No description available.

An automated procedure was developed to identify and extract confined poultry facilities from color 35-mm slide imagery collected by the United States Department of Agriculture/Farm Service Agency (USDA/FSA). The imagery is used by the USDA/FSA to monitor compliance with various farm support programs and to determine crop production acreage within a given county. The imagery is generally available for all counties within the state on an annual basis. The imagery, however, is not flown to rigid specifications as flight height, direction, and overlap can vary significantly. The USDA/FSA attempts to collect imagery with reasonably clear skies, as visual interpretations could be drastically impacted by cloudiness. The goal of this study was to develop procedures to effectively utilize this imagery base to identify and extract poultry facilities using automated techniques based on image processing and GIS. The procedure involved pre-screening the slides to determine coverage, geopositioning to USGS quadrangle base, color scanning to convert slide image to a digital format and archiving each data file with a naming convention that would allow rapid retrieval in later analysis. Image processing techniques were developed for identifying poultry facilities based on spectral characteristics. GIS tools were used to select poultry facilities from an array of features with similar spectral characteristics. A training data set was selected from which the spectral characteristics of poultry facilities were analyzed and compared with background conditions. Poultry facilities were found to have distinguishable characteristics. Descriptive statistics were used to define the range of spectral characteristics encompassing poultry facilities. Thresholding analyses were then utilized to eliminate all image features with spectral characteristics outside of this range. Additional analyses were made to remove noise in the spectral image due to the sun angle, line of sight of camera, variation in roof reflectance due to rust and/or aging, shading by trees, etc. A primary objective in these analyses was to enhance the spectral characteristics for the poultry facility while, at the same time, retaining physical characteristics, i.e. the spectral characteristic is represented by a single blue color with a high brightness value. The techniques developed to achieve a single blue color involved the use of Principal Component Analysis (PCA) on the red color band followed by RGB to Hue and RGB to Saturation analyses on the red and green color bands, respectively, from the resulting image. The features remaining from this series of analyses were converted into polygons (shape file) using ArcView GIS, which was then used to calculate the area and perimeter of each polygon. The parameters utilized to describe the shape of a poultry house included width, length, compactness, length-width ratio, and polygon centroid analysis. Poultry facilities were found to have an average width of approximately 12.6m with a low standard deviation indicating that the widths of all houses were very similar. The length of poultry facilities ranged from 63m to 261m with and average length of 149m. The compactness parameter, which also is related to length and width, ranged from 30 to 130 with a mean value of approximately 57. The shape parameters were used by ArcView GIS to identify polygons that represent poultry facilities. The order of selection was found to be compactness followed by length-width ratio and polygon centroid analysis. A data set that included thirty 35-mm slide images randomly selected from the Rockingham County data set, which contained over 2000 slides, was used to evaluate the automated procedure. The slides contained 182 poultry houses previously identified through manual procedures. Seven facilities were missed and 175 were correctly identified. Ninety-seven percent (97%) of existing poultry facilities were correctly identified which compares favorably with the 97 % accuracy resulted by manual procedures. . The manual procedure described by Mostaghimi, et. al.(1999) only gave the center coordinates for each poultry facility. The automated procedure not only gives the center coordinate for each poultry building but also gives estimates for geometric parameters area, length and width along with an estimate of the capacity of building (i.e. number of birds), and waste load generated by birds including nutrient and bacteria content. The nutrient and bacteria load generated by each poultry facility is important information for conducting TMDL studies currently being developed for impaired Virginia streams. The information is expected to be very helpful to consultants and state agencies conducting the studies. Agricultural support agencies such as USDA/NRCS and USDA/FSA, Extension Service, consultants, etc. will find the information very helpful in the development of implementation plans designed to meet TMDL target water quality goals. The data also should be useful to Water Authorities for selection of appropriate treatment of water supplies and to county and local government jurisdictions for developing policies to minimize the degradation of water supplies. / Ph. D.

