<p> Permanent Global Positioning System (GPS) networks have been established around the globe for a variety of uses, most notably to monitor the activity of fault lines and tectonic plate motion. A model for utilizing GPS as a tool for hydrologic monitoring is also developed. </p><p> First, observations of the recent movement of the land surface throughout California by the Scripps Orbit and Permanent Array Center (SOPAC) GPS network are explored. Significant seasonal cycles and long term trends are related to historical observations of land subsidence. The pattern of deformation throughout the state appears to be caused by the occurrence of poroelastic deformation of the aquifer in the Central Valley, and elastic crustal loading by surface water and the winter snowpack in the Sierra Nevada Mountains. The result is a sort of teeter-totter motion between the Valley and the mountains where the Valley sinks in the dry season while the mountains lift, and the mountains sink in the wet season while the Valley lifts. </p><p> Next, the elastic crustal deformation caused by the winter snowpack is explored more thoroughly at 6 high elevations throughout the Western United States. Expected annual deformation as a result of thermoelastic and snow water equivalent are calculated using SNOTEL observations and an elastic half-space model. The results demonstrate the dominance of snow loading on the seasonal vertical land surface deformation at all 6 GPS stations. The model is then reversed and applied to the GPS vertical site-position observations in order to predict snow water equivalent. The results are compared to SNOTEL observations of snow water equivalent and soil moisture. The study concludes that GPS site-position observations are able to predict variations in snow water equivalent and soil moisture with good accuracy. </p><p> Then a model which incorporates both elastic crustal loading and poroelastic deformation was used to predict groundwater storage variations at 54 GPS stations throughout the Central Valley, CA. The results are compared to USGS water table observations from 43 wells. The predictions and observations show a similar magnitude and spatial pattern of groundwater depletion on both a seasonal and long term timescales. Depletion is focused on the southernmost part of the Valley where GPS reveals seasonal fluctuation of the water table around 2 m and 8 m/yr of water table decline during the study period. GPS also appears to respond to deformation from peat soils and changing reservoir storage in the northern parts of the Valley. </p><p> Finally, preliminary work exploring the potential for using GPS as a tool for monitoring snowmelt runoff and infiltration is explored at one station in Eastern Idaho. Taking the difference between the change in GPS water storage estimates with time and the change in SNOTEL observed snow water equivalent with time produces a time series of infiltration, or the amount of water added to storage in the geologic profile. Then subtracting the estimated infiltration and snow water equivalent from the total precipitation observed by SNOTEL produces a time series of runoff. The estimated runoff at the GPS site was compared to observations from a nearby stream gauge and the foundation for a more extensive comparison is laid out. </p><p> The overall impact of this work is to introduce the unique hydrologic information and monitoring capabilities which can be accessed through monitoring of the land surface position using GPS. As GPS networks grow and expand worldwide, the available data should be harnessed by the hydrologic community for the benefit of local water management as well as improvements to data assimilated models. The work presented here represents only a small fraction of the wealth of knowledge that could result from a budding field of GPS hydrologic remote sensing. (Abstract shortened by UMI.)</p>

Thesis (Ph.D.)--The Ohio State University, 2006. / (UnM)AAI3220994. Adviser: C. K. Shum. Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3017.

Thesis (Ph.D.)--Lehigh University, 2009. / Adviser: Edward Everson.

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains ix, 125 p.; also includes graphics (some col.). Includes bibliographical references (p. 101-103).

Thesis (Ph.D.)--The Ohio State University, 2008. / (UMI)AAI3292725. Source: Dissertation Abstracts International, Volume: 68-12, Section: B, page: 7892. Adviser: Terry J. Wilson.

Tesis (Ingeniero topografo y geodesta)--Universidad Autónoma de México, Escuela Nacional de Ingeniera, 1959.

Thesis (Ph. D.)--University of Louisville, 2009. / Department of Mathematics. Vita. "August 2009." Includes bibliographical references (leaves 97-99).

Thesis (Ph. D.)--Ohio State University, 2009. / Title from first page of PDF file. Includes vita. Includes bibliographical references (p. 147-155).

No description available.

<p> Federal, state and local officials are planning for a (M) 7.8 San Andreas Earthquake Scenario in the Southern California Catastrophic Earthquake Response Plan that would require initial emergency food and water resources to support from 2.5 million to 3.5 million people over an eight-county region in Southern California. However, a model that identifies locations of affected populations&mdash;with consideration for social vulnerability, estimates of their emergency logistical resource requirements, and their resource requirements over time&mdash;has yet to be developed for the emergency response plan.</p><p> The aim of this study was to develop a modeling methodology for emergency logistical resource requirements of affected populations in the (M) 7.8 San Andreas Earthquake Scenario in Southern California. These initial resource requirements, defined at three-days post-event and predicted through a probabilistic risk model, were then used to develop a relative risk ratio and to estimate resources requirements over time. The model results predict an &ldquo;at-risk&rdquo; population of 3,352,995 in the eight-county study region. In Los Angeles County, the model predicts an &ldquo;at- risk&rdquo; population of 1,421,415 with initial requirements for 2,842,830 meals and 4,264,245 liters of water. The model also indicates that communities such as Baldwin Park, Lancaster-Palmdale and South Los Angeles will have long-term resource requirements.</p><p> Through the development of this modeling methodology and its applications, the planning capability of the Southern California Catastrophic Earthquake Response Plan is enhanced and provides a more effective baseline for emergency managers to target emergency logistical resources to communities with the greatest need. The model can be calibrated, validated, generalized, and applied in other earthquake or multi-hazard scenarios through subsequent research.</p><p>