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Developing a modified SEBAL algorithm that is responsive to advection by using limited weather data

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dc.contributor.authorMkhwanazi, Mcebisi, author
dc.contributor.authorChávez, José, advisor
dc.contributor.authorAndales, Allan, committee member
dc.contributor.authorHam, Jay, committee member
dc.contributor.authorTrout, Thomas, committee member
dc.date.accessioned2007-01-03T06:32:36Z
dc.date.available2007-01-03T06:32:36Z
dc.date.issued2014
dc.description.abstractThe use of Remote Sensing ET algorithms in water management, especially for agricultural purposes is increasing, and there are more models being introduced. The Surface Energy Balance Algorithm for Land (SEBAL) and its variant, Mapping Evapotranspiration with Internalized Calibration (METRIC) are some of the models that are being widely used. While SEBAL has several advantages over other RS models, including that it does not require prior knowledge of soil, crop and other ground details, it has the downside of underestimating evapotranspiration (ET) on days when there is advection, which may be in most cases in arid and semi-arid areas. METRIC, however has been modified to be able to account for advection, but in doing so it requires hourly weather data. In most developing countries, while accurate estimates of ET are required, the weather data necessary to use METRIC may not be available. This research therefore was meant to develop a modified version of SEBAL that would require minimal weather data that may be available in these areas, and still estimate ET accurately. The data that were used to develop this model were minimum and maximum temperatures, wind data, preferably the run of wind in the afternoon, and wet bulb temperature. These were used to quantify the advected energy that would increase ET in the field. This was a two-step process; the first was developing the model for standard conditions, which was described as a healthy cover of alfalfa, 40-60 cm tall and not short of water. Under standard conditions, when estimated ET using modified SEBAL was compared with lysimeter-measured ET, the modified SEBAL model had a Mean Bias Error (MBE) of 2.2 % compared to -17.1 % from the original SEBAL. The Root Mean Square Error (RMSE) was lower for the modified SEBAL model at 10.9 % compared to 25.1 % for the original SEBAL. The modified SEBAL model, developed on an alfalfa field in Rocky Ford, was then tested on other crops; beans and wheat. It was also tested on well-irrigated corn and also corn under deficit irrigation. The modified SEBAL model performed fairly well in wheat and beans, just slightly underestimating ET, and it performed well with irrigated corn. However, modified SEBAL, similar to the original SEBAL and also METRIC, could not accurately estimate ET for drier conditions or at early stages of plant growth.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierMkhwanazi_colostate_0053A_12636.pdf
dc.identifier.urihttp://hdl.handle.net/10217/83792
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectmodified
dc.subjectevapotranspiration
dc.subjectSEBAL
dc.subjectenergy fluxes
dc.subjectevaporative fraction
dc.titleDeveloping a modified SEBAL algorithm that is responsive to advection by using limited weather data
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineCivil and Environmental Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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