Excessive sedimentation is a leading cause of aquatic life use impairments in Virginia. As required by the Clean Water Act, a total maximum daily load (TMDL) must be developed for impaired waters. When developing a TMDL for aquatic life use impairment where sediment has been identified as the primary pollutant, the target sediment load is often determined using a non-impaired reference watershed, i.e., the reference watershed approach (RWA). The RWA has historically been used in Virginia to establish TMDL target sediment loads because there is no numeric ambient water quality criterion for sediment. The difference between the sediment load generated by the reference watershed and the load generated by the impaired watershed is used to determine the sediment load reduction required to meet the TMDL target load in the impaired watershed. Recent quantification of the Chesapeake Bay TMDL based on Phase 5.3 of the Chesapeake Bay Watershed Model (CBWM) offers a simpler and potentially more consistent method of calculating target sediment loads for impaired watersheds within the Chesapeake Bay watershed. Researchers in the Biological Systems Engineering department at Virginia Tech have developed the "disaggregate method" (DM) which uses landuse inputs to, and pollutant load outputs from, the CBWM to determine pollutant load reductions needed in watersheds whose areas are smaller than the smallest modeling segments generally used in the CBWM. The DM uses landuse-specific unit area loads from two CBWM model runs (an existing condition run and TMDL target load run) and a finer-scale, locally assessed landuse inventory to determine sediment loads. The DM is simpler and potential more consistent than the reference watershed approach. This study compared the reference watershed approach and the disaggregate method in terms of required sediment load reduction. Three sediment-impaired watersheds (Long Meadow Run, Taylor Creek and Turley Creek) within the Chesapeake Bay watershed were used for the study. Study results showed that the TMDL development method used to determine sediment loads would have noticeable effects on resulting sediment-load reduction requirements. For Taylor Creek, the RWA required 20.4 times greater reductions in sediment load (tons/yr) when compared to the DM. The RWA also required 9.2 and 10.4 times greater reductions for Turley Creek and Long Meadow Run watersheds, respectively. On a percentage basis, the RWA for reduction Taylor Creek was 7.3 times greater than that called for by the DM. The RWA called for 4.4 and 4.6 times greater percent reductions for Turley Creek and Long Meadow Run watersheds, respectively. An ancillary objective of this research was to compare the sediment load reductions required for the impaired and their respective RWA-reference watersheds, using the DM. This comparison revealed that, both Taylor Creek and Turley Creek watersheds required less sediment load reduction than their respective reference watersheds, while the load reductions required for Long Meadow Run were slightly greater than its reference watershed. There are several issues associated with either the RWA or the DM for developing sediment TMDLs. Those issues are discussed in detail. Recommendations the need for further studies, based in questions raised by the research presented here are also discussed. / Master of Science

This research addresses the Reference Watershed Approach (RWA) in the TMDL process for benthic impairments. In the RWA, do different land use sources (DOQQ and NLCD) or use of alternative water quality models (GWLF and SWAT) result in different stressor loadings? Is there a difference in stressor loadings when different reference watersheds are used? Study results showed that using different land use sources resulted in required stressor reductions that were different by greater than 10%. In one scenario, use of the NLCD-based land use parameters results in 3.5 times greater reductions than use of DOQQ-based land use parameters. With respect to water quality model selection, in two of the three scenarios considered, a difference in stressor reduction requirements of greater than 10% resulted from using different models. Differences in load reduction requirements are also seen when different reference watersheds are used, regardless of the water quality model or the land use source used. Different references result in a difference of as much as 73% in required sediment reductions in the impaired watershed: the required reductions using one reference watershed are 6.2 times as great as when another is used. Possible alternatives to the RWA include water quality standards to set the target level for many of the common stressors on the benthic assemblage, regression equations that relate benthic stressors to the RBP II score, or averaging of stressor reduction requirements obtained from using the Reference Watershed Approach on several different reference watersheds. / Master of Science

Water quality issues in the Leon River watershed in Texas exemplify the challenges water resource managers and the public face in the ongoing effort to improve water quality in our nation’s water bodies. Some pollutant sources are difficult to regulate and likely managed through non-regulatory means, such as voluntary action. The Leon River challenge is how to go beyond regulations to address the concerns of citizens and produce options they want to develop and implement voluntarily that address a common good. This dissertation argues that voluntary measures work only if those who must take action support the action, otherwise conflict can occur. Thus, it is critical to learn what people are willing to do to promote the public good (e.g., swimmable streams). This can be achieved through an effective public process. Public participation processes may have barriers that impede success, such as inadequate access, intimidation, competing interests, limited accountability, and scientific mistrust. This dissertation developed process enhancements to overcome these barriers based on documented public participation principles. This research tested whether specific enhancements can improve the quality of a public process and achieve desired process outcomes. This dissertation reports on quasi-experiments with stakeholders making actual environmental decisions. The findings suggest that these enhancements are capable of reducing conflict and reducing the time to produce environmental policy. Five process enhancements (representation, film, narratives, deliberative decision-making, and decision support) were put into operation to provide options for government agencies and stakeholders to consider when undertaking public participation processes. The lack of access can be avoided by giving stakeholders voice with representation through different types of meetings levels (e.g., focus groups and town hall meetings). Films, when captured, edited, and shown to others, can remove the mechanisms typically associated with the intimidation perceived by speakers during discussions. Narratives were used to collect information about stakeholders to develop a deeper understanding of the diversity of interests affected by a policy, avoiding gridlock from positional bargaining. Deliberative decision-making (no voting) can assure stakeholders have real and equitable decision-making power, with scenarios collaboratively developed that address the common good. Application of a decision support system (DSS) as an overlay to a scientific model can provide stakeholders direct access to science so they can develop scenarios, evaluate alternatives, and choose solutions. / text

