Thesis (M.Eng.Sc.)--University of Adelaide, Dept. of Civil and Environmental Engineering, 1995. / Undertaken in conjunction with JUMP (Joint Universities Masters Programme in Hydrology and Water Resources). Bibliography: leaves 76-83.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 134-143). Also available via the Internet.

Thesis (B.S.)--University of Missouri, School of Mines and Metallurgy, 1910. / L. J. Porri determined to be Louis Joseph Porri and J. L. Pickering determined to be John Lyle Pickering from "Forty-First Annual Catalogue. School of Mines and Metallurgy, University of Missouri". The entire thesis text is included in file. Typescript. Illustrated by authors. Title from title screen of thesis/dissertation PDF file (viewed February 23, 2009)

<p> In this project we created a hydrodynamic model of the Lower Green Bay of Lake Michigan in Wisconsin, United States using the Visual Environmental Fluid Dynamics Code (EFDC). The model includes four tributary rivers to Lower Green Bay as well as the open boundary flow conditions at Chambers Island. This case study is used to: 1) compare the results obtained with a previous study of Lower Green Bay to validate the creation of the model 2) examine the hydrodynamics of the bay, and 3) create a framework for future studies at Lower Green Bay. The Geographic Information used to build the Grid was obtained from the NOAA web site. Meteorological and flow information was obtained from the National Weather Service and USGS web sites, respectively. It was necessary to create a new model grid as a platform for future studies of Lower Green Bay, and the Visual EFDC 1.2 code was a useful tool in the development of the grid. However, some limitations in the code made the creation of the grid a challenge. In this project, we summarize the process used to overcome challenges in creating a correct grid, and analyze the hydrodynamic results of the model simulation for the period between June and October 2011. Overall, we conclude that the model reproduces field data reasonably well, and a correct modeling framework for hydrodynamic modeling of Lower Green Bay was created. </p>

<p> Snow is a key component of the Earth&rsquo;s energy balance, climate, environment, and a major source of freshwater in many regions. Seasonal and perennial snow cover affect up to 50% of the Northern Hemisphere landmass, which accounts for vast regions of the Earth that influence climate, culture, and commerce significantly. Information on snow properties such as snow cover, depth, and wetness is important for making hydrological forecasts, monitoring climate change, weather prediction, and issuing snowmelt runoff, flash flood, and avalanche warnings. Hence, adequate knowledge of the areal extent of snow and its properties is essential for hydrologists, water resources managers, and decision-makers. </p><p> The use of infrared (IR) and microwave (MW) remote sensing (RS) has demonstrated the capability of estimating the presence of snow cover and snowpack properties with accuracy. However, there are few publicly accessible, operational RS-based snow depth products, and these only provide the depth of recently accumulated dry snow because retrievals lose accuracy drastically for wet snow (late winter - early spring). Furthermore, it is common practice to assume snow grain size and wetness to be constant to retrieve certain snow properties (e.g. snow depth). This approach is incorrect because these properties are space- and time- dependent, and largely impact the MW signal scattering. Moreover, the remaining operational snow depth products have not been validated against in-situ observations; which is detrimental to their performance and future calibrations. </p><p> This study is focused on the discovery of patterns in geospatial data sets using data mining techniques for mapping snow depth globally at 10 km spatial resolution. A methodology to develop a RS MW-based snow depth and water equivalent (SWE) product using regression tree algorithms is developed. The work divided into four main segments includes: (1) validation of RS-based IR and MW-retrieved Land Surface Temperature (LST) products, (2) studying snow wetness by developing, validating, and calibrating a Snow Wetness Profiler, (3) development of a regression tree algorithm capable of estimating snow depth based on radiative (MW observations) and physical snowpack properties, and (4) development of a global MW-RS-based snow depth product built on the regression tree algorithm. </p><p> A predictive model based on Regression Tree (RT) is developed in order to model snow depth and water equivalent at the Cooperative Remote Sensing Science and Technology Center &ndash; Snow Analysis and Field Experiment (CREST-SAFE). The RT performance analyzed based on contrasting training error, true prediction error, and variable importance estimates. The RT algorithm is then taken to a broader scale, and Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission &ndash; Water 1 (GCOM-W1) MW brightness temperature measurements were used to provide snow depth and SWE estimates. These SD and SWE estimates were evaluated against twelve (12) Snow Telemetry (SNOTEL) sites owned by the National Resources Conservation Service (NRCS) and JAXA&rsquo;s own snow depth product. Results demonstrated that a RS MW-based RT algorithm is capable of providing snow depth and SWE estimates with acceptable accuracy for the continental United States, with some limitations. The major setback to the RT algorithm is that it will only provide estimates based on the data with which it was trained. Therefore, it is recommended that the work be expanded, and data from additional in-situ stations be used to re-train the RT algorithm. The CREST snow depth and water equivalent product, as it was named, is currently operational and publicly accessible at https://www.noaacrest.org//snow/products/. </p><p>

