USING GEOSTATISTICS, PEDOTRANSFER FUNCTIONS TO GENERATE 3D SOIL AND HYDRAULIC PROPERTY DISTRIBUTIONS FOR DEEP VADOSE ZONE FLOW SIMULATIONS

Persistent Link:
http://hdl.handle.net/10150/193439
Title:
USING GEOSTATISTICS, PEDOTRANSFER FUNCTIONS TO GENERATE 3D SOIL AND HYDRAULIC PROPERTY DISTRIBUTIONS FOR DEEP VADOSE ZONE FLOW SIMULATIONS
Author:
Fang, Zhufeng
Issue Date:
2009
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:
We use geostatistical and pedotrasnfer functions to estimate the three-dimensional distributions of soil types and hydraulic properties in a relatively large volume of vadose zone underlying the Maricopa Agriculture Center near Phoenix, Arizona. Soil texture and bulk density data from the site are analyzed geostatistically to reveal the underlying stratigraphy as well as finer features of their three-dimensional variability in space. Such fine features are revealed by cokriging soil texture and water content measured prior to large-scale long-term infiltration experiments. Resultant estimates of soil texture and bulk density data across the site are then used as input into a pedotransfer function to produce estimates of soil hydraulic parameter (saturated and residual water content θs and θr, saturated hydraulic conductivity Ks, van Genuchten parameters αand n) distributions across the site in three dimensions. We compare these estimates with laboratory-measured values of these same hydraulic parameters and find the estimated parameters match the measured well for θs, n and Ks but not well for θr nor α, while some measured extreme values are not captured. Finally the estimated soil hydraulic parameters are put into a numerical simulator to test the reliability of the models. Resultant simulated water contents do not agree well with those observed, indicating inverse calibration is required to improve the modeling performance. The results of this research conform to a previous work by Wang et al. at 2003. Also this research covers the gaps of Wang’s work in sense of generating 3-D heterogeneous fields of soil texture and bulk density by cokriging and providing comparisons between estimated and measured soil hydraulic parameters with new field and laboratory measurements of water retentions datasets.
Type:
text; Electronic Thesis
Keywords:
Artificial Neural Network; Geostatistics; Modeling; Pedotransfer Functions
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Hydrology; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Neuman, Shlomo P.
Committee Chair:
Neuman, Shlomo P.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleUSING GEOSTATISTICS, PEDOTRANSFER FUNCTIONS TO GENERATE 3D SOIL AND HYDRAULIC PROPERTY DISTRIBUTIONS FOR DEEP VADOSE ZONE FLOW SIMULATIONSen_US
dc.creatorFang, Zhufengen_US
dc.contributor.authorFang, Zhufengen_US
dc.date.issued2009en_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.abstractWe use geostatistical and pedotrasnfer functions to estimate the three-dimensional distributions of soil types and hydraulic properties in a relatively large volume of vadose zone underlying the Maricopa Agriculture Center near Phoenix, Arizona. Soil texture and bulk density data from the site are analyzed geostatistically to reveal the underlying stratigraphy as well as finer features of their three-dimensional variability in space. Such fine features are revealed by cokriging soil texture and water content measured prior to large-scale long-term infiltration experiments. Resultant estimates of soil texture and bulk density data across the site are then used as input into a pedotransfer function to produce estimates of soil hydraulic parameter (saturated and residual water content θs and θr, saturated hydraulic conductivity Ks, van Genuchten parameters αand n) distributions across the site in three dimensions. We compare these estimates with laboratory-measured values of these same hydraulic parameters and find the estimated parameters match the measured well for θs, n and Ks but not well for θr nor α, while some measured extreme values are not captured. Finally the estimated soil hydraulic parameters are put into a numerical simulator to test the reliability of the models. Resultant simulated water contents do not agree well with those observed, indicating inverse calibration is required to improve the modeling performance. The results of this research conform to a previous work by Wang et al. at 2003. Also this research covers the gaps of Wang’s work in sense of generating 3-D heterogeneous fields of soil texture and bulk density by cokriging and providing comparisons between estimated and measured soil hydraulic parameters with new field and laboratory measurements of water retentions datasets.en_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
dc.subjectArtificial Neural Networken_US
dc.subjectGeostatisticsen_US
dc.subjectModelingen_US
dc.subjectPedotransfer Functionsen_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineHydrologyen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorNeuman, Shlomo P.en_US
dc.contributor.chairNeuman, Shlomo P.en_US
dc.contributor.committeememberFerre, Paul A.en_US
dc.contributor.committeememberSchaap, Marcel G.en_US
dc.identifier.proquest10295en_US
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