Hexavalent chromium is an extremely carcinogenic chemical that has been widely produced in the United States. This has led to major waste contamination and pollution throughout the country. According to the Environmental Working Group Hexavalent chromium has been found in 89% of city tap water. Most people believe they are safe using regular home filter systems however that is not true. A more expensive ion exchange water treatment unit is required. Therefore to protect yourselves from this carcinogenic metal a reliable test is required. In this study we have developed a Zinc Sulfide Manganese doped Quantum Rod technology to detect for presence of chromate and other harmful transitional metals in drinking water. Quantum Rods were synthesized using a hydrothermal reaction method. They were fully characterized using UV-visible absorption spectroscopy, fluorescence emission spectroscopy, X-ray Photoelectric Spectroscopy (XPS) and High Resolution Transmission Electron Microscopy (HRTEM). Quantum Rod metal detection studies were done with 28 different ions in a 96-well fluorescent plate reader. Results show that highest sensitivity to 8 ions including the toxic ions of chromate and mercury allowing us to create a sensor to detect these items.

Modern streamflow measuring equipment, water quality sampling techniques and a better understanding of pollutant washoff are continuously being developed as today's society is in critical need of improving water management, minimizing developmental impacts and preventing environmental hazards. In particular, the study of the spatial, temporal and volumetric characteristics of annual pollutant loading caused by variations in precipitation, land use and other anthropogenic factors is of great significance due to their relation to future global water demands. The research presented here falls in three parts. In the first part of the dissertation, an acoustical Doppler velocity profiler installed in a submerged concrete channel is proposed to continually measure the annual fluctuation in streamflow levels down to dry channel conditions. The tailwater influenced, intermittent streamflow conditions for the City of Kissimmee, Florida were selected for the evaluation of this approach under a 3-year study from 2006 to 2008. The performance of these concrete channels were systematically evaluated by comparisons with established field measurement techniques over various stream configurations and flow conditions. The second part of this research investigates the dynamics of flood wave detection with respect to enabling an automatic water quality sampler to start collecting samples. The main focus was on the accurate detection of flood waves in the absence of rainfall and the presence of fluctuating baseflows and stream stages. In the 3-year study, it was shown that a dual parameter trigger, utilizing independent measuring equipment, resulted in accurate flood wave detection with minimal false triggering of the autosampler. In addition, an incremental or percent deviation from a moving average of stage or flow proved to be a more consistent indicator for the presence of a flood wave. In the third part of this work, the frequency of water quality sampling and the associated level of detail for sampling of rainfall events were investigated with respect to accurately depicting annual pollutant loads. It was found that the seasonal variations in baseflow pollutant loads are not accurately represented by current 4-quarter grab sampling. Also, significant pollutant loading within rainfall events may not be captured by only performing grab sampling during baseflow conditions. In addition, although increased pollutant concentrations were observed within the initial 30 minutes of the flood wave, their actual loadings did not represent a significant impact on the annual pollutant loads. A biweekly grab sampling frequency was found to be adequate in many cases to depict the annual pollutant loads, but depending upon the targeted constituent and particular streamflow condition, rainfall event sampling might also be necessary. The results of this research complemented with other studies will promote better understanding of intermittent streamflows, accurate flood wave detection, and assessment of annual pollutant loads to our nation's waterbodies.

Research into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In particular, intensity return values for each point in a LiDAR grid have been found to provide a useful data set for wet and dry channel classification. LiDAR intensity return data are, in essence, a numeric representation of the characteristic light reflectivity of the object being scanned; the more reflective the object is, the higher the intensity return will be. Intensity data points are collected along the course of the channel network and within the perceived banks of the channel. Intensity data do not crisply reflect a perfectly wet or dry condition, but instead vary over a range such that each location can be viewed as partially wet and partially dry. It is advantageous to assess problems of this type using the methods of fuzzy logic. Specifically, the variance in LiDAR intensity return data is such that the use of fuzzy logic to identify intensity cluster centers, and thereby assign wet and dry condition identifiers based on fuzzy memberships, is a possibility. Membership within a fuzzy data set is characterized by a value representing the degree of membership. Typically, membership values range from 0 (representing non-membership) through 1 (representing full membership), with many observations found to be not at either extreme but instead at some intermediate value representing partial membership. The ultimate goal of this research was to design and develop an automated algorithm to identify wet and dry channel sections, given a previously identified channel network based on topographic elevation, using a combination of intensity return values from LiDAR data and fuzzy logic clustering methods, and to implement that algorithm in such a way as to produce reliable multi-class channel segments in ArcGIS. To enable control of calculations, limiting parameters were defined, specifically including the maximum allowable bank slope, and a filtering percentage to more accurately accommodate the study area. Alteration of the maximum allowable bank slope has been shown to affect the comparative quantity of high and low intensity centroids, but only in extreme bank slope conditions are the centroids changed enough to hamper results. However, interference from thick vegetation has been shown to lower intensity values in dry channel sections into the range of a wet channel. The addition of a filtering algorithm alleviates some of the interference, but not all. Overall results of the tool show an effective methodology where basic channel conditions are identified, but refinement of the tool could produce more accurate results.

