Thesis (J.C.L.)--Catholic University of America, 2001. / Includes bibliographical references (leaves 75-79).

Thesis (M. Sc.)--University of Hong Kong, 1994. / Includes bibliographical references.

Thesis (Ph. D.)--University of North Texas, August, 2008. / Title from title page display. Includes bibliographical references.

Thesis (J.C.L.)--Catholic University of America, 2001. / Includes bibliographical references (leaves 75-79).

Thesis (Ph. D.)--University of California, Los Angeles, 1992. / Vita. Includes bibliographical references (leaves 288-300).

Thesis (Ph. D.)--University of Maryland at College Park, 1997. / Includes bibliographical references.

<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> The Lawn to Garden Program incentivizes replacing front yard turf grass with California Friendly gardens in Long Beach, CA. The Long Beach Water Department introduced the program in 2009 as a means of water conservation and since that time 2% of the City&rsquo;s single-family homes have successfully completed the program. As of the end of 2014, 3,461 applications had been submitted, but only 1,849 Lawn to Garden projects had been completed, a success rate of 53% and withdrawal rate of 47%. This study seeks to identify some of the factors that contribute to the program&rsquo;s high withdrawal rate, exploring the resources available to participants as well as the barriers to success. Methods used include geographic analysis in the form of density mapping, analysis of survey responses submitted by participants who withdrew from the program, and my professional observations. This study considers which neighborhoods display greater rates of success and withdrawn applications as well as what resources would have most aided those who did not complete their projects. </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> Along the coastlines of the Hawaiian Islands, there is a valuable and critical resource known for its brackish water habitat &ndash; loko i&lsquo;a (Hawaiian fishponds). They are dynamic systems dependent on the balance between fresh groundwater inputs from uka (uplands) and landward flow of kai (seawater), which all vary depending on the behavior of our climate, including rainfall, tides, and storms. Nutrient-rich groundwater mixing with the seawater at the coast allows for an abundant growth of limu or primary productivity attracting many of Hawaii&rsquo;s favorable native brackish water and herbivorous species. Having an intimate relationship with this natural coastal nursery, Hawaiians effectively modified these coastal habitats into loko i&rsquo;a to provide a sustainable food source for the communities in which they reside. In support of these invaluable resources and practices, this study seeks to understand primary productivity and salinity relationships along the same coastline at Honokea Loko of Waiuli, and Hale o Lono and Wai&amacr;hole/Kapalaho of Honohononui, Hawaii. Weekly water quality monitoring by kiai' loko (fishpond steward) and biweekly water column sampling, salinity in the three loko i&rsquo;a ranged from 3.1 to 18.8 and was significantly different throughout different areas of each pond. Benthic primary productivity experiments, found significantly more growth at higher salinity locations across all sites. Due to these strong correlations, loko i'a communities would greatly benefit from these methodologies to quantify the variability of environmental changes through time and specific impacts of climate phenomena, changes in rainfall and sea level. These factors have the potential to interfere with primary productivity and alter loko i'a systems interactions entirely.</p><p>