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Colorado State University. Libraries

Use of global datasets for downscaling soil moisture with the EMT+VS model

Abstract

dc:description.abstract

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.

Degree

thesis:*
Name thesis:degree_name
Master of Science (M.S.)
Level thesis:degree_level
Masters
Discipline thesis:degree_discipline
Civil and Environmental Engineering
Grantor dc:publisher
Colorado State University. Libraries
Year dc:date.issued
2017

Author and committee

dc:creator, dc:contributor.*
Authors dc:creator
  • Grieco, Nicholas R., author
  • Niemann, Jeffrey D., advisor
  • Green, Timothy R., committee member
  • Butters, Gregory L., committee member

Rights

dc:rights
Statement dc:rights
  • 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.
Language dc:language.iso
eng, English

Identifiers

dc:identifier.*
OAI identifier oai:identifier
oai:mountainscholar.org:10217/185633

Chain of custody

source
Harvested from
Colorado State University
Base URL
api.mountainscholar.org/server/oai/request
Last updated
2026-07-27
Source record
OAI-PMH GetRecord
related terms
citation

Grieco, Nicholas R., author; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Butters, Gregory L., committee member. Use of global datasets for downscaling soil moisture with the EMT+VS model. Masters thesis, Colorado State University. Libraries, 2017. https://hdl.handle.net/10217/185633