The following is a compilation of field data collected in 2011 and 2012 in Apalachicola, FL as part of a five year study assessing the ecological effects of sea level rise in the northern Gulf of Mexico. Many coastal communities, both natural and developed, will soon be working to mitigate the effects of sea level rise, if they are not already doing so. This thesis investigates the natural patterns of the Apalachicola estuarine system through the collection and analysis of in situ water, sediment, and biomass samples. Additionally, results of the field samples are presented and recommendations for additional sampling are given. The field methods and procedures developed in this study were designed to be repeated in other estuaries to build upon the work that has been conducted in Apalachicola. Water samples were tested for total suspended solids (TSS) and compared against hydrodynamic (tidal circulation and streamflow) and meteorological (wind and precipitation) characteristics. Streamflow was determined to influence a seasonal base level concentration of TSS. Wind strength and direction consistently influenced small TSS concentration fluctuations, an effect amplified by the shallow nature of the estuary. Tidal circulation appeared to have minor influences on TSS concentration fluctuations within the base level concentration range. Precipitation appeared to influence large TSS concentration fluctuations; however, due to limited data collection during storm events, more data is required to conclusively state this. Sediment cores throughout the lower Apalachicola River revealed that coarse particles settled out in upstream areas while fine particles tended to stay in suspension until low energy areas in the lower portions of the river or marsh system were reached. Finally, biomass samples were used to iv develop regression models utilizing remotely sensed data to predict biomass density in marsh areas with unprecedented accuracy. The documented patterns of this system are to be used as inputs and validation points to update an existing hydrodynamic model and to aid in the coupling and development of sediment transport and marsh equilibrium models. The field campaign developed and implemented here provides a foundation for this novel coupled modeling effort of estuarine systems. From the 2011 and 2012 sampling conducted, it is apparent that Apalachicola can be modeled as a closed system with river inflow and sediment influx as boundary conditions. Forcing local conditions should accurately represent the system. Ultimately, these models will be used to simulate future sea level rise scenarios and will provide useful decision making tools to coastal managers. Future work will include replicating water sampling in subsequent wet and dry seasons in Apalachicola, FL to confirm observed trends, in addition to implementing this sampling in Grand Bay, MS and Weeks Bay, AL. Additional biomass samples will be taken to validate the strong correlations found between remotely sensed data and in situ samples. In similar studies, it is recommended that water samples be taken to adequately represent influences from tidal cycles and riverine inflow. It is also recommended that spatially distributed biomass samples be taken to validate regression models.

Sea level rise (SLR) is posing a great risk of flooding and inundation to coastal areas in Florida. Some coastal nesting species, including sea turtle species, have experienced diminished habitat from SLR. In an effort to assess the economic and ecosystem service loss to coastal areas with respect to sea turtles Contingent Valuation Method (CVM) and Habitat Equivalency Analysis (HEA) were used. The CVM was used to measure the economic impacts of SLR on sea turtles. Open-ended and dichotomous choice CVM was used to obtain the willingness to pay (WTP) values of Florida residents to implement certain mitigation strategies which would protect Florida’s east coast sea turtle nesting areas. The problem of sample selection bias was reduced by surveying residents of two cities that would potentially have varying interest in coastal conservation due to their relative distance from the coast. The hypothetical WTP of Florida households to implement policies designed to protect sea turtle habitat from development encroachment was estimated to be between $21 and $29 per year for a maximum of five years. Characteristics of respondents were found to have statistically significant impacts on their WTP. Findings include a negative correlation between the age of a respondent and the probability of an individual willing to pay the hypothetical WTP amount. Counter intuitively, it was found that WTP of an individual was not dependent on prior knowledge of the effects of SLR on sea turtle habitat. As the level of this awareness increased, the probability to pay the hypothetical WTP value decreased. The greatest indicators of whether or not an individual was willing to pay to protect sea turtle habitat were the respondents’ perception regarding the importance of sea turtle population health to the ecosystem, and their confidence in the conservation methods used. iii Concepts of Habitat Equivalency Analysis were used in order to determine the ecosystem service lost due to SLR. The study area of Archie Carr National Wildlife Refuge (ACNWR) has a continually increasing sea turtle population due to various conservation efforts. However, how the inundation of the coastal area will injure this habitat was assessed, and if mitigation strategies to compensate for the loss are necessary. The carrying capacity (CC) of the refuge was chosen as the metric of the ecosystem service. Using the estimated area of ACNWR and the approximate area needed by a sea turtle to nest, the theoretical number of sea turtle nests possible on the refuge was calculated. This value was then projected to the year 2100 using the sea level rise scenarios provided by IPCC (2007) and NRC (2010). In order to quantify the injury caused by the decrease in the refuge’s CC, the number of sea turtle nests on the refuge was projected to the year 2100 using the data obtained over the past 30 years. The analysis concludes a potential loss of service to be experienced as early as 2060’s due to the carrying capacity of the refuge diminishing with the loss of the habitat due to the increase in the mean sea level.

