This study investigates the impact on water quality of combined agricultural best management practices cover crop and minimum tillage, alongside an examination of techniques used to collect those samples. Edge-of-field (EOF) water quality samples were collected from 11 working farms during a two-year paired field experiment. Results showed significant reductions in nutrient concentrations, increased discharge, and mixed findings regarding nutrient mass transport post-treatment. A suite of EOF collection techniques were compared using in-situ automated water sampling systems sampling the same runoff events. Sampling protocols influenced nutrient concentrations in composite samples, but unexpected variance in velocity sensors affected measured discharge, making it challenging to confidently attribute differences in nutrient loading estimates to sampling protocol. The findings provide regionally specific evidence for mitigating on-farm nutrient enrichment in the Lower Mississippi Alluvial Valley and enhancing monitoring techniques.

No description available.

Animal and human wastes introduce pathogens into rivers and streams, creating human health and economic burdens. While direct monitoring for pathogens is possible, it is impractical due to the sporadic distribution of pathogens, cost to identify, and health risks to laboratory workers. To overcome these issues, fecal indicator organisms are used to estimate the presence of pathogens. Although fecal indicators generally protect public health, they fall short in their utility because of difficulties in public health risk characterization, inconsistent correlations with pathogens, weak source identification, and their potential to persist in environments with no point sources of fecal pollution. This research focuses on characterizing the ecology of fecal indicators using both modeling and metabolic indicators to better understand the processes that drive fecal pollution. Fecal indicator impairment was modeled in Sinking Creek, a 303 (d) listed stream in Northeast Tennessee, using the ecological niche model, Maxent, for two different fecal indicators. While the use of Maxent has been well demonstrated at the macroscale, this study introduces its application to ecological niches at the microscale. Stream impairment seasonality was exhibited in two different indicators over multiple years and different resolutions (quarterly versus monthly sampling programs). This stresses the need for multiple year and month sampling to capture heterogeneity in fecal indicator concentrations. Although discharge is strongly associated with dissolved solutes, fecal indicator impairment was governed by other ecological factors such as populations of heterotrophic bacteria, enzyme activity, nutrient conditions, and other metabolic indicators. This research also incorporated metabolic indicators to characterize spatiotemporal variability in microbial community function, making connections to fecal and other pollution gradients. Communities differed in their ability to use a wide variety of substrates, and metabolic inhibition in sediments captured most of the interaction of aquatic and benthic communities. Sediment substrate activity was also indicative of degrees of pollution, suggesting that sediment is a potential reservoir for Escherichia coli in this stream, and there is possibility for resuspension, extended residence times, and increased duration for exposure. This research highlights the benefit of using models and other microbial indicators to better understand how environment shapes the niche of fecal indicators.

As the human population increases, the way we use and manage our supply of drinking water becomes even more important. The purpose of this study was to determine residents’ satisfaction level of and performance rating of new water savings devices installed in their apartments. Specifically the investigation focused on ratings of shower heads, kitchen faucet aerators, bath faucet aerators, and fill valve and flapper systems. This quantitative survey included residents at 4 apartment complexes in Tennessee using a paper questionnaire (N = 626). The participants were grouped by age, ethnicity, gender, and whether or not they had experienced both nonrestrictive devices or restrictive low flow devices within their apartment. An independent samples t test was conducted from the research questions for each of these 4 groups. The testing variables for each group consisted of the overall performances of the low flow devices, and the satisfaction of the time to get hot water to shower heads and faucets. There was no significant difference between the 4 grouping variables; residents aged 62 and over compared to 61 and younger, males compared to females, whites compared to nonwhites, and those who had experienced both nonrestrictive and restrictive devices while living in the same apartment when compared to these variables; performance rating of low flow shower heads, kitchen faucet aerators, bath faucet aerators, and low flow toilet devices. The variables also included the satisfaction rating of the time needed to get hot water to the new low flow shower heads and kitchen and bath faucet aerators. These findings support the effort to save clean water and reduce water and sewer costs by installing low flow shower heads, bath and faucet aerators, and water saving toilets. Mean score suggest satisfactory ratings were encountered in every testing category and within every group. In particular, the satisfactory mean score of residents who experienced both nonrestrictive and low flow devices while in the same apartment led to the conclusion that the reduction of water can be achieved satisfactorily in all types of residences.