<p> Redevelopment of the Granite Run Mall, a nearly 100% impervious 34 ha site in Media, Pennsylvania, required upgrading the site&rsquo;s 40-year old stormwater control measures (SCMs) to comply with local modern ordinances. With its headwaters adjacent to the site, Chrome Run has received the mall&rsquo;s stormwater runoff for decades with deleterious effects on all levels of stream functions. Thus, the mall&rsquo;s redevelopment was an ideal opportunity to examine the effectiveness of current stormwater practices and validate the approach of focusing SCM implementation in headwaters. Specifically the study aimed to determine if the damage to the receiving waters could be reversed by conducting a 3 year long Before-After-Control-Impact study. To these ends in the summer of 2016, a rigorous monitoring program was established in Chrome Run along with three control streams. Areas of investigation included hydrology, hydraulics, geomorphology, biology, and numerous physicochemical parameters. This thesis details the materials and methods employed in addition to an analysis of the pre-redevelopment data to establish the baseline conditions along Chrome Run and quantify the stream&rsquo;s impairment.</p><p>

<p> During <i>in situ</i> remediation of contaminated groundwater, a treatment chemical is injected into the contaminated groundwater to degrade a contaminant through chemical reaction that occurs in the subsurface. Reactions and subsequent contaminant degradation occur only where the treatment chemical contacts the contaminant long enough to complete degradation reactions. Traditional <i> in situ</i> groundwater remediation relies on background groundwater flow to spread an injected treatment chemical into a plume of contaminated groundwater. </p><p> Engineered Injection and Extraction (EIE), in which time-varying induced flow fields are used to actively spread the treatment chemical into the contaminant plume, has been developed to increase contact between the contaminant and treatment chemical, thereby enhancing contaminant degradation. EIE has been investigated for contaminants degrading through irreversible, bimolecular reaction with a treatment chemical, but has not been investigated for a contaminant governed by complex biogeochemical processes. Uranium fate and transport in subsurface environments is governed by adsorption, oxidation reduction, solution, and solid-phase interactions with naturally occurring solution species, microbial communities, minerals and aquifer media. Uranium primarily occurs in aqueous, mobile U(VI) complexes in the environment but can be reduced to sparingly soluble, immobile U(IV) solid-phase complexes by native dissimilatory metal reducing bacteria. </p><p> This work investigates the ability of EIE to promote subsurface delivery of an acetate-amended treatment solution throughout a plume of uranium-contaminated groundwater to promote <i>in situ</i> growth of native microbial communities to immobilize uranium. Simulations in this investigation are conducted using a semi-synthetic flow and reactive transport model based on physical and biogeochemical conditions from two uranium contaminated sites: the Naturita Uranium Mill Tailings Remedial Action (UMTRA) Project site in southwestern Colorado and the Old Rifle UMTRA Project site in western Colorado.</p><p>

<p> The Saint-Venant shallow water equations are commonly used to model river hydraulics. The equations utilize a hydrostatic assumption with a recommendation to limit use to a bed slope less than 1:10, vertical to horizontal. This recommended limit was made in an era when calculations were performed by hand and therefore minimized by performing a one-dimensional analysis with the distance between river stations maximized. Current technology makes a more detailed analysis accessible. </p><p> This study investigates the effects of applying a two-dimensional hydraulic model that utilizes the Saint-Venant shallow water equations without correction for non-hydrostatic conditions to a bed slope of 1:8. By doing so it was hoped to show that there exists an effective and economical method for engineers to analyze hydraulic effects in these conditions. </p><p> A comparative analysis of the results from the 2D model and a 3D non-hydrostatic model was utilized to investigate the theoretical limit of slope on the hydrostatic assumption. The models consisted of an existing 2D model previously developed for an engineering study and a 3D model developed for this study, which employed a novel approach to approximate the effects of surface roughness. The analysis compared model results for depth, velocity, and flow rate at nine cross sections on the study reach. While the findings from the research are not conclusive they do illustrate that a well resolved 2D model is able to push the 1:10 slope limit on the hydrostatic assumption for the shallow water equations. It was found that a uniform flow applied to the 2D model and allowed to come to steady state maintained a relatively consistent flow rate throughout the length of the reach. This demonstrates that the model did not produce any artificial gains or losses. Surprisingly, the 2D model accomplished this while the 3D model did not. </p><p> These findings are important in locations where the accepted methods of 3D non-hydrostatic modeling would be computationally cumbersome and cost prohibitive. The lack of efficient and affordable analysis tools rated for steep slopes leads to the construction of facilities with unknown hydraulic risk to life and property. Fully verifying the methods of this study would provide needed support to hydraulic engineers for these conditions. </p><p> Concurrent to the research for this thesis, was the development of a series of lessons on introductory hydraulic engineering for middle school students. Engineering is characterized by its hands on, real world application of science and math and is rooted in a tradition of disseminating knowledge through mentorship. Many engineering topics provide opportunity to spark the minds of our youth. The final chapter of this paper is a summary of this work. It is included it here to encourage more engineers to share their work with the next generation.</p><p>