Agriculture is the mainstay of the economy in Malawi and accounts for 40% of the Gross Domestic Product (GDP) and 90% of the export revenues. Corn (maize) is the major cereal crop grown as staple food under rainfed conditions, covers over 92% of the total agricultural area, and contributes 54% of the caloric intake. Corn production is the principle occupation and major source of income for over 85% of the total population in Malawi. Issues of hunger and food insecurity for the entire nation are associated with corn scarcity and low production. Global warming is expected to cause climate change in Malawi, including changes in temperature and precipitation amounts and patterns. These climate changes are expected to affect corn production in Malawi. This study evaluates the impacts of climate change on rainfed corn production in Malawi. Lilongwe District, with about 1,045 square miles of agriculture area, has been selected as a representative area. First, outputs of 15 General Circulation Models (GCMs) under different emission scenarios are statistically downscaled. For this purpose, a weather generator (LARSWG) is calibrated and validated for the study area and daily precipitation as well as minimum and maximum temperature are projected for 15 GCMs for three time horizons of 2020s, 2050s and 2090s. Probability assessment of bounded range with known distributions is used to deal with the uncertainties of GCMs’ outputs. These GCMs outputs are weighted by considering the ability of each model to simulate historical records. AquaCrop, a new model developed by FAO that simulates the crop yield response to water deficit conditions, is employed to assess potential rainfed corn production in the study area with and without climate change. Study results indicate an average temperature increase of 0.52 to 0.94oC, 1.26 to 2.20oC and 1.78 to 3.58oC in the nearterm (2020s), mid-term (2050s) and long-term (2090s) future, respectively. The expected changes in precipitation during these periods are -17 to 11%, -26 to 0%, and -29 to -3%. Corn iii yields are expected to change by -8.11 to 0.53%, -7.25 to -14.33%, and -13.19 to -31.86%, during the same time periods. The study concludes with suggestion of some adaptation strategies that the Government of Malawi could consider to improve national food security under climate change.

Streams are classified into perennial, intermittent, and ephemeral streams based on flow durations. Perennial stream is the basic network, while intermittent or ephemeral stream is the expanded network. Connection between perennial stream and base flow at the mean annual scale exists since one of the hydrologic functions of perennial stream is to deliver runoff even in low flow seasons. The partitioning of precipitation into runoff and evaporation at the mean annual scale, on the first order, is captured by the ratio of potential evaporation to precipitation (Ep/P called climate aridity index) based on the Budyko hypothesis. The primary focus of this thesis is the relationship between base flow and perennial stream density (Dp) in the Budyko framework. In this thesis, perennial stream density is quantified from the high resolution National Hydrography Dataset for 185 watersheds; the climate control (represented by the climate aridity index) on perennial stream density and on base flow is quantified; and the correlation between base flow and perennial stream density is analyzed. Perennial stream density declines monotonically with the climate aridity index, and an inversely proportional function is proposed to model the relationship between Dp and Ep/P. This monotonic trend of perennial stream density reconciles with the Abrahams curve, and the perennial stream density is only a small portion of the total drainage density. The dependences of base flow ratio (Qb/P) and the normalized perennial stream density on the climate aridity index follow a similar complementary Budyko-type curve. The correlation coefficient between iv the ratio of base flow to precipitation and perennial stream density is found to be 0.74. The similarity between the base flow and perennial stream density reveals the co-evolution between water balance and perennial stream network.