Recent advances in remote-sensing technologies and analysis methods, specifically airborne-LiDAR elevation data and corresponding geographical information system (GIS) tools, present new opportunities for automated and rapid fluvial geomorphic (FGM) assessments that can cover entire watersheds. In this thesis, semi-automated GIS tools are used to extract channel centerlines and bankfull width values from digital elevation models (DEM) for five New England watersheds. For each study site, four centerlines are mapped. LiDAR and NED lines are delineated using ArcGIS spatial analyst tools with high-resolution (1-m to 2-m) LiDAR DEMs or USGS National Elevation Dataset (NED) DEMs, respectively. Resampled LiDAR decreases LiDAR DEM resolution and then runs spatial analyst tools. National Hydrography Dataset (NHD) lines are mapped by the USGS. All mapped lines are compared to centerlines delineated from photography and LiDAR DEMs. Bankfull widths at each site are determined through three methods. Regional regression equations are applied using variables derived from LiDAR and NED DEMs separately, producing two sets of width results. Additionally, the Hydrogeomorphological Geoprocessing Toolset (HGM) is used to extract widths from LiDAR data. Widths are also estimated visually from aerial photos and LiDAR DEMs. Widths measured directly in the field or derived from field-data are used as a baseline for comparison. I find that with a minimal amount of preprocessing, specifically through DEM resampling, LiDAR data can be used to model a channel that is highly correlated with the shape and location of the mapped channel. NED-derived channels model the mapped channel shape with even greater accuracy, and model the channel location only minimally less accurately. No tool used in this study accurately extracted bankfull width values, but analysis of LiDAR data by the HGM toolset did capture details that could not be resolved using regression equations. Overall, I conclude that automated, computerized LiDAR interpretation needs to improve significantly for the expense of data collection to be cost-effective at a watershed scale.

Monitoring the heterogeneous aquatic environment such as the Stormwater Treatment Areas (STAs) located at the northeast of the Everglades is extremely important in understanding the land processes of the constructed wetland in its capacity to remove nutrient. Direct monitoring and measurements of ecosystem evolution and changing velocities at every single part of the STA are not always feasible. Integrated remote sensing, monitoring, and modeling technique can be a state-of-the-art tool to estimate the spatial and temporal distributions of flow velocity regimes and ecological functioning in such dynamic aquatic environments. In this presentation, comparison between four computational intelligence models including Extreme Learning Machine (ELM), Genetic Programming (GP) and Artificial Neural Network (ANN) models were organized to holistically assess the flow velocity and direction as well as ecosystem states within a vegetative wetland area. First the local sensor network was established using Acoustic Doppler Velocimeter (ADV). Utilizing the local sensor data along with the help of external driving forces parameters, trained models of ELM, GP and ANN were developed, calibrated, validated, and compared to select the best computational capacity of velocity prediction over time. Besides, seasonal images collected by French satellite Pleiades have been analyzed to address the seasonality effect of plant species evolution and biomass changes in the constructed wetland. The key finding of this research is to characterize the interactions between geophysical and geochemical processes in this wetland system based on ground-based monitoring sensors and satellite images to discover insight of hydraulic residence time, plant species variation, and water quality and improve the overall understanding of possible nutrient removal in this constructed wetland.

No description available.

