rates for Arizona irrigated soils

Persistent Link:
http://hdl.handle.net/10150/279790
Title:
Measuring and predicting steady state infiltration rates for Arizona irrigated soils
Author:
Bagour, Mohammed Hussien
Issue Date:
2001
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
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.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Agriculture, Soil Science.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Soil, Water and Environmental Science
Degree Grantor:
University of Arizona
Advisor:
Post, Donald F.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleMeasuring and predicting steady state infiltration rates for Arizona irrigated soilsen_US
dc.creatorBagour, Mohammed Hussienen_US
dc.contributor.authorBagour, Mohammed Hussienen_US
dc.date.issued2001en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractFive 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.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectAgriculture, Soil Science.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSoil, Water and Environmental Scienceen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorPost, Donald F.en_US
dc.identifier.proquest3016501en_US
dc.identifier.bibrecord.b41939335en_US
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