Climate change and human activities alter the hydrologic systems and exerted global scale impacts on our environment with significant implications for water resources. Climate change can be characterized by the change of precipitation and temperature, and both precipitation pattern change and global warming are associated with the increase in frequency of flooding or drought and low flows. With increasing water demand from domestic, agricultural, commercial, and industrial sectors, humans are increasingly becoming a significant component of the hydrologic cycle. Human activities have transformed hydrologic processes at spatial scales ranging from local to global. Human activities affecting watershed hydrology include land use change, dam construction and reservoir operation, groundwater pumping, surface water withdrawal, irrigation, return flow, and others. In this thesis, the hydromorphology (i.e., the change of coupled hydrologic and human systems) of the Econlockhatchee River (Econ River for short) is studied. Due to the growth of the Orlando metropolitan area the Econ basin has been substantially urbanized with drastic change of the land cover. The land use / land cover change from 1940s to 2000s has been quantified by compiling existing land cover data and digitizing aerial photography images. Rainfall data have been analyzed to determine the extent that climate change has affected the river flow compared to land use change. The changes in stream flow at the annual scale and low flows are analyzed. The Econ River has experienced minimal changes in the amount of annual streamflow but significant changes to the amount of low flows. These changes are due to urbanization and other human interferences.

The Environmental Justice (EJ) movement has long highlighted the disproportionate exposure to environmental hazards experienced by Black, Indigenous, People of Color (BIPOC) and low-income communities across the country. Environmental practitioners have recently focused on utilizing EJ screening tools, which combine environmental and social data to visualize vulnerable communities, to begin to address environmental injustice rampant in BIPOC and low-income communities. This project explores EJ theoretical frameworks and the historical context of social oppression and environmental pollution in the Elizabeth River watershed (ERW) of Virginia to: 1) understand the social, political, and economic context behind environmental injustice; and 2) generate goals to address environmental injustice with a particular focus on utilizing EJ screening tools. This project highlights five EJ theoretical frameworks that can be used to explain disproportionate exposure to environmental hazards: 1) Racism and Discrimination; 2) Exploitation, Manipulation, Enticement, and Intimidation; 3) Institutional Practices; 4) Economics; and 5) Physical Characteristics and provides an overview of the history of the ERW to highlight the operation of these frameworks. Further, this project suggests three major goals to address environmental injustice: 1) empowering communities through equitable and just community engagement; 2) mapping distributions of environmental hazards, social factors, and institutional practices using EJ screening tools; and 3) ensuring that environmental amenities, burdens, and practices are equitably distributed and target vulnerable communities using EJ screening tools. This project serves as a framework for exploring the social, political, and economic contexts that give rise to environmental injustice and how EJ screening tools can be used to begin addressing them.

Uncertainty in climate change plays a major role in watershed systems. The increase in variability and intensity in temperature and precipitation affects hydrologic cycle in spatial and temporal dimensions. Predicting uncertainty in climate change impacts on watershed systems can help to understand future climate-induced risk on watershed systems and is essential for designing policies for mitigation and adaptation. Modeling the temporal patterns of uncertainties is assessed in the New England region for temperature and precipitation patterns over a long term. The regional uncertainty is modeled using Python scripting and GIS to analyze spatial patterns of climate change uncertainties over space and time. The results show that the regional uncertainty is significant in variation for changes in location and climatic scenarios. Watershed response to climate change under future scenarios is assessed using hydrologic simulation modeling for the Connecticut River watershed. Changes in water budgets are assessed for each of the subbasins using spatial analysis and process modeling using GIS and Soil and Water Assessment tool (SWAT). The results show that climate change uncertainty in precipitation and temperature can lead to uncertainty in both quantity and quality in the watershed system. A spatiotemporal, dynamic model was applied to subbasins within the Chicopee River Watershed to estimate climate change uncertainty impacts at a micro scale. These changes were assessed relative to changes in land use and climatic change. The results show that there is a significant potential for climate change to increase evaporation, watershed runoff and soil erosion rates and this varied with climate change uncertainty. Finally, water sustainability gradient analysis was applied to the Volga River watershed in Russia to assess potential climate change impacts by combining with downscaled Global Circulation Model estimates and spatial assessment. Results show that runoff and evapotranspiration are projected to increase with potential for more localized floods and drought events effecting both water resources and food supply. Overall results show that climate change uncertainty can impact watershed systems and spatial and temporal assessments is important for developing strategies for adaptation to climatic change conditions at local and regional scales.