This Is AuburnElectronic Theses and Dissertations

Evaluating Water Quality Impacts of Alternative Management Practices through Development of a BMP Database




Butler, Gary

Type of Degree



Civil Engineering


Agriculture and forestry are two important industries in the State of Alabama, and each is historically known to cause nonpoint source (NPS) pollution problems. Currently, the U.S. Environmental Protection Agency (USEPA) considers NPS pollution to be its biggest water quality problem. Millions of dollars and an enormous amount of time and effort have been spent on NPS pollution abatement. Best management practices (BMPs) are often used to control NPS pollution in agricultural, forested, and urban watersheds. A BMP is any practice or method that is used to reduce or prevent NPS pollution. BMPs can be categorized as structural or nonstructural. For instance, a silt fence would be considered a structural BMP where as growing crops in a rotation would be a nonstructural BMP. Effectiveness of BMPs can be determined by collecting monitoring data under various hydrologic, geomorphic, and weather conditions. However, collecting monitoring data can be expensive and time consuming. Furthermore, determining watershed-level reduction in NPS pollution due to the implementation of a specific BMP at a particular site is extremely difficult, if not impossible, through monitoring. Therefore, to assess watershed-level reduction in NPS pollutant loads derived from BMP implementation and to devise future NPS abatement plans, watershed-scale NPS pollution models are used. The overarching goal of this project is to develop a comprehensive database of commonly used agricultural and forestry BMPs in Alabama and to evaluate the effectiveness of the Alabama P index (a BMP to reduce transport of P from land-applied broiler litter) in reducing watershed-level water quality impact using the BMP database and the Soil and Water Assessment Tool (SWAT). The SWAT model, supported by the USEPA, is one of the most commonly used watershed-scale models for developing Total Maximum Daily Loads (TMDLs) and BMP implementation plans. The specific objectives of this project were to: (1) develop a database of commonly used BMPs in the agricultural and forestry industries for the State of Alabama, (2) create an ArcView 3.X GIS (geographic information system) extension to load the database into the SWAT model, and (3) determine the effectiveness of the Alabama P index (as a BMP) in reducing P loads at the watershed-scale through the use of the Alabama BMP database and the SWAT model. The BMP database will provide detailed information on how agricultural and forested lands are usually managed (i.e., how much fertilizer is used, what pesticides are used, how much animal waste is applied). This detailed information on agricultural and forestry BMPs is currently unavailable for the State of Alabama. Using the BMP database along with the SWAT model, it will be possible to evaluate the site-specific effectiveness of BMPs and conduct more accurate assessments of NPS pollution, TMDLs, and BMP implementation plans. This will allow environmental professionals to make more confident BMP recommendations and manage watersheds more effectively and efficiently. Overall, the BMP database and the ArcView 3.X extension will significantly help reduce NPS pollution in agricultural and forested watersheds. P pollution has become a major environmental concern in recent years, especially in agricultural watersheds where animal waste is being utilized as a fertilizer source. The P index is a BMP that is used to rate an area for the potential risk of contributing to P pollution. While this BMP has been shown to reduce P loads at the field scale, this study evaluates the P index effectiveness on a watershed-scale. The results of this study indicated that the Alabama P index is effective at reducing P loads at the watershed-scale; however, climate variability plays an important role in determining the level of effectiveness. The P index is most effective in dryer years, as opposed to years of heavy precipitation. The resu