A Framework for Assessing Water Quality, Prioritizing Recovery Potential, and Analyzing Placement of Best Management Practice

Motivated by the U.S. EPA goals, this research developed a framework to support identification and restoration of nutrient-impaired water bodies. The study objectives were developing total nitrogen (TN) and total phosphorus (TP) prediction models, evaluating the impact of social indicators on assessing recovery potential, and developing a spatial decision support system for choice and placement of best management practices (BMPs). An artificial neural network was used to develop TN and TP predictive regional models for U.S. lakes using easily measurable and cost-effective variables. The performance of models was superior for regions trained with larger datasets and/or regions with lower temperature and precipitation variability. The use of datasets larger than existing records and obtained from homogeneous climatic region was suggested to achieve the desired performance. The impact of social indicators on assessing a recovery potential was studied by comparing four watersheds using ecological, stressor, and social indicators. Social indicators were grouped into Socio-Economic, Organizational, and Information and Planning subcategories. The existing U.S. EPA Recovery Potential Screening tool prioritizes restoration for a water body with the most favorable ecological and social condition as well as the least stressing factors. In the present study, water bodies ranked lowest were observed with lower social scores associated with lower Socio-Economic conditions. This could mean a manager would take a water body with lower Socio-Economic condition as the lowest priority for restoration. It is suggested that such prioritization plan should carefully incorporate community goals in a prioritization effort because restoration supports an improvement of quality of life. A spatial decision support system was developed with the necessary information to assess nitrogen (N) pollution and methods to estimate an annual exported N load into Beasley Lake, Mississippi. A decision analysis of choice and placement of BMPs was performed based on performance, site suitability, and establishment cost criteria. From this analysis, a BMP scenario that reduces 25% of the exported load at an establishment and an annual opportunity cost-to-performance ratios of 148 $/kg and 29 $/kg, respectively, was developed. The presented approach supports similar efforts when the use of existing watershed models is limited by data availability.