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oai:mountainscholar.org:10217/1856332025-12-30T03:40:51Zcom_10217_100532com_10217_100000com_10217_100369com_10217_100303col_10217_100538col_10217_100381

Grieco, Nicholas R., author Niemann, Jeffrey D., advisor Green, Timothy R., committee member Butters, Gregory L., committee member 2018-01-17T16:45:31Z 2018-01-17T16:45:31Z 2017 Grieco_colostate_0053N_14444.pdf https://hdl.handle.net/10217/185633 https://doi.org/10.25675/3.018967 Satellite remote sensing and land-surface models provide coarse-resolution (9-40 km) soil moisture estimates, but various applications require fine-resolution (10-30 m) soil moisture patterns. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moisture using fine-resolution topography, vegetation, and soil data. It has been shown to reproduce temporally unstable soil moisture patterns (i.e. patterns where the spatial structure varies in time). It can also reproduce hillslope dependent patterns (wetter locations occur on hillslopes oriented away from the sun) and valley dependent patterns (wetter locations occur in valley bottoms). However, the EMT+VS model requires several parameters to characterize the local climate, soil, and vegetation characteristics. In previous applications, the parameters were calibrated using point soil moisture data, but many regions of interest may not have such data. The purpose of this study is to evaluate EMT+VS model performance when the parameters are estimated from global datasets without site-specific calibration. Reliable and accessible global datasets were identified and methods were developed to estimate the parameters from the datasets. The global model (without site-specific calibration) was applied to six study sites, and its results were compared to local soil moisture observations and the results from the locally calibrated model. The use of global datasets decreased downscaling performance and the spatial variability of soil moisture was underestimated. Overall, only 5 of the 16 parameters can be estimated from global datasets. However, the global model still provides more reliable soil moisture estimates than the coarse-resolution input for most sampling dates at all six study sites. born digital masters theses English eng Colorado State University. Libraries 2000-2019 Copyright 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. Use of global datasets for downscaling soil moisture with the EMT+VS model Text Master of Science (M.S.) Masters Civil and Environmental Engineering Colorado State University This 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). open.access