Sinking Creek, a stream in northeast Tennessee, was added to the state 303 (d) list and a TMDL for fecal coliforms developed. The study objectives were to 1) identify areas in Sinking Creek with elevated levels of fecal coliforms and 2) compare data collected to results from watershed models used in the TMDL. Fourteen sites on Sinking Creek were monitored monthly and concentrations of total coliforms, fecal coliforms, and selected physical water quality parameters measured. Fecal coliform concentrations were >1000 CFU/100 ml at sites 1 through 4, and <400 CFU/100 ml, at all other sites indicating significant inputs between sites 4 and 5. Comparisons of results from Monte Carlo simulations and watershed models indicated geometric means listed in the TMDL were on average 64% higher than results from simulations calibrated with collected data. Proposed Best Management Practices (BMP’s) included; septic tank surveys, and the use of vegetative buffer zones.

No description available.

To propose a methodology for the uncertainty estimation in water quality modeling as related to TMDL development, four Monte Carlo (MC) based techniques—single-phase MC, two-phase MC, Generalized Likelihood Uncertainty Estimation (GLUE), and Markov Chain Monte Carlo (MCMC) —were applied to a Hydrological Simulation Program–FORTRAN (HSPF) model developed for the Mossy Creek bacterial TMDL in Virginia. Predictive uncertainty in percent violations of instantaneous fecal coliform concentration criteria for the prediction period under two TMDL pollutant allocation scenarios was estimated. The average percent violations of the applicable water quality criteria were less than 2% for all the evaluated techniques. Single-phase MC reported greater uncertainty in percent violations than the two-phase MC for one of the allocation scenarios. With the two-phase MC, it is computationally expensive to sample the complete parameter space, and with increased simulations, the estimates of single and two-phase MC may be similar. Two-phase MC reported significantly greater effect of knowledge uncertainty than stochastic variability on uncertainty estimates. Single and two-phase MC require manual model calibration as opposed to GLUE and MCMC that provide a framework to obtain posterior or calibrated parameter distributions based on a comparison between observed and simulated data and prior parameter distributions. Uncertainty estimates using GLUE and MCMC were similar when GLUE was applied following the log-transformation of observed and simulated FC concentrations. GLUE provides flexibility in selecting any model goodness of fit criteria for calculating the likelihood function and does not make any assumption about the distribution of residuals, but this flexibility is also a controversial aspect of GLUE. MCMC has a robust formulation that utilizes a statistical likelihood function, and requires normal distribution of model errors. However, MCMC is computationally expensive to apply in a watershed modeling application compared to GLUE. Overall, GLUE is the preferred approach among all the evaluated uncertainty estimation techniques, for the application of watershed modeling as related to bacterial TMDL development. However, the application of GLUE in watershed-scale water quality modeling requires further research to evaluate the effect of different likelihood functions, and different parameter set acceptance/rejection criteria. / Ph. D.

Sinking Creek, located in upper east Tennessee, is on the 303(d) list for not meeting minimum water quality standards for recreation. A Total Maximum Daily Load (TMDL) for fecal coliforms was developed. The purpose of this study was to investigate the use of water quality parameters to identify areas that contribute to the fecal coliform loading. Concentrations of nitrate-N, orthophosphates, BOD, alkalinity, hardness, and optical brighteners (OB) were monitored at fourteen stations monthly for one year. Site 3 (agricultural region) exhibited the highest average nitrate-N loadings (627.34 mg/sec) and orthophosphate (as PO43-) loadings (84.83 mg/sec). Alkalinity loadings ranged from 10.00 mg as CaCO3/sec to 163,500.00 mgCaCO3/sec. Hardness loadings ranged from 2.00 mg as CaCO3/sec to 96,200.00 mgCaCO3/sec. The agricultural sites exhibited higher loadings for all water quality parameters measured (except OB) than the urban and forest areas. Nutrient loadings appeared to be related to agricultural land use patterns.

In the development of the watershed, hydrodynamic, and water quality models for Back Bay of Biloxi in Mississippi, the Better Assessment Science Integrating Point and Nonpoint Sources (BASINS 2.0) - Nonpoint Source Model (NPSM) was selected as the watershed model. The hydrodynamic and water quality models DNYHYD5 and EUTRO5 were selected as the tidally influenced bay models. The watershed model simulated nonpoint source flow and pollutant loadings for all sub-watersheds, routed flow and water quality, and accounted for all major point source discharges in the Back Bay of Biloxi watershed. Time varying output from the watershed model was applied directly to the Back Bay of Biloxi model. The Bay models, in turn simulated hydrodynamics and water quality, including water depth, velocities, and fecal coliform concentrations. Both watershed and Bay models were calibrated and verified against observed data. The calibrated/verified model was used as a planning tool to assess the water quality in the Watershed and the Bay as well as for calculating Total Maximum Daily Load (TMDL) and Waste Load Allocation (WLA).