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Improving soil water determination in spatially variable field using fiber optic technology and Bayesian decision theory

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dc.contributor.advisor English, Marshall J.
dc.contributor.advisor Selker, John S.
dc.creator Sayde, Chadi
dc.date.accessioned 2012-04-17T18:13:34Z
dc.date.available 2012-04-17T18:13:34Z
dc.date.copyright 2012-03-22
dc.date.issued 2012-03-22
dc.identifier.uri http://hdl.handle.net/1957/28778
dc.description Graduation date: 2012 en_US
dc.description.abstract Achieving and maintaining sustainability in irrigated agriculture production in the era of rapidly increasing stress on our natural resources require, among other essential actions, optimum control and management of the applied water. Thus, a significant upgrade of the currently available soil water monitoring technologies is needed. The primary goal of this work was to reduce the uncertainties of spatially variable soil water in the field. Two approaches are suggested: 1) The Bayesian decision model that implicitly accounts for spatial variability at minimal cost based on limited field data, and 2) The Actively Heated Fiber Optic (AHFO) method that explicitly accounts for spatial variability with high sampling density at relatively low cost per measurement point. The Bayesian decision model uses an algorithm to integrate information embodied in independent estimates of soil water depletion to derive a posterior estimation of soil water status that has the potential to reduce the risk of costly errors in irrigation scheduling decisions. The sources of information are obtained from an ET based water balance model, soil water measurements, and expert opinion. The algorithm was tested in a numerical example based on a field experiment where soil water depletion measurements were made at 43 sites in an agricultural field under center pivot irrigation. The results showed that the estimates of the average soil water depletion in the field obtained from the posterior distributions of soil water depletion proved to outperform simple averaging of n soil water depletion measurements, up to n = 35 measurements. For n< 3, the model also provided a 39% average reduction in risk of error derived from non-representative measurements. The AHFO method observes the heating and cooling of a buried fiber optic (FO) cable through the course of a pulse application of energy as monitored by a distributed temperature sensing (DTS) system to reveal soil water content simultaneously at sub-meter scale along the FO cable that can potentially exceeds kilometers in length. A new and simple interpretation of heat data that takes advantage of the characteristics of FO temperature measurements is presented. The results demonstrate the feasibility of AHFO method application to obtain <0.05 m³m⁻³ error distributed measurements of soil water content under laboratory controlled conditions. The AHFO method was then tested under field conditions using 750 m of FO cables buried at 30, 60, and 90 cm depths in agricultural field. The calibration curve relating soil water content to the thermal response of the soil to a heat pulse was developed in the lab. It was successively applied to the 30 and 60 cm depths cables, while the 90 cm depth cable illustrated the challenges of soil heterogeneity for this technique. The method was used to map with high spatial (1m) and temporal (1hr) resolution the spatial variability of soil water content and fluxes induced by the non-uniformity of water application at the surface. en_US
dc.language.iso en_US en_US
dc.subject Water content en_US
dc.subject Bayesian en_US
dc.subject DTS en_US
dc.subject Irrigation en_US
dc.subject Fiber Optics en_US
dc.subject Actively Heated Fiber Optics en_US
dc.subject Fluxes en_US
dc.subject Hydrology en_US
dc.subject.lcsh Irrigation scheduling -- Decision making en_US
dc.subject.lcsh Soil moisture -- Measurement en_US
dc.subject.lcsh Fiber optic cables en_US
dc.subject.lcsh Uncertainty (Information theory) en_US
dc.subject.lcsh Bayesian statistical decision theory en_US
dc.title Improving soil water determination in spatially variable field using fiber optic technology and Bayesian decision theory en_US
dc.type Thesis/Dissertation en_US
dc.degree.name Doctor of Philosophy (Ph. D.) in Water Resources Engineering en_US
dc.degree.level Doctoral en_US
dc.degree.discipline Engineering en_US
dc.degree.grantor Oregon State University en_US
dc.contributor.committeemember Higgins, Chad
dc.contributor.committeemember Gitelman, Alix
dc.contributor.committeemember Deborah, Pence
dc.description.peerreview no en_us


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