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Virginia Tech

Observing Drought-Induced Crustal Loading Deformation Around Lake Mead Region via GNSS and InSAR: A Comparison With Elastic Loading Models

Abstract

dc:description.abstract

Lake Mead, the largest reservoir in the United States along the Colorado River on the border between the states of Nevada and Arizona, is one of the nation's most important sources of freshwater. As reported by the U.S. drought monitor (USDM), the entire region has been experiencing recurring severe to extreme droughts since the early 2000s, which have further intensified during the past two years. The drought-driven water deficit caused Lake Mead's water volume to decrease to approximately one-third of its capacity, creating a water crisis and negatively affecting soil and groundwater storage across the region. Water deficits have further reduced the mass of water loading on the Earth's crust, causing it to elastically deform. I observe this process from the ground by recording the vertical land motion occurring at Global Navigation Satellite System (GNSS) stations, or from space via Interferometric Synthetic Aperture Radar (InSAR) technology. In this study, I analyze vertical deformation observations from GNSS sites and multi-temporal InSAR analysis of Sentinel-1A/B to investigate the contribution of water mass changes in lake, soil, and groundwater to the deformation signal. To achieve this, I remove the effects of glacial isostatic adjustment and non-tidal mass loads from GNSS/InSAR observations. Our findings indicate that recent drought periods led to a notable uplift near Lake Mead, averaging 7.3 mm/year from 2012 to 2015 and an even larger rate of 8.6 mm/year from 2020 to 2023. Further, I provide an estimate of the expected vertical crustal deformation in response to well-known changes in lake and soil moisture storage. For that, I quantify hydrological loads through two different loading models. These include the application of Green's functions for an elastic, layered, self-gravitating, spherical Earth, and the Love load numbers from the Preliminary Reference Earth Models (PREMs), as well as elastic linearly homogeneous half-space Earth models. I further test various load models against the GNSS observations. Our research further investigates the impact of local crustal properties and evaluates the output of several elastic loading models using crustal properties and different model types under non-drought and drought conditions. For future studies, I suggest a comprehensive analysis of the deformation field InSAR data. Also, rigorous monitoring of groundwater levels is essential to accurately predict changes in water masses based on deformation. In addition, for each data set, I suggest implementing an uncertainty analysis to assess the predictability of groundwater level changes based on vertical loading deformation observed by INSAR/GNSS data around the region. Obtaining such estimates will provide valuable insight into the dynamic interactions of the local aquifers with Lake Mead.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Geosciences
Department dc:contributor.department
Geosciences
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Zehsaz, Sonia
Chair dc:contributor.committeechair
  • Werth, Susanna
Committee members dc:contributor.committeemember
  • Shirzaei, Manoochehr
  • Stamps, D. Sarah
  • Schreiber, Madeline E.

Subjects

dc:subject × 5

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:38539
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/116320

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Zehsaz, Sonia. Observing Drought-Induced Crustal Loading Deformation Around Lake Mead Region via GNSS and InSAR: A Comparison With Elastic Loading Models. masters thesis, Virginia Tech, 2023. http://hdl.handle.net/10919/116320