Green adsorption media with the inclusion of renewable and recycled materials can be applied as a stormwater best management practice for copper removal. A green adsorption media mixture composed of recycled tire chunk, expanded clay aggregate, and coconut coir was physicochemically evaluated for its potential use in an upflow media filter. The results found that the use of the green adsorption media mixture in isolation or the coconut coir with an expanded clay filtration chamber could be an effective and reliable stormwater best management practice for copper removal. A suite of tests were conducted on the media mixture and the individual media components including studies of isotherm, reaction kinetics, column adsorption and reaction kinetics. Batch adsorption tests revealed that the media and media mixture follow both the Freundlich and Langmuir isotherm models and that the coconut coir had the highest affinity for copper. A screening of desorbing agents revealed that hydrochloric acid has good potential for copper desorption, while batch tests for desorption with hydrochloric acid as the desorbing agent showed the data fit the Freundlich isotherm model. Reaction kinetics revealed that the adsorption reaction took less than 1 hour to reach equilibrium and that it followed pseudo-second order kinetics for the mixture and coconut. Desorption kinetic data had high correlation with the pseudo-second order model and revealed a rapid desorption reaction. Batch equilibrium data over 3 adsorption/desorption cycles found that the coconut coir and media mixture were the most resilient and demonstrated that they could be used through 3 or more adsorption/desorption cycles. The coconut coir also performed the best under dynamic conditions, having an equilibrium uptake of 1.63 mg?g-1, compared to 0.021 mg?g-1 at an influent concentration of 1.0 mg?L-1 and a hydraulic retention time of 30 minutes. A physical evaluation of the media found the macro-scale properties, such as particle size distribution and mass-volume relationships, and observed the micro-scale properties such as surface and pore microstructures, crystalline structures, and elemental composition. FE-SEM imaging found a strong correlation between the porosity of the micro pore structure and the adsorptive capacity. The equilibrium and dynamic adsorption testing results were confirmed by elemental analysis, which showed measureable quantities of copper in the coconut coir and media mixture after adsorption followed by partial desorption. A new scaling-up theory was developed through a joint consideration of the Damköhler and Péclet numbers for a constant media particle size such that a balance between transport-controlled and reaction-controlled kinetics can be harmonized. A series of column breakthrough tests at varying hydraulic residence times revealed a clear peak adsorptive capacity for the media mixture at a Damköhler number of 2.7. The Péclet numbers for the column breakthrough tests indicated that mechanical dispersion is an important effect that requires further consideration in the scaling-up process. However, perfect similitude of the Damköhler number cannot be maintained for a constant media particle size, and relaxation of hydrodynamic similitude through variation of the Péclet number must occur.

The following presents a method for determining topographic elevation error for overland unstructured finite element meshes derived from bare earth LiDAR for use in a shallow water equations model. This thesis investigates the development of an optimal interpolation method to produce minimal error for a given element size. In hydrodynamic studies, it is vital to represent the floodplain as accurately as possible since terrain is a critical factor that influences water flow. An essential step in the development of a coastal inundation model is processing and resampling dense bare earth LiDAR to a DEM and ultimately to the mesh nodes; however, it is crucial that the correct DEM grid size and interpolation method be employed for an accurate representation of the terrain. The following research serves two purposes: 1) to assess the resolution and interpolation scheme of bare earth LiDAR data points in terms of its ability to describe the bare earth topography and its subsequent performance during relevant tide and storm surge simulations

In this thesis, the role of soil water storage change on the annual water balance is evaluated based on observations at a large number of watersheds located in a spectrum of climate regions, and an annual water balance model is developed at the seasonal scale based on Budyko hypthesis. The annual water storage change is quantified based on water balance closure given the available data of precipitation, runoff, and evaporation estimated from remote sensing data and meteorology reanalysis. The responses of annual runoff, evaporation, and storage change to the interannual variability of precipitation and potential evaporation are then analyzed. Both runoff and evaporation sensitivities to potential evaporation are higher under energy-limited conditions, but storage change seems to be more sensitive to potential evaporation under the conditions in which water and energy are balanced. Runoff sensitivity to precipitation is higher under energylimited conditions; but both evaporation and storage change sensitivities to precipitation are higher under water-limited conditions. Therefore, under energy-limited conditions, most of precipitation variability is transferred to runoff variability; but under waterlimited conditions, most of precipitation variability is transferred to storage change and some of precipitation variability is transferred to evaporation variability. The main finding of this part is that evaporation variability will be overestimated by assuming negligible storage change in annual water balance, particularly under water-limited conditions. Budyko framework which expresses partitioning of water supply at the mean annual scale, is adapted to be applicable in modeling water cycle in short terms i.e., iv seasonal and interannual scales. Seasonal aridity index is defined as the ratio of seasonal potential evaporation and the difference between precipitation and storage change. The seasonal water balance is modeled by using a Budyko-type curve with horizontal shifts which leads prediction of seasonal and annual storage changes and evaporation if precipitation, potential evaporation, and runoff data are available.