Thesis (M.S.)--University of Wisconsin--Madison, 1958. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 34-37).

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (p. 157-165).

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (p. 140-146).

Thesis (M.S.)--Florida State University, 2006. / Advisor: Kwang-Yul Kim, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed June 13, 2006). Document formatted into pages; contains x, 69 pages. Includes bibliographical references.

Thesis (Ph. D.)--Oregon Graduate Center, 1986.

High fuel prices have created an economic climate in which oil and gas development is increasingly profitable, and consequently, is increasing rapidly in the United States. The development includes drilling new wells and expanding the pipeline network to deliver gas and oil. This is especially true in the northern Appalachian region where the relatively undeveloped Marcellus shale formation is located. The Marcellus formation has been called a "super giant" gas reservoir possibly containing 50 trillion cubic feet of recoverable natural gas (Harper, 2008). In Pennsylvania alone, over 375 wells targeting the Marcellus shale formation have been approved between 2003 and the end of 2007. However, environmental impacts from well pads and pipelines are relatively unknown. Sediment concentrations and yields were measured from four sections of an in-road pipeline in the Monongahela National Forest in Tucker County, West Virginia during summer and fall 2007 and spring 2008. The objectives of this study were to determine the influence of vegetation cover and precipitation characteristics on sediment concentrations in runoff and sediment yields from the in-road pipeline, and to compare sediment yields to forest roads. Poorly vegetated pipeline sections produced 30.92 kg of sediment throughout the study compared to 13.49 kg for the well vegetated sections. Despite this, percent vegetative ground cover had no statistically significantly effect on sediment concentrations or yields except during very intense storms. Several precipitation characteristics, especially intensity, played a significant role in explaining sediment yields and concentrations. Precipitation patterns changed with seasons, and therefore, sediment concentrations and yields varied significantly by season. The most intense storms occurred during the summer months, which is when most soil loss also occurred. Erosion rates from the pipeline were greater than from undisturbed or well-managed forest plots, but were less than rates reported for logging and skid roads when normalized for rainfall. The reduced erosion rates suggest that routing new pipelines along closed roads may be a good method to reduce erosion compared to clearing new pipeline rights-of-ways. To further reduce erosion potential, steps in the installation process that expose mineral soil should be timed to avoid periods of intense rainfall.

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In an effort to improve upon rainfall forecasts produced by simple storm advection methods (nowcasts) and to broach the gap between them and the forecasts of complex Numerical Weather Prediction (NWP) models, in terms of the spatial detail and length of lead-time each provides, the research presented explores the possibility of combining elements of each into a physically-based algorithm for rainfall forecasting. It is an algorithm that uses as its foundation the rainfall prediction model of Mark French and Witold Krajewski, developed in 1994. Their model was designed to take advantage of the high resolution rainfall observations and tracking abilities provided by weather radar and to achieve a rainfall forecast by augmenting extrapolation techniques with a representation of storm dynamics in the form of "rising parcel" theory. The new algorithm/model retains those features but incorporates NWP data to assist with forecasting, using it as a means to enable an informed choice of algorithm pathways and, more specifically, to identify the ingredients of precipitation, namely ascending air of high moisture content. A case study application of the new rainfall forecasting model to storms in Northern England shows its performance, at a lead-time of one hour, compares favourably with respect to extrapolation and persistence techniques and also NWP forecasts, and that it is able to provide more assured forecasts than persistence and nowcasts at longer lead-times. The robustness of the model is tested and confirmed by way of another case study, this time using Mediterranean storms and with predictions made in the context of urban hydrology. The case studies help to identify aspects of the model that need improvement, with representation of orographic forcing being a key one. Both the model's encouraging performance and its pinpointed weaknesses provide impetus for further research in the area of integrated mesoscale-hydrometeorological modelling for flood forecasting.

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