According to the 2004 303(d) List, 192 segments are impaired by bacteria in the State of Texas. Impairment of streams due to bacteria is of major concern in several urban watersheds in Texas. In order to assess, monitor and manage water quality, it is necessary to characterize the sources of pathogens within the watershed. The objective of this study was to develop a spatially explicit method that allocates E.coli loads in the Plum Creek watershed in East Central Texas. A section of Plum Creek is classified as impaired due to bacteria. The watershed contains primarily agricultural activity and is in the midst of an urban housing boom. Based on a stakeholder input, possible sources E. coli were first identified in the different regions of the watershed. Locations of contributing non-point and point sources in the watershed were defined using Geographic Information Systems (GIS). By distributing livestock, wildlife, wastewater treatment plants, septic systems, and pet sources, the bacterial load in the watershed was spatially characterized. Contributions from each source were then quantified by applying source specific bacterial production rates. The rank of each contributing source was then assessed for the entire watershed. Cluster and discriminant analysis was then used to identify similar regions within the watershed for assistance in selection of appropriate best management practices. The results of the cluster analysis and the spatially explicit method were compared to identify regions that require further refinement of the SELECT method and data inputs.

Fecal contamination is the pollution caused by the microorganisms residing in the intestine of warm blooded animals and humans. Bacteria are the prime cause of contamination of surface waters in the US. The transport of microorganisms into waterways can have detrimental effects on water quality and human health especially if the pathogenic strains are ingested. E. coli is used as an indicator of fecal contamination. Detection of these bacteria in a water body above set limits poses a potential health hazard. Various sources contribute to the bacterial contamination of a water body. The sources need to be identified and quantified for their E. coli content to measure bacteria loads in the waterbody accurately. In many cases, in-situ re-growth is also believed to be a considerable source of E. coli. Also re-growth of E. coli in landscapes due to favorable environmental conditions (e.g., rainfall after dry weather conditions) is one of the major phenomena affecting E. coli concentration in streams. Thus the environmental factors like temperature and soil moisture that influence transport, persistence, re-growth, and survival of E. coli in landscapes were studied. The objective of this study was to identify, characterize and quantify E. coli loads from feces of four different animals and monitor survival, growth and re-growth at four different temperatures and moisture contents over a period of seven days. Findings of this research will aid in Watershed Protection Plan (WPP) development and Total Maximum Daily Load (TMDL) development to address impairment from point and non-point source pollution of E. coli. Wildlife and range cattle manure samples responsible for fecal contamination of Cedar Creek were identified and four fecal sources out of those were quantified for the E. coli concentrations. No significant difference was found upon comparing the E. coli concentration for each species between the genders. Sub-adult cattle demonstrated significantly higher E. coli concentrations than adult cattle. Growth and die-off rates were measured at different temperatures (0degreesC, 10degreesC, 25degreesC, and 50degreesC) and moisture conditions (1%, 25% 56.5% and 83%). E. coli concentrations in cattle and raccoons feces showed highest survivability and growth at 20degreesC out of all the temperatures studied. There was no survival of E. coli from either species at 50degreesC after 24 h. E. coli in cattle and raccoons samples exhibited greater growth at lower, nearly aerobic soil moisture content (25%) for all days compared to nearly anaerobic soil moisture content (83%).

The objective of this study was to obtain a bioassessment using benthic macroinvertebrates to meet TMDL (Total Maximum Daily Load) criteria for an oligotrophic (Lake Seminary) and an eutrophic (Lake Jesup) freshwater system in Seminole County, Florida. Monthly sampling of the benthic macroinverterbrate communities provided important biological data necessary to construct TMDL protocol and trophic state. Since macroinvertebrates are near the base of the food chain, they not only provide a critical role in the natural flow of energy and cycling of nutrients through the food web, but also provide a good indication of water quality by their presence and abundance. This study suggests that TMDL protocol and reversal trends in eutrophication can be successfully monitored using benthic macroinvertebrate data. Comparative methodology between the LCI and conventional methods indicate that the LCI is a valid, cost-effective and rapid bioassessment method when compared to the conventional method and that the conventional method is an effective tool when more in depth benthic studies are needed as it shows distinct seasonal patterns and accounts for more of the sensitive, intolerant taxa. Furthermore, this type of biological monitoring and trend analysis aids in the implementation of anthropogenic controls that targets waters for TMDLs in suspect systems. When integrated within a watershed management plan, multi-metric indexing functions as an effective overall indicator of the biological condition within a waterbody responding to its watershed.