<p> Recent research and technological advances in the field of hydrology and water resources call for parallel educational reforms at the undergraduate level. This thesis describes the design, development, and evaluation of a series of undergraduate learning modules that engage students in investigative and inquiry-based learning experiences and introduces data analysis and numerical modeling skills. The modules are situated in the coastal hydrologic basins of Louisiana, USA. Centered on the current crisis of coastal land loss in the region, the modules immerse students in a suite of active-learning experiences in which they prepare and analyze data, reproduce model simulations, interpret results, and balance the beneficial and detrimental impacts of several real-world coastal restoration projects. The modules were developed using a web-based design that includes geospatial visualization via a built-in map-interface, textual instructions, video tutorials, and immediate feedback mechanisms. Following pilot implementations, an improvement-focused evaluation was conducted to examine the effectiveness of the modules and their potential for advancing students&rsquo; experiences with modeling-based analysis in hydrology and water resources. Both qualitative and quantitative data was collected including Likert-scale surveys, student performance grades, informal interviews, and text-response surveys. Students&rsquo; perceptions indicated that data and modeling-driven pedagogy using local real-world projects contributed to their learning and served as an effective supplement to instruction. The evaluation results also pointed out some key aspects on how to design effective and conducive undergraduate learning experiences that adopt technology-enhanced, data and modeling-based strategies, and how to pedagogically strike a balance between sufficient module complexity, ensurance of students&rsquo; continuous engagement, and flexibility to fit within existing curricula limitations. Additionally, to investigate how such learning modules can achieve large scale adoption, a total of 100 interviews were conducted with academic instructors and practicing professionals in the field of hydrology and water resources engineering. Key perspectives indicate that future efforts should appease hindering factors such as steep learning curves, lack of assessment data, refurbishment requirements, rigidness of material, time limitations.</p><p>

Water resources systems are designed to operate under a wide range of potential climate conditions. Traditionally, systems have been designed using stationarity-based methods. Stationarity is the assumption that the climate varies within an envelope of variability, implying that future variability will be similar to past variability. Due to anthropogenic climate change, the credibility of the stationarity-based assumptions has been reduced. In response, climate change assessments have been developed to quantify the potential impacts due to climatic change. While these methods quantify potential changes, they lack the probabilistic information that is needed for a risk-based approach to decision-analysis. This dissertation seeks to answer two crucial questions. First, what is the best way to evaluate water resource systems given uncertainty due to climate change? Second, what role should climate projections or scenarios play in water resources evaluation? A decision analytic approach is applied that begins by considering system decisions and proceeds to determine the information relevant to decision making. Climate based predictor variables are used to predict system hazards using a climate response function. The function is used with climate probability distributions to determine metrics of system robustness and risk. Climate projections and additional sources of climate information are used to develop conditional probability distributions for future climate conditions. The robustness and risk metrics are used to determine decision sensitivity to assumptions about future climate conditions. The methodology is applied within the context of the International Upper Great Lakes Study, which sought to determine a new regulation plan for the releases from Lake Superior that would perform better than the current regulation plan and be more robust to potential future climate change. The methodology clarifies the value of climate related assumptions and the value of GCM projections to the regulation plan decision. The approach presented in this dissertation represents a significant advancement in accounting for potential climate change in water resources decision making. The approach evaluates risk and robustness in a probabilistic context that is familiar to decision makers and evaluates the relevance of additional climate information to decisions.