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Assessing the influence of model inputs on performance of the EMT+VS soil moisture downscaling model for a large foothills region in northern Colorado

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dc.contributor.authorFischer, Samantha C., author
dc.contributor.authorNiemann, Jeffrey D., advisor
dc.contributor.authorScalia, Joseph, advisor
dc.contributor.authorStright, Lisa, committee member
dc.date.accessioned2024-05-27T10:31:44Z
dc.date.available2024-05-27T10:31:44Z
dc.date.issued2024
dc.description.abstractSoil moisture is an important driving variable of the hydrologic cycle and a key consideration for decision-making in off-road vehicle mobility, crop modeling, drought forecasting, flood prediction, and a variety of other applications. Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing or land surface models; however, coarse resolution estimates are unsuitable for many applications. Downscaling these products to finer resolutions (~10 m) creates soil moisture maps that are more useful. This study applies the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model to Maxwell Ranch, a 4,000-ha cattle ranch in Northern Colorado that represents a diverse range of topographic, vegetation, and soil characteristics and a wide range of soil moisture conditions. The EMT+VS model is a physically based geo-information method that downscales coarse resolution soil moisture estimates using ancillary fine resolution datasets of topography and vegetation. Input data to the EMT+VS model contain inherent sources of error that can impact the uncertainty of downscaled estimates. The objective of this study is to identify sources of uncertainty in inputs and assess their influence on the error of the EMT+VS model output. The study finds changes in vegetation input or digital elevation model (DEM) resolution introduce substantial errors in the EMT+VS model output; however, these errors can be mostly overcome when recalibration with local in-situ data is possible. The highest errors (RMSE = 0.20 cm3/cm3) tend to occur in locations with thick vegetation and high contributing area, which are difficult to accurately estimate with available remote sensing data sources.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierFischer_colostate_0053N_18183.pdf
dc.identifier.urihttps://hdl.handle.net/10217/238357
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
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.subjectEMT+VS
dc.subjectdownscaling
dc.subjectsoil moisture
dc.titleAssessing the influence of model inputs on performance of the EMT+VS soil moisture downscaling model for a large foothills region in northern Colorado
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.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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