The amount of time cattle spend standing in streams is one input parameter needed by computer models when total maximum daily load (TMDL) plans are being developed. This input parameter is estimated using professionals' best judgment because experimental data are not available, and estimations are generally inconsistent. The goal of this study was to gain a better estimate of the amount of time cows spend in streams, since this has a significant impact on direct fecal coliform loadings to streams. Significant factors influencing the amount of time cattle spend in streams were identified, and a relationship was developed for predicting the amounts of time cattle spend in streams. Five farms were studied in southwest Virginia from August 2001 through February 2002. Camera surveillance systems were set up on two beef farms and three dairy heifer operations, and cattle activity in streams was recorded during daylight hours. Climatic data, pasture characteristics, feed characteristics, and farm management practices were collected from each site, and their relationships with the amount of time cattle spend in streams were investigated. No significant difference (p=0.82) was found between the amount of time beef cattle and dairy heifers spent in streams. Overall, cows spent an average of 10.12 min day-1cow-1 standing in streams during the observation period. Throughout the study period from August to February, cows spent the highest amount of time in streams during the month of November (14.3 min day-1cow-1). Feed, climatic, and pasture parameters were found to influence the amount of time cattle spent in streams. These significant parameters were used to develop an empirical equation for predicting cattle presence in streams. This model may not accurately predict the amount of time spent in streams by cows during warmer summer months, since data was collected during fall and winter months. Other limitations may be encountered when using the model to predict the amount of time lactating dairy cows spend in streams when streams are their sole water source, since they have a much higher water requirement than those cows studied. / Master of Science

Bacteria are the major cause of surface water contamination in the United States. US Environmental Protection Agency (USEPA) uses the Total Maximum Daily Load (TMDL) process to regulate the E. coli loads from fecal sources in a watershed. Different point and non-point sources can contribute to the fecal contamination of a waterbody including municipal and on-site wastewater treatment plants, livestock, birds, and wildlife. Unfortunately, wildlife sources in many rural watersheds are poorly characterized. E. coli is also known to persist in waterbodies when no known fecal sources are present. In this study, E. coli from wildlife fecal material was enumerated. It was found that E. coli concentrations varied with the season the fecal samples were collected. When studying the fate of E. coli under different environmental factors, no growth was observed in soil at 4% moisture content and in water at 10 degrees C. The highest E. coli growth was recorded in water at 30 degrees C. It can be seen from these results that there was variation in the fate of E. coli under different environmental conditions. The fate of E. coli in the environment is a complex process and is influenced by many factors and their interactions, making it difficult to predict. The findings from this study along with additional studies can be used to improve the accuracy of model predictions to estimate the E. coli loads in watersheds.

The North Bosque River watershed of central Texas hosts a large portion of diary production in the state. In recent years, the Texas Commission on Environmental Quality (TCEQ), formerly known as the Texas Natural Resource Conservation Commission (TNRCC), has applied a Total Maximum Daily Load Program for soluble phosphorus to the watershed. Best management practices (BMPs) are now necessary to remedy the issue of excess phosphorus. This thesis explores the application of GIS as an agricultural planning tool in support of a BMP for the region. The suggested BMP calls for the production of turfgrass sod using composted dairy manure; this sod is then transported at a profit from the watershed and provides an economically sustainable means to reduce the nutrient loading in the watershed. Using GIS, a geospatial database was developed with available data from government and institutional sources. As part of the development process, these sites were verified by field technicians and the results were combined in the GIS to refine the database. This database demonstrates the suitability of GIS as a tool for large-scale planning in agriculture.

No description available.

This study compared physical characteristics used when selecting comparison (healthy) watersheds for the All-Forested Load Multiplier (AllForX) Approach, and examined a quantitative watershed characteristic as a selection criterion. The AllForX Approach uses a regression relationship between Virginia Stream Condition Index (VSCI) scores and AllForX values (a unit-less multiplier that is the ratio of a modeled existing sediment load divided by a modeled all-forested load condition) for an impaired watershed and several comparison watersheds to develop sediment TMDL target loads. The Generalized Watershed Loading Function (GWLF) model was used to simulate sediment loads for 20 watersheds (four impaired and 16 comparison) in the Upper James and New River basins in Virginia's Ridge and Valley physiographic region. Results suggest that within Virginia's Ridge and Valley physiographic region it may be possible to select comparison watersheds that are of a different stream order (watershed size) and lie in different river basins from the impaired watershed. Results further indicated that the topographic index (TI) distributions were not different across the modeled watersheds, indicating the watersheds are hydrologically similar. These results support selecting comparison watersheds regardless of river basin or stream order within Virginia's Ridge and Valley physiographic region. Finally, there was no statistical difference between the AllForX regressions when using the entire period of record or the two most recent VSCI data points. Therefore, for the watersheds modeled for this study, either all of the VSCI samples or the two most recent may be used in the AllForX Approach. / Master of Science

