Thesis (Ph. D.)--University of Wisconsin--Madison, 1975. / Vita. Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 266-270.).

Thesis (Ph. D.)--University of Delaware, 2005. / Principal faculty advisor: Victor V. Klemas, College of Marine Studies. Includes bibliographical references.

A theoretical energy balance model was used to predict water temperatures in small clear streams which have limited heat transfer due to ground water flow. The model was designed for application to coastal streams in central California to determine reservoir release policies that would improve downstream salmon habitat by altering stream temperatures. The results of the model are predicted to be applicable to small clear streams in moderately calm and humid areas throughout the temperate zone, where the winters are not prolonged, and where there is little ground Hater flow.. It predicts that all turbulently flowing wateracts ,. identically. It initially heats more rapidly than non-turbulent water, but reaches a lower asymptotic limit. The predicted water temperature was linearly related (in approximate descending order of importance) ·to the reciprocal of the square root of the thermal diffusivity of the substrate, the ambient water vapor pressure, the initial water temperature, the albedo of the stream bed, and the daily mean air temperature. It was found to have important curvilinear relationships with the angle of shading, the wind speed, and the depth. It was curvilinearly related to the latitude, but was surprisingly insensitive to it. It was insensitive to the annual mean temperature, and the range of daily air temperatures.

Thesis (M.S.)--Oregon State University, 2004. / Printout. Includes bibliographical references (leaves 86-91). Also available on the World Wide Web.

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 102-110). Also available on the World Wide Web.

Indiana University-Purdue University Indianapolis (IUPUI) / Numerical models are important tools for simulating processes within complex natural systems, such as hydrodynamics and water quality processes within a water body. From decision makers’ perspectives, such models also serve as useful tools for predicting the impacts of water quality problems or develop early warning systems. However, accuracy of a numerical model developed for a specific site is dependent on multiple model parameters and variables whose values are attained via calibration processes and/or expert knowledge. Real time variations in the actual aquatic system at a site necessitate continuous monitoring of the system so that model parameters and variables are regularly updated to reflect accurate conditions. Multiple sources of observations can help adjust the model better by providing benefits of individual monitoring technology within the model updating process. For example, remote sensing data provide a spatially dense dataset of model variables at the surface of a water body, while in-situ monitoring technologies can provide data at multiple depths and at more frequent time intervals than remote sensing technologies. This research aims to present an overview of an integrated modeling and data assimilation framework that combines three-dimensional numerical model with multiple sources of observations to simulate water column temperature in a eutrophic reservoir in central Indiana. A variational data assimilation approach is investigated for incorporating spatially continuous remote sensing observations and spatially discrete in-situ observations to change initial conditions of the numerical model. This research addresses the challenge of improving the model performance by combining water temperature from multi-spectral remote sensing analysis and in-situ measurements. Results of the approach on a eutrophic reservoir in Central Indiana show that with four images of multi-spectral remote sensing data assimilated, the model results oscillate more from the in-situ measurements during the data assimilation period. For validation, the data assimilation has negative impacts on the root mean square error. According to quantitative analysis, more significant water temperature stratification leads to larger deviations. Sampling depth differences for remote sensing technology, in-situ measurements and model output are considered as possible error source.

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Civil Engineering. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 125-129. / by Kathleen Ann Hurley. / M.S.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 130-137). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 130-137). Also available on the Internet.

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