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Stochastic analysis and probabilistic downscaling of soil moisture

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dc.contributor.authorDeshon, Jordan P., author
dc.contributor.authorNiemann, Jeffrey D., advisor
dc.contributor.authorGreen, Timothy R., committee member
dc.contributor.authorCooley, Daniel S., committee member
dc.date.accessioned2018-09-10T20:04:38Z
dc.date.available2019-09-06T20:04:15Z
dc.date.issued2018
dc.description.abstractMany applications require fine-resolution soil-moisture maps that exhibit realistic statistical properties (e.g., spatial variance and correlation). Existing downscaling models can estimate soil-moisture based on its dependence on topography, vegetation, and soil characteristics. However, observed soil-moisture patterns also contain stochastic variations around such estimates. The objectives of this research are to perform a geostatistical analysis of the stochastic variations in soil moisture and to develop downscaling models that reproduce the observed statistical features while including the dependence on topography, vegetation, and soil properties. Extensive soil-moisture observations from two catchments are used for the geostatistical analysis and model development, and two other catchments are used for model evaluation. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model is used to downscale soil moisture, and the difference between the point measurements and the EMT+VS estimates are considered to be the stochastic variations. The stochastic variations contain a temporally stable pattern along with temporally unstable patterns. All of these patterns include spatially correlated and uncorrelated variations. Moreover, the spatial variance of the stochastic patterns increases with the mean moisture content. The EMT+VS model can reproduce the observed statistical features if it is generalized to include stochastic deviations from equilibrium soil moisture, variations in porosity, and measurement errors. It can also reproduce most observed properties if stochastic variations are inserted directly in its soil moisture outputs. These analyses and downscaling models provide insight into the nature of stochastic variations in soil moisture and can be further tested by application to other catchments and larger regions.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierDeshon_colostate_0053N_14933.pdf
dc.identifier.urihttps://hdl.handle.net/10217/191345
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.subjectgeostatistical analysis
dc.subjectsoil moisture
dc.subjectvariability
dc.subjectsemivariograms
dc.subjectdownscaling
dc.subjectspatial statistics
dc.titleStochastic analysis and probabilistic downscaling of soil moisture
dc.typeText
dcterms.embargo.expires2019-09-06
dcterms.embargo.terms2019-09-06
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.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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