Pathogens are the most common cause of water quality impairment in Virginia. Bacteria TMDLs (Total Maximum Daily Loads) for watersheds are typically created using a modeling approach. These models require characterization of all residential, agricultural, and wildlife sources of bacteria. Wildlife bacteria source characterization is typically conducted with estimates of population and fecal coliform production. A sensitivity analysis was performed on the bacteria TMDL development process and the HSPF (Hydrological Simulation Program-FORTRAN) model to determine how wildlife population and fecal coliform density variability impacts simulated in-stream bacteria loads. The population and fecal coliform density values for seven wildlife species were sequentially varied and run through the TMDL model to analyze the changes in bacteria loads. For population density, high, median, and low values were tested, and for fecal coliform density, high and low values were tested. The analysis was conducted on three watersheds (Abrams Creek, Upper Opequon Creek, and Happy Creek), each with a different dominant land use. The results revealed that all watersheds were sensitive to the high fecal coliform densities of deer, muskrats, and raccoons. However, Happy Creek, the watershed with majority forested land use, was additionally sensitive to the high fecal coliform densities of ducks and the high population density for all species. Using the three watersheds as surrogates for comparing different land uses, the study showed the TMDL modeling process is most sensitive to changes in wildlife in watersheds dominated by forested land use. The results also demonstrated that TMDL calibration is more efficient when adjusting wildlife fecal coliform density rather than population density to match the modeled watershed with the observed water quality data. / Master of Science

Sources of E. coli were determined from impaired waterways in the Appomattox River watershed (in the lower Piedmont and South-Central Virginia) for the development of bacterial Total Maximum Daily Loads (TMDLs). The Appomattox River watershed is primarily undeveloped with 70.8% of the land forested, 17.0% used for agriculture (mainly livestock production), and 7.7% classified as water, wetland or barren land. The remaining 4.5% is developed for residential, commercial, and industrial land uses (mainly within the city of Petersburg). Using Antibiotic Resistance Analysis, a known source library of 1,280 E. coli isolates (320 isolates per source) was constructed. Water samples were collected monthly for between eleven and fourteen months (11/02-12/03) from 40 locations throughout the Appomattox watershed and analyzed for fecal coliforms, E. coli, and resistance to 7 antibiotics of varying concentrations. A total of 486 water samples (9,907 isolates) were analyzed during the study. The objectives of this study were verify that each sampling site exceeded state bacterial count standards (using fecal coliform data), to compare the Discriminate Analysis and Logistic Regression statistical models for use in the classification of isolates, and finally to determine the source of contamination at each site. The fecal coliform and E.coli data was used to determine if each site exceeded state standards during the assessment period. Thirty-eight of the sites exceeded the fecal coliform standard at least 10% of the time, and thirty-three exceeded the E.coli standard at least 10% of the time. Discriminate Analysis (DA) is typically used to classify isolates, but the results obtained from the DA model were unrealistic based on the watershed land uses. By statistically analyzing the original 1,280 E.coli isolates six different ways, a more appropriate classification of isolates was determined. The six analyzing methods were Regular DA and Logistic Regression (LR); DA and LR where each isolate whose probability fell below 80% was deleted; DA and LR where each isolate whose probability fell below 80% was used to create an Unknown category. The Logistic Regression model with an Unknown category proved to be the most appropriate. By using the Logistic Regression model, with Unknown category, to classify isolates, twenty five of the forty sites were discovered to be contaminated predominately with Livestock and fourteen of the sites predominately by Wildlife. One site was equally divided between these two categories. Human and Pet contamination were not dominant at any of the forty sites. This comparison of the DA and LR statistical methods could change the analysis standard for Bacterial Source Tracking and suggests that the model required to classify isolates depends on the watershed characteristics